Learning-based pattern-data-driven forecast approach for predicting future well responses

Abstract Forecasting well responses, such as flow rates and pressures, is crucial for effective reservoir management and investment decision-making in the development of subsurface reservoir resources. Recently, data-driven forecast methods, such as data-space inversion (DSI) and a learning-based data-driven forecast approach (LDFA), have been introduced to mitigate the computational cost and geological constraint issues of history-matching methods. However, DSI and LDFA have extrapolation, conditioning, and prediction variance issues. In this study, we propose two simpler alternatives, a learning-based pattern-data-driven forecast approach (LPFA) and an ensemble conditioning step (ECS), to resolve the issues associated with DSI and LDFA. LPFA mitigates the extrapolation issue by scaling well responses for each instance using the mean and variance of an observation period. ECS addresses …