Effective Prediction and Management of a CO₂ Flooding Process for Enhancing Oil Recovery Using Artificial Neural Networks

The injection of CO₂ has been in global use for enhanced oil recovery (EOR) as it can improve oil production in mature fields. It also has environmental benefits for reducing greenhouse carbon by permanently sequestrating CO₂ (carbon capture and storage (CCS)) in reservoirs. As a part of numerical studies, this work proposed a novel application of an artificial neural network (ANN) to forecast the performance of a water-alternating-CO₂ process and effectively manage the injected CO₂ in a combined CCS–EOR project. Three targets including oil recovery, net CO₂ storage, and cumulative gaseous CO₂ production were quantitatively simulated by three separate ANN models for a series of injection frames of 5, 15, 25, and 35 cycles. The concurrent estimations of a sequence of outputs have shown a relevant application in scheduling the injection process based on the progressive profile of the targets. For a specific surface design, an increment of 5.8% oil recovery and 4% net CO₂ storage was achieved from 25 cycles to 35 cycles, suggesting ending the injection at 25 cycles. Using the models, distinct optimizations were also computed for oil recovery and net CO₂ sequestration in various reservoir conditions. The results expressed a maximum oil recovery from 22% to 30% oil in place (OIP) and around 21,000–29,000 tons of CO₂ trapped underground after 35 cycles if the injection began at 60% water saturation. The new approach presented in this study of applying an ANN is obviously effective in forecasting and managing the entire CO₂ injection process instead of a single output as presented in previous studies.