Studies by the Gas Research Institute have revealed that improved methods are needed to cost-effectively identify high-potential restimulation candidate wells. Subsequent research has had the objective of developing such methodologies,... more
Studies by the Gas Research Institute have revealed that improved methods are needed to cost-effectively identify high-potential restimulation candidate wells. Subsequent research has had the objective of developing such methodologies, and testing them in the field. The techniques being investigated include production statistics, virtual intelligence, and type-curves. For various reasons, field activities have been slow to implement, limiting the feedback needed to fully test each candidate selection method. Therefore a reservoir simulation study was performed to test the methods. The simulation field model consisted of four reservoir layers of variable properties. Wells were drilled in three rounds over a 12-year period (120 total wells). Completion intervals were varied for each well, as were skin factors for individual layers. Before providing the data to the project team for analysis, noise was added. These model features and noise were incorporated into the exercise to best replicate actual field conditions. Restimulation potential was established by "restimulating" each well in the model and observing the incremental production response. Application of the various candidate selection techniques, and comparing the results to the known answer, has yielded several important conclusions. First, simple production data comparisons are not effective at identifying high-potential restimulation candidates; better producing wells tend to be better restimulation candidates. Virtual intelligence techniques were the most successful, correctly identifying over 80% of the theoretical maximum available potential. The type-curve technique was not as effective as virtual intelligence, but still achieved a 75% candidate selection efficiency.
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Abstract Is it the quality of the formation or the quality of the completion that determines or controls the productivity of a shale well? In this paper we attempt to address this important question. We present a case study using a... more
Abstract Is it the quality of the formation or the quality of the completion that determines or controls the productivity of a shale well? In this paper we attempt to address this important question. We present a case study using a fit-for-purpose approach with no attempt to generalize the final conclusions. The analysis presented in this article is based on field measurements. No assumptions are made regarding the physics of the storage and/or the transport phenomena in shale. Our objective is to let the data speak for itself. The case study includes a large number of wells in a Marcellus shale asset in the northeast of the United States. Characteristics such as net thickness, porosity, water saturation, and TOC are used to qualitatively classify the formations surounding each well. Furthermore, wells are classified based on their productivity. We examine the hypothesis that reservoir quality has a positive correlation with the well productivity (wells completed in shale with better reservoir quality will demonstrate better productivity). The data from the field will either confirm or dispute this hypothesis. If confirmed, then it may be concluded that completion practices have not harmed the productivity and are, in general, in harmony with the reservoir characteristics. The next step in the analysis is to determine the dominant trends in the completion and judge them as best practices. However, if and when the hypothesis is disproved (wells completed in shale with better reservoir quality will NOT demonstrate better productivity), one can and should conclude that completion practices are the main culprit for the lack of better production from better quality shale. In this case, analysis of the dominant trends in the completion practices should be regarded as identifying the practices that need to be modified. Results of this study show that production from shale challenges many of our preconceived notions. It shows that the impact of completion practices in low quality shale are quite different from those of higher quality shale. In other words, completion practices that results in good production in low quality shale are not necessarily just as good for higher quality shale. Results of this study will clearly demonstrate that when it comes to completion practices in shale, “One-Size-fit-All” is a poor prescription.
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Mitchell and his team of geologists and engineers began working on the shale challenge in 1981, trying different combinations of processes and technologies before ultimately succeeding in 1997.
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Tech 101 Shahab D. Mohaghegh of West Virginia University discusses the key role of artificial intelligence and data mining in smart-fields technology.
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... Well Logs Using Intelligent Systems Shahab D. Mohaghegh Petroleum & Natural Gas Engineering West Virginia University Morgantown, WV 26506, USA Email:shahab@wvu.edu ... Razi Gaskari', Steve Wolhart', Bob Siegfried3 &... more
... Well Logs Using Intelligent Systems Shahab D. Mohaghegh Petroleum & Natural Gas Engineering West Virginia University Morgantown, WV 26506, USA Email:shahab@wvu.edu ... Razi Gaskari', Steve Wolhart', Bob Siegfried3 & Sam Ameri I ...
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Well-based Surrogate Reservoir Model (SRM) may be classified as a new technology for building proxy models that represent large, complex numerical reservoir simulation models. The well-based SRM has several advantages over traditional... more
Well-based Surrogate Reservoir Model (SRM) may be classified as a new technology for building proxy models that represent large, complex numerical reservoir simulation models. The well-based SRM has several advantages over traditional proxy models, such as response surfaces or reduced models. These advantages include (1) to develop an SRM one does not need to approximate the existing simulation model, (2) the number of simulation runs required for the development of an SRM is at least an order of magnitude less than traditional proxy models, and (3) above and beyond representing the pressure and production profiles at each well individually, SRM can replicate, with high accuracy, the pressure and saturation changes at each grid block. Well-based SRM is based on the pattern recognition capabilities of artificial intelligence and data mining (AI&DM) that is also referred to as predictive analytics. During the development process the SRM is trained to learn the principles of fluid flow...
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Summary Selection of candidate wells for stimulation treatment to increase their productivity is a challenging task. A systematic approach that uses a three-layer backpropagation neural network, introduced in this paper, assists engineers... more
Summary Selection of candidate wells for stimulation treatment to increase their productivity is a challenging task. A systematic approach that uses a three-layer backpropagation neural network, introduced in this paper, assists engineers in predicting post-stimulation well performance to select candidate wells for stimulation treatment. This approach can also be used to optimize the stimulation design parameters. Unlike conventional simulators that are based on mathematical modeling of the fracturing process, the process introduced in this paper uses no specific mathematical model. As a result, access to explicit reservoir data, such as porosity, permeability-thickness, and stress profile, is not essential. This is a major advantage over conventional hydraulic fracturing simulators, which can translate to considerable savings because it eliminates the need for expensive data collection. The application of this methodology to a gas-storage field is presented in this paper. The devel...
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Today applications of drilling require proper identification of operations where a cost reduction is possible. Many indicators are present when one tries to optimize the drilling operations such as casing size and mud properties. On the... more
Today applications of drilling require proper identification of operations where a cost reduction is possible. Many indicators are present when one tries to optimize the drilling operations such as casing size and mud properties. On the other hand the selection of the optimum bit requires information from a variety of sources. The parameters affecting the bit performance are complex and their relationship is not easily recognized. The general trend is to evaluate the performance of the bit from an offset well. A new methodology was developed to model the rate of penetration and bit wear under various formation types and operating parameters. This method introduces a new approach with improved bit wear prediction. A simulator was used to generate drilling data to eliminate errors coherent to field measurements. The data generated was used to establish the relationship between the complex patterns such as weight on bit, rotary speed, pump rates, formation hardness, and bit type. The m...
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This paper presents a study on the impact of reservoir characteristics such as matrix porosity, matrix permeability, initial reservoir pressure and pay thickness as well as the length and the orientation of horizontal wells on gas... more
This paper presents a study on the impact of reservoir characteristics such as matrix porosity, matrix permeability, initial reservoir pressure and pay thickness as well as the length and the orientation of horizontal wells on gas production in New Albany Shale. The study was conducted using a publicly available numerical model, specifically developed to simulate gas production from naturally fractured reservoirs. Reasons for selecting a non-commercial simulator for this study were two folds. First, we wanted to make sure that our results, discussions, and conclusions are accessible and repeatable by all interested operators and individuals that are currently producing or plan to produce from New Albany Shale since the simulator we used is readily available. Secondly, we wanted to demonstrate the utility and ease of use of this publicly available simulation software. The study focuses on several New Albany Shale wells in Western Kentucky. Production from these wells is analyzed and ...
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Although the New Albany Shale of the Illinois Basin has been estimated to contain approximately 86 TCF of natural gas in place, the full development of this potentially large resource has not yet occurred. The intent of this study is to... more
Although the New Albany Shale of the Illinois Basin has been estimated to contain approximately 86 TCF of natural gas in place, the full development of this potentially large resource has not yet occurred. The intent of this study is to reassess the potential of New Albany shale using a novel integrated workflow, which incorporates field production data and well logs using a series of traditional reservoir engineering analyses with artificial intelligence & data mining techniques. The model developed using this technology is a full filed model and its objective is to predict future reservoir/well performance in order to recommend field development strategies. In this integrated workflow unlike traditional reservoir simulation and modeling, we do not start from building a geo-cellular model. Top-Down intelligent reservoir modeling(TDIRM) starts by analyzing the production data using traditional reservoir engineering techniques such as Decline Curve Analysis, Type Curve Matching, Sing...
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Many computer software programs have been developed to assist petroleum engineers and scientists in designing hydraulic fractures. These programs use analytical, numerical or empirical methods (or a combination of two or all three... more
Many computer software programs have been developed to assist petroleum engineers and scientists in designing hydraulic fractures. These programs use analytical, numerical or empirical methods (or a combination of two or all three methods) to model fracture propagation in ...
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In this paper we present a new method to measure the velocity of objects using two sequential images from a camera with a pan angle that can be determined by this method. The algorithm first extracts the moving object in two consecutive... more
In this paper we present a new method to measure the velocity of objects using two sequential images from a camera with a pan angle that can be determined by this method. The algorithm first extracts the moving object in two consecutive images using image processing methods. It then uses a simple geometrical approach to calculate the displacement of the
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ABSTRACT
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We introduce a new application of artificial neural network technology in the characterization of reservoir heterogeneity. Different reservoir properties, such as porosity, permeability and fluid saturation, in highly heterogeneous... more
We introduce a new application of artificial neural network technology in the characterization of reservoir heterogeneity. Different reservoir properties, such as porosity, permeability and fluid saturation, in highly heterogeneous formations can be predicted with good accuracy ...
