Physics-Informed Machine Learning for Real-time Reservoir Management

We present a physics-informed machine learning (PIML) workflow for real-time unconventional reservoir management. Reduced-order physics and high-fidelity physics model simulations, lab-scale and sparse field-scale data, and machine learning (ML) models are developed and combined for real-time forecasting through this PIML workflow. These forecasts include total cumulative production (e.g., gas, water), production rate, stage-specific production, and spatial evolution of quantities of interest (e.g., residual gas, reservoir pressure, temperature, stress fields). The proposed PIML workflow consists of three key ingredients: (1) site behavior libraries based on fast and accurate physics, (2) ML-based inverse models to refine key site parameters, and (3) a fast forward model that combines physical models and ML to forecast production and reservoir conditions. First, synthetic production data from multi-fidelity physics models are integrated to develop the site behavior library. Second, ML-based inverse models are developed to infer site conditions and enable the forecasting of production behavior. Our preliminary results show that the ML-models developed based on PIML workflow have good quantitative predictions (>90% based on R2-score). In terms of computational cost, the proposed MLmodels are OO(104) to OO(107) times faster than running a high-fidelity physics model simulation for evaluating the quantities of interest (e.g., gas production). This low computational cost makes the proposed ML-models attractive for real-time history matching and forecasting at shale-gas sites (e.g., MSEEL – Marcellus Shale Energy and Environmental Laboratory) as they are significantly faster yet provide accurate predictions.