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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
RESERVOIR CHARACTERIZATION The second volume in the series, "Sustainable Energy Engineering," written by some of the foremost authorities in the world on reservoir engineering, this groundbreaking new volume presents the most comprehensive and updated new processes, equipment, and practical applications in the field. Long thought of as not being "sustainable," newly discovered sources of petroleum and newly developed methods for petroleum extraction have made it clear that not only can the petroleum industry march toward sustainability, but it can be made "greener" and more environmentally friendly. Sustainable energy engineering is where the technical, economic, and environmental aspects of energy production intersect and affect each other. This collection of papers covers the strategic and economic implications of methods used to characterize petroleum reservoirs. Born out of the journal by the same name, formerly published by Scrivener Publishing, most of the articles in this volume have been updated, and there are some new additions, as well, to keep the engineer abreast of any updates and new methods in the industry. Truly a snapshot of the state of the art, this groundbreaking volume is a must-have for any petroleum engineer working in the field, environmental engineers, petroleum engineering students, and any other engineer or scientist working with reservoirs. This outstanding new volume: * Is a collection of papers on reservoir characterization written by world-renowned engineers and scientists and presents them here, in one volume * Contains in-depth coverage of not just the fundamentals of reservoir characterization, but the anomalies and challenges, set in application-based, real-world situations * Covers reservoir characterization for the engineer to be able to solve daily problems on the job, whether in the field or in the office * Deconstructs myths that are prevalent and deeply rooted in the industry and reconstructs logical solutions * Is a valuable resource for the veteran engineer, new hire, or petroleum engineering student
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Table of Contents
Cover
Title Page
Copyright
Foreword
Preface
Part 1: Introduction
1 Reservoir Characterization: Fundamental and Applications – An Overview
1.1 Introduction to Reservoir Characterization?
1.2 Data Requirements for Reservoir Characterization
1.3 SURE Challenge
1.4 Reservoir Characterization in the Exploration, Development and Production Phases
1.5 Dynamic Reservoir Characterization (DRC)
1.6 More on Reservoir Characterization and Reservoir Modeling for Reservoir Simulation
1.7 Conclusion
1.8 References
Part 2: General Reservoir Characterization and Anomaly Detection
2 A Comparison Between Estimated Shear Wave Velocity and Elastic Modulus by Empirical Equations and that of Laboratory Measurements at Reservoir Pressure Condition
2.1 Introduction
2.2 Methodology
2.3 Laboratory Set Up and Measurements
2.4 Results and Discussion
2.5 Conclusions
2.6 Acknowledgment
References
3 Anomaly Detection within Homogenous Geologic Area
3.1 Introduction
3.2 Anomaly Detection Methodology
3.3 Basic Anomaly Detection Classifiers
3.4 Prior and Posterior Characteristics of Anomaly Detection Performance
3.5 ROC Curve Analysis
3.6 Optimization of Aggregated AD Classifier Using Part of the Anomaly Identified by Universal Classifiers
3.7 Bootstrap Based Tests of Anomaly Type Hypothesis
3.8 Conclusion
References
4 Characterization of Carbonate Source-Derived Hydrocarbons Using Advanced Geochemical Technologies
4.1 Introduction
4.2 Samples and Analyses Performed
4.3 Results and Discussions
4.4 Summary and Conclusions
References
5 Strategies in High-Data-Rate MWD Mud Pulse Telemetry
5.1 Summary
5.2 New Technology Elements
5.3 Directional Wave Filtering
5.4 Conclusions
Acknowledgments
References
6 Detection of Geologic Anomalies with Monte Carlo Clustering Assemblies
6.1 Introduction