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Designing optimal frac jobs is a complex and time-consuming process. It usually involves the use of a two-or three-dimensional computer model. For the computer models to perform as intended, a wealth of input data is required. The input... more
Designing optimal frac jobs is a complex and time-consuming process. It usually involves the use of a two-or three-dimensional computer model. For the computer models to perform as intended, a wealth of input data is required. The input data includes wellbore ...
Research Interests: Engineering, Earth Sciences, Computer Science, Genetic Algorithms, Neural Networks, and 13 moreNeural Network, Genetic Algorithm, Job Design, Design process, Computer Model, Job Scheduling, Design optimization, Schedule, Fitness Function, Three Dimensional, Overburden, Software Tool, and Artificial Neural Network
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Research Interests: Engineering, Earth Sciences, Geology, Computer Science, Algorithms, and 15 moreNeural Networks, Interpretation, Neural Network, Computers, Models, Reservoir, Correlation, Carboniferous, Paleozoic, Inverse Problem, High Resolution, Lessons Learned, Data Processing, Model Building, and Divide and Conquer
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This book describes the application of modern information technology to reservoir modeling and well management in shale. While covering Shale Analytics, it focuses on reservoir modeling and production management of shale plays, since... more
This book describes the application of modern information technology to reservoir modeling and well management in shale. While covering Shale Analytics, it focuses on reservoir modeling and production management of shale plays, since conventional reservoir and production modeling techniques do not perform well in this environment. Topics covered include tools for analysis, predictive modeling and optimization of production from shale in the presence of massive multi-cluster, multi-stage hydraulic fractures. Given the fact that the physics of storage and fluid flow in shale are not well-understood and well-defined, Shale Analytics avoids making simplifying assumptions and concentrates on facts (Hard Data - Field Measurements) to reach conclusions. Also discussed are important insights into understanding completion practices and re-frac candidate selection and design. The flexibility and power of the technique is demonstrated in numerous real-world situations.
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It is almost impossible to solve the modern fluid flow problems without the use of Computational Fluid Dynamics (CFD). In petroleum industry, flow simulations assist engineers to develop the most efficient well design and it is essential... more
It is almost impossible to solve the modern fluid flow problems without the use of Computational Fluid Dynamics (CFD). In petroleum industry, flow simulations assist engineers to develop the most efficient well design and it is essential to understand the multiphase flow details. However, despite the high accuracy, performing the numerical simulation fall short in providing the required results in timely manner. This article presents two case studies of Smart Proxy Models (SPM) utilizing artificial intelligence (AI) and Machine Learning (ML) techniques to appraise the behavior of the chaotic system and predict the dynamic features including pressure, velocity and the evolution of phase fraction within the process at each time-step at a much lower run time. Proposed cases concentrate on 2-D dam-break and 3-D fluidized bed problems, using OpenFOAM and MFiX, CFD software applications, respectively. This paper focuses on building and improving the artificial neural network (ANN) models ...
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Using commercial numerical reservoir simulators to build a full field reservoir model and simultaneously history match multiple dynamic variables for a highly complex, offshore mature field in Malaysia, had proven to be challenging,... more
Using commercial numerical reservoir simulators to build a full field reservoir model and simultaneously history match multiple dynamic variables for a highly complex, offshore mature field in Malaysia, had proven to be challenging, manpower intensive, highly expensive, and not very successful. This field includes almost two hundred wells that have been completed in more than 60 different, non-continuous reservoir layers. The field has been producing oil, gas and water for decades. The objective of this article is to demonstrate how Artificial Intelligence (AI) and Machine Learning is used to build a purely data-driven reservoir simulation model that successfully history match all the dynamic variables for all the wells in this field and subsequently used for production forecast. The model has been validated in space and time. The AI and Machine Learning technology that was used to build the dynamic reservoir simulation and modeling is called spatio-temporal learning. Spatio-tempora...
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Managers, geologists, reservoir and completion engineers are faced with important challenges and questions when it comes to producing from and operating shale assets. Some of the important questions that need to be answered are: What... more
Managers, geologists, reservoir and completion engineers are faced with important challenges and questions when it comes to producing from and operating shale assets. Some of the important questions that need to be answered are: What should be the distance between wells (well spacing)? How many clusters need to be included in each stage? What is the optimum stage length? At what point we need to stop adding stages in our wells (what is the point of diminishing returns)? At what rate and at what pressure do we need to pump the fluid and the proppant? What is the best proppant concentration? Should our completion strategy be modified when the quality of the shale (reservoir characteristics) and the producing hydrocarbon (dry gas, vs. condensate rich, vs. oil) changes in different parts of the field? What is the impact of soak time (starting production right after the completion versus delaying it) on production? Shale Analytics is the collection of the state of the art data driven tec...
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The goal of this chapter in the book is to demonstrate the utility of Shale Analytics in providing insight into hydraulic fracturing practices in the shale.
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Is it the quality of the formation or the quality of the completion that determines or controls the productivity of a shale well?
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Carbon Capture and Storage (CCS) has been gaining support and popularity as one of the most viable CO2 emission mitigation methods. In order to assure underground CO2 storage safety and reduce leakage risk, different CO2 Monitoring... more
Carbon Capture and Storage (CCS) has been gaining support and popularity as one of the most viable CO2 emission mitigation methods. In order to assure underground CO2 storage safety and reduce leakage risk, different CO2 Monitoring techniques must be utilized. In-zone reservoir pressure, which is transmitted by Permanent Down-hole Gauges (PDG), is a widely used monitoring parameter that can provide important indications when CO2 migration/leakage occurs. As part of a monitoring package, a Real-Time Intelligent CO2 Leakage Detection System (RT-ILDS) was developed for CO2 storage project at Citronelle Dome, Alabama. This system, which is designed based on Pattern Recognition Technology and Smart Wells, is able to identify the location and amount of the CO2 leakage at the reservoir level using real-time pressure data from PDGs. In this work, history matched reservoir simulation model (based on 11 months of actual injection/pressure data) was used for CO2 leakage modeling study. High fr...
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Majority of mineralogical studies directed at hydrocarbon producing formations are conventional studies that treat the formation as a bulk entity. Focusing on pore surface mineralogy, which is the identification of the elemental... more
Majority of mineralogical studies directed at hydrocarbon producing formations are conventional studies that treat the formation as a bulk entity. Focusing on pore surface mineralogy, which is the identification of the elemental composition of the pore surface, seems to be a more realistic approach, since fluids in the formation come into direct contact with these elements on the surface. Rock-fluid properties such as relative permeability, wettability, capillary pressure and certain rock properties, are influenced by pore surface mineralogy. Hence, characterization of pore surface mineralogy will enhance understanding of the interaction between fluid and the porous medium. This paper discusses Multiple Voltage Scanning Electron Microscopy as a novel method for characterization of pore surface mineralogy. Multiple Voltage Scanning Electron Microscopy, a new method that has successfully been used to study the surface of particles, has been implemented to identify the elemental compos...
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A methodology to generate synthetic wireline logs is presented. Synthetic logs can help analyze the reservoir properties in areas where the set of logs that are necessary, are absent or incomplete. The approach presented involves the use... more
A methodology to generate synthetic wireline logs is presented. Synthetic logs can help analyze the reservoir properties in areas where the set of logs that are necessary, are absent or incomplete. The approach presented involves the use of Artificial Neural Networks as the main tool, in conjunction with data obtained from conventional wireline logs. Implementation of this approach aims to reduce costs to companies. Development of the neural network model was completed using Generalized Regression Neural Network, and wireline logs from four wells that included gamma ray, density, neutron, and resistivity logs. Synthetic logs were generated through two different exercises. Exercise one involved all four wells for training, calibration and verification process. The second exercise used three wells for training and calibration and the fourth well was used for verification. In order to demonstrate the robustness of the methodology, three different combinations of inputs/outputs were cho...
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Magnetic resonance logs provide the capability of in-situ measurement of reservoir characteristics such as effective porosity, fluid saturation, and rock permeability. This study presents a new and novel methodology to generate synthetic... more
Magnetic resonance logs provide the capability of in-situ measurement of reservoir characteristics such as effective porosity, fluid saturation, and rock permeability. This study presents a new and novel methodology to generate synthetic magnetic resonance logs using readily available conventional wireline logs such as spontaneous potential, gamma ray, density, and induction logs. The study also examines and provides alternatives for situations in which all required conventional logs are unavailable for a particular well. Synthetic magnetic resonance logs for wells with an incomplete suite of conventional logs are generated and compared with actual magnetic resonance logs for the same well. In order to demonstrate the feasibility of the concept being introduced here, the methodology is applied to a highly heterogeneous reservoir in East Texas. The process was verified by applying it to a well away from the wells used during the development process. This technique is capable of provi...
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Conventional reservoir simulation and modeling is a bottom-up approach. It starts with building a geological model of the reservoir that is populated with the best available petrophysical and geophysical information at the time of... more
Conventional reservoir simulation and modeling is a bottom-up approach. It starts with building a geological model of the reservoir that is populated with the best available petrophysical and geophysical information at the time of development. Engineering fluid flow principles are added and solved numerically so as to arrive at a dynamic reservoir model. The dynamic reservoir model is calibrated using the production history of multiple wells and the history matched model is used to strategize field development in order to improve recovery. Top-Down, Intelligent Reservoir Modeling approaches the reservoir simulation and modeling from an opposite angle by attempting to build a realization of the reservoir starting with the measured well production behavior (history). The production history is augmented by core, log, well test and seismic data in order to increase the accuracy of the Top-Down modeling technique. Although not intended as a substitute for the conventional reservoir simul...
