Handling Spatial Heterogeneity in Reservoir Parameters Using Proper Orthogonal Decomposition Based Ensemble Kalman Filter for Model-Based Feedback Control of Hydraulic Fracturing

2018 American Chemical Society. Accurate characterization of reservoir properties is of central importance to achieve a desired fracture geometry during a hydraulic fracturing process. However, the estimation of spatially varying geological properties in hydraulic fracturing is inherently ill-posed due to a limited number of measurements. In this work, parametrization is performed to reduce the dimensionality of spatially varying Young's modulus profiles via proper orthogonal decomposition (POD), and a data assimilation technique called ensemble Kalman filter (EnKF) is used to estimate the parameter values in the reduced low-dimensional subspace. Through a series of simulation results, it is demonstrated that the POD-based EnKF technique provides a process model with updated spatially varying geological parameters, which is able to make an accurate prediction of the fracture propagation dynamics in hydraulic fracturing. Next, we use the updated high-fidelity process model in a model predictive control framework to construct a closed-loop system of hydraulic fracturing to achieve uniform proppant concentration at the end of pumping.

Handling Spatial Heterogeneity in Reservoir Parameters Using Proper Orthogonal Decomposition Based Ensemble Kalman Filter for Model-Based Feedback Control of Hydraulic Fracturing Conference Paper