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...