Machine Learning in Computer Vision Software for Geomechanics Modeling

Abstract

AI-GEM (Artificial Intelligence of Geomechanics Earth Modelling) tool aims to detect the geomechanical features, especially the elastic parameters and stresses. Characterizing the wellbore instability issues is one of the factors increases cost of drilling and creating an AI-based tool will enhance and present a real-time solution for wellbore instability. These features are usually interpreted manually, depending on the experience and usually impacted by inconsistencies due to biased or unexperienced interpreters. Therefore, there is a need for a robust automatic or semiautomatic approach to reduce time, manual efficiency and consistency. The range of Geomechanics issues is wide and interfaces with many other upstream disciplines (e.g., Petrophysics, Geophysics, Production Geology, Drilling and Reservoir Engineering). Safe and effective field operation is built on the understanding and implementation of the subsurface in-situ stress state throughout the life of the field; the quantification of key subsurface uncertainties through well thought-out data gathering and characterization programs. The integration with appropriate Geomechanics modelling and the field surveillance /monitoring strategy. There are two major aspects that must be addressed during the design phase of any Geomechanics project. The first and most important is developing a realistic estimate of the expected mechanical behaviour of the rocks and its potential response as a result of drilling. The second is to design an economic, safe well and support method for the determined rocks behaviour. The design process begins with the feasibility study followed by preliminary design, the detail design, tender design and throughout the construction. The design is constantly updated during each phase as more information becomes available and this requires the involvement of Geologists, Engineers and Subject Matter Expert throughout the phases of a project. A central concern for all geomechanical designs is the well-rock interaction, which is not only includes the final state but also the transient effects of the well processes as well as time and stress of the dependent rock properties. The end-to-end workflow to achieve the mechanical earth model is automated, guided and orchestrated with the help of machine learning framework such as recommendation engine for offset well data, prediction of well logs, and optimization for all calibration with existing test results, enabling end users to run sensitivity and scenario analysis so on and so forth.