Gas transport in shale gas reservoirs is largely affected by rock properties such as permeability. These properties are often sensitive to the in-situ stress state changes. Accurate modeling of shale gas transport in shale reservoir rocks considering the stress sensitive effects on rock petrophysical properties is important for successful shale gas extraction. Nonlinear elasticity in stress sensitive reservoir rocks depicts the nonlinear stress-strain relationship, yet it is not thoroughly studied in previous reservoir modeling works. In this study, an improved coupled flow and geomechanics model that considers nonlinear elasticity is proposed. The model is based on finite element methods, and the nonlinear elasticity in the model is validated with experimental data on shale samples selected from the Longmaxi Formation in Sichuan Basin China. Numerical results indicate that, in stress sensitive shale rocks, nonlinear elasticity affects shale permeability, shale porosity, and distributions of effective stress and pore pressure. Elastic modulus change is dependent on not only in-situ stress state but also stress history path. Without considering nonlinear elasticity, the modeling of shale rock permeability in Longmaxi Formation can overestimate permeability values by 1.6 to 53 times.
This paper presents an uncertainty assessment project using Artificial Neural Network (ANN) for a giant multi-layered sandstone reservoir in Middle East, which contains several uncertainties and associated risks. Uncertainty quantification in history matching, production forecasting and optimization approaches often requires hundreds of thousands of forward flow simulations to explore the uncertain parameter space, causing forbidden computational time requirement, especially for large-scale reservoir models. In order to bypass this limitation, one can use a proxy to replace the time-consuming flow simulator. In this work, an optimized ANN is used as the proxy and an uncertainty assessment workflow is implemented for the giant Cretaceous multi-layered sandstone reservoir using a global optimizer. Using the ANN based uncertainty assessment framework, the impacts of the main uncertain parameters on production forecasting are assessed for this multi-layered sandstone reservoir. Then, field development optimization is also performed to optimize wells injection and production rates to maximize the economic measures considering uncertainties. Using a real field as a case study, the capability of our uncertainty assessment workflow is demonstrated. The optimized ANN successfully captures the complex nonlinear dynamics as a proxy and significantly reduced the computation time.
Thief zones are highly permeable zones in oil reservoirs which affect the performance greatly during water flooding. The most effective way to prevent the invalid circle of thief layer is the injection of high-concentration polymer slugs. In this paper, the plugging effect of nano-micron polymer flooding on the thief zone is studied by experimental and theoretical analysis. The changes of water content and flow resistance were analyzed under different conditions. The result shows that the model presented here has good agreement with the experimental results. The displacement effect is the best when the thief zone is located on the upper part of the reservoir. And the water content will decrease with the increase of flow resistance after nano-micron polymer injection. Besides, the higher the polymer concentration, the more obvious the decrease of water content, and more effective the plugging. This study has provided a quick and reasonable guide in the later adjustment of water flooding development of carbonate reservoirs with thief layers.
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