Abstract
Geological surface modeling is typically based on seismic data, well data, and models of regional geology. However, structural interpretation of these data is error-prone, especially in the absence of structural morphology information, Existing geological surface models suffer from high levels of uncertainty, which exposes oil and gas exploration and development to additional risk. In this paper, we achieve a reconstruction of the uncertainties associated with a geological surface using chance-constrained programming based on multi-source data. We also quantified the uncertainty of the modeling data and added a disturbance term to the objective function. Finally, we verified the applicability of the method using both synthetic and real fault data. We found that the reconstructed geological models met geological rules and reduced the reconstruction uncertainty.
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Acknowledgments
The authors are very grateful to the three reviewers for their critiques, helpful comments, and valuable suggestions which improved this manuscript significantly. The authors would like to thank Dr. Dennis K for the improvement of English language.
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This work was supported by National Science and Technology Major Project(Grant No. 2017ZX05018004004) and the National Natural Science Foundation of China (No. U1562218 & 41604107).
Yu Shi-Cheng received his B.S. in Mathematics and Applied Mathematics form Chengdu University of Technology, Chengdu, China (2004) and his M. S. from the College of Information Science & Technology, Chengdu University of Technology, Chengdu, China (2009). He is a lecturer at the Engineering & Technical College of Chengdu University of Technology since 2010. Currently, he is a Ph.D. student in the School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China. His research interest are structural modeling.
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Yu, SC., Lu, C. & Hu, GM. Reconstruction of geological surfaces using chance-constrained programming. Appl. Geophys. 16, 125–136 (2019). https://doi.org/10.1007/s11770-019-0744-7
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DOI: https://doi.org/10.1007/s11770-019-0744-7