6.2 Analysis of Inhomogeneity of the Training and Test Sets and Instability of Clustering
6.3 Formation of Multiple Randomized Test Sets and Construction of the Clustering Assemblies
6.4 Irregularity Index of Individual Clusters in the Cluster Set
6.5 Anomaly Indexes of Individual Records and Clustering Assemblies
6.6 Prior and Posterior True and False Discovery Rates for Anomalous and Regular Records
6.7 Estimates of Prior False Discovery Rates for Anomalous Cluster Sets, Clusters, and Individual Records. Permeability Dataset
6.8 Posterior Analysis of Efficiency of Anomaly Identification. High Permeability Anomaly
6.9 Identification of Records in the Gas Sand Dataset as Anomalous, using Brine Sand Dataset as Data with Regular Records
6.10 Notations
6.11 Conclusions
References
7 Dissimilarity Analysis of Petrophysical Parameters as Gas-Sand Predictors
7.1 Introduction
7.2 Petrophysical Parameters for Gas-Sand Identification
7.3 Lithologic and Fluid Content Dissimilarities of Values of Petrophysical Parameters
7.4 Parameter Ranking and Efficiency of Identification of Gas-Sands
7.5 ROC Curve Analysis with Cross Validation
7.6 Ranking Parameters According to AUC Values
7.7 Classification with Multidimensional Parameters as Gas Predictors
7.8 Conclusions
Definitions and Notations
References
8 Use of Type Curve for Analyzing Non-Newtonian Fluid Flow Tests Distorted by Wellbore Storage Effects
8.1 Introduction
8.2 Objective
8.3 Problem Analysis
8.4 Use of Finite Element
8.5 Analysis Methodology
8.6 Test Data Examples
8.7 Conclusion
Nomenclature
References
Appendix A: Non-Linear Boundary Condition and Laplace Transform
Appendix B: Type Curve Charts for Various Power Law Indices
Part 3: Reservoir Permeability Detection
9 Permeability Prediction Using Machine Learning, Exponential, Multiplicative, and Hybrid Models
9.1 Introduction
9.2 Additive, Multiplicative, Exponential, and Hybrid Permeability Models
9.3 Combination of Basis Function Expansion and Exhaustive Search for Optimum Subset of Predictors
9.4 Outliers in the Forecasts Produced with Four Permeability Models
9.5 Additive, Multiplicative, and Exponential Committee Machines
9.6 Permeability Forecast with First Level Committee Machines. Sandstone Dataset
9.7 Permeability Prediction with First Level Committee Machines. Carbonate Reservoirs
9.8 Analysis of Accuracy of Outlier Replacement by The First and Second Level Committee Machines. Sandstone Dataset
9.9 Conclusion
Notations and Definitions
References
10 Geological and Geophysical Criteria for Identifying Zones of High Gas Permeability of Coals (Using the Example of Kuzbass CBM Deposits)
10.1 Introduction
10.2 Physical Properties and External Load Conditions on a Coal Reservoir
10.3 Basis for Evaluating Physical and Mechanical Coalbed Properties in the Borehole Environment
10.4 Conclusions
Acknowledgement
References
11 Rock Permeability Forecasts Using Machine Learning and Monte Carlo Committee Machines
11.1 Introduction
11.2 Monte Carlo Cross Validation and Monte Carlo Committee Machines
11.3 Performance of Extended MC Cross Validation and Construction MC Committee Machines
11.4 Parameters of Distribution of the Number of Individual Forecasts in Monte Carlo Cross Validation
11.5 Linear Regression Permeability Forecast with Empirical Permeability Models
11.6 Accuracy of the Forecasts with Machine Learning Methods
11.7 Analysis of Instability of the Forecast
11.8 Enhancement of Stability of the MC Committee Machines Forecast Via Increase of the Number of Individual Forecasts
11.9 Conclusions
Nomenclature
Appendix 1- Description of Permeability Models from Different Fields
Appendix 2- A Brief Overview of Modular Networks or Committee Machines
References
Part 4: Reserves Evaluation/Decision Making
12 The Gulf of Mexico Petroleum System – Foundation for Science-Based Decision Making
Introduction
Basin Development and Geologic Overview
Petroleum System
Reservoir Geology
Hydrocarbons
Salt and Structure
Conclusions
Acknowledgments and Disclaimer
References