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A methodology to generate synthetic wireline logs is presented. Synthetic logs can help to analyze the reservoir properties in areas where the set of logs that are necessary, are absent or incomplete. The approach presented involves the... more
A methodology to generate synthetic wireline logs is presented. Synthetic logs can help to analyze the reservoir properties in areas where the set of logs that are necessary, are absent or incomplete. The approach presented involves the use of artificial neural networks as the main tool, in conjunction with data obtained from conventional wireline logs. Implementation of this approach aims
Research Interests: Engineering, Computer Science, Artificial Intelligence, Petrophysics, Neural Networks, and 13 moreNeural Network, Reservoir Characterization, Calibration, Artificial Intelligent, Well Logging, Multiple Regression, Input Output, Well logs, Gamma Ray, Wireline, Artificial Neural Network, General Regression Neural Network, and Neural Network Model
This manuscript was selected for presentation by the ADIPEC 2013 Technical Committee Review and Voting Panel upon online submission of an abstract by the named author(s). Abstract: A novel approach to reservoir simulation and modeling... more
This manuscript was selected for presentation by the ADIPEC 2013 Technical Committee Review and Voting Panel upon online submission of an abstract by the named author(s). Abstract: A novel approach to reservoir simulation and modeling applied to a mature giant oilfield in the Middle East is presented. This is a prolific brown field producing from multiple horizons with production data going back to mid-1970s. Periphery water injection in this filed started in mid-1980s. The field includes more than 200 active producers and injectors. The production wells are deviated or horizontal and have been completed in multiple formations. Different types of field data (measurements) used in this empirical, full field reservoir simulation and modeling technology such as production and injection history, well configurations, well-head pressure, well logs, time-lapse saturation logs, and well tests. The well tests were used to estimates the static pressure of the reservoir as a function of space ...
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Summary Underground storage of natural gas is an efficient process that balances the variable market demand against the constant supply of natural gas from the pipelines for engineering and economic advantages. Storage reservoirs are... more
Summary Underground storage of natural gas is an efficient process that balances the variable market demand against the constant supply of natural gas from the pipelines for engineering and economic advantages. Storage reservoirs are unique warehouses that store natural gas in times of low demand and provide a ready supply of gas in times of high demand. The various attributes that impacts design and performance of the gas storage reservoirs namely inventory, deliverability, and containment are presented in detail. In addition, the various methods that are used for inventory analysis are discussed.
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This study discusses and compares, from a practical point of view, three different approaches for permeability determination from logs. These are empirical, statistical, and the recently introduced "virtual measurement" methods.... more
This study discusses and compares, from a practical point of view, three different approaches for permeability determination from logs. These are empirical, statistical, and the recently introduced "virtual measurement" methods. They respectively make use of empirically determined models, multiple variable regression, and artificial neural networks. All three methods are applied to well log data from a heterogeneous formation and the results are compared with core permeability, which is considered to be the standard. In this first part of the paper we present only the model development phase in which we are testing the capability of each method to match the presented data. Based on this, the best two methods are to be analyzed in tenns of prediction performance in the second part of this paper
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Reservoir management requires tools that can (a) provide fast track and accurate assessment of a large variety of operations, while (b) are capable of quantifying uncertainties associated with management decisions. Reservoir managers must... more
Reservoir management requires tools that can (a) provide fast track and accurate assessment of a large variety of operations, while (b) are capable of quantifying uncertainties associated with management decisions. Reservoir managers must be able to compare and contrast a large number of development scenarios, while taking into account the uncertainties and risks involved with each scenario, in a relatively short period of time. To achieving this important task with traditional technologies one must either sacrifice the accuracy or the speed. While numerical reservoir simulation models can provide the required accuracy, they fall short in providing the required speed. On the other hand, reduced models (conventional proxy models that rely on analytical solutions, simplified physics-based models or statistics-based response surfaces) can provide fast output (speed) but fail to fulfill the required accuracy. Surrogate Reservoir Model (SRM) is a "smart" proxy of the numerical ...
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Surrogate Reservoir Model (SRM) is new solution for fast track, comprehensive reservoir analysis (solving both direct and inverse problems) using existing reservoir simulation models. SRM is defined as a replica of the full field... more
Surrogate Reservoir Model (SRM) is new solution for fast track, comprehensive reservoir analysis (solving both direct and inverse problems) using existing reservoir simulation models. SRM is defined as a replica of the full field reservoir simulation model that runs and provides accurate results in real-time (one simulation run takes only a fraction of a second). SRM mimics the capabilities of a full field model with high accuracy. Reservoir simulation is the industry standard for reservoir management. It is used in all phases of field development in the oil and gas industry. The routine of simulation studies calls for integration of static and dynamic measurements into the reservoir model. Full field reservoir simulation models have become the major source of information for analysis, prediction and decision making. Large prolific fields usually go through several versions (updates) of their model. Each new version usually is a major improvement over the previous version. The updat...
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MRI logs are well logs that use nuclear magnetic resonance to accurately measure free fluid, irreducible water (MBVI), and effective porosity (MPHI). Permeability is then calculated using a mathematical function that incorporates these... more
MRI logs are well logs that use nuclear magnetic resonance to accurately measure free fluid, irreducible water (MBVI), and effective porosity (MPHI). Permeability is then calculated using a mathematical function that incorporates these measured properties. This paper describes the methodology developed to generate synthetic Magnetic Resonance Imaging logs using data obtained by conventional well logs such as SP, Gamma Ray, Caliper, and Resistivity. The synthetically generated logs are named Virtual Magnetic Imaging Logs or "VMRI" logs for short. This methodology incorporates artificial neural networks as its main tool. Virtual MRI logs for irreducible water saturation (MBVI) and effective porosity (MPHI) as well as permeability (MPERM) were generated for four wells. These wells are located in East Texas, Gulf of Mexico, Utah, and New Mexico. The results are quite encouraging. It is shown that MPHI, MBVI, and MPERM logs can be generated with a high degree of accuracy. For e...
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A novel approach to reservoir management applied to a mature giant oilfield in the Middle East is presented. This is a prolific brown field producing from multiple horizons with production data going back to mid-1970s. Periphery water... more
A novel approach to reservoir management applied to a mature giant oilfield in the Middle East is presented. This is a prolific brown field producing from multiple horizons with production data going back to mid-1970s. Periphery water injection in this filed started in mid-1980s. The field includes more than 400 producers and injectors. The production wells are deviated (slanted) or horizontal and have been completed in multiple formations. An empirical, full field reservoir management technology, based on a data-driven reservoir model was used for this study. The model was conditioned to all available types of field data (measurements) such as production and injection history, well configurations, well-head pressure, completion details, well logs, core analysis, time-lapse saturation logs, and well tests. The well tests were used to estimates the static reservoir pressure as a function of space and time. Time-lapse saturation (Pulse-Neutron) logs were available for a large number o...
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Intelligent Production Data Analysis – IPDA, is a new methodology for Reservoir Characterization based only on monthly production rate data. This technique combines conventional methods of production data analysis (decline curve analysis,... more
Intelligent Production Data Analysis – IPDA, is a new methodology for Reservoir Characterization based only on monthly production rate data. This technique combines conventional methods of production data analysis (decline curve analysis, type curve matching and history matching) with intelligent systems. The study targets the validation of this methodology under a controlled environment, attempting three main objectives: Identifying Sweet Spots, Forecasting Reserves and recognizing under-performer wells. The study investigates the behavior of five different reservoirs, modeled using a commercial simulator. The structure, parameters and heterogeneity of each configuration was inspired by existing formations. Records of production rate data were generated from the simulated fields (both single and multi-layer formations) and used as input to perform an "Intelligent Production Data Analysis". The findings highlight strength of this technique in tracking the fluid movement in...
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A family of pressure and production decline curves are generated for wet-gas sands with closed outer boundaries. Wet-gas sands are characterized as gas reservoirs which produce substantial amounts of water together with gas. Production of... more
A family of pressure and production decline curves are generated for wet-gas sands with closed outer boundaries. Wet-gas sands are characterized as gas reservoirs which produce substantial amounts of water together with gas. Production of water introduces complications when practicing engineers use decline curves designed for gas reservoirs in which gas is the only flowing phase. This usually translates to over estimation of the production performance of the reservoir. In this paper a series of pressure and production decline curves which accounts for the water production in wet-gas sands is presented. These decline curves provide a simple way of extracting valuable information from available data, using graphical methods and simple calculations. The proposed decline curves are generated for a radial system with closed outer boundary with one centrally located well which fully penetrates the formation. The application of the proposed decline curves is illustrated through a series of...
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Reservoir characterization plays a critical role in appraising the economic success of reservoir management and development methods. Nearly all reservoirs show some degree of heterogeneity, which invariably impacts production. As a... more
Reservoir characterization plays a critical role in appraising the economic success of reservoir management and development methods. Nearly all reservoirs show some degree of heterogeneity, which invariably impacts production. As a result, the production performance of a complex reservoir cannot be realistically predicted without accurate reservoir description. Characterization of a heterogeneous reservoir is a complex problem. The difficulty stems from the fact that sufficient data to accurately predict the distribution of the formation attributes are not usually available. Generally the geophysical logs are available from a considerable number of wells in the reservoir. Therefore, a methodology for reservoir description and characterization utilizing only well logs data represents a significant technical as well as economic advantage. One of the key issues in the description and characterization of heterogeneous formations is the distribution of various zones and their properties....
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Summary We discuss and compare three different approaches for permeability determination from logs from a practical point of view. The three methods, empirical, statistical, and the recently introduced "virtual measurement,"... more
Summary We discuss and compare three different approaches for permeability determination from logs from a practical point of view. The three methods, empirical, statistical, and the recently introduced "virtual measurement," make use of empirically determined models, multiple variable regression, and artificial neural networks, respectively. We apply all three methods to well log data from a heterogeneous formation and compare the results with core permeability, which is considered to be the standard. Our comparison focuses on the predictive power of each method.
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Neural network, a nonalgorithmic, nondigital, intensely parallel and distributive information processing system, is being used more and more everyday. The main interest in neural networks is rooted in the recognition that the human brain... more
Neural network, a nonalgorithmic, nondigital, intensely parallel and distributive information processing system, is being used more and more everyday. The main interest in neural networks is rooted in the recognition that the human brain processes information in a different manner than conventional digital computers. Computers are extremely fast and precise at executing sequences of instructions that have been formulated for them. A human information processing system is composed of neurons switching at speeds about a million times slower than computer gates. Yet, humans are more efficient than computers at such computationally complex tasks as speech and other pattern-recognition problems. Artificial neural systems, or neural networks, are physical cellular systems that can acquire, store, and use experiential knowledge. The knowledge is in the form of stable states or mapping embedded in networks that can be recalled in response to the presentation of cues. In a typical neural dat...
Research Interests: Engineering, Computer Science, Artificial Intelligence, Pattern Recognition, Neural Network, and 10 moreInformation Processing, Natural Gas, Petroleum Technology, Data Collection, Human Brain, West Virginia, Petroleum Industry, Time delay neural network TDN, Artificial Neural Network, and Information Embedding
Pressure drop prediction in pipes is an old petroleum engineering problem. There is a long history of attempts to develop empirical correlations to predict the pressure drop in pipes. Some of these attempts have produced correlations that... more
Pressure drop prediction in pipes is an old petroleum engineering problem. There is a long history of attempts to develop empirical correlations to predict the pressure drop in pipes. Some of these attempts have produced correlations that provide good prediction in some cases. However, their general applicability is question-able. Correlations that address only a specific class of problems exist. These types of correlation usually perform better than those which attempt to meet the need of av ariety of problems. Usually, the higher the_ number of variables in the model the lesser the reliability and general applicability of the correlations. This is the result of using methodologies such as conventional regression analysis. In such methodologies, the chances of correctly and completely captwing the relationship between variables decreases as the number of variables increases. Many parameters could be involved in these types of problems, such as gas-oil ratios in two phase systems, w...
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This paper presents a new methodology to predict the wear for three-cone bits under varying operating conditions. In this approach, six variables (weight on bit, rotary speed, pump rate, formation hardness, bit type, and torque) were... more
This paper presents a new methodology to predict the wear for three-cone bits under varying operating conditions. In this approach, six variables (weight on bit, rotary speed, pump rate, formation hardness, bit type, and torque) were studied over a range of values. A simulator was used to generate drlling data to eliminate arrors coherent to field measurements. The data generated was used to establish the relationship between complex patterns. A three-layer artificial neural network was designed and trained with measured data. This method incorporates computational intelligence to define the relationship between the variables. Further, it can be used to estimate the rate of penetration and formation characteristics. The new model was successful in predicting the condition of the bit. In this study, the value of 0.997 was obtained by the model as the correlation coefficient between the predicted and measured bearing wear and tooth wear values. The validity of the model was demonstrat...
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Smart Fields are distinguished with two characteristics: Big Data and Real-Time access. A small smart field with only ten wells can generate more than a billion data points every year. This data is streamed in real-time while being stored... more
Smart Fields are distinguished with two characteristics: Big Data and Real-Time access. A small smart field with only ten wells can generate more than a billion data points every year. This data is streamed in real-time while being stored in data historians. The challenge for operating a smart field is to be able to process this massive amount of information in ways that can be useful in reservoir management and relevant operations. In this paper we introduce a technology for processing and utilization of data generated in a smart field. The project is a CO2 storage demonstration at Citronelle Dome, Alabama and the objective is to use smart field technology to build a real-time, long-term, CO2 Intelligent Leakage Detection System (ILDS). The main concern for geologic CO2 sequestration is the capability of the underground carbon dioxide storage to confine and sustain the injected CO2 for very long time. If a leakage from a geological sink occurs, it is crucial to find the approximate...
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Production data analysis has extensively been applied to predicting the future production performance and field recovery. These applications are mostly on a single well basis. This paper presents a new approach to production data analysis... more
Production data analysis has extensively been applied to predicting the future production performance and field recovery. These applications are mostly on a single well basis. This paper presents a new approach to production data analysis using Artificial Intelligence (AI) techniques where production history is used to build a field-wide performance prediction models. In this work AI techniques and data driven modeling are utilized to predict future production of both synthetic (for validation purposes) and real field cases.In the approach presented in this article production history is paired with geological information from the field to build datasets containing the spatio-temporal dependencies amongst different wells. These dependencies are addressed by compiling information from Closest Offset Wells (COWs) that includes their geological and reservoir characteristics (spatial data) as well as their production history (temporal data).Once the dataset is assembled, a series of neur...
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Summary Recent years have witnessed a renewed interest in development of coalbed methane (CBM) reservoirs. Optimizing CBM production is of interest to many operators. Drilling horizontal and multilateral wells is gaining popularity in... more
Summary Recent years have witnessed a renewed interest in development of coalbed methane (CBM) reservoirs. Optimizing CBM production is of interest to many operators. Drilling horizontal and multilateral wells is gaining popularity in many different coalbed reservoirs, with varying results. This study concentrates on variations of horizontal-and multilateral-well configurations and their potential benefits. In this study, horizontal and several multilateral drilling patterns for CBM reservoirs are studied. The reservoir parameters that have been studied include gas content, permeability, and desorption characteristics. Net present value (NPV) has been used as the yard stick for comparing different drilling configurations. Configurations that have been investigated are single-, dual-, tri-, and quad-lateral wells along with fishbone (also known as pinnate) wells. In these configurations, the total length of horizontal wells and the spacing between laterals (SBL) have been studied. It...
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ABSTRACT We propose the application of an artificial neural network to a Taguchi orthogonal experiment to develop a robust and efficient method of depositing alloys with a favorable surface morphology by a specific microwelding hardfacing... more
ABSTRACT We propose the application of an artificial neural network to a Taguchi orthogonal experiment to develop a robust and efficient method of depositing alloys with a favorable surface morphology by a specific microwelding hardfacing process. An artificial neural network model performs self-learning by updating weightings and repeated learning epochs. The artificial neural network construct can be developed based on data obtained from experiments. The root of mean squares (RMS) error can be minimized by applying results obtained from training and testing samples, such that the predicted and experimental values exhibit a good linear relationship. An analysis of variance indicates that the significant factors explain approximately 70% of the total variance. Consequently, the Taguchi-based neural network model is experimentally confirmed to estimate accurately the hardfacing roughness performance.The experimental results reveal the hardfacing roughness performance of the product of PTA coating is greatly improved by optimizing the coating conditions and is accurately predicted by the artificial neural network model. The combination of the neural network model with Taguchi-based experiments is demonstrated as an effective and intelligent method for developing a robust, efficient, high-quality coating process.
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SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been
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Reservoir simulation models are the major tools for studying fluid flow behavior in hydrocarbon reservoirs. These models are constructed based on geological models, which are developed by integrating data from geology, geophysics, and... more
Reservoir simulation models are the major tools for studying fluid flow behavior in hydrocarbon reservoirs. These models are constructed based on geological models, which are developed by integrating data from geology, geophysics, and petro-physics. As the complexity of a reservoir simulation model increases, so does the computation time. Therefore, to perform any comprehensive study which involves thousands of simulation runs, a very long period of time is required. Several efforts have been made to develop proxy models that can be used as a substitute for complex reservoir simulation models. These proxy models aim at generating the outputs of the numerical fluid flow models in a very short period of time. This research is focused on developing a proxy fluid flow model using artificial intelligence and machine learning techniques. In this work, the proxy model is developed for a real case CO2 sequestration project in which the objective is to evaluate the dynamic reservoir paramete...
Research Interests: Engineering, Environmental Science, Geology, Computer Science, Artificial Intelligence, and 15 moreMachine Learning, Petroleum, Infertility, Artificial Neural Networks for modeling purposes, Fluids, Artificial Neural Networks, Computation, Energetics, Assisted Reproductive Technology, Air Conditioning, Geology and Geophysics, Evaporator, Elsevier, Cooling Capacity, and Petroleum Engineering
The preferred common tool to estimate the performance of oil and gas fields under different production scenarios is numerical reservoir simulation. A comprehensive numerical reservoir model has tens of millions of grid blocks. The massive... more
The preferred common tool to estimate the performance of oil and gas fields under different production scenarios is numerical reservoir simulation. A comprehensive numerical reservoir model has tens of millions of grid blocks. The massive potential of the existing numerical reservoir simulation models go unrealized because they are computationally expensive and time-consuming [1]. Therefore, an effective alternative tool is required for fast and reliable decision making. To reduce the required computational time, proxy models are developed. Traditional proxy models are either statistical or reduced order models (ROM). They are developed to substitute the complex numerical simulation by producing a representation of the system at a lower computational cost. However, there are shortcomings associated with these approaches when applied to complex systems. In this study, a novel proxy model approach is presented. The smart proxy model presented in this article is based on artificial int...
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CO2 sequestration into a coal seam project was studied and a numerical model was developed in this paper to simulate the primary and secondary coal bed methane production (CBM/ECBM) and carbon dioxide (CO2) injection. The key geological... more
CO2 sequestration into a coal seam project was studied and a numerical model was developed in this paper to simulate the primary and secondary coal bed methane production (CBM/ECBM) and carbon dioxide (CO2) injection. The key geological and reservoir parameters, which are germane to driving enhanced coal bed methane (ECBM) and CO2 sequestration processes, including cleat permeability, cleat porosity, CH4 adsorption time, CO2 adsorption time, CH4 Langmuir isotherm, CO2 Langmuir isotherm, and Palmer and Mansoori parameters, have been analyzed within a reasonable range. The model simulation results showed good matches for both CBM/ECBM production and CO2 injection compared with the field data. The history-matched model was used to estimate the total CO2 sequestration capacity in the field. The model forecast showed that the total CO2 injection capacity in the coal seam could be 22,817 tons, which is in agreement with the initial estimations based on the Langmuir isotherm experiment. To...
