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Rule-Based Reservoir Modeling by Integration of Multiple Information Sources: Learning Time- Varying Geologic Processes

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Rule-Based Reservoir Modeling by Integration of Multiple Information Sources: Learning Time- Varying Geologic Processes RULE-BASED RESERVOIR MODELING BY INTEGRATION OF MULTIPLE INFORMATION SOURCES: LEARNING TIME- VARYING GEOLOGIC PROCESSES A REPORT SUBMITTED TO THE DEPARTMENT OF ENERGY RESOURCES ENGINEERING OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE By Yinan Wang March 2015 I certify that I have read this report and that in my opinion it is fully adequate, in scope and in quality, as partial fulfillment of the degree of Master of Science in Petroleum Engineering. __________________________________ Prof. Tapan Mukerji (Principal Advisor) iii Abstract Rule-based modeling methodology has been developed to improve the integration of geologic information into geostatistical reservoir models. Quantifying modeling rules significantly aid in building geologically accurate reservoir models and reproduce the intrinsic complexity of subsurface conditions. Especially when field data and geological knowledge are both limited the way we utilize the rules is important. To expand the application of rule-based reservoir modeling in various field cases, we propose a systematic methodology of creating rules from other information sources. Physical geomorphic experiments and process-based models contain time series information we need for a reservoir model. Incorporating these two information sources facilitate the rule induction for rule-based modeling and therefore help capture the underlying uncertainty. Two examples are demonstrated in our study. A reference class from tank experiments is created for turbidite lobe system, while a realization of a process-based model is used to mimic and simulate channel network patterns and their behaviors on a delta plain. In our study, we assume that if an experiment is comparable to field data at a certain interpretation scale, then the sedimentary processes and associated structures are informative and provide at least some information about the resulting sedimentary features at the comparable scale. Ripley’s K-function is utilized to analyze and extract spatial clustering information of lobe elements at a given scale from experimental strata. We convert the K function to modeling rules, allowing us to integrate clustering patterns of turbidite lobes into surface-based models. Surface-based models successfully produce a clustered point behavior and a stratigraphic framework comparable to the chosen physical tank experiment. These models can be used to better assess subsurface spatial uncertainty under such a stochastic process framework constrained by experimental information. To facilitate the utilization of process-based models, an automated tool for extraction of channel features is developed with adjustable parameters for the optimal result. Multi- Scale Line Tracking Algorithm is embedded and shows robust and accurate extraction of channel networks from Delft3D models. Space colonization algorithm is proposed to capture the developmental processes of channel network and reproduce a network pattern. It is able to integrate theoretical knowledge and simulate a network coupling with feature extraction tool.. The overall methodology is able to efficiently simulate channel networks and their progradation through time given information from one or more realizations of process-based models. v Acknowledgments First of all, I want to thank my advisor Professor Tapan Mukerji. I am deeply indebted to Professor Mukerji for his insightful advice and endless support over the past few years. Life is always full of new journeys. Before I arrived in Stanford for a Petroleum Engineering degree, I had been possessed by the beauty of geology for years. Consequently, for quite a long term at Stanford I always tried to look at engineers' world from the perspective of geologists and was extremely confused with my value to the team. Instead of shaping me into an efficient worker, Professor Mukerji encouraged me to be who I am and gave me freedom to explore my interest. I am very fortunate and grateful to have him as my advisor. I also owe my thanks to Professor Jef Caers and Professor Tim McHargue. They have a very profound impact on me by sharing their expertise and research passion. Every time when I have a conversation with them, I feel I grow up gradually, not just on my development as a researcher, but on a general way of thinking. The best resources and assets at Stanford are professors who guide students. I have been moving forward to be a better man by talking with them and learning from them. And sometimes I only think of taking classes and doing research as approaches to get in touch with them. I would also like to thank Dr. David Hoyal and Dr. Hongmei Li for their meticulous guidance during my internship with ExxonMobil. Both of them are true caring mentors to me in the company. Working with them was such a pleasant experience. In the meantime I also received great help from my advisor Professor Kyle Straub at Tulane University and Professor Douglas Edmonds at Indiana University. Our many discussions and subsequent work together has enriched my thoughts and studies immensely. One of my magical changes happened at Tulane. Without Prof. Straub, I have not come this far from where I started from. Finally, I would like to acknowledge the financial support from Stanford Center for Reservoir Forecasting (SCRF). And thanks to all the colleagues in SCRF, especially my mentor, Siyao Xu. He guided me into fundamental thinking as an engineer. I owe him a special thank you. vii Contents Abstract ............................................................................................................................... v Acknowledgments............................................................................................................. vii Contents ............................................................................................................................. ix List of Tables ..................................................................................................................... xi List of Figures .................................................................................................................. xiii 1. Introduction ....................................................................................................................1 1.1. Geologic Contexts ................................................................................................. 3 1.1.1. Submarine turbidite fan system ..................................................................... 3 1.1.2. Deltaic channel network system .................................................................... 4 1.2. Rule-based Modeling ............................................................................................ 6 1.3. Geomorphic Experiments ................................................................................... 10 1.4. Process-based numerical modeling ..................................................................... 11 2.Rule-based Model Construction ....................................................................................15 2.1. Rules and Their Classification ............................................................................ 15 2.2. Discussion on Model Complexity ....................................................................... 18 2.3. Modeling A Typical Channel-lobe System......................................................... 19 2.3.1. Compensational Stacking............................................................................. 21 2.3.2. Clustered Stacking ....................................................................................... 24 2.4. Rasterizing Rule-based Models .......................................................................... 26 3. Modeling Turbidite Lobes with Experimental Data .....................................................29 3.1. Methodology Overview ...................................................................................... 28 3.2. Quantitative Metric ............................................................................................. 30 3.3. Analyzing Experimental Data ............................................................................. 32 3.4. Modeling Sub-resolution Lobes .......................................................................... 35 3.5. Discussion ........................................................................................................... 37 4. Feature Extraction of Process-based Models ................................................................40 4.1. Network Skeleton................................................................................................ 39 4.2. Pre-processing of Topography Images ............................................................... 41 4.2.1. Supervised and Unsupervised Segmentation Methods ................................ 42 4.2.2. Multi-scale Line Tracking Algorithm .......................................................... 43 4.2.3. Tracking channel networks .......................................................................... 44 4.3. Post-processing of Topography Images .............................................................. 48 4.4. Application to Satellite Images ........................................................................... 52 4.5. Evaluation and Optimization of Feature Extraction Tool ................................... 53 4.6. Statistical Similarity ...........................................................................................
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