Accurate Prediction of CO2 Minimum Miscibility Pressure Using Adaptive Neuro-Fuzzy Inference Systems

Abstract

In CO2 gas injection, the oil recovery rate of the miscible flooding is significantly higher than that of the non-miscible flooding. The miscibility of oil and CO2 can only be achieved when pressure is above the minimum miscible pressure (MMP), hence MMP is an important parameter for the optimal design of the CO2 injection in the reservoir. The MMP can be determined by traditional methods such as experimental and empirical correlation approaches. The experimental method is accurate but time-consuming and expensive, while the correlation methods are efficient but have limited application range due to its theoretical assumption's limitation. Thus, a more efficient and accurate method for MMP measurement is in need. Artificial neuro network (ANN) is an effective tool for engineering estimation, hence some MMP prediction model based on ANN was proposed by researchers, but the accuracy of models still can be improved. In this study, four factors (heavy hydrocarbon molecular weight, reservoir temperature, volatile and intermediate ratio oil) of MMP were designed as input, MMP as output, and ANFIS model is constructed for MMP prediction, the accuracy is estimated by the root mean square error (RMSE). The simulation results indicate that the ANFIS model has higher accuracy (average RMSE=1.846) and wider application range than those traditional correlation approaches (best-performed correlation RMSE=4.25). ANFIS runs faster and cheaper than the experimental approach. Among all the ANFIS models, the hybrid algorithm optimized ANFIS with Gaussian member function is most accurate with RMSE=1.44. BP algorithm optimized ANFIS with PSigmoid membership function produces the largest error (RMSE=2.83). Therefore, the ANFIS model developed in this study is more accurate and time-saving than traditional methods in predicting MMP and is more accurate than the previous neural network MMP prediction models.

Comprehensive Review of the Determination and Reduction of the Minimum Miscibility Pressure during CO2 Flooding

“…Hamdi et al 74 used heavy hydrocarbon molecular weight, reservoir temperature, volatile matter, and intermediate components as input variables, and MMP as the output variable. They employed an ANFIS model to predict the MMP, and the accuracy was estimated using the root-mean-square error (RMSE).…”

Utilizing Artificial Intelligence Techniques for Modeling Minimum Miscibility Pressure in Carbon Capture and Utilization Processes: A Comprehensive Review and Applications

“…The developed GMDH-explicit-based correlations showed acceptable prediction performance. Hamdi and Chenxi investigated and compared the performance of five ANFIS models using different membership functions (MFs) for predicting the MMP of pure CO 2 – oil systems. The study demonstrated the superiority of the ANFIS model based on the Gaussian MF compared with the other types.…”

Integration of Domain Knowledge and Data-Driven Modeling Evaluation Process for Predicting Minimum Miscible Pressure of CO2–Oil Systems in CCUS

“…Based on four factors (heavy hydrocarbon molecular weight, reservoir temperature, and volatile and intermediate ratio oil), Hamdi and Chenxi created the adaptive fuzzy reasoning system (ANFIS) to predict MMP. However, Hamdi et al ignored the influence of high-dimensional nonlinear full-component data of injected gas and crude oil.…”