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Reservoir simulation models are used extensively to model complex physics associated with fluid flow in porous media. Such models are usually large with high computational cost. The size and computational footprint of these models make it... more
Reservoir simulation models are used extensively to model complex physics associated with fluid flow in porous media. Such models are usually large with high computational cost. The size and computational footprint of these models make it impractical to perform comprehensive studies which involve thousands of simulation runs. Uncertainty analysis associated with the geological model and field development planning are good examples of such studies.In order to address this problem, efforts have been made to develop proxy models which can be used as a substitute for a complex reservoir simulation model in order to reproduce the outputs of the reservoir models in short periods of time (seconds).In this study, by using artificial intelligence techniques a Grid-Based Surrogate Reservoir Model (SRMG) is developed. Grid- based SRM is a replica of the complex reservoir simulation models that is trained, calibrated and validated to accurately reproduce grid block level results. This technolog...
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Developing proxy models has a long history in our industry. Proxy models provide fast approximated solutions that substitute large numerical simulation models. They serve specific useful purposes such as assisted history matching and... more
Developing proxy models has a long history in our industry. Proxy models provide fast approximated solutions that substitute large numerical simulation models. They serve specific useful purposes such as assisted history matching and production/injection optimization. Most common proxy models are either reduced models or response surfaces. While the former accomplishes the run-time speed by grossly approximating the problem the latter accomplishes it by grossly approximating the solution space. Nevertheless, they are routinely developed and used in order to generate fast solutions to changes in the input space. Regardless of the type of model simplifications that is used, these conventional proxy models can only provide, at best, responses at the well locations, i.e. pressure or rate profiles at the well. In this paper we present application of a new approach to building proxy models. This method has one major difference with the traditional proxy models. It has the capability of re...
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Performing look back studies to evaluate the economic and technical impacts of filed management decisions, is not a common occurrence in our industry. Even when such studies are performed, the results are hardly ever published for... more
Performing look back studies to evaluate the economic and technical impacts of filed management decisions, is not a common occurrence in our industry. Even when such studies are performed, the results are hardly ever published for evaluation and scrutiny by the larger community of industry professionals. This paper presents such a study in the case of a mature giant oil field in the Middle East. This prolific mature asset that includes more than 160 production wells has been the subject of peripheral water injection for many years to maintain pressure and help displace oil toward the production wells. Production was restricted to 1,500 bbls of fluid per day per well to avoid excessive water production as well as pulling oil from an over produced overlaying prolific reservoir. In 2005 a reservoir management study was commissioned to evaluate the impact of rate relaxation in this asset. The objective was to explore the likelihood of increasing oil production from the asset while minim...
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Application of the Surrogate Reservoir Model (SRM) to an onshore green field in Saudi Arabia is the subject of this paper. SRM is a recently introduced technology that is used to tap into the unrealized potential of the reservoir... more
Application of the Surrogate Reservoir Model (SRM) to an onshore green field in Saudi Arabia is the subject of this paper. SRM is a recently introduced technology that is used to tap into the unrealized potential of the reservoir simulation models. High computational cost and long processing time of reservoir simulation models limit our ability to perform comprehensive sensitivity analysis, quantify uncertainties and risks associated with the geologic and operational parameters or to evaluate a large set of scenarios for development of green fields. SRM accurately replicates the results of a numerical simulation model with very low computational cost and low turnaround period and allows for extended study of reservoir behavior and potentials. SRM represents the application of artificial intelligence and data mining to reservoir simulation and modeling. In this paper, development and the results of the SRM for an onshore green field in Saudi Arabia is presented. A reservoir simulatio...
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Reservoir simulation is routinely used as a reservoir management tool. The static model that is used as the basis for simulation is the result of an integrated effort that usually includes the latest geological, geophysical and... more
Reservoir simulation is routinely used as a reservoir management tool. The static model that is used as the basis for simulation is the result of an integrated effort that usually includes the latest geological, geophysical and petro-physical measurements and interpretations. As such, it is inherently a model with some uncertainty. Analysis of these uncertainties and quantification of their effects on oil production and water cut using a new and efficient technique is the subject of this paper. Typical uncertainty analysis techniques require many realizations and runs of the reservoir model. In the day and age that reservoir models are getting larger and more complicated, making hundreds or sometimes thousands of simulation runs can put considerable strain on the resources of an asset team. This paper summarizes the results of uncertainty analysis on a giant oil field in the Middle East using a new technique that incorporates a Surrogate Reservoir Model (SRM). A Surrogate Reservoir ...
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Just like any other industry, the bottom-line in gas storage is economics. Any tool, new or old, conventional or unconventional, is evaluated by its contribution to the bottom-line. Fracture Optimization eXpert (FOX) is a new software... more
Just like any other industry, the bottom-line in gas storage is economics. Any tool, new or old, conventional or unconventional, is evaluated by its contribution to the bottom-line. Fracture Optimization eXpert (FOX) is a new software tool that has been developed using the cutting edge of information technology, namely Computational Intelligence. Two different paradigms of Computational Intelligence was used in development of FOX. They are Artificial Neural Networks and Genetic Programming. What distinguishes FOX from conventional frac simulators (Frac-pro, …) is its unique capability to A) not only predict post-frac deliverability but also B) suggest the best possible combination of frac parameters for optimum results. Furthermore, FOX is able to do this using only historical (production / injection) data and well completion data. In other words, FOX can do without reservoir data such as porosity, permeability, and stress profile data that are an essential part of any conventional ...
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For the past few years Artificial Neural Networks (ANN) have made a strong comeback to the scientific community. They are used in a variety of tasks where adaptive computing can enhance process performance. There has been a handful of... more
For the past few years Artificial Neural Networks (ANN) have made a strong comeback to the scientific community. They are used in a variety of tasks where adaptive computing can enhance process performance. There has been a handful of papers suggesting the use of artificial neural networks in the petroleum industry1-3. These papers can be classified into two major categories. First category includes papers that recommend the use of ANN in classification of lithologies from well logs. Second category includes papers that employ ANN to pick the proper reservoir model for well testing purposes. This paper introduces a new implementation of the neuro-computing technology in petroleum engineering. It is shown in this study that artificial neural networks possess numerous capabilities, and can be much more useful to petroleum engineers than previously thought. An implementation of artificial neural networks in characterization of reservoir heterogeneity is presented in this paper. A metho...
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Simulation models are routinely used as a powerful tool for reservoir management. The underlying static models are the result of integrated efforts that usually includes the latest geophysical, geological and petrophysical measurements... more
Simulation models are routinely used as a powerful tool for reservoir management. The underlying static models are the result of integrated efforts that usually includes the latest geophysical, geological and petrophysical measurements and interpretations. As such, these models carry an inherent degree of uncertainty. Typical uncertainty analysis techniques require many realizations and runs of the reservoir simulation model. In this day and age, as reservoir models are getting larger and more complicated, making hundreds or sometimes thousands of simulation runs can put considerable strain on the resources of an asset team, and most of the times are simply impractical. Analysis of these uncertainties and their effects on well performance using a new and efficient technique is the subject of this paper. The analysis has been performed on a giant oil field in the Middle East using a surrogate reservoir model. The surrogate reservoir model that runs and provides results in real-time i...
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While CO2 Capture and Sequestration (CCS) is considered a part of the solution to overcoming the ever increasing level of CO2 in the atmosphere, one must be sure that significant new hazards are not created by the CO2 injection process.... more
While CO2 Capture and Sequestration (CCS) is considered a part of the solution to overcoming the ever increasing level of CO2 in the atmosphere, one must be sure that significant new hazards are not created by the CO2 injection process. The risks involved in different stages of a CO2 sequestration project are related to geological and operational uncertainties. This paper presents the application of a grid-based Surrogate Reservoir Model (SRM) to a real case CO2 sequestration project in which CO2 were injected into a depleted gas reservoir. An SRM is a customized model that accurately mimics reservoir simulation behavior by using Artificial Intelligence & Data Mining techniques. Initial steps for developing the SRM included constructing a reservoir simulation model with a commercial software, history matching the model with available field data and then running the model under different operational scenarios or/and different geological realizations. The process was followed by extra...
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This paper examines the validity of a recently introduced reservoir simulation and modeling technique. The technique, that is named Top-Down Intelligent Reservoir Modeling, TDIRM (not to be confused with BP's TDRM history matching... more
This paper examines the validity of a recently introduced reservoir simulation and modeling technique. The technique, that is named Top-Down Intelligent Reservoir Modeling, TDIRM (not to be confused with BP's TDRM history matching technique), integrates traditional reservoir engineering analysis with Artificial Intelligence & Data Mining (AI&DM) technology in order to arrive at a full field model and to predict reservoir performance in order to recommend field development strategies. The distinguishing feature of this technology is its data requirement for its analysis. Although it can incorporate almost any type and amount of data that is available in the modeling process, it only requires field production rate and some well log data (porosity, thickness and initial water saturation) in order to start the analysis and provide a full field model. Presence and incorporation of other types of data can increase the accuracy and validity of the developed model. In this work three di...
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Uncertainties of CO2 injection-sequestration in a CBM is driven by the complex process of gas desorption-controlled mechanism attributed to the natural characteristics of CBM and to the impact of the wells engaged in the reservoir. This... more
Uncertainties of CO2 injection-sequestration in a CBM is driven by the complex process of gas desorption-controlled mechanism attributed to the natural characteristics of CBM and to the impact of the wells engaged in the reservoir. This work presents the reservoir characterization and simulation process focused on natural gas production and subsequent CO2 injection into an unmineable coal seam in the Marshall Country West Virginia. Two coal seams (Pittsburgh and Upper Freeport) are the subject of this pilot CO2 sequestration project. Methane is produced from both coal seams; however CO2 is injected only in the Upper Freeport which includes four wells. The shallower Pittsburgh coal is used to observe and detect any possible leakage. The objective is to build a reservoir simulation model that is capable of matching the methane production history and forecast field potential capacity for CO2 injection and sequestration. Although injection has taken place in two reasonably close wells, ...
