Optimizing a Waterflood Using a Combination of Machine Learning and Reservoir Physics. A Field Application for Reducing Fresh Water Injection with no Impact on Oil Production and Improved Carbon Intensity

As the oil and gas industry navigates the energy transition, companies face increasing pressure from regulators, investors, and the public to establish clear net-zero goals and align their operations with sustainability initiatives. One potential avenue for emissions reduction lies in optimizing mature fields, where operational efficiencies can simultaneously enhance production while improving greenhouse gas (GHG) emissions intensity. By leveraging advanced modeling techniques, operators can achieve these dual objectives, ensuring both economic and environmental benefits.

This study introduces a novel approach that integrates Data Physics, a hybrid methodology combining machine learning with the underlying physics of reservoir simulators, to optimize waterflood operations in mature fields. Unlike traditional simulation models, Data Physics models are computationally efficient, capable of integrating diverse datasets, and possess strong long-term predictive capabilities. Applied to a mature field in Argentina’s Neuquén Basin, this technology successfully reduced water injection volumes without negatively impacting production. By accurately predicting performance, even for wells without historical data, the model provided a scalable solution for optimizing reservoir management.

Additionally, the study utilized a new Carbon Intensity (CI) modeling tool to assess the emissions impact of these operational improvements. The results demonstrated a substantial reduction in CI, achieving three key objectives: significantly lowering water injection volumes and associated treatment costs, maintaining production levels despite natural field decline, and reducing overall GHG emissions intensity. This approach underscores how advanced optimization strategies can simultaneously enhance economic performance and sustainability, providing a viable pathway for oil and gas companies to meet their energy transition goals.