Three-dimensional Prediction of In-situ Stress in Oil and Gas Field Based on Intelligence Algorithms

doi:10.11885/j.issn.1674-5086.2019.03.27.03

Abstract

Abstract: Complex sedimentary and tectonic environment results in the difficulty of predicting in-situ stress quantitatively. Two new three-dimensional in-situ stress prediction methods for different working conditions based on intelligence algorithm are proposed using the relationships between interval velocity, in-situ stress and post stack seismic information. With abundant well logging and test data, in-situ stress is predicted by the rock physics and mechanics method based on spatial velocity information obtained using BP neural network algorithm. With sparse well points and a small number of data, simulated annealing algorithm is utilized to directly search for the stress solution vector with the best match of synthetic and actual seismic records. These methods have been applied in an oilfield in eastern China and achieves high accuracy and resolution prediction results, which proves the feasibility of these methods.

Key words: in-situ stress in oil and gas field, three-dimensional prediction, intelligence algorithm, BP neural network, simulated annealing

CLC Number:

TE21

YUAN Duo, WU Chao, LU Yunhu, ZHANG Dongqing. Three-dimensional Prediction of In-situ Stress in Oil and Gas Field Based on Intelligence Algorithms[J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2021, 43(2): 84-92.

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