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E-raamat: Reservoir Characterization: Fundamentals and Applications, Volume 2

Fred Aminzadeh (University of Southern California, CA; University of Houston, TX)

Formaat: PDF+DRM
Ilmumisaeg: 08-Dec-2021
Kirjastus: Wiley-Scrivener
Keel: eng
ISBN-13: 9781119556251

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Ilmumisaeg: 08-Dec-2021
Kirjastus: Wiley-Scrivener
Keel: eng
ISBN-13: 9781119556251

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RESERVOIR CHARACTERIZATION FUNDAMENTALS AND APPLICATIONS

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

Foreword

xix

Preface

xxiii

Part 1: Introduction

(22)

1 Reservoir Characterization: Fundamental and Applications - An Overview

(20)

Fred Aminzadeh

1.1 Introduction to Reservoir Characterization?

(2)

1.2 Data Requirements for Reservoir Characterization

(2)

1.3 SURE Challenge

(3)

1.4 Reservoir Characterization in the Exploration, Development and Production Phases

(2)

1.4.1 Exploration Stage/Development Stage

(1)

1.4.2 Primary Production Stage

(1)

1.4.3 Secondary/Tertiary Production Stage

(1)

1.5 Dynamic Reservoir Characterization (DRC)

(3)

1.5.1 4D Seismic for DRC

(1)

1.5.2 Microseismic Data for DRC

(1)

1.6 More on Reservoir Characterization and Reservoir Modeling for Reservoir Simulation

(5)

1.6.1 Rock Physics

(1)

1.6.2 Reservoir Modeling

(3)

1.7 Conclusion

(1)

References

(3)

Part 2: General Reservoir Characterization and Anomaly Detection

(172)

2 A Comparison Between Estimated Shear Wave Velocity and Elastic Modulus by Empirical Equations and that of Laboratory Measurements at Reservoir Pressure Condition

(22)

Haleh Azizia

Hamid Reza Siahkoohi

Brian Evans

Nasser Keshavarz Farajkhah

Ezatollah KazemZadeh

2.1 Introduction

(2)

2.2 Methodology

(4)

2.1.2 Estimating the Shear Wave Velocity

(3)

2.2.2 Estimating Geomechanical Parameters

(1)

2.3 Laboratory Set Up and Measurements

(3)

2.3.1 Laboratory Data Collection

(1)

2.4 Results and Discussion

(6)

2.5 Conclusions

(2)

2.6 Acknowledgment

(1)

References

(4)

3 Anomaly Detection within Homogenous Geologic Area

(22)

Simon Katz

Fred Aminzadeh

George Chilingar

Leonid Khilyuk

3.1 Introduction

(1)

3.2 Anomaly Detection Methodology

(1)

3.3 Basic Anomaly Detection Classifiers

(2)

3.4 Prior and Posterior Characteristics of Anomaly Detection Performance

(3)

3.5 ROC Curve Analysis

(3)

3.6 Optimization of Aggregated AD Classifier Using Part of the Anomaly Identified by Universal Classifiers

(3)

3.7 Bootstrap Based Tests of Anomaly Type Hypothesis

(3)

3.8 Conclusion

(1)

References

(4)

4 Characterization of Carbonate Source-Derived Hydrocarbons Using Advanced Geochemical Technologies

(12)

Hossein Alimi

4.1 Introduction

(1)

4.2 Samples and Analyses Performed

(1)

4.3 Results and Discussions

(7)

4.4 Summary and Conclusions

(1)

References

(1)

5 Strategies in High-Data-Rate MWD Mud Pulse Telemetry

(54)

Yinao Su

Limin Sheng

Lin Li

Hailong Bian

Rong Shi

Xiaoying Zhuang

Wilson Chin

5.1 Summary

(6)

5.1.1 High Data Rates and Energy Sustainability

(1)

5.1.2 Introduction

(2)

5.1.3 MWD Telemetry Basics

(2)

5.1.4 New Telemetry Approach

(1)

5.2 New Technology Elements

(23)

5.2.1 Downhole Source and Signal Optimization

(3)

5.2.2 Surface Signal Processing and Noise Removal

(1)

5.2.3 Pressure, Torque and Erosion Computer Modeling

(3)

5.2.4 Wind Tunnel Analysis: Studying New Approaches

(12)

5.2.5 Example Test Results

108

(3)

5.3 Directional Wave Filtering

111

(21)

5.3.1 Background Remarks

111

(1)

5.3.2 Theory

112

(4)

5.3.3 Calculations

116

(16)

5.4 Conclusions

132

(1)

Acknowledgments

133

(1)

References

133

(2)

