Petroleum Reservoir Connectivity Patterns Reconstruction Using Deep Convolutional Generative Adversarial Networks

Abstract

In this paper, we propose a deep convolutional generative adversarial network model to reconstruct the petroleum reservoir connectivity patterns. In the petroleum exploration industry, the critical issue is determining the internal reservoir structure and connectivity, aiming to find a flow channel for placing the injection and the production wells. The state-of-the-art methods propose a combination of seismic inversion with multipoint geostatistics, which imposes connectivity patterns during the optimization. However, this approach has a high computational cost, no learning ability and do not provide a probability through the connection. Results show that our approach is able to learn the petroleum reservoir connectivity patterns from the data and reproduce them also in facies images obtained by the seismic inversion.