13 Forecast and Uncertainty Analysis of Production Decline Trends with Bootstrap and Monte Carlo Modeling
13.1 Introduction
13.2 Simulated Decline Curves
13.3 Nonlinear Least Squares for Decline Curve Approximation
13.4 New Method of Grid Search for Approximation and Forecast of Decline Curves
13.5 Iterative Minimization of Least Squares with Multiple Approximating Models
13.6 Grid Search Followed by Iterative Minimization with Levenberg-Marquardt Algorithm
13.7 Two Methods for Aggregated Forecast and Analysis of Forecast Uncertainty
13.8 Uncertainty Quantile Ranges Obtained Using Monte Carlo and Bootstrap Methods
13.9 Monte Carlo Forecast and Analysis of Forecast Uncertainty
13.10 Block Bootstrap Forecast and Analysis of Forecast Uncertainty
13.11 Comparative Analysis of Results of Monte Carlo and Bootstrap Simulations
13.12 Conclusions
References
14 Oil and Gas Company Production, Reserves, and Valuation
14.1 Introduction
14.2 Reserves
14.3 Production
14.4 Factors that Impact Company Value
14.5 Summary Statistics
14.6 Market Capitalization
14.7 International Oil Companies
14.8 U.S. Independents
14.9 Private Companies
14.10 National Oil Companies of OPEC
14.11 Government Sponsored Enterprises and Other International Companies
14.12 Conclusions
References
Part 5: Unconventional Reservoirs
15 An Analytical Thermal-Model for Optimization
15.1 Introduction
15.2 Mathematical Model
15.3 Model Comparison
15.4 Sensitivity Analysis
15.5 Model Applications
15.6 Conclusions
Nomenclature
Acknowledgements
References
Appendix A: Steady Heat Transfer Solution for Fluid Temperature in Counter-Current Flow
Assumptions
Governing Equation
Boundary Conditions
Solution
16 Development of an Analytical Model for Predicting the Fluid Temperature Profile in Drilling Gas Hydrates Reservoirs
16.1 Introduction
16.2 Mathematical Model
16.3 Case Study
16.4 Sensitivity Analysis
16.5 Conclusions
Acknowledgements
Nomenclature
References
17 Distinguishing Between Brine-Saturated and Gas-Saturated Shaly Formations with a Monte-Carlo Simulation of Seismic Velocities
17.1 Introduction
17.2 Random Models for Seismic Velocities
17.3 Variability of Seismic Velocities Predicted by Random Models
17.4 The Separability of (
V
p
,
V
s
) Clusters for Gas- and Brine-Saturated Formations
17.5 Reliability Analysis of Identifying Gas-Filled Formations
17.6 Conclusions
References
18 Shale Mechanical Properties Influence Factors Overview and Experimental Investigation on Water Content Effects
18.1 Introduction
18.2 Influence Factors
18.3 Experimental Investigation of Water Saturation Effects on Shale’s Mechanical Properties
18.4 Conclusions
Acknowledgements
References
Part 6: Enhance Oil Recovery
19 A Numerical Investigation of Enhanced Oil Recovery Using Hydrophilic Nanofuids
19.1 Introduction
19.2 Simulation Framework
19.3 Coupling of Mathematical Models
19.4 Verification Cases
19.5 Results
19.6 Discussions
19.7 Conclusions and Future Work
References
20 3D Seismic-Assisted CO
2
-EOR Flow Simulation for the Tensleep Formation at Teapot Dome, USA
20.1 Presentation Sequence
20.2 Introduction
20.3 Geological Background
20.4 Discrete Fracture Network (DFN)
20.5 Petrophysical Modeling
20.6 PVT Analysis
20.7 Streamline Analysis
20.8 CO
2
-EOR
20.9 Conclusions
Acknowledgement
References
Part 7: New Advances in Reservoir Characterization-Machine Learning Applications
21 Application of Machine Learning in Reservoir Characterization
21.1 Brief Introduction to Reservoir Characterization
21.2 Artificial Intelligence and Machine (Deep) Learning Review
21.3 Artificial Intelligence and Machine (Deep) Learning Applications to Reservoir Characterization
21.4 Machine (Deep) Learning and Enhanced Oil Recovery (EOR)
21.5 Conclusion
Acknowledgement
References
Index
End User License Agreement
Tables
Chapter 3
Table 3.1 Mean values of false and true discovery rates for three classifers in ...
Table 3.2 Mean and three quantiles of distribution of AUC values.
Table 3.3 Percent of values of
anomalyIndicator
exceeding significance cutoff
α
...
