Machine Learning in Oil and Gas Exploration: A Review

Lawal, Ahmad; Yang, Yingjie; He, Hongmei; Baisa, Nathanael L.

Abstract

A comprehensive assessment of machine learning applications is conducted to identify the developing trends for Artificial Intelligence (AI) applications in the oil and gas sector, specifically focusing on geological and geophysical exploration and reservoir characterization. Critical areas, such as seismic data processing, facies and lithofacies classification, and the prediction of essential petrophysical properties (e.g., porosity, permeability, and water saturation), are explored. Despite the vital role of these properties in resource assessment, accurate prediction remains challenging. This paper offers a detailed overview of machine learning’s involvement in seismic data processing, facies classification, and reservoir property prediction. It highlights its potential to address various oil and gas exploration challenges, including predictive modelling, classification, and clustering tasks. Furthermore, the review identifies unique barriers hindering the widespread application of machine learning in the exploration, including uncertainties in subsurface parameters, scale discrepancies, and handling temporal and spatial data complexity. It proposes potential solutions, identifies practices contributing to achieving optimal accuracy, and outlines future research directions, providing a nuanced understanding of the field’s dynamics. Adopting machine learning and robust data management methods is crucial for enhancing operational efficiency in an era marked by extensive data generation. While acknowledging the inherent limitations of these approaches, they surpass the constraints of traditional empirical and analytical methods, establishing themselves as versatile tools for addressing industrial challenges. This comprehensive review serves as an invaluable resource for researchers venturing into less-charted territories in this evolving field, offering valuable insights and guidance for future research.

Citation

Lawal, A., Yang, Y., He, H., & Baisa, N. L. (2024). Machine Learning in Oil and Gas Exploration: A Review. IEEE Access, 12, 19035-19058. https://doi.org/10.1109/access.2023.3349216

• Journal Article Type: Article
• Acceptance Date: Dec 22, 2023
• Publication Date: Feb 23, 2024
• Deposit Date: Feb 22, 2024
• Publicly Available Date: Feb 23, 2024
• Journal: IEEE Access
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 12
• Pages: 19035-19058
• DOI: https://doi.org/10.1109/access.2023.3349216
• Keywords: General Engineering, General Materials Science, General Computer Science, Electrical and Electronic Engineering