6 Detection of Geologic Anomalies with Monte Carlo Clustering Assemblies

135

(16)

Simon Katz

Fred Aminzadeh

George Chilingar

Leonid Khilyuk

Matin Lockpour

6.1 Introduction

135

(1)

6.2 Analysis of Inhomogeneity of the Training and Test Sets and Instability of Clustering

136

(2)

6.3 Formation of Multiple Randomized Test Sets and Construction of the Clustering Assemblies

138

(1)

6.4 Irregularity Index of Individual Clusters in the Cluster Set

139

(2)

6.5 Anomaly Indexes of Individual Records and Clustering Assemblies

141

(1)

6.6 Prior and Posterior True and False Discovery Rates for Anomalous and Regular Records

142

(1)

6.7 Estimates of Prior False Discovery Rates for Anomalous Cluster Sets, Clusters, and Individual Records. Permeability Dataset

142

(2)

6.8 Posterior Analysis of Efficiency of Anomaly Identification. High Permeability Anomaly

144

(2)

6.9 Identification of Records in the Gas Sand Dataset as Anomalous, using Brine Sand Dataset as Data with Regular Records

146

(3)

6.10 Notations

149

(1)

6.11 Conclusions

149

(1)

References

150

(1)

7 Dissimilarity Analysis of Petrophysical Parameters as Gas-Sand Predictors

151

(18)

Simon Katz

George Chilingar

Fred Aminzadeh

Leonid Khilyuk

7.1 Introduction

152

(1)

7.2 Petrophysical Parameters for Gas-Sand Identification

152

(2)

7.3 Lithologic and Fluid Content Dissimilarities of Values of Petrophysical Parameters

154

(1)

7.4 Parameter Ranking and Efficiency of Identification of Gas-Sands

155

(4)

7.5 ROC Curve Analysis with Cross Validation

159

(2)

7.6 Ranking Parameters According to AUC Values

161

(2)

7.7 Classification with Multidimensional Parameters as Gas Predictors

163

(1)

7.8 Conclusions

164

(2)

Definitions and Notations

166

(1)

References

166

(3)

8 Use of Type Curve for Analyzing Non-Newtonian Fluid Flow Tests Distorted by Wellbore Storage Effects

169

(26)

Fand Siddiqui

Mohamed Y. Soliman

8.1 Introduction

170

(3)

8.2 Objective

173

(1)

8.3 Problem Analysis

173

(3)

8.3.1 Model Assumptions

174

(1)

8.3.2 Solution Without the Wellbore Storage Distortion

175

(1)

8.3.3 Wellbore Storage and Skin Effects

175

(1)

8.3.4 Solution by Mathematical Inspection

175

(1)

8.3.5 Solution Verification

176

(1)

8.4 Use of Finite Element

176

(1)

8.5 Analysis Methodology

177

(3)

8.5.1 Finding the n Value

177

(1)

8.5.2 Dimensionless Wellbore Storage

178

(1)

8.5.3 Use of Type Curves

178

(1)

8.5.4 Match Point

179

(1)

8.5.5 Uncertainty in Analysis

180

(1)

8.6 Test Data Examples

180

(8)

8.6.1 Match Point

182

(1)

8.6.2 Match Point

183

(2)

8.6.3 Analysis Recommendations

185

(1)

8.6.4 Match Point

185

(1)

8.6.5 Analysis Recommendations

186

(1)

8.6.6 Match point

186

(2)

8.7 Conclusion

188

(1)

Nomenclature

188

(1)

References

189

(1)

Appendix A: Non-Linear Boundary Condition and Laplace Transform

189

(2)

Appendix B: Type Curve Charts for Various Power Law Indices

191

(4)

Part 3: Reservoir Permeability Detection

195

(58)

9 Permeability Prediction Using Machine Learning, Exponential, Multiplicative, and Hybrid Models

197

(20)

Simon Katz

Fred Aminzadeh

George Chilingar

M. Lackpour

9.1 Introduction

197

(1)

9.2 Additive, Multiplicative, Exponential, and Hybrid Permeability Models

198

(2)

9.3 Combination of Basis Function Expansion and Exhaustive Search for Optimum Subset of Predictors

200

(1)

9.4 Outliers in the Forecasts Produced with Four Permeability Models

201

(2)

9.5 Additive, Multiplicative, and Exponential Committee Machines

203

(3)

9.6 Permeability Forecast with First Level Committee Machines. Sandstone Dataset

206

(4)

9.7 Permeability Prediction with First Level Committee Machines. Carbonate Reservoirs

210

(2)

9.8 Analysis of Accuracy of Outlier Replacement by The First and Second Level Committee Machines. Sandstone Dataset

212

(2)

