The Arbuckle Group aquifer is the principal disposal zone for oil and gas field brines and hazardous/nonhazardous wastewater across the U.S. midