As 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.
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Table of Contents
1. Introduction2. Assisted History Matching Workflow
3. Shale Gas Well in Longmaxi with Bi-wing Hydraulic Fractures
4. Shale Gas Well in Marcellus with Bi-wing Hydraulic Fractures
5. Proxy Comparison between Neural Network and K-nearest Neighbors
6. Shale Gas Well with and without Enhanced Permeability Area (EPA)
7. Shale Gas Well with and without Natural Fractures
8. Shale Oil Well with and without Natural Fractures
9. Multi Shale Gas Wells with and without Natural Fractures
10. Shale Oil Well with Slanted Hydraulic Fractures
11. Shale Oil and Shale Gas with Varying Fractures Intensity around Wellbore
Authors
Sutthaporn Tripoppoom Reservoir Engineer, PTT Exploration and Production, PLC, Bangkok, Thailand. 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. Wei Yu Chief Technology Officer, Sim Tech LLC, Houston, Texas, USAResearch Associate, Hildebrand Department of Petroleum and Geosystems Engineering, University of Texas at Austin, TX, USA. 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. Kamy Sepehrnoori Professor, Hildebrand Department of Petroleum and Geosystems Engineering, The University of Texas at Austin, USA. 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. Jijun Miao CEO, Sim Tech LLC, Houston, Texas,
Research Advisor, Hildebrand Department of Petroleum and Geosystems Engineering, The University of Texas at Austin, USA. 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.