Chapter 4
Table 4.1 Results of TOC and Rock-Evaol Pyrolysis for the rock samples analyzed.
Table 4.2 Typical biomarker characteristics of shale and carbonate-derived hydro...
Chapter 6
Table 6.1 Prior rates of false discovery of anomalous clusters, and cluster sets...
Table 6.2 Parameters of clusters, labeled as anomalous with individual cluster a...
Table 6.3 Thresholds, true, and false discovery rates of anomalous records using...
Chapter 7
Table 7.1 Dissimilarities among values of 14 parameters in 4 combinations of gas...
Table 7.2 AUC values for four classification methods and 14 predictor parameters...
Chapter 8
Table 8.1 Input parameters for Example 1.
Table 8.2 Comparison of analyses for Example 1.
Table 8.3 Input parameters for Example 2.
Table 8.4 Comparison of analyses for Example 2.
Table 8.5 Input parameters for Example 3.
Table 8.6 Simulation input vs. analysis results for Example 3.
Table 8.7 Input parameters for Example 4.
Table 8.8 Simulation input vs. analysis results for Example 4.
Chapter 9
Table 9.1 Percent of identified outliers among individual forecasts for five val...
Table 9.2 Number of outliers at the output of the first level committee machine....
Table 9.3 Bias of the forecast with four predictors for four permeability models...
Table 9.4 Absolute bias of the output of first level committee machine produced ...
Table 9.5 Root Mean Squared error (mD) for individual forecasts with hybrid mode...
Table 9.6 Mean instability of individual forecasts with hybrid model for the rec...
Table 9.7 Outliers of permeability forecasts with multiplicative model and their...
Chapter 11
Table 11.1 Parameters of distribution of a number of individual forecasts for in...
Table 11.2 Percent of identified outliers among individual forecasts for five va...
Table 11.3 Mean absolute bias of the forecasts by Monte Carlo committee machines...
Table 11.4 Instability index of individual forecasts with six machine learning m...
Table 11.5 Correlation coefficients between outputs of Monte Carlo committee mac...
Table 11.6 Instability of individual forecasts for eight permeability records.
Chapter 13
Table 13.1 Example of the estimated parameters of the SEPD-type decline curve.
Table 13.2 Example of estimated covariance matrix for parameters of SEPD decline...
Table 13.3 Monte Carlo simulated coefficients of the SEPD model. Mean 10,000: me...
Table 13.4 Forecasted mean and median production values derived from Monte Carlo...
Chapter 14
Table 14.1 Model variables and definitions.
Table 14.2 Integrated oil company reserves and financial data (2010).
Table 14.3 Statistical summary of the independent oil and gas companies sampled ...
Table 14.4 Regression results of integrated oil companies (2010).
Table 14.5 Regression results of independent oil companies (2010). CAP.
Table 14.6 Regression results of independent oil companies – three independent v...
Table 14.7 Regression results of independent oil companies – four independent va...
Table 14.8 Estimated reserves and assets of private companies (2010).
Table 14.9 Effective market capitalization of National Oil Companies in OPEC (20...
Table 14.10 Actual and computed market capitalization of companies outside North...
Table A.1 Sample of independent oil producers
3
(2010).
Table A.2 Sample of independent gas producers
a
(2010).
Chapter 15
Table 15.1 Data used in comparison of analytical solutions.
Chapter 16
Table 16.1 Well and operation parameter values in drilling well NGHP-01-17A.
Table 16.2 Estimated material properties in drilling well NGHP-01-17A.
Chapter 18
Table 18.1 Uniaxial compressive strength of typical shales [28] (Steiger and Leu...
Table 18.2 Summary of difference in strength for the upper & middle woodford sha...
Table 18.3 Different mineral constituents of three-layer formation [47] (Marouby...
Table 18.4 Where
φ
is porosity;
S
w
is water saturation; E is Young’s modulus, ps...
Table 18.5 The result of error analysis.
Chapter 19
Table 19.1 Parameters used in simulation.
Table 19.2 Oil and water relative permeability data with water flooding.
Table 19.3 Oil and water relative permeability data with nanofuids flooding.
Table 19.4 Geometric parameters of the model.
