Application of ML & AI to model petrophysical and geomechanical properties of shale reservoirs -A systematic literature review

Abstract

Extensive reviews and cross-comparison studies are essential to analyze the emerging developments in a specific field of research. In the past decade, hydrocarbon exploration and exploitation from the shale reservoirs have been the most discussed and researched area around the globe. A dramatic development in shale formations became the game-changer, especially in the US. On the other hand, Artificial Intelligence (AI) and Machine Learning (ML) are playing an important role in the rapid development in all the industries through automating most of the routine operations.

The oil industry is also getting equal benefits of AI and ML for the reservoir development planning and its operational accuracy through a series of automated systems. For the field development, computerized static and dynamic simulation models are generated based on several Petrophysical and Geomechanical properties gathered through different resources that are quite time-taking and expensive. AI and ML have made this process much easier, faster, and economical by means of learning through uncounted experiences from already explored and developed reservoirs, their rock properties, and the cross-ponding fluid flow behavior under different circumstances and hence, predicts accordingly.

This article provides a comprehensive literature review in the area of AI and ML applications to model Petrophysical and Geomechanical properties using different approaches and algorithms. Also, a systematic publication counts in each field of subject study per year in different literature databases are presented that infect reflects the trending interest in this subject. Finally, multiple AI and ML techniques are discussed in detail which have been tested in the last decade for the sake of achieving higher accuracy in Petrophysical and Geo-Mechanical simulation models.

Keywords

AI / ML / Petrophysics / GeoMechanics

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