Assisted History Matching for Unconventional Reservoirs
- 1st Edition - August 5, 2021
- Imprint: Gulf Professional Publishing
- Authors: Sutthaporn Tripoppoom, Wei Yu, Kamy Sepehrnoori, Jijun Miao
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 2 2 4 2 - 3
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 2 2 4 3 - 0
As unconventional reservoir activity grows in demand, reservoir engineers relying on history matching are challenged with this time-consuming task in order to characterize hy… Read more
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Request a sales quoteAs unconventional reservoir activity grows in demand, reservoir engineers relying on history matching are challenged with this time-consuming task in order to characterize hydraulic fracture and reservoir properties, which are expensive and difficult to obtain. Assisted History Matching for Unconventional Reservoirs delivers a critical tool for today’s engineers proposing an Assisted History Matching (AHM) workflow. The AHM workflow has benefits of quantifying uncertainty without bias or being trapped in any local minima and this reference helps the engineer integrate an efficient and non-intrusive model for fractures that work with any commercial simulator. Additional benefits include various applications of field case studies such as the Marcellus shale play and visuals on the advantages and disadvantages of alternative models. Rounding out with additional references for deeper learning, Assisted History Matching for Unconventional Reservoirs gives reservoir engineers a holistic view on how to model today’s fractures and unconventional reservoirs.
- Provides understanding on simulations for hydraulic fractures, natural fractures, and shale reservoirs using embedded discrete fracture model (EDFM)
- Reviews automatic and assisted history matching algorithms including visuals on advantages and limitations of each model
- Captures data on uncertainties of fractures and reservoir properties for better probabilistic production forecasting and well placement
Reservoir engineers; production engineers; petroleum engineers; engineers specializing in unconventional reservoirs
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- About the authors
- Preface
- Chapter 1. Introduction and literature review
- Abstract
- 1.1 Motivation
- 1.2 Literature review
- 1.3 Assisted history matching in unconventional reservoirs
- References
- Chapter 2. Methodology
- Abstract
- 2.1 Assisted history-matching framework
- 2.2 Embedded discrete fracture model
- 2.3 Reservoir simulator
- 2.4 Proxy model
- 2.5 Proxy-based Markov chain Monte Carlo algorithm
- 2.6 Steps in assisted history-matching workflow
- References
- Chapter 3. Validation of assisted history matching for a synthetic shale gas well
- Abstract
- 3.1 Introduction
- 3.2 Case 1: hydraulic fractures only
- 3.3 Case 2: hydraulic fractures and natural fractures
- 3.4 Remarks
- Chapter 4. Shale-gas well in Longmaxi Shale with bi-wing hydraulic fractures
- Abstract
- 4.1 Introduction
- 4.2 Reservoir model
- 4.3 Comparison between EDFM and LGR
- 4.4 Parameters identification and screening
- 4.5 History matching
- 4.6 Probabilistic production forecasting
- 4.7 Remarks
- References
- Chapter 5. Shale-gas well in Marcellus Shale with bi-wing hydraulic fractures
- Abstract
- 5.1 Introduction
- 5.2 Reservoir model
- 5.3 Sensitivity analysis
- 5.4 History matching
- 5.5 Posterior distribution of matrix and fracture parameters
- 5.6 Probabilistic production forecasting
- 5.7 Remarks
- Reference
- Chapter 6. Proxy comparison between neural network and k-nearest neighbors
- Abstract
- 6.1 Introduction
- 6.2 Reservoir model
- 6.3 Parameters identification and screening
- 6.4 History matching
- 6.5 Probabilistic forecasting
- 6.6 Remarks
- References
- Chapter 7. Shale-gas well with and without enhanced permeability area
- Abstract
- 7.1 Introduction
- 7.2 Parameters identification and screening
- 7.3 History matching
- 7.4 Probabilistic forecasting
- 7.5 Remarks
- References
- Chapter 8. Shale-gas well with and without natural fractures
- Abstract
- 8.1 Introduction
- 8.2 Reservoir model
- 8.3 History matching
- 8.4 Discussion
- 8.5 Probabilistic production forecast
- 8.6 1000 History-matching solutions from neural networks
- 8.7 Benefits from the study
- 8.8 Remarks
- References
- Chapter 9. Shale-oil well with and without natural fractures
- Abstract
- 9.1 Introduction
- 9.2 Reservoir model
- 9.3 History matching
- 9.4 History-matching results and discussion
- 9.5 Probabilistic production forecast
- 9.6 History-matching solutions from neural networks
- 9.7 Benefits from the study
- 9.8 Remarks
- References
- Chapter 10. Investigation of different production performances in multiple shale-gas wells
- Abstract
- 10.1 Introduction
- 10.2 Reservoir model
- 10.3 Automatic history matching
- 10.4 Probabilistic production forecast
- 10.5 Remarks
- References
- Appendix A Wells A and B results
- Chapter 11. Concluding remarks
- Abstract
- 11.1 Key conclusions
- 11.2 Recommendations
- Index
- Edition: 1
- Published: August 5, 2021
- No. of pages (Paperback): 288
- No. of pages (eBook): 288
- Imprint: Gulf Professional Publishing
- Language: English
- Paperback ISBN: 9780128222423
- eBook ISBN: 9780128222430
ST
Sutthaporn Tripoppoom
Mr. Sutthaporn Tripoppoom received his MS degree in the Hildebrand Department of Petroleum and Geosystems Engineering at The University of Texas at Austin in 2019. Currently, he is reservoir engineer at the Thai national oil and gas company, PTT Exploration and Production Plc. His research interests include development of Assisted History Matching (AHM) for naturally fractured reservoirs, hydraulically fractured reservoirs, and unconventional resources. He was the developer of an AHM workflow for unconventional reservoirs and he also won the first place in SPE Regional Paper Contest (2019). He holds a BS degree in Petroleum Engineering from Chulalongkorn University in Thailand with First Class Honors and the outstanding student certificate. He is a member of SPE.
