Integration of Pressure-Derived Permeability with Static Reservoir Modeling: An Adaptive Approach for Improved History matching

Abstract Integration of pressure-derived permeability thickness with other geological data plays a crucial role in estimating the apparent reservoir permeability, which is a key reservoir property required for reliable reservoir characterization as it governs fluid flow and greatly impacts decisions related to production, field development, and reservoir management. The geological model provides a representation of the subsurface reservoir, capturing the spatial distribution of lithology, porosity, permeability, and other geological properties. Analysis of pressure data provides valuable information on well condition, reservoir extent, and dynamic reservoir parameters. Integrating such data with the geological model is an enabler to better quantify and manage the uncertainty in the spatial 3D distribution of permeability away from well control. This work proposes a methodology to build high-resolution geological models based on the available dynamic data, seismic data, and geologic interpretations. The geological model typically provides an estimate of intrinsic permeability, which depends on the pore structure and flow path within the rock matrix. In general, the 3D-modeled permeability honors core and well log data, which are sparse and at a significantly smaller scale compared to the reservoir size. Hence, it may not be representative of permeability farther away from the well locations, especially in carbonate reservoirs, which may have varying complex pore structures and widely varying porosity/permeability relationships. The proposed workflow incorporates pressure-derived permeability into the reservoir model prior to the matching. The workflow iteratively modifies the permeability in the geological model to minimize the mismatch between the simulated and measured production and pressure data. The proposed workflow has been implemented on a synthetic reservoir model, showing an overall improvement in the history match quality for both deviated and vertical wells. The process enabled a more robust permeability adjustment and improved the integrated asset team's efficiency through guided iterations. The presented methodology offers a systematic and adaptive approach leveraging pressure-derived permeability and geological information to adjust the intrinsic permeability. By integrating these datasets and enabling multi-disciplinary collaboration, the accuracy of the reservoir model is enhanced, leading to more reliable reservoir characterization, improved decision-making in reservoir management, and optimization of IRS resources and time.