Pore-to Reservoir-Scale Modeling of Depletion-Induced Compaction and Implications on Production Rate

Sun, Zhuang (The University of Texas at Austin) | Tang, Hewei (Texas A&M University) | Espinoza, D. Nicolas (The University of Texas at Austin) | Balhoff, Matthew T. (The University of Texas at Austin) | Killough, John E. (Texas A&M University)

OnePetro 

Abstract

The reduction of pore pressure caused by depletion can induce significant reservoir compaction, especially in unconsolidated reservoirs. Experiments using unconsolidated core samples are often sparse and costly. We develop a numerical approach based on computer-based simulations of rock samples and mechanical tests. The numerical sample consists of crushable grains simulated with the discrete element method (DEM) and the bonded-particle model (BPM). Model parameters are calibrated through numerical single-grain-crushing tests which reproduce the experimentally-measured sand strength. Grain crushing induced by the uniaxial strain stress path results in a pronounced reduction of porosity and permeability, which manifests more readily for samples with large grain size. The change of particle size distribution indicates that the high effective stress causes grain crushing and produces a significant amount of fines. We perform numerical uniaxial strain tests on numerical samples comprising stiff and soft mineral grains. Simulation results indicate that the presence of soft grains and inclusions (e.g. shale fragments) facilitates the grain crushing. Reservoir simulations, incorporating the change of porosity and permeability as a compaction table, show that the upscaled compaction can enhance production due to compaction drive but also reduces production rate by impairing the reservoir permeability. This multiscale numerical workflow bridges particle-scale compaction behavior and field-scale reservoir production. In this paper, (a) DEM simulations provide a useful tool to investigate compaction effects and complement laboratory experiments; (b) the multi-scale numerical approach can predict the depletion-induced evolution of reservoir production.

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