9.9 Conclusion

214

(1)

Notations and Definitions

215

(1)

References

216

(1)

10 Geological and Geophysical Criteria for Identifying Zones of High Gas Permeability of Coals (Using the Example of Kuzbass CBM Deposits)

217

(14)

A.G. Pogosyan

10.1 Introduction

217

(2)

10.2 Physical Properties and External Load Conditions on a Coal Reservoir

219

(6)

10.3 Basis for Evaluating Physical and Mechanical Coalbed Properties in the Borehole Environment

225

(3)

10.4 Conclusions

228

(1)

Acknowledgement

228

(1)

References

229

(2)

11 Rock Permeability Forecasts Using Machine Learning and Monte Carlo Committee Machines

231

(22)

Simon Katz

Fred Aminzadeh

Wennan Long

George Chilingar

Matin Lackpour

11.1 Introduction

232

(1)

11.2 Monte Carlo Cross Validation and Monte Carlo Committee Machines

233

(3)

11.3 Performance of Extended MC Cross Validation and Construction MC Committee Machines

236

(1)

11.4 Parameters of Distribution of the Number of Individual Forecasts in Monte Carlo Cross Validation

237

(1)

11.5 Linear Regression Permeability Forecast with Empirical Permeability Models

238

(4)

11.6 Accuracy of the Forecasts with Machine Learning Methods

242

(2)

11.7 Analysis of Instability of the Forecast

244

(2)

11.8 Enhancement of Stability of the MC Committee Machines Forecast Via Increase of the Number of Individual Forecasts

246

(1)

11.9 Conclusions

247

(1)

Nomenclature

247

(1)

Appendix 1 Description of Permeability Models from Different Fields

248

(1)

Appendix 2 A Brief Overview of Modular Networks or Committee Machines

249

(2)

References

251

(2)

Part 4: Reserves Evaluation/Decision Making

253

(84)

12 The Gulf of Mexico Petroleum System - Foundation for Science-Based Decision Making

255

(14)

Corinne Disenhof

MacKenzie Mark-Moser

Kelly Rose

Introduction

256

(1)

Basin Development and Geologic Overview

257

(2)

Petroleum System

259

(1)

Reservoir Geology

259

(2)

Hydrocarbons

261

(1)

Salt and Structure

262

(1)

Conclusions

263

(1)

Acknowledgments and Disclaimer

264

(1)

References

265

(4)

13 Forecast and Uncertainty Analysis of Production Decline Trends with Bootstrap and Monte Carlo Modeling

269

(20)

Simon Katz

George Chilingar

Leonid Khilyuk

13.1 Introduction

270

(1)

13.2 Simulated Decline Curves

271

(2)

13.3 Nonlinear Least Squares for Decline Curve Approximation

273

(1)

13.4 New Method of Grid Search for Approximation and Forecast of Decline Curves

273

(2)

13.5 Iterative Minimization of Least Squares with Multiple Approximating Models

275

(1)

13.6 Grid Search Followed by Iterative Minimization with Levenberg-Marquardt Algorithm

276

(1)

13.7 Two Methods for Aggregated Forecast and Analysis of Forecast Uncertainty

277

(2)

13.8 Uncertainty Quantile Ranges Obtained Using Monte Carlo and Bootstrap Methods

279

(1)

13.9 Monte Carlo Forecast and Analysis of Forecast Uncertainty

280

(4)

13.10 Block Bootstrap Forecast and Analysis of Forecast Uncertainty

284

(1)

13.11 Comparative Analysis of Results of Monte Carlo and Bootstrap Simulations

285

(2)

13.12 Conclusions

287

(1)

References

288

(1)

14 Oil and Gas Company Production, Reserves, and Valuation

289

(48)

Mark J. Kaiser

14.1 Introduction

290

(2)

14.2 Reserves

292

(2)

14.2.1 Proved Reserves

292

(1)

14.2.2 Proved Reserves Categories

292

(1)

14.2.3 Reserves Reporting

293

(1)

14.2.4 Probable and Possible Reserves

293

(1)

14.2.5 Contractual Differences

294

(1)

14.3 Production

294

(1)

14.4 Factors that Impact Company Value

295

(8)

14.4.1 Ownership

295

(1)

14.4.1.1 International Oil Companies

295

(1)

14.4.1.2 National Oil Companies

296

(1)

14.4.1.3 Government Sponsored Entities

296

(1)

14.4.1.4 Independents and Juniors

297

(1)

14.4.2 Degree of Integration

297

(1)

14.4.3 Product Mix

298

(1)

14.4.4 Commodity Price

298

(1)