Affiliations and expertise
Reservoir Engineer, PTT Exploration and Production, PLC, Bangkok, ThailandWY
Wei Yu
Dr. Wei Yu is the chief technology officer for Sim Tech LLC and a research associate in the Hildebrand Department of Petroleum and Geosystems Engineering at The University of Texas at Austin. He is an Associate Editor for the SPE Journal and the Journal of Petroleum Science and Engineering. His research interests include EDFM (Embedded Discrete Fracture Model) technology for modeling any complex fractures, shale gas and tight oil reservoir simulation, EDFM-AI for automatic history matching and complex fracture characterization. Yu has authored or coauthored more than 200 technical papers and two books (Shale Gas and Tight Oil Reservoir Simulation and Embedded Discrete Fracture Modeling and Application in Reservoir Simulation), and holds five patents. He holds a PhD degree in petroleum engineering from The University of Texas at Austin.
Affiliations and expertise
Chief Technology Officer, Sim Tech LLC, Houston, Texas, USA
Research Associate, Hildebrand Department of Petroleum and Geosystems Engineering, University of Texas at Austin, TX, USAKS
Kamy Sepehrnoori
Dr. Kamy Sepehrnoori is a professor in the Hildebrand Department of Petroleum and Geosystems Engineering at The University of Texas at Austin, where he holds the Texaco Centennial Chair in Petroleum Engineering. His research interest and teaching include computational methods, reservoir simulation, simulation of unconventional reservoirs, enhanced oil recovery modeling, flow assurance modeling, naturally fractured reservoirs, high-performance computing, and CO2 sequestration. He has been teaching at The University of Texas for over 35 years and has graduated more than 230 MS and PhD students under his supervision working mainly in the areas of reservoir simulation and enhanced oil recovery modeling. For the last several years, he has been supervising a research group in simulation of unconventional reservoirs (shale gas and tight oil reservoirs). Sepehrnoori’s research team along with his colleagues have been in charge of development of several compositional reservoir simulators (i.e., UTCOMPRS, UTCHEMRS, and UTGEL). Also, more recently, he supervised the development of a software package for embedded discrete fracture modeling for application in naturally and hydraulically fractured reservoirs. He has published more than 650 articles in journals and conference proceedings in his research areas. He has also coauthored three books, which have been published by Elsevier. Sepehrnoori is the director of the Reservoir Simulation Joint Industry Project in the Center for Subsurface Energy and the Environment. He holds a PhD degree in petroleum engineering from The University of Texas at Austin.
Affiliations and expertise
Professor, Hildebrand Department of Petroleum and Geosystems Engineering, The University of Texas at Austin, USAJM
Jijun Miao
Dr. Jijun Miao is the CEO at Sim Tech LLC and a research advisor in the Hildebrand Department of Petroleum and Geosystems Engineering at The University of Texas at Austin. His research interests include fracture characteristics, reservoir modeling and simulation, EDFM development and application. Dr. Miao authored or coauthored more than 35 technical papers and holds 5 patents. He holds a PhD degree in petroleum exploration from The Research Institute of Petroleum Exploration & Development, PetroChina. Dr. Miao has hands-on experience in oilfield for oil & gas exploration and development and leads multi-disciplinary research teams undertaking various major R&D programs.
Affiliations and expertise
CEO, Sim Tech LLC, Houston, Texas,
Research Advisor, Hildebrand Department of Petroleum and Geosystems Engineering, The University of Texas at Austin, USARead Assisted History Matching for Unconventional Reservoirs on ScienceDirect