Table 19.5 Base data for model analysis.
Table 19.6 Oil recovery and mass of trapped nanoparticle under different injecti...
Table 19.7 Oil recovery and nanoparticles trapped under different slug size.
Table 19.8 Oil recovery and nanoparticle recovery under different flow rate rati...
Table 19.9 Input data for 3D simulation.
Chapter 20
Table 20.1 The mean and standard deviation of apertures observed in the CT of Co...
Table 20.2 Matrix porosity (ft
3
/ft
3
), permeability (mD.), and average cell thick...
Table 20.3 Oil properties of the Tensleep reservoir at Teapot Dome, Wyoming [41]...
Table 20.4 The oil sample composition at the surface condition [41].
Table 20.5 The 8-components Peng-Robinson EOS parameters.
Chapter 21
Table 21.1 Chemical EOR input and output data from [31].
Guide
Cover
Table of Contents
Title page
Copyright
Foreword
Preface
Begin Reading
Index
Also of Interest
End User License Agreement
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Scrivener Publishing
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Sustainable Energy Engineering
Series Editor: Fred Aminzadeh
Scope: This series’s mission is to publish peer-reviewed, original research seeking sustainable methods of worldwide energy production, distribution, and utilization through engineering, scientific, and technological advances in the areas of both fossil fuels and renewable energy. Energy issues cannot be addressed in isolation, without attention to the economy and the environment. Thus, this series introduces the “E cubed” concept, addressing sustainability with a three-pronged emphasis on energy, economy and environment, publishing research in all of these areas, and their intersections, as they apply to global energy sustainability. This unique multi-disciplinary theme allows the introduction of new and cutting-edge processes and technologies across all areas of energy production, transportation, and transmission, including fossil fuels and renewable energy and their intersection with the economy and environment. Papers are invited on any individual topic or those which are interdisciplinary.
Publishers at Scrivener
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Phillip Carmical ([email protected])
Reservoir Characterization
Fundamentals and Applications
Fred Aminzadeh
This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA © 2022 Scrivener Publishing LLC For more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 9781119556213
Cover image: Geo/Rock Wall, 31647625 © Pzaxe | Dreamstime.com Cover design by Kris Hackerott
Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines
Printed in the USA
10 9 8 7 6 5 4 3 2 1
Foreword
What is reservoir characterization? As you will see from this book, this is a very advanced topic so let’s break it down a bit and start form the basics. What is a reservoir? This is ‘a place where something is kept in store’. And what is characterization? That is ‘to describe the character or quality’ all according to the Webster dictionary. So, we are arrived at: ‘describe the character of something that’s kept in store’. It seems relatively benign and easy but ‘the devil is in the details’ is perhaps the best way to get the readers intrigued and immersed in this topic. So, we are left wondering what are these details where the devil resides? And here starts the story…..
In fact, a better wording would be ‘Subsurface Reservoir Characterization’ or SRC. There have been on the order of thousands of studies in reservoir characterization over the life time of this field. As such, this topic has evolved and matured with many learnings. As illustrated in this book, there are now well established and tested workflows SRC and I‘d like to go over some aspects of these understandings and workflows.
First, it is key to understand that SRC is a continuously changing, multi-discipline and multi-scale topic. For continuously changing a good example would be the recent impact of say machine learning methods. I have learned that if our data quality is good enough and there are physical relationships between reservoir data and properties, machine learning can be an excellent way to quickly uncover relationships in a multi-variable universe. However, once again, even here, the devil is in the details…. Multi-discipline is a word we easily use but have difficulty implementing. In many projects the geologist is tasked with building a static reservoir model and then passing it on to the reservoir engineer to build a dynamic model and history match production. However, it has been challenging to form a loop versus a linear workflow or for the dynamic model to be updated with new static information or cover a range of possible models that fit the data…… As for multi-scale, the discipline involves integration of data from a wide range of data, say, nanometer (electron microscope), to centimeter (cutting and core samples), to decimeter (well log), to meter (seismic) scale. Spatially most of these data are acquired within a small portion of one or several wells and geophysical data gives the capability to extrapolate away from the wells with lower resolution. Due to uncertainties in the data, rapid variations in the subsurface, and sparse sampling multi-scale integration can be a challenging task. There is a good discussion of “SURE Challenge” in the book where the author addresses the above mentioned challenges of integration incolving multitude of data set with different Scale, Unvertainty, Resoultion and Environemnet. It is suggested that different AI and Data Analytics techniques may be best equipped to handle the SURE Challenge.