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Coalbed methane is becoming one of the major natural gas resources. CO2 injection into CBM reservoirs is used as an effective method for CBM production enhancement (ECBM) and for long term sequestration of CO2 (CO2Seq). Reservoir... more
Coalbed methane is becoming one of the major natural gas resources. CO2 injection into CBM reservoirs is used as an effective method for CBM production enhancement (ECBM) and for long term sequestration of CO2 (CO2Seq). Reservoir simulation is used regularly for building representative ECBM and CO2Seq models. Given the wide range of uncertainties that are associated with the geological models (that forms the foundation of any reservoir simulation), comprehensive analysis and uncertainty quantification of ECBM and CO2Seq models become very time consuming if not impossible. This paper addresses the uncertainty quantification of a complex ECBM reservoir model. We use a new technique by developing a Surrogate Reservoir Model (SRM) that can accurately mimic the behavior of the commercial reservoir model. Upon validation of SRM, we perform Monte Carlo Simulation (MCS) in order to quantify the uncertainties associated with the geological (CBM) model. Performing MCS requires thousands of si...
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Research Interests: Geology, Computer Science, Seismology, Neural Network, Low Frequency, and 15 moreReservoir Computing, Intelligent System, Well Logging, New Mexico, Seismic Inversion, Inverse Method, High Resolution, Reservoir modeling, Correlation coefficient, Gamma Ray, Estimation Method, Low Resolution, General Regression Neural Network, Structural model, and Elementary Particles
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Summary Production-data analysis has been applied extensively to predicting future production performance and field recovery. These applications operate mostly on a single-well basis. This paper presents a new approach to production-data... more
Summary Production-data analysis has been applied extensively to predicting future production performance and field recovery. These applications operate mostly on a single-well basis. This paper presents a new approach to production-data analysis using artificial-intelligence (AI) techniques in which production history is used to build a fieldwide performance-prediction model. In this work, AI and data-driven modeling are used to predict future production of both synthetic- (for validation purposes) and real-field cases. In the approach presented in this article, production history is paired with field geological information to build data sets containing the spatio-temporal dependencies among different wells. These dependencies are addressed by compiling information from closest offset wells (COWs) that includes their geological and reservoir characteristics (spatial data) as well as their production history (temporal data). Once the data set is assembled, a series of neural network...
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Data-Driven Reservoir Modeling introduces new technology and protocols (intelligent systems) that teach the reader how to apply data analytics to solve real-world, reservoir engineering problems. The book describes how to utilize... more
Data-Driven Reservoir Modeling introduces new technology and protocols (intelligent systems) that teach the reader how to apply data analytics to solve real-world, reservoir engineering problems. The book describes how to utilize machine-learning-based algorithmic protocols to reduce large quantities of difficult-to-understand data down to actionable, tractable quantities. Through data manipulation via artificial intelligence, the user learns how to exploit imprecision and uncertainty to achieve tractable, robust, low-cost, effective, actionable solutions to challenges facing upstream technologists in the petroleum industry.
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Abstract: Numerical simulation and modeling has dominated the computation-al sciences for decades. From Computational Fluid Dynamics (CFD) to Numer-ical Reservoir Simulation (NRS) most of the computational modeling is per-formed by... more
Abstract: Numerical simulation and modeling has dominated the computation-al sciences for decades. From Computational Fluid Dynamics (CFD) to Numer-ical Reservoir Simulation (NRS) most of the computational modeling is per-formed by numerically solving a set of nonlinear partial differential equations. When historical or experimental data exists, it is used to calibrate the computa-tional model. In this paper we propose a technology to use the historical and/or simulated to build data driven models. These data driven models that are devel-oped using artificial intelligence and data mining technologies have many uses some of which are: when the physics of the phenomenon being models is poorly understood and when the numerical modeling is computational expensive. In this paper we use modeling of petroleum reservoirs to introduce this new mod-eling technology based on pattern recognition capabilities of artificial intelli-gence and data mining.
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This paper presents a study on the impact of reservoir characteristics such as matrix porosity, matrix permeability, initial reservoir pressure and pay thickness as well as the length and the orientation of horizontal wells on gas... more
This paper presents a study on the impact of reservoir characteristics such as matrix porosity, matrix permeability, initial reservoir pressure and pay thickness as well as the length and the orientation of horizontal wells on gas production in New Albany Shale. The study was conducted using a publicly available numerical model, specifically developed to simulate gas production from naturally fractured reservoirs. Reasons for selecting a non-commercial simulator for this study were two folds. First, we wanted to make sure that our results, discussions, and conclusions are accessible and repeatable by all interested operators and individuals that are currently producing or plan to produce from New Albany Shale since the simulator we used is readily available. Secondly, we wanted to demonstrate the utility and ease of use of this publicly available simulation software. The study focuses on several New Albany Shale wells in Western Kentucky. Production from these wells is analyzed and ...
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Oil and gas production from shale has increased significantly in the United States. Forecasting production and estimating ultimate recovery (EUR) using Decline Curve Analysis (DCA) is performed routinely during development and planning.... more
Oil and gas production from shale has increased significantly in the United States. Forecasting production and estimating ultimate recovery (EUR) using Decline Curve Analysis (DCA) is performed routinely during development and planning. Different methods to calculate EUR have been used in the industry (Hyperbolic Decline, Power Law, Stretched Exponential, Dung's and Tail-end Exponential). Traditionally, the decline curve analysis method by Arps (1945) was considered to be the best common tool for estimating ultimate recovery (EUR) and reserves. However, the Arps' equations over estimate of reserves when they are applied to unconventional reservoirs. Multiple modifications to Arp's method have been proposed in order to extend the applicability of DCA to forecast production and estimate the recovery from shale wells. Decline Curve Analysis, including all its flavors that recently have surfaced, is essentially a curve fitting technique that does not take into account reserv...
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Estimating ultimate recovery (EUR) in shale is a function of rock properties, well, and completion design parameters. The variation associated with these parameters are in source of uncertainty. In this paper the combined decline curve... more
Estimating ultimate recovery (EUR) in shale is a function of rock properties, well, and completion design parameters. The variation associated with these parameters are in source of uncertainty. In this paper the combined decline curve (CDC) approach is used to estimate the EUR of shale wells. CDC is a conservative approach that combines hyperbolic (in early time) and exponential (in later time) declines for production analysis. The major objective of this work is to condition the results of the CDC-EUR of shale wells to rock properties, well characteristics, and completion design parameters in a given shale asset. As the first step CDC-EUR is estimated. In the second step data-driven analytics using artificial neural networks is employed to condition the CDC-EUR to rock properties, well characteristics, and completion design parameters. Then, artificial Intelligence techniques are used in order to extract the nonlinear relationship between well productivity and reservoir characteri...
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Summary Performing hydraulic fractures on gas storage wells to improve their deliverability is a common practice in the eastern part of the U.S. Most fields used for storage in this region are old, and the reservoir characteristic data... more
Summary Performing hydraulic fractures on gas storage wells to improve their deliverability is a common practice in the eastern part of the U.S. Most fields used for storage in this region are old, and the reservoir characteristic data necessary for most reservoir studies and hydraulic fracture design and evaluation are scarce. This paper introduces a new method by which parameters that influence the response of gas storage wells to hydraulic fracturing may be identified in the absence of sufficient reservoir data. Control and manipulation of these parameters, once identified correctly, could enhance the outcome of frac jobs in gas storage fields. We conducted the study on a gas storage field in the Clinton formation of northeastern Ohio. We found that well-performance indicators before a hydraulic fracture play an important role in how good the well will respond to a new frac job. We also identified several other important factors. The identification of controlling parameters serve...
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The methodology developed in this study uses several artificial neural networks and genetic algorithm routines to help engineers select restimulation candidates based on available data. The neural networks provide realistic models of the... more
The methodology developed in this study uses several artificial neural networks and genetic algorithm routines to help engineers select restimulation candidates based on available data. The neural networks provide realistic models of the hydraulic frac jobs and chemical treatments in this field. The genetic algorithms provide design optimization and economic analysis (capital investment allocation). Historically wells in this storage field have been stimulated/restimulated by hydraulic fracturing or by being chemically treated using one, two or sometimes three different chemicals. Several neural network models were developed for different stimulation processes. The first series of genetic algorithm routines are used with each of the neural network models to provide optimum treatment design for each of the stimulation processes. A separate genetic algorithm uses several economic parameters and provides the engineer with an optimum stimulation combination of the candidate wells. A sof...
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Summary This paper summarizes the efforts conducted toward the development of a new and novel methodology for optimal design of hydraulic fracture treatments in a gas storage field. What makes this methodology unique is its capability to... more
Summary This paper summarizes the efforts conducted toward the development of a new and novel methodology for optimal design of hydraulic fracture treatments in a gas storage field. What makes this methodology unique is its capability to provide engineers with a near optimum design of a frac job despite very little (almost none) reservoir data availability. Lack of engineering data for hydraulic fracture design and evaluation had made use of 2D or 3D hydraulic fracture simulators impractical. As a result, prior designs of hydraulic frac jobs had been reduced to guess works and in some cases dependent on engineers with many years of experience on this particular field, who had developed an intuition about this formation and its possible response to different treatments. This was the main cause of several frac job failures every year. On the other hand, in case of relocation of engineers with experience on this particular field the risk of even more frac job failures was imminent. The...
