Physics-Informed Deep-Learning Models Improve Forecast Scalability, Reliability
Authors: Carpenter Chris 
Published: October 2024

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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTeC 4042557, “Physics-Informed Deep-Learning Models for Improving Shale and Tight Forecast Scalability and Reliability,” by Kainan Wang, SPE, Lichi Deng, and Yuzhe Cai, SPE, Chevron, et al. The paper has not been peer reviewed.

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In the complete paper, the authors present a workflow that combines probabilistic modeling and deep-learning models trained on an ensemble of physics models to improve scalability and reliability for shale and tight reservoir forecasting. Their approach is applied to synthetic cases and many wells in the Permian Basin. Through hindsight studies, these models have been demonstrated to generate realistic and diverse production curves, capture the physics of unconventional flow, quantify well-production-outlook uncertainty, and help interpretation of subsurface uncertainty.

Introduction

In the realm of unconventional asset development, scalable forecasting is a key component in forecast reliability. In recent years, data-driven machine-learning models and workflows have emerged as potent tools for predicting well performance, particularly in scenarios where wells share similar reservoir properties, completion designs, and operational conditions.

A novel approach known as physics-informed machine learning (PIML) has gained prominence. These models leverage the strengths of machine learning while honoring the constraints imposed by physical laws. By incorporating observational, inductive, or learning bias, they bridge the gap between empirical data and fundamental physics.

In this paper, the authors showcase the application of a PIML system tailored specifically for unconventional reservoirs. Their methodology involves the selection of an appropriate physics modeling framework, coupled with the development of multiple machine-learning models that augment the limited field data set.

Data Acquisition and Generation

Bottomhole pressure (BHP) data are key to reliable forecasting because they reflect constraints to well production. Downhole pressure gauges can provide measurements of BHP accurately, but these measurements are not always available. Numerical models can be used to correlate surface measurements to BHP with some accuracy, taking into consideration the well configuration and fluid properties, yet it is too time-consuming to develop such models for many wells in relation to the speed needed for unconventional reservoir development.

An in-house machine-learning model was developed for BHP calculation to further augment the data set of gauge measurements and numerical modeling. Through a workflow that chooses the wells with higher-fidelity data as a training set, and careful calibration and iterations between incrementally adding wells of high-fidelity data to reduce uncertainty, the trained machine- learning model is able to predict BHP accurately for many more wells.

Another type of engineering data important for reliable well-performance analysis and forecasting is reservoir-fluid description. In this study, the authors leveraged an internally developed pressure/volume/temperature (PVT) machine-learning model trained on proprietary PVT data, which was also shown to provide reliable predictions to key PVT properties such as saturation pressure, formation volume factor, and viscosity from limited inputs at the reservoir and separator level.

These PVT models not only enhance scalability of any downstream forecasting models but also play a crucial role in physics-informed uncertainty analysis for these models.