14.4.5 Production Cost

299

(1)

14.4.6 Finding Cost

299

(1)

14.4.7 Assets

300

(1)

14.4.8 Capital Structure

300

(1)

14.4.9 Geologic Diversification

301

(1)

14.4.10 Geographic Diversification

301

(1)

14.4.11 Unobservable Factors

302

(1)

14.5 Summary Statistics

303

(6)

14.5.1 Sample

303

(1)

14.5.2 Variables

303

(2)

14.5.3 Data Source

305

(1)

14.5.4 International Oil Companies

305

(3)

14.5.5 Independents

308

(1)

14.6 Market Capitalization

309

(1)

14.6.1 Functional Specification

309

(1)

14.6.2 Expectations

309

(1)

14.7 International Oil Companies

310

(2)

14.8 U.S. Independents

312

(6)

14.8.1 Large vs. Small Cap, Oil vs. Gas

312

(2)

14.8.2 Consolidated Small-Caps

314

(1)

14.8.3 Multinational vs. Domestic

314

(1)

14.8.4 Conventional vs. Unconventional

315

(1)

14.8.5 Production and Reserves

316

(1)

14.8.6 Regression Models

316

(2)

14.9 Private Companies

318

(2)

14.10 National Oil Companies of OPEC

320

(1)

14.11 Government Sponsored Enterprises and Other International Companies

320

(3)

14.12 Conclusions

323

(1)

References

324

(13)

Part 5: Unconventional Reservoirs

337

(90)

15 An Analytical Thermal-Model for Optimization of Gas-Drilling in Unconventional Tight-Sand Reservoirs

339

(24)

Boyun Guo

Gao Li

Jinze Song

15.1 Introduction

340

(1)

15.2 Mathematical Model

341

(5)

15.3 Model Comparison

346

(2)

15.4 Sensitivity Analysis

348

(1)

15.5 Model Applications

349

(2)

15.6 Conclusions

351

(1)

Nomenclature

352

(1)

Acknowledgements

353

(1)

References

353

(2)

Appendix A: Steady Heat Transfer Solution for Fluid Temperature in Counter-Current Flow

355

(1)

Assumptions

355

(1)

Governing Equation

355

(5)

Boundary Conditions

360

(1)

Solution

360

(3)

16 Development of an Analytical Model for Predicting the Fluid Temperature Profile in Drilling Gas Hydrates Reservoirs

363

(20)

Liqun Shan

Boyun Guo

Xiao Cai

16.1 Introduction

364

(1)

16.2 Mathematical Model

365

(8)

16.3 Case Study

373

(1)

16.4 Sensitivity Analysis

374

(3)

16.5 Conclusions

377

(1)

Acknowledgements

378

(1)

Nomenclature

378

(1)

References

379

(4)

17 Distinguishing Between Brine-Saturated and Gas-Saturated Shaly Formations with a Monte-Carlo Simulation of Seismic Velocities

383

(16)

Simon Katz

George Chilingar

Leonid Khilyuk

17.1 Introduction

384

(1)

17.2 Random Models for Seismic Velocities

385

(2)

17.3 Variability of Seismic Velocities Predicted by Random Models

387

(1)

17.4 The Separability of (Vp, Vs) Clusters for Gas- and Brine-Saturated Formations

388

(1)

17.5 Reliability Analysis of Identifying Gas-Filled Formations

389

(7)

17.5.1 Classification with K-Nearest Neighbor

391

(1)

17.5.2 Classification with Recursive Partitioning

392

(2)

17.5.3 Classification with Linear Discriminant Analysis

394

(1)

17.5.4 Comparison of the Three Classification Techniques

395

(1)

17.6 Conclusions

396

(1)

References

397

(2)

18 Shale Mechanical Properties Influence Factors Overview and Experimental Investigation on Water Content Effects

399

(28)

Hui Li

Bitao Lai

Shuhua Lin

18.1 Introduction

400

(1)

18.2 Influence Factors

400

(14)

18.2.1 Effective Pressure

401

(1)

18.2.2 Porosity

402

(1)

18.2.3 Water Content

403

(2)

18.2.4 Salt Solutions

405

(1)

18.2.5 Total Organic Carbon (TOC)

406

(1)

18.2.6 Clay Content

407

(1)

18.2.7 Bedding Plane Orientation

408

(3)

18.2.8 Mineralogy

411

(2)

18.2.9 Anisotropy

413

(1)

18.2.10 Temperature

413

(1)

18.3 Experimental Investigation of Water Saturation Effects on Shale's Mechanical Properties

414

(4)

18.3.1 Experiment Description

414

(1)

18.3.2 Results and Discussion

414

(3)

