Challenges and Opportunities in Implementing Machine Learning for Predictive Modeling in Unconventional Petroleum Reservoirs

EasyChair Preprint 14468

Abstract

The integration of machine learning (ML) into predictive modeling for unconventional petroleum reservoirs represents a frontier in the field of petroleum engineering. These reservoirs, which include formations like shale gas and tight oil, are characterized by their complex and heterogeneous geological features. Traditional modeling techniques, which have been effective in conventional reservoirs, often fall short when applied to unconventional ones due to the intricate subsurface dynamics. Machine learning, with its capability to analyze vast amounts of data and uncover hidden patterns, emerges as a promising tool to address these challenges. However, the application of ML in this context is not straightforward, as it introduces several challenges, including issues with data quality, the interpretability of models, and the need for substantial computational resources. This article delves deeply into these challenges while also highlighting the significant opportunities ML presents for improving prediction accuracy, enhancing automation, and reducing operational costs. Through a comprehensive review of the literature and an analysis of relevant case studies, this paper outlines the current state of the art and offers insights into future research directions, aiming to provide a balanced perspective on the potential of ML in transforming the management of unconventional petroleum reservoirs.

Keyphrases: Challenges, Oil and Gas, Opportunities, Unconventional Petroleum Reservoirs, machine learning, predictive modeling

@booklet{EasyChair:14468,
  author    = {Kayode Sheriffdeen},
  title     = {Challenges and Opportunities in Implementing Machine Learning for Predictive Modeling in Unconventional Petroleum Reservoirs},
  howpublished = {EasyChair Preprint 14468},
  year      = {EasyChair, 2024}}