Improving Reservoir Characterization and Prediction Via Machine Learning-Driven Integration of Subseismic Geologic Concepts, Geophysical Attributes, and Wells

Abstract

Summary Reservoir dynamic performance is affected by subseismic (below the resolution of seismic imaging) geologic features such as submeter thin barriers and baffles, or high-permeability streaks. Here, we present an application of machine learning that has the potential to improve and significantly accelerate the workflow for generating reservoir realizations and scenarios with relevant subseismic features consistent with both well-log and 3D/4D seismic data and/or attributes. We leverage unsupervised machine learning methods, in particular, a modification of diffusion probabilistic modeling (DPM).