Residual Learning to Integrate Neural Network and Physics-Based Models for Improved Production Prediction in Unconventional Reservoirs

Abstract

Summary The flow and transport processes that take place during hydrocarbon production from hydraulically fractured unconventional reservoirs are not well understood. As a result, current simulators cannot provide reliable predictions of the production behavior in the field. In addition to imperfect physics, the prediction errors can be caused by the inability to conveniently integrate important field data, such as well logs, drilling, and completion parameters, into existing physical models. A neural network (NN) model is developed to learn the (residual) errors in simulation-based production prediction as a funcation of input parameters of an unconventional well. Once trained, the NN model augments the physics-based predictions by adding the learned reiodual to predict the production response of a new well. To learn the discrepancy between the simulated and observed production data, the NN model is trained using a labeled dataset consisting of the prediction errors (as labels) and the corresponding input parameters (features), such as formation, completion, and fluid properties. During training, a mapping is identified from the input parameters to their respective prediction errors. To facilitate the residual learning, first a convolutional autoencoder architecture is used to map the simulated and observed production responses to a low-dimensional latent space. This step is followed by a regression model that learns the mapping between the collected field parameters and the corresponding latent space representation of the prediction errors. The two steps are included in a single NN architecture and trained simultaneously. The proposed residual learning method is designed to compensate for prediction errors originating from a combination of imperfect representation of the physics and inaccurate simulation inputs, including uncertain descriptions of the reservoir and fracture properties. The performance of the proposed residual learning approach is evaluated using synthetic data as well as a field case study from the Bakken play in North Dakota.