The second component can be categorized into input data quality (informally ‘garbage in, garbage out’). Any workflow that is lets say cutting edge cant work without high quality input data. Further, it may cause mis-interpretation that a workflow is ‘not’ a good workflow or appropriate simply because the input data was the culprit. The input data in fact starts from data acquisition, then to data processing and finally to data interpretation and integration. One of the pitfalls along the way is to simply obtain the data as an interpreter and not be aware of lets say the ‘history’. An example would be to apply amplitude based seismic analysis to data that non-amplitude preserving processing was applied to (Automatic Gain Control or AGC would be a simple example). However, the same could be happening with say well-log or production data. The good news is that over time in every SRC related discipline data quality has been improving with not only better tools but also more frequent data acquisition during the life of a reservoir. Further, over time we have learned to build much better processing tools that provide high quality data for the integration component. The net result of this has improved our ability to conduct integrated studies and quantitative products. One example of this near to my heart is joint seismic inversion of PP reflected waves with PS (or converted) reflected waves from a reservoir. We have seen that with improved acquisition and processing, the joint PP/PS inversion can substantially improve pre-stack seismic inversion providing a stable S-impedance as well as a P-impedance that can provide valuable information such as formation properties, porosity, Total Organic Carbon (TOC,) fluid types, and time-lapse reservoir pressure and saturation changes over the life of the reservoir. Such improvements are going on in all the subsurface disciplines thanks to modern acquisition and more diverse data with higher quality.
This book is an excellent resource for beginners in SRC to get an overview of the topic and for expert to study most recent advances in their own and related disciplines. The book covers a wide range of topics from conventional to unconventional reservoirs, from geology to geophysics to petroleum engineering, from laboratory measurements to field applications, from deterministic to statistical methods, from primary depletion to EOR with CO2 injection, from static to dynamic SRC, as well as use of AI for reservoir charcaterization. In the end, SRC requires best practices to be implemented to be value generating. This books certainly provides the necessary best practices.
Dr. Ali Tura
Professor of Geophysics Co-director of Reservoir Characterization Project (RCP) Colorado School of Mine, Denver, Colorado
Preface
An important step in exploration development, monitoring, and management a reservoir as well optimizing production and planning for post primary production decisions is reservoir characterization. Upon the completion of the preliminary task of reservoir characterization, and as we continue to produce from the reservoir or use different methods to stimulate it, many of its properties change. This requires updating the reservoir model, bringing up the concept of dynamic reservoir characterization. To achieve this goal, we incorporate the newly acquired petrophysical, seismic, micro seismic and production data. The updated model would be a better representative of the status of the reservoir. Both static reservoir properties, such as porosity, permeability, and facies type; and dynamic reservoir properties, such as pressure, fluid saturation, and temperature, needs to be updated as more field data become available.
Among the reason for focusing on reservoir characterization is the fact based on the estimates by experts, more than 95% of the world’s oil production in the 21st century will come from existing fields. This will require significant improvements in the current recovery rates of less than 50% in most reservoirs. Improved secondary and tertiary recovery through enhanced recovery of oil and gas require by better understanding and monitoring of the reservoir will be an important element of the much-needed increase in the recovery factor. Increased production will be made possible only through effective dynamic reservoir characterization.
We need to recognize the fact that reservoir characterization is a multidisciplinary field. It attempts to describe petroleum deposits and the nature of the rocks that contain hydrocarbons using a variety of data types. Reservoir characterization relies on expertise from petroleum engineers, geologists, geochemists, petrophysicists, and of course geophysicists, The integration of information from these fields, with the aid of advanced data analysis techniques as well as artificial intelligence (AI) based methods will make our reservoir models more accurate and the updating process much faster.
