New Approach to History Matching of Simulation Models with Discrete Fracture Networks

Abstract

Simulations of fractured reservoirs are usually performed by dual porosity, dual permeability models. Traditional deterministic workflows that model prominent fracture lineaments often fail to integrate quantification of uncertainty, inherent in fracture spatial distribution and properties. This poses significant challenges on history matching and frequently requires extensive manual beyond geological consistency to achieve the match. We present a method for assisted history matching (AHM) that calibrates models of fractured reservoirs by dynamically updating matrix properties and discrete fracture networks (DFN), while retaining the highest levels of geological consistency and model predictability. The new workflow simultaneously interfaces between applications for building the geo-model and the DFN model "on the fly" and integrates them into a Closed-Loop framework for global stochastic optimization using evolutionary algorithms. Rigorous uncertainty quantification is performed with sensitivity analysis and variability refinement, using the multi-level design of experiments (DoE). We deploy the workflow on the model of a fractured and faulted reservoir developed under natural aquifer drive. Geo-modeling uncertainty workflow generates multiple realizations of seismic-inverted acoustic impedance, used as a 3D trend for populating porosity, with varying variogram parameters. Uncertainty in porosity-permeability correlation coefficients is leveraged to generate multiple, spatially diverse permeability models. Realizations of porosity and permeability are used to generate corresponding realizations of water saturation. By sampling probability distributions of fracture density, geometry, aperture and orientation, 3D realizations of fracture porosity, fracture horizontal and vertical permeability and matrix-fracture transfer parameters are generated. The workflow produces statistically and geologically diverse ensemble of matrix and DFN model realizations that results in excellent variability in dynamic simulation response and confines the observed data. The multi-objective misfit function (OF), subject to minimization in the AHM process, incorporates static well pressures and was evaluated with a reservoir simulator that employs Massive Parallel Processing to achieve practical computation times, even with large-scale simulation grids. The presented AHM workflow demonstrates a unique functionality that enables the integration of DFN geo-mechanical properties (e.g. paleo-stress, pore-pressure) as predictors for fracture network attributes in the process of Closed-Loop model inversion and optimization. The method enables a robust, multivariate reservoir uncertainty quantification and dynamic calibration and delivers geologically consistent fractured reservoir models for reservoir forecasting under uncertainty.