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This paper presents a new and novel technique for determining the in-situ stress profile of hydrocarbon reservoirs from geophysical well logs using a combination of fuzzy logic and neural networks. It is well established, that in-situ... more
This paper presents a new and novel technique for determining the in-situ stress profile of hydrocarbon reservoirs from geophysical well logs using a combination of fuzzy logic and neural networks. It is well established, that in-situ stress cannot be generated from well logs alone. This is because two sets of formations may have very similar geologic signatures but possess different in-situ stress profiles because of varying degrees of tectonic activities in each region. By using two new parameters as surrogates for tectonic activities, fuzzy logic to interpret the logs and rank parameter influence, and neural networks as a mapping tool, it has become possible to accurately generate in-situ stress profiles from logs. This paper demonstrates the improved performance of this new approach over conventional approaches used in the industry.
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Production from unconventional reservoirs has gained an increased attention among operators in North America during past years and is believed to secure the energy demand for next decades. Economic production from unconventional... more
Production from unconventional reservoirs has gained an increased attention among operators in North America during past years and is believed to secure the energy demand for next decades. Economic production from unconventional reservoirs is mainly attributed to realizing the complexities and key fundamentals of reservoir formation properties. Geomechanical well logs (including well logs such as total minimum horizontal stress, Poisson’s ratio, and Young, shear, and bulk modulus) are secured source to obtain these substantial shale rock properties. However, running these geomechanical well logs for the entire asset is not a common practice that is associated with the cost of obtaining these well logs. In this study, synthetic geomechanical well logs for a Marcellus shale asset located in southern Pennsylvania are generated using data-driven modeling. Full-field geomechanical distributions (map and volumes) of this asset for five geomechanical properties are also created using gener...
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Producing hydrocarbon from Shale plays has attracted much attention in the recent years. Advances in horizontal drilling and multi-stage hydraulic fracturing have made shale reservoirs a focal point for many operators. Our understanding... more
Producing hydrocarbon from Shale plays has attracted much attention in the recent years. Advances in horizontal drilling and multi-stage hydraulic fracturing have made shale reservoirs a focal point for many operators. Our understanding of the complexity of the flow mechanism in the natural fracture and its coupling with the matrix and the induced fracture, impact of geomechanical parameters and optimum design of hydraulic fractures has not necessarily kept up with our interest in these prolific and hydrocarbon rich formations. In this paper we discuss using a new and completely different approach to modeling, history matching, forecasting and analyzing oil and gas production in shale reservoirs. In this new approach instead of imposing our understanding of the flow mechanism and the production process on the reservoir model, we allow the production history, well log, and hydraulic fracturing data to force their will on our model and determine its behavior. In other words, by carefu...
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This paper demonstrates the validity of a recently developed reservoir modeling technique called "Top-Down, Intelligent Reservoir Modeling" (will be referred to Top-Down Modeling for short). This new modeling technology... more
This paper demonstrates the validity of a recently developed reservoir modeling technique called "Top-Down, Intelligent Reservoir Modeling" (will be referred to Top-Down Modeling for short). This new modeling technology integrates reservoir engineering analytical techniques with Artificial Intelligence & Data Mining (AI&DM) in order to arrive at an empirical, cohesive and spatiotemporally calibrated full field model. The model is used to predict reservoir performance in order to recommend field development strategies. One of the distinctive features of this technology is its data requirement for analysis. Although Top-Down Modeling can incorporate almost any type and amount of data that is available in the modeling process, it only requires monthly production rate and some well log data (porosity, initial water saturation and thickness) in order to start the analysis and provide a full field model. Presence and incorporation of other types of data such as core analysis, pr...
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Shale gas in the United States went almost instantly from a practically invisible resource to massive reserves that challenge the largest conventional gas accumulations in the world. Shale gas success is directly the result of... more
Shale gas in the United States went almost instantly from a practically invisible resource to massive reserves that challenge the largest conventional gas accumulations in the world. Shale gas success is directly the result of economically managed deployment of petroleum technology, namely horizontal wells .Horizontal drilling and multi-stage stimulation technologies are driving the successful development of shale plays. Modeling and simulation of shale gas reservoirs poses a unique problem. The geological complexity of shale gas reservoirs—containing both natural and hydraulic fractures—makes accurate modeling a significant challenge. To overcome these challenges and maximize recovery of a shale gas field requires specialized methods and state-of-the-art technology. In the first part of this paper an integrated workflow, which demonstrates a quantitative platform for shale gas production optimization through capturing the essential characteristics of shale gas reservoirs was discus...
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In this paper a new class of reservoir models that are developed based on the pattern recognition technologies collectively known as Artificial Intelligence and Data Mining (AI&DM) is introduced. The workflows developed based on this new... more
In this paper a new class of reservoir models that are developed based on the pattern recognition technologies collectively known as Artificial Intelligence and Data Mining (AI&DM) is introduced. The workflows developed based on this new class of reservoir simulation and modeling tools break new ground in modeling fluid flow through porous media by providing a completely new and different angle on reservoir simulation and modeling. The philosophy behind this modeling approach and its major commonalities and differences with numerical and analytical models are explored and two different categories of such models are explained. Details of this technology are presented using examples of most recent applications to several prolific reservoirs in the Middle East and in the Gulf of Mexico. AI-Based reservoir models can be developed for green or brown fields. Since these models are developed based on spatio-temporal databases that are specifically developed for this purpose, they require t...
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Latest advances in shale gas reservoir simulation and modeling have made it possible to optimize and enhance the production from organic rich shale gas reservoirs. Reservoir simulator is no longer used with a simple description of the... more
Latest advances in shale gas reservoir simulation and modeling have made it possible to optimize and enhance the production from organic rich shale gas reservoirs. Reservoir simulator is no longer used with a simple description of the complex shale gas reservoirs, but with multiple, equally probable realizations to allow risk assessment. Nevertheless, the perennial challenge in shale reservoir modeling is to strike a balance between explicit representation of reservoir complexity and long simulation run time for multiple realizations. Focus of this study is on the development, calibration and validation of a Shale Surrogate Reservoir Model (AI-based proxy model) that represents a series of complex shale numerical simulation models. The Shale Surrogate Reservoir Model is then used for fast track analysis of the shale numerical model. Reservoir simulation model for a generic shale gas reservoir are constructed using a popular commercial simulator that is capable of handling complex fr...
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History matching is the process of adjusting uncertain reservoir parameters until an acceptable match with the measured production data is obtained. Complexity and insufficient knowledge of reservoir characteristics makes this process... more
History matching is the process of adjusting uncertain reservoir parameters until an acceptable match with the measured production data is obtained. Complexity and insufficient knowledge of reservoir characteristics makes this process time-consuming with high computational cost. In the recent years, many efforts mainly referred as assisted history matching have attempted to make this process faster; nevertheless, the degree of success of these techniques continues to be a subject for debate. This study aims to examine the application of a unique pattern recognition technology to improve the time and efforts required for completing a successful history matching project. The pattern recognition capabilities of Artificial Intelligence and Data Mining (AI&DM) are used to develop Surrogate Reservoir Model (SRM) for utilization as the engine to drive the history matching process. SRM is an intelligent prototype of the full-field reservoir simulation model that runs in fractions of a secon...
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Rock typing is an essential part of building geological model for an asset. Millions of dollars are invested in logs, core measurements, SCAL studies and geological interpretation that result in definition of different rock types. In most... more
Rock typing is an essential part of building geological model for an asset. Millions of dollars are invested in logs, core measurements, SCAL studies and geological interpretation that result in definition of different rock types. In most caes rock types that are identified in a reservoir do not have crisp boundaries and display overlapping characteristics. During the upscaling process, multiple rock types that have been identified in a high resolution geological (geo-cellular) model are approximated into a dominant rock type for any grid block in a reservoir simulation flow model.This defeats the original purpose of performing detail geological and petrophysical studies as far as reservoir flow models are concerned. The objective of this study is to develop a new upscaling methodology based on fuzzy set theory principles. Fuzzy rock typing refers to taking into account the inherent uncertainties and vagueness associated with rock typing in hydrocarbon bearing reservoirs. In this pa...
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Abstract: The intent of this study is to reassess the potential of New Albany Shale formation using a novel and integrated workflow, which incorporates natural fracture system modeling with top-down intelligent reservoir modelling... more
Abstract: The intent of this study is to reassess the potential of New Albany Shale formation using a novel and integrated workflow, which incorporates natural fracture system modeling with top-down intelligent reservoir modelling technique. Top-down, intelligent reservoir modeling technology integrates reservoir engineering analytical techniques with artificial intelligence and data mining in order to arrive at an empirical, cohesive and spatiotemporally calibrated full field model. The model is used to predict reservoir performance. Analytical reservoir engineering techniques used in the top-down modelling presented in this study include production decline analysis, type curve matching, single well history matching, volumetric reserve and recovery factor estimations are integrated with Voronoi graph theory, geostatistics, two-dimensional fuzzy pattern recognition, and discrete, data driven predictive modelling. The resulting full field top-down modeling is used to identify the
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SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been
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In this paper a fast track reservoir modeling and analysis of the Lower Huron Shale in Eastern Kentucky is presented. Unlike conventional reservoir simulation and modeling which is a bottom up approach (geo-cellular model to history... more
In this paper a fast track reservoir modeling and analysis of the Lower Huron Shale in Eastern Kentucky is presented. Unlike conventional reservoir simulation and modeling which is a bottom up approach (geo-cellular model to history matching) this new approach starts by attempting to build a reservoir realization from well production history (Top to Bottom), augmented by core, well-log, well-test and seismic data in order to increase accuracy. This approach requires creation of a large spatial-temporal database that is efficiently handled with state of the art Artificial Intelligence and Data Mining techniques (AI & DM), and therefore it represents an elegant integration of reservoir engineering techniques with Artificial Intelligence and Data Mining. Advantages of this new technique are a) ease of development, b) limited data requirement (as compared to reservoir simulation), and c) speed of analysis. All of the 77 wells used in this study are completed in the Lower Huron Shale and...