This book will provide a comprehensive body of technical material on different aspects of reservoir characterization. It is divided into 7 parts: Part 1 is an introductory chapter covering the general concepts, of reservoir characterization. It includes an overview of what is meant by reservoir characterization as it is applied in different stages of its life, from exploration to post primary production stages. It also highlights the challenges of data integration of different data types, the previously mentioned dynamic reservoir characterization, and reservoir stimulation for enhanced oil (or gas) recovery.
Part 2 deals with general issues on reservoir characterization and anomaly detection. It is comprised of 7 chapters on different related topics such as: (1) Comparison between estimated shear wave velocity and elastic modulus at in situ pressure condition (2) Anomaly detection (3) geochemical analysis on characterization of carbonate source-derived hydrocarbons, (4) MWD mud pulse telemetry, (5) Use of Monte Carlo clustering to detect geologic anomalies, (6) Gas-sand predictors using dissimilarity analysis, and (7) Fluid flow tests distorted by wellbore storage effects. Part 3 is dedicated to reservoir permeability detection, being one of the most important reservoir properties. What are covered here are three different techniques. Two of them involves use of two different machine learning techniques to predict permeability, namely, exponential/multiplicative and Monte Carlo/committee machines. The other chapter discusses geoscience criteria identifying high gas permeability zones.
One of the reasons for reservoir characterization is to assess the recoverable reserves in the reservoir. Part 4 addresses reserves evaluation and decision-making issues. The first chapter of this part discusses foundation for science-based decision making, using data from the Gulf of Mexico. The next chapter in Part 4 investigates decline trends in a reservoir using Bootstrap and Monte Carlo modeling. This Part concludes with a typical production, reserves, and valuation method used in an oil and gas company.
Given the tremendous success with the development and production from shale reservoirs over the last 2 decades, Part 5 is dedicated to the unconventional reservoirs. The chapters in Part 5 include: (1) Optimization of Gas-Drilling in Unconventional Tight-Sand Reservoirs, (2) Predicting the Fluid Temperature Profile in Drilling Gas Hydrates Reservoirs, (3) Distinguishing between brine and gas-saturated shaly formations, and (4) Influence of shale mechanical properties on water content effects.
Part 6 is about enhanced oil recovery. It covers EOR with hydrophilic nanofluids, as well as CO2-EOR Flow Simulation for the Tensleep Formation using 3D seismic data. Part 7 is the concluding section, highlighting new advances in reservoir characterization. It discusses the recent application of machine learning in reservoir characterization. It also discusses the future trends in reservoir characterization and the impact of data explosion associated with the real time reservoir monitoring and reservoir surveillance. It also describes how the “Big Data” concepts and data analytics techniques will play a role in the next generation reservoir characterization technology developments.
It should be understood reservoir characterization is an evolving technology. It is our hope that this volume will be a meaningful addition to the current body of literature and will help pave the way for further advances on the subject matter in the future.
Fred Aminzadeh
Santa Barbara, California September 22, 2021
Part 1INTRODUCTION
1Reservoir Characterization: Fundamental and Applications - An Overview
Fred Aminzadeh
FACT Inc., Santa Barbara, CA, USA
Abstract
This article provides a brief overview of reservoir characterization at different stages of a field from exploration to development to production and post primary production. It demonstrates the challenges associated with integration of different data types. It also shows how “Dynamic Reservoir Characterization” can assist in monitoring of the field for various well stimulation processes such as enhanced oil recovery as well as reservoir stimulation. Different sections of this entry attempt to highlight different aspects of reservoir characterization, as an exploration tool, development tool, production tool and monitoring tool. As reservoirs age, different measures are taken to extend their productive life. This includes different types of reservoir stimulation and enhanced oil (or gas) recovery.
Keywords: Reservoir characterization, data integration challenges, 3D/4D seismic, micro-seismic data, reservoir monitoring, dynamic reservoir characterization, rock physics and enhanced oil recovery (EOR)
1.1 Introduction to Reservoir Characterization?
As discussed in Aminzadeh and Dasgupta [2], Reservoir Characterization is to assess reservoir condition and its properties using the available data from core/log data to seismic and production data. This is done to assist in delineating or describing a reservoir. Reservoir characterization and modeling have become increasingly important for optimizing field development.
