The Application of Physics Informed Neural Networks to Compositional Modeling

Compositional modeling is essential when simulating processes involving significant changes in reservoir fluid composition. It is computationally expensive because we typically need to predict the states and properties of multicomponent fluid mixtures at several different points in space and time. To speed up this process, several researchers have used machine learning algorithms to train deep learning (DL) models on data from the rigorous phase-equilibrium (flash) calculations. However, one shortcoming of the DL models is that there is no explicit consideration for the governing physics. So, there is no guarantee that the model predictions will honor the thermodynamical constraints of phase equilibrium (Ihunde & Olorode, 2022). This work is the first attempt to incorporate thermodynamics constraints into the training of DL models to ensure that they yield two-phase flash predictions that honor the physical laws that govern phase equilibrium. A space-filling mixture design is used to generate one million different compositions at different pressures (Ihunde & Olorode, 2022). Stability analysis and flash calculations are performed on these compositions to obtain the corresponding phase compositions and vapor fraction (Ihunde & Olorode, 2022). Physics-informed neural network (PINN) and standard deep neural network (DNN) models were trained to predict two-phase flash results using the data from the actual phase-equilibrium calculations (Ihunde & Olorode, 2022). Considering the stochasticity of the deep learning optimization process, we used the seven-fold cross-validation to obtain reliable estimates of average model accuracy and variance (Ihunde & Olorode, 2022). Comparing the PINN and standard DNN models reveals that PINNs can incorporate physical constraints into DNNs without significantly lowering the model accuracy (Ihunde & Olorode, 2022). The evaluation of the model results with the test data shows that both PINN and standard DNN models yield coefficients of determination of ~97% (Ihunde & Olorode, 2022). However, the root-mean-square error of the physics-constraint errors in the PINN model is over 55% lower than that of the standard DNN model (Ihunde & Olorode, 2022). This indicates that PINNs significantly outperform DNNs in honoring the governing physics. Finally, we demonstrate the significance of honoring the governing physics by comparing the resulting phase envelopes obtained from overall compositions computed from the PINN, DNN, and linear regression model predictions (Ihunde & Olorode, 2022)

Investigation of Fluid Phase Behavior in Shale Reservoirs Using Equation of State, Molecular Simulation and Machine Learning

Fluid phase behavior in shale reservoirs differs significantly from phase behavior in conventional reservoirs due to the strong interactions between fluid and boundary in nanopores. In this study, we applied equation-of-state (EOS) modeling, machine learning (ML) technique and molecular simulation to investigate fluid phase behavior in shale reservoirs. One common issue observed in liquid-rich shale (LRS) production is that oil recovery of LRS reservoirs is much lower compared to oil recovery from a conventional reservoir with the same drawdown. To understand this phenomenon, EOS modeling is developed to analyze the fluid compositions in the bulk and confined regions. Our simulation results indicate that hydrocarbons distribute heterogeneously with respect to pore size on a nanoscale. The leaner bulk composition leads to the reduction in oil recovery from LRS reservoirs. Although EOS modeling can accurately simulate fluid phase behavior in shale reservoirs, the required simulation time is much longer than that for models of conventional reservoirs. To solve this problem, ML techniques were applied to accelerate the phase-equilibrium calculations in the EOS modeling. In contrast to previous models designed for a specific type of hydrocarbon, we have developed a generalized, ML-assisted phase-equilibrium calculation model that is suitable for shale reservoirs. In total, the average CPU time required for phase-equilibrium calculation using the generalized ML-assisted phase-equilibrium model was reduced by more than two orders of magnitude while maintaining an accuracy of 97%. With the development of shale oil and gas, depleted shale gas reservoirs may be attractive candidates for hydrogen (H2) storage. Molecular simulation was used to investigate the potential for H2 storage in depleted shale gas reservoirs. The results of the simulation suggest that a higher proportion of H2 exists in the bulk region. Because fluid is mainly produced from the bulk region, the high percentage of H2 in bulk fluid would lead to high purity of H2 during the recovery process. This work contributes to the understanding and application of fluid phase behavior in shale reservoirs