Explicit and explainable artificial intelligent model for prediction of CO2 molecular diffusion coefficient in heavy crude oils and bitumen

Abstract

Optimizing CO2 injection for enhanced oil recovery (EOR) requires a precise estimation of the CO2-diffusivity coefficient in porous media. This study developed a predictive model for the molecular diffusivity coefficient of CO2 in bitumen and heavy crude oils using a Bayesian regularized artificial neural network. The unique contributions of the developed model compared to existing models include: First, a simple and accurate mathematical correlation between inputs and CO2-diffusivity has been presented, making it easy to use particularly for individuals with limited understanding of machine learning. Secondly, the time it takes the model to make a prediction (its inference latency) has been established and the model has been physically validated using trend analysis. Fourthly, independent datasets were used to test for the model generalizability.The model was evaluated using 260 data points from the literature, with 70 % for training and 30 % for testing. Key performance metrics were calculated, including root mean square error (RMSE = 0.03), and coefficient of determination (R2 = 0.996). Furthermore, an outlier detection analysis using the statistical leverage approach demonstrated that the data used was of high quality. A relevancy factor analysis revealed that pressure had the greatest impact on CO2 diffusivity, followed by temperature, while CO2 mass fraction had the least impact. The developed model operates within a temperature range of 295 K – 363 K and a pressure range of 1 MPa – 8MPa. Overall, the outcomes of this study contribute to the efficient prediction of CO2 diffusivity in heavy crude oil and bitumen.