Combining Gradual Deformation and Upscaling Techniques for Direct Conditioning of Fine-Scale Reservoir Models to Interference Test Data

Abstract

Reconciling multiscale data for reservoir characterization is a crucial issue, because different data types provide different information about the reservoir architecture and heterogeneity. It is essential that reservoir models preserve small-scale property variations observed in well logs and core measurements and capture the large-scale structure and continuity observed in global measures such as well-test and production data.In this paper, we present a new methodology to directly update fine-scale geostatistically-based reservoir models by combining gradual deformation parameterization for the fine-scale geostatistical model, and an upscaling technique for the coarse-scale flow simulation model. As consequence, both fine-and coarse-scale models are updated by dynamic data during the history matching process.The proposed methodology includes:• Perturbation of the fine-scale geostatistical model using the gradual deformation parameterization. Gradual deformation ensures the preservation of the overall geostatistical properties of the fine model.• Generation of the coarse-scale flow simulation model by upscaling the fine-scale geostatistical model.• Fluid-flow simulation and sensitivity computation of the flow simulation results with respect to the fine-scale parameterization. This sensitivity computation is analytical and takes into account the upscaling process.• Direct updating of the fine-scale geostatistical model using classical optimization process. Direct updating ensures consistency between the fine-and coarse-scale models.The accuracy of the proposed methodology was improved by calibrating the flow simulation model. The objective of this calibration is to reduce the error introduced by the upscaling step during the flow simulation.We applied successfully our methodology for fine-scale reservoir characterization process by integrating permanent downhole gauge measurements directly into a 3D-geostatistical model containing about two million gridblocks.This test is designed to highlight several key issues of the proposed methodology:• Efficiency of the upscaling step, coupled with gradient-based optimization to speed up history matching.• Usefulness of the calibration step for a correct integration of upscaling techniques in history matching.• Capability of the methodology for maintaining consistency and coherency between fine-scale and coarse-scale models.• Improvement of the reservoir characterization process by integrating dynamic data at both fine geostatistical scale and coarse simulation scale.

A review of applications to constrain pumping test responses to improve on geological description and uncertainty

“…At this time I believe that it is best to directly optimize on variables that describe the properties of the fine‐scale model rather than the properties of the coarse‐scale model as suggested by Landa [2001]; see also Mezghani and Roggero [2004]. These studies focus principally on random variations within structures that are discrete flow or geological units.…”

Effect of Scaleup and Aggregation on the Analysis of Interference Tests

“…1. 20,21 Conclusions Based on this study, the following principal conclusions are warranted: 1. 34 Curves A and AЈ are pressure and derivative responses, respectively, at the active well, and Curves B and BЈ are pressure and derivative responses, respectively, at the observation well.…”

Complex geology and pressure tests

“…If we are to incorporate considerations based on geological modeling, then we believe that it is best to directly optimize on variables that describe the properties of the fine-scale model (Landa, 2001;Mezghani and Roggero, 2004). These studies focus principally on random variations within structures that are discrete flow or geological units.…”