Dynamical counterfactual inference under time-series model for waterflooding oilfield - Guoquan Wen; Chao Min; Qingxia Zhang; Guoyong Liao (Petroleum)
The performances of numerical simulation and machine learning in production forecasting are severely dependent on precise geological modeling and high-quality history matching. To address these challenges, causal inference is an effective methodology since it can provide a causality for formalizing causality in history, not statistical dependence. In this paper, to dynamically predict oil production from causality existed in waterflooding oilfield, a dynamical counterfactual inference framework is built to predict oil production. The proposed framework can forecast the oil production under non-observation of engineering factors, i.e., counterfactual, and provide the causal effect of engineering factors impacting on oil production. Meanwhile, combining with the practice exploitation in engineering factor impacting on production, a counterfactual experiment is designed to execute counterfactual prediction. Compared with general machine learning and statistical models, our results not only show better performance in oil production flooding but also guide the specific optimization in improving production, which holds more practical application significance.