18.3.3 Error Analysis of Experiments

417

(1)

18.4 Conclusions

418

(2)

Acknowledgements

420

(1)

References

420

(7)

Part 6: Enhance Oil Recovery

427

(60)

19 A Numerical Investigation of Enhanced Oil Recovery Using Hydrophilic Nanofuids

429

(34)

Yin Feng

Liyuan Cao

Erxiu Shi

19.1 Introduction

430

(2)

19.2 Simulation Framework

432

(5)

19.2.1 Background

432

(1)

19.2.2 Two Essential Computational Components

433

(1)

19.2.2.1 Flow Model

433

(1)

19.2.2.2 Nanoparticle Transport and Retention Model

435

(2)

19.3 Coupling of Mathematical Models

437

(2)

19.4 Verification Cases

439

(4)

19.4.1 Effect of Time Steps on the Performance of the in House Simulator

439

(1)

19.4.2 Comparison with Eclipse

440

(2)

19.4.3 Comparison with Software MNM1D

442

(1)

19.5 Results

443

(14)

19.5.1 Continuous Injection

445

(1)

19.5.1.1 Effect of Injection Time on Oil Recovery and Nanoparticle Adsorption

445

(1)

19.5.1.2 Effect of Injection Rate on Oil Recovery and Nanoparticle Adsorption

447

(2)

19.5.2 Slug Injection

449

(1)

19.5.2.1 Effect of Injection Time on Oil Recovery and Nanoparticle Adsorption

449

(1)

19.5.2.2 Effect of Slug Size on Oil Recovery and Nanoparticle Adsorption

451

(1)

19.5.3 Water Postflush

452

(1)

19.5.3.1 Effect of Injection Time Length

452

(1)

19.5.3.2 Effect of Flow Rate Ratio Between Water and Nanofluids on Oil and Nanoparticle Recovery

452

(3)

19.5.4 3D Model Showcase

455

(2)

19.6 Discussions

457

(2)

19.7 Conclusions and Future Work

459

(2)

References

461

(2)

20 3D Seismic-Assisted CO2-EOR Flow Simulation for the Tensleep Formation at Teapot Dome, USA

463

(24)

Payam Kavousi Ghahfarokhi

Thomas H. Wilson

Alan Lee Brown

20.1 Presentation Sequence

464

(1)

20.2 Introduction

464

(4)

20.3 Geological Background

468

(1)

20.4 Discrete Fracture Network (DFN)

469

(4)

20.5 Petrophysical Modeling

473

(1)

20.6 PVT Analysis

473

(6)

20.7 Streamline Analysis

479

(1)

20.8 CO2-EOR

479

(4)

20.9 Conclusions

483

(1)

Acknowledgement

483

(1)

References

484

(3)

Part 7: New Advances in Reservoir Characterization-Machine Learning Applications

487

(38)

21 Application of Machine Learning in Reservoir Characterization

489

(36)

Fred Aminzadeh

21.1 Brief Introduction to Reservoir Characterization

489

(2)

21.2 Artificial Intelligence and Machine (Deep) Learning Review

491

(11)

21.2.1 Support Vector Machines

492

(1)

21.2.2 Clustering (Unsupervised Classification)

492

(5)

21.2.3 Ensemble Methods

497

(1)

21.2.4 Artificial Neural Networks (ANN)-Based Methods

498

(4)

21.3 Artificial Intelligence and Machine (Deep) Learning Applications to Reservoir Characterization

502

(11)

21.3.1 3D Structural Model Development

503

(3)

21.3.2 Sedimentary Modeling

506

(2)

21.3.3 3D Petrophysical Modeling

508

(4)

21.3.4 Dynamic Modeling and Simulations

512

(1)

21.4 Machine (Deep) Learning and Enhanced Oil Recovery (EOR)

513

(4)

21.4.1 ANNs for EOR Performance and Economics

514

(2)

21.4.2 ANNs for EOR Screening

516

(1)

21.5 Conclusion

517

(1)

Acknowledgement

518

(1)

References

518

(7)

Index

525

Fred Aminzadeh, PhD, is a world-renowned academic and scientist in the energy industry. With over 20 years of teaching experience at the University of Southern California and at the University of Houston, he also has extensive industry experience not only in oil and gas, but also in geothermal energy and other areas of energy. He also served as the president of Society of Exploration Geophysicists. He has been author of multiple books and has written numerous papers that have been well-received by academics and industry experts alike. He served as the editor in chief of the journal, The Journal of Sustainable Energy Engineering, formerly of Scrivener Publishing. He is currently editing the series, Sustainable Energy Engineering, for the Wiley-Scrivener imprint.

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