Reservoir valuation and producing from a field demands a realistic description of the reservoir, requiring an integrated reservoir characterization and modeling. An integrated approach for reservoir modeling bridges the traditional disciplinary divides and tears down interdisciplinary barriers, leading to better handling of uncertainties and improvement of the reservoir model for field development. Integrated reservoir management requires better characterization of the reservoir and it is imperative to a successful operation throughout the life of the reservoir. Dynamic reservoir characterization is to understand the changes in reservoir properties to monitor its performance as we produce from reservoir and/or stimulate the reservoir to enhance production. This is accomplished by the analysis of data from combination of different sources, to extract additional information about the in-situ conditions of the reservoir, including the formation temperature, pressure, and the properties of the oil, gas, and brine. Other reservoir properties that can affect measured data are density, hydrocarbon viscosity, stresses, and fractures. We start with the reservoir description process that generates models of reservoir architecture, lithologies and facies. The geometry of the flow units is established, physical rock properties such as porosities and permeabilities of flow layers. Three properties are related to the pore space: porosity--the fraction of the entire volume part occupied by pores, cracks and fractures, internal surface: the magnitude of the surface of pores as related to the rock mass pore volume and controls interface-effects at the boundary grain - pore fluid, permeability: the ability to flow fluid through rock pores. Porosity and specific internal surface are scalar properties, permeability is a tensor.
Figure 1.1 shows integration of reservoir structure or architecture and reservoir detailed properties from calibration with well data for the reservoir model. Reservoir description is an iterative process and need.
Different aspects of Figure 1.1, from the input data to the process (well data, seismic data, production data, etc.) will be discussed in Section 1.2 on the data requirements. The difficulties associated with the integration of different data sources will be addressed in Section 1.3, under “SURE Challenge”. In Section 1.4 we discuss different aspects of reservoir characterizations fin different stages of reservoir life. The exploration and development stage deal with preliminary determination of the reservoir structural model, stratigraphic and facies models. This is followed by the production phase with a focus on porosity, permeability and fluid saturation, involving reservoir/flow simulation and history matching. The recovery stages involve injection of water/CO2 or steam to increase production. We discuss Dynamic Reservoir Characterization (DRC) in Section 1.5. We note that 4D seismic and microseismic data play an important role in geo-model updating monitoring production and the EOR/reservoir stimulation process. Sections 1.6 goes into more details on rock physics and reservoir modeling and how reservoir characterization can be used as an input to reservoir simulation and help with enhanced oil recovery and other well stimulation processes.
Figure 1.1 Different components of reservoir characterization, from Fornel and Estublier [5].
1.2 Data Requirements for Reservoir Characterization
Well data provides vertically high-resolution model at the well location, however, the distribution of well in a field are sparse. Combining well information with geophysical and geological data allows the necessary constraints for extrapolating high resolution well data beyond where they are measured thus increasing the coverage. For every phase of the reservoir life cycle from discovery to development to operating to maturity and well stimulation (enhanced oil recovery) phase, geophysical tools are used to create reservoir model with the associated properties and update the model based new data collected.
Integration of geophysical data with geologic data, and engineering measurements improves our understanding of the reservoir, reduces uncertainties and mitigates the risk. The detailed spatial coverage offered are calibrated with analysis of well logs, pressure tests, cores, fracture system, geologic depositional knowledge and other information from appraisal wells. 3D seismic is the primary geophysical technique used to create the original reservoir models. 4D seismic (time lapse data) and other new measurements (micro-seismic, new log/pressure data and production data help create updated (dynamic) reservoir model. In addition, gravity, controlled source EM, borehole measurements such as vertical seismic profiling-VSP, borehole gravimeter-BRGM, cross well seismic, cross well EM are also used to build the original and updated reservoir models.
The required information for the petroleum engineers and geologists includes subsurface lithology, net pay, porosity, permeability, reservoir fluid-fill, fluid contacts, reservoir pressure and stress regime. Geophysical tools infer reservoir properties from the measured physical observations by blending these with measurements made at the wells like well logs, well tests and core analyses. During the field appraisal and development stages, understanding of the reservoir matrix properties and fluid distribution within the reservoir are of great importance.
Figure 1.2
