Abstract
In petroleum engineering, the horizontal crustal stress magnitude and direction are necessary geomechanical parameters to conduct the wellbore stability analysis, geomechanics simulation and optimization of production well location and direction. For this purpose, this paper presents an artificial intelligence technique of multiple parameters identification. In this method, we apply the field measured wellbore deformation information to identify the field crustal stresses backward based on the artificial intelligence optimization algorithm. The algorithm is to combine back propagation neural network (BPNN) with genetic algorithm (GA) to implement the parameters determination. There is an obvious linear/nonlinear relationship between the horizontal crustal stresses and the wellbore deformation, which naturally applies the BPNN to map it. For the parameter optimization determination, the GA is used to search the optimal values in a large search space on the basis of the fitness function. Results from the learning samples and identification horizontal crustal stress state show that the hybrid BPNN-GA displacement back analysis model can effectively map the relationship between geomechanical parameters and mechanical behavior, and accurately determine the horizontal crustal stress magnitude and direction by the field measured wellbore deformation when drilling.
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Acknowledgements
This study was supported by Henan Science and Technology Research Planning Project (182102310804, 182102310763), Natural Science Foundation of Henan Province (182300410160), Training Project for Young Backbone Teachers of Higher Education of Henan (2018GGSJ122), Key Research Project of Institution of Higher Learning in Henan Province (20B560002), Anyang Science and Technology Research Planning Project (Anke[2018]66), College Student Innovation Fund Project (ASCX/2019-Z154).
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Zhang, S., Yuan, Y., Fu, D. (2020). An Application of Artificial Intelligence Technique in Horizontal Crustal Stress Measurement. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1074. Springer, Cham. https://doi.org/10.1007/978-3-030-32456-8_72
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DOI: https://doi.org/10.1007/978-3-030-32456-8_72
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