Data-Driven Modeling Of Fractured Shale Reservoirs

Abstract

Summary Performing robust modeling and forecasting is an overarching challenge for unconventional reservoirs. Due to the lack of efficient and dependable physical models for adequately describing fluid/rock interactions on fractured geometries, there has been an increasing interest in seeking alternative solutions via data-driven models. Despite a few encouraging outcomes reported in the literature, off-the-shelf data-driven models may not be able to generalize well in realistic reservoir scenarios. In this work, we strive to emulate first-order flow dynamics with data-driven models that have recently emerged in model reduction and machine learning. We rely on the assumption that complex flows on fractured systems can be decomposed into a simple representation based on coherent spatiotemporal structures. When field and simulation data are both integrated with the proposed approach, it is possible to extract additional patterns that enhance our capabilities for understanding predictions on different unconventional reservoir systems. We implement a single-phase flow model on structured curvilinear grids to capture first-order physics associated with unconventional shale production dynamics. Latin hypercube sampling is carried out to represent a different number of fractures (stages), fractures length and, geological uncertainty across distinct field scenarios. The data-driven model consists of the application of the recently proposed Dynamic Mode Decomposition (DMD) approach for modeling the evolution of pressure field and Long Short-Term Memory (LSTM) network, a powerful class of Recurrent Neural Network (RNN), to track gas production consistently and accurately. Our experiments show that our approach is accurate for a relatively small number of samples and reflects the relevant dynamics determining the production. Our model may not be as practical as empirical models employed in decline curve analysis, but it offers the potential to be more reliable as it can be based on complex simulations and field data. Numerical results support the accuracy of our approach with the possibility to impact forecast, reserves estimation and economics studies of unconventional assets in much shorter turnarounds.