Data science’s pivotal role in enhancing oil recovery methods while minimizing environmental footprints: An insightful review

The oil and gas industry must do two things at once: pull as much hydrocarbon as possible from complex reservoirs and keep the environmental impact low. Traditional reservoir‑engineering tools often cannot handle the huge, varied data sets that come from exploration and production. This review looks at how data science, machine‑learning (ML) and big‑data analytics can help. By combining many data streams—seismic surveys, well logs and real‑time production numbers—engineers can build highly accurate, dynamic reservoir models. Those models guide the design of optimized recovery plans. At the same time, ML algorithms applied to live sensor data can spot anomalies early, cutting downtime and lowering the chance of leaks or spills.

The paper also points out limits. Integrating all the data is complex, and strong validation methods are still needed. It offers structured guidance for M.Tech students who want to review, simulate and assess data‑driven reservoir‑management workflows, with a focus on choosing the right algorithms for anomaly detection and predictive modeling.

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