Data-Limited Machine Learning Approaches for Identifying High-Risk Geological Zones and Predicting Methane Leakage for Active Oil & Gas Wells

Authors: Stanley Uchenna Opara, Abass Aliu, Andrews Ayim Oduro

DOI: https://doi.org/10.37082/IJIRMPS.v14.i2.232962

Short DOI: https://doi.org/hbxbth

Country: United States

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Abstract: Methane emissions from active oil and gas wells pose a significant challenge to environmental resilience and energy governance due to their high radiative impact and the persistent uncertainty surrounding subsurface leakage processes. Accurate identification of high-risk geological zones and reliable leakage prediction are constrained by sparse, heterogeneous, and incomplete data, limiting the effectiveness of conventional deterministic and purely data-driven models. This study presents a systematic review of data-limited machine learning (ML) approaches developed to address these challenges in methane leakage assessment. Following PRISMA guidelines, thirty peer-reviewed studies published between 2020 and 2026 were analyzed to evaluate how ML frameworks operate under data scarcity, integrate geological knowledge, and quantify uncertainty. The review finds a clear methodological shift toward hybrid physics-ML and probabilistic models, which outperform traditional approaches by embedding physical constraints, leveraging heterogeneous data sources, and producing risk-based predictions rather than single-value estimates. Results consistently indicate that methane emissions are dominated by a small number of high-risk geological and operational conditions, including faulted reservoirs, degraded wellbores, and legacy infrastructure, which data-limited ML models can effectively prioritize even with sparse observations. The findings highlight important operational and regulatory implications, demonstrating that uncertainty-aware ML supports targeted monitoring, adaptive regulation, and transparent risk governance. Finally, the review highlights that data limitation is not merely a constraint but a defining condition for methane leakage modeling, positioning data-limited machine learning as a critical tool for decision-relevant methane mitigation in active oil and gas systems.

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Paper Id: 232962

Published On: 2026-04-15

Published In: Volume 14, Issue 2, March-April 2026

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