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Hydraulic fracturing is an economic way of increasing gas well productivity. Hydraulic fracturing is routinely performed on many gas wells in fields that contain hundreds of wells. Companies have developed databases that include... more
Hydraulic fracturing is an economic way of increasing gas well productivity. Hydraulic fracturing is routinely performed on many gas wells in fields that contain hundreds of wells. Companies have developed databases that include information such as methods and materials used during the fracturing process of their wells. These databases usually include general information such as date of the job, name of the service company performing the job, fluid type and fluid amount, proppant type and proppant amount, and pumped rate. Sometimes more detail information may be available such as breakers, amount of nitrogen, and ISIP, to name a few. These data usually is of little use if some of the complex 3-D hydraulic fracture simulators are used to analyze them. But valuable information can be deduced from such data using virtual intelligence tools. The process covered in this paper takes the available data and couples it with general information from each well (things like latitude, longitude ...
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In 1996, the Gas Research Institute (GRI) performed a scoping study to investigate the potential for natural gas production enhancement via restimulation in the United States. The results indicated that the potential was substantial,... more
In 1996, the Gas Research Institute (GRI) performed a scoping study to investigate the potential for natural gas production enhancement via restimulation in the United States. The results indicated that the potential was substantial, particularly in the tight sands of the Rocky Mountain, Mid-Continent and South Texas regions. However it was also determined that industry's historical experience with restimulation is mixed, and that considerable effort is required in candidate selection, problem diagnosis, and treatment selection/design/implementation for a restimulation program to be successful. As a result GRI initiated a subsequent two-year R & D project with the objectives: 1) to develop efficient, cost-effective, and reliable methodologies to identify wells with restimulation potential, 2) to identify and classify various mechanisms leading to well underperformance, and 3) to develop and test various restimulation techniques tailored to different causes of well underperforman...
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In 1996, the Gas Research Institute (GRI) performed a scoping study to investigate the potential for natural gas production enhancement via restimulation in the United States (lower-48 onshore). The results indicated that the potential... more
In 1996, the Gas Research Institute (GRI) performed a scoping study to investigate the potential for natural gas production enhancement via restimulation in the United States (lower-48 onshore). The results indicated that the potential was substantial (over a Tcf in five years), particularly in tight sand formations of the Rocky Mountain, Mid-Continent and South Texas regions. However, it was also determined that industry's current experience with restimulation is mixed, and that considerable effort is required in candidate selection, problem diagnosis, and treatment selection/design/ implementation for a restimulation program to be successful. Given a general lack of both specialized (restimulation) technology and "spare" engineering manpower to focus on restimulation, GRI initiated a subsequent R&D project in 1998 with several objectives. Those objectives are to 1) develop efficient, cost-effective, reliable methodologies to identify wells with high restimulation pot...
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The Marcellus Shale play has attracted much attention in recent years. Our full understanding of the complexities of the flow mechanism in matrix, sorption process and flow behavior in complex fracture system (natural and hydraulic) still... more
The Marcellus Shale play has attracted much attention in recent years. Our full understanding of the complexities of the flow mechanism in matrix, sorption process and flow behavior in complex fracture system (natural and hydraulic) still has a long way to go in this prolific and hydrocarbon rich formation. In this paper, we present and discuss a novel approach to modeling and history matching of hydrocarbon production from a Marcellus shale asset in southwestern Pennsylvania using advanced data mining & pattern recognition technologies. In this new approach instead of imposing our understanding of the flow mechanism, the impact of multi-stage hydraulic fractures, and the production process on the reservoir model, we allow the production history, well log, and hydraulic fracturing data to force their will on our model and determine its behavior. The uniqueness of this technique is that it incorporates the so-called "hard data" directly into the reservoir model, such that t...
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Bakken shale has been subjected to more attention during the last decade. Recently released reports discussing the high potential of the Bakken formation coupled with advancements in horizontal drilling, increased the interest of oil... more
Bakken shale has been subjected to more attention during the last decade. Recently released reports discussing the high potential of the Bakken formation coupled with advancements in horizontal drilling, increased the interest of oil companies for investment in this field. Bakken formation is comprised of three layers. In this study upper and middle parts are the core of attention. Middle member which is believed to be the main reserve is mostly a limestone and the upper member is black shale. The upper member plays as a source and seal which has been subject to production in some parts as well. In this study, a Top-Down Intelligent Reservoir Modeling technique has been implemented to a part of Bakken shale formation in Williston basin of North Dakota. This innovative technique utilizes a combination of conventional reservoir engineering methods, data mining and artificial intelligence to analyze the available data and to build a full field model that can be used for field developme...
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Economic production from shale has been intimately tied to hydraulic fracturing since the first signs of success in Barnet Shale in the late 90s. The introduction of horizontal wells and multi-stage hydraulic fracturing was met by a huge... more
Economic production from shale has been intimately tied to hydraulic fracturing since the first signs of success in Barnet Shale in the late 90s. The introduction of horizontal wells and multi-stage hydraulic fracturing was met by a huge move by operators towards developing shale formations that were mainly ignored in the past. Today using pad drilling, multiple horizontal wells share surface facilities and infrastructure, a development that minimizes the industry's environmental footprint. To understand production from shale reservoirs one must understand the network of natural fractures in the shale and the role of hydraulically induced fractures and their interaction. In this article author proposes a new view of the network of natural fractures in shale that when interfaced with the induced hydraulic fractures, will provide a completely different picture of how stored hydrocarbon is produced. Modeling this new network of natural fractures and its interactions with induced fr...
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The main goal of this paper is to modify and apply the state-of-the-art intelligent, optimum portfolio management to the gas storage field in order to optimize the return on investment associated with well remedial operations. It... more
The main goal of this paper is to modify and apply the state-of-the-art intelligent, optimum portfolio management to the gas storage field in order to optimize the return on investment associated with well remedial operations. It continues the development of a methodology for candidate selection and stimulation design and optimization using Artificial Intelligence techniques. The data of an actual gas storage field was used to test the results. The project data include Well-bore, Completion, Perforation, Stimulation, Well-test and Reservoir Data. To make candidate selection for gas storage fields operators predict the effectiveness of the stimulation commonly using three parameters. One in Peak Day rate second is Absolute open flow and third is change in skin provided permeability values in the field don't vary much. The software developed in parallel with this selection methodology includes an easy to use interface that allows the user to edit the data for a gas storage field, ...
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The importance of production from Shale and its impact on the total US energy equation has focused much attention on this prolific source of hydrocarbon. Consequently, research related to unconventional reservoirs has increased... more
The importance of production from Shale and its impact on the total US energy equation has focused much attention on this prolific source of hydrocarbon. Consequently, research related to unconventional reservoirs has increased significantly in order to better understand the inherent complexities of their behavior. Analytical, numerical and statistical analyses have been applied to large multi-variable data set from Shale assets with different degrees of success. The notion that shale is a "statistical play" may be attributed to the fact that many of our preconceived notions on storage and flow mechanisms in shale are not supported by facts. Therefore, we set out to examine the possibility of learning from the data in order to be able to answer some of the questions that rise during the production process. Data Driven Analytics, having roots in pattern recognition and machine learning, have proven to be capable of extracting useful information from large data sets and are ...
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Summary The most common data that engineers can count on, especially in mature fields, is production rate data. Practical methods for production data analysis (PDA) have come a long way since their introduction several decades ago and... more
Summary The most common data that engineers can count on, especially in mature fields, is production rate data. Practical methods for production data analysis (PDA) have come a long way since their introduction several decades ago and fall into two categories: decline curve analysis (DCA) and type curve matching (TCM). DCA is independent of any reservoir characteristics, and TCM is a subjective procedure. State of the art in PDA can provide reasonable reservoir characteristics, but it has two shortcomings: First, for reservoir characterization, the process requires bottomhole or wellhead pressure data in addition to rate data. Bottomhole or wellhead pressure data are not usually available in most of the mature fields. Second, a technique that would allow the integration of results from hundreds of individual wells into a cohesive fieldwide or reservoirwide analysis for business decision making is not part of today's PDA tool kit. To overcome these shortcomings, a new methodology...
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State-of-the-art data analysis in production allows engineers to characterize reservoirs using production data. This saves companies large sums that should otherwise be spend on well testing and reservoir simulation and modeling. There... more
State-of-the-art data analysis in production allows engineers to characterize reservoirs using production data. This saves companies large sums that should otherwise be spend on well testing and reservoir simulation and modeling. There are two shortcomings with today's production data analysis: It needs bottom-hole or well-head pressure data in addition to data for rating reservoirs' characterization. Analysis remains at the individual well level. It does not offer integration of results from individual wells to create a field-wide analysis. A new technique called Intelligent Production Data Analysis, IPDA, addresses both of these short-comings. Through an iterative technique, IPDA integrates Decline Curve Analysis, Type Curve Matching, and Numerical Reservoir Simulation (History Matching) in order to converge to a set of reservoir characteristics, compatible with all three techniques. Furthermore, once reservoir characteristics for individual wells in the field are identi...
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Copyright 1999, Society of Petroleum Engineers Inc. This paper was prepared for presentation at the 1999 SPE Eastern Regional Meeting held in Charleston, WV, 2122 October 1999. This paper was selected for presentation by an SPE Program... more
Copyright 1999, Society of Petroleum Engineers Inc. This paper was prepared for presentation at the 1999 SPE Eastern Regional Meeting held in Charleston, WV, 2122 October 1999. This paper was selected for presentation by an SPE Program Committee following review of ...
Copyright 2002, Society of Petroleum Engineers Inc. This paper was prepared for presentation at the SPE Annual Technical Conference and Exhibition held in San Antonio, Texas, 29 September2 October 2002. This paper was selected for... more
Copyright 2002, Society of Petroleum Engineers Inc. This paper was prepared for presentation at the SPE Annual Technical Conference and Exhibition held in San Antonio, Texas, 29 September2 October 2002. This paper was selected for presentation by an SPE ...