A machine‐learning framework to estimate saturation changes from 4D seismic data using reservoir models

Abstract

Time-lapse seismic (four-dimensional seismic) data play a preeminent role in closed-loop reservoir management by providing a full-field image of dynamic reservoir behaviour during production. Nonetheless, the multidisciplinary nature of four-dimensional closed-loop approaches demands more quantitative and fast methods to integrate rock physics models, reservoir flow simulation models and four-dimensional seismic analysis. In this work, we tackle this time-consuming and expensive process and develop a data-driven quantitative approach to leverage the inherent physics between four-dimensional seismic and reservoir property changes. We propose an inversion flow method using machine learning strategies to estimate changes in reservoir properties directly from a fast-derived four-dimensional seismic attribute. This study was carried out in a Brazilian deep-water field where production started in 2013 with 3 years of production and injection history. For this reservoir, the estimation of fluid saturation maps is a critical objective to assist engineers with data assimilation procedures. We also generated millions of training data samples using 200 simulation models from the field mentioned above (before history matching) to highlight the benefits of restricting training samples to proper fluid flow consistent combinations. Results demonstrate high prediction accuracy for the targeted reservoir property changes. Additionally, it provides insights for the detection of sweet spots and positioning of infill wells. We significantly simplify the four-dimensional seismic integration process, allowing initial engagements of reservoir engineers with decision-making processes and data assimilation applications.