Field Application of a Novel Multiresolution Multiwell Unconventional Reservoir Simulation: History Matching and Parameter Identification

Abstract

Summary This study focuses on developing an efficient workflow by integrating a multiresolution simulation model and a multiobjective evolutionary algorithm (MOEA) for application to multiwell unconventional reservoirs. In this approach, hydraulic fractures are represented using a dual porosity, dual permeability system facilitated by an embedded discrete fracture model (EDFM). A novel fast-marching simulation method is used to cut down on computational expenses by an order of magnitude, greatly accelerating the history-matching process. A variety of integrated monitoring technologies were implemented to map out the hydraulic fracture network. Insights into hydraulic fracture locations were gleaned from warm-back analysis of distributed temperature sensing data, and these locations were then assimilated into the simulation model as embedded discrete fractures. For the simulation, a fast-marching-based multiresolution model was used to partition the reservoir into local and shared domains guided by the diffusive-time-of-flight (DTOF) principle. The local domain maintained the original 3D grids near the wells while transforming the remaining area into 1D grids to accelerate the simulation process. Before history matching, a thorough sensitivity analysis was conducted to pinpoint the most impactful parameters. Subsequently, the model was fine-tuned using production data through an MOEA. The most sensitive parameters in history matching were identified as fracture geometry and conductivity, fluid saturations, and rock compressibility in the stimulated reservoir volume (SRV) areas. After history matching, there was a noteworthy reduction in the uncertainty of these tuning parameters. The calibrated parameters are valuable to evaluate the effectiveness of the well completion design. Overall, this work emphasizes the innovative combination of techniques applied, the efficiency gains in the history-matching process, and the scalability of the approach to other oilfield applications.