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Application of simple machine learning algorithms to critical stress analysis
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, Second EAGE Workshop on Machine Learning, Mar 2021, Volume 2021, p.1 - 3
Abstract
Critical stress analysis is a geomechanical technique used to understand whether faults are likely to reactivate, given an input effective stress field and fault mechanical properties, and is an important component of fault seal workflows for hydrocarbon exploration and carbon capture and storage applications. As the inputs to the analysis are all typically highly uncertain, it is difficult to cover the range of likely values for all the input parameters. In this contribution we outline a workflow for applying simple machine learning techniques to the results of a large number of stochastic critical stress analysis realizations. The aim is to allow us to better summarize the results of those realizations, gaining insights that can be used in our decision-making process.