Integrated Deep-Learning and Physics-Based Models Improve Production Prediction

Abstract

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 204864, “Integrating Deep-Learning and Physics-Based Models for Improved Production Prediction in Unconventional Reservoirs,” by Syamil M. Razak, SPE, Jodel Cornelio, SPE, and Atefeh Jahandideh, SPE, University of Southern California, et al. The paper has not been peer reviewed. _ The physics of fluid flow and transport processes in hydraulically fractured unconventional reservoirs is not well understood. As a result, predicted production behavior using conventional simulation often does not agree with observed field performance data. The discrepancy is caused by potential errors in the simulation model and the physical processes that take place in complex fractured rocks subjected to hydraulic fracturing. In the complete paper, the authors discuss the development of a deep-learning model to investigate the errors in simulation-based performance prediction in unconventional reservoirs. Introduction One of the major challenges for petroleum engineers working with unconventional reservoirs is a lack of models that accurately represent a physical relationship between the formation, completion and fluid properties, and production responses. Statistical predictive models typically are used to extract sets of input parameters that better represent the properties of the field. However, a major drawback with statistical models is their inability to extrapolate from the training data set. The authors propose a deep-learning predictive model based on a combination of physics and data to account for the errors that may have come from undiscovered physics or imperfect description of unconventional reservoirs. The model leverages the power of deep learning to account for systematic prediction inaccuracies resulting from incomplete knowledge about the reservoir model and the underlying flow processes. Deep neural network models trained with observed production responses and other related data in the field are used to enhance the prediction performance of simulation models with limited knowledge of flow physics in complex, unconventional reservoirs. The data the authors use for their model consist of the following: - Formation, completion, and fluid properties for physics-based reservoir simulation (xsim) - Corresponding simulated production responses (dsim) - Formation, completion, and fluid properties collected from the field (xfield) - Related observed production response data (dfield) The authors define simulation errors (derr) as the difference between dsim and dfield. Each derr is paired with xerr that is the concatenation of the corresponding xsim and xfield. The predictive model integrates a 1D convolutional autoencoder (AE) that extracts temporal trends in the simulation errors as a set of latent variables. These components are used to train deep regression neural networks to represent the complex relationship between the simulation errors and these latent variables.