Microfluidic insights into CO2 sequestration and enhanced oil recovery in laminated shale reservoirs: Post-fracturing interface dynamics and micro-scale mechanisms

Confined phase behavior plays a critical role in predicting production from shale reservoirs. In this work, a pseudo-potential lattice Boltzmann method is applied to directly model the phase equilibrium of fluids in nanopores. First, vapor-liquid equilibrium is simulated by capturing the sudden jump on simulated adsorption isotherms in a capillary tube. In addition, effect of pore size distribution on phase equilibrium is evaluated by using a bundle of capillary tubes of various sizes. Simulated coexistence curves indicate that an effective pore size can be used to account for the effects of pore size distribution on confined phase behavior. With simulated coexistence curves from pore-scale simulation, a modified equation of state is built and applied to model the thermodynamic phase diagram of shale oil. Shifted critical properties and suppressed bubble points are observed when effects of confinement is considered. The compositional simulation shows that both predicted oil and gas production will be higher if the modified equation of state is implemented. Results are compared with those using methods of capillary pressure and critical shift.

In the domain of deep learning (DL), dimension reduction is crucial for enhancing training efficiency and mitigating overfitting, particularly when managing complex data such as three-dimensional (3D) saturation data. The 3D saturation data in the context of geological carbon storage (GCS) presents unique challenges due to its inherent sparsity and the abrupt transitions at plume boundaries, known as shock fronts. To address the challenges, we proposed a novel DL framework that integrates dimension reduction with advanced 3D reconstruction techniques. Our model leveraged latent variables derived from 2D average saturation data, offering a robust and efficient solution tailored to the intricate dynamics of 3D saturation fields. The proposed framework can extract the critical features of the high-dimensional data while reducing the variable numbers, which is more tractable for DL models and enhances the model robustness and accuracy. Therefore, it provides a novel approach for modeling and analyses in complex geological scenarios, which finds great potential applications in environmental monitoring and energy storage.

In this study, we used deep learning techniques, which are a form of artificial intelligence, to create fast and effective models for predicting how fluids flow in underground geological formations. This is important for managing geological carbon storage, a method used to fight climate change by storing carbon dioxide underground. The challenge lies in the complex nature of these underground spaces and the large amount of data needed to accurately simulate them. To overcome these issues, we developed a new workflow that reduces the data’s complexity before training the deep learning model and then reconstructs the predicted results in their original form. We also proposed a unique approach to handle the specific complexities found in 3D saturation fields, a crucial aspect of fluid flow prediction. We tested our method using real-world data from the Gulf of Mexico. Our results show that our approach not only accurately predicts fluid behavior but also significantly reduces computation time. This will greatly improve real-time decision-making and risk assessment in large-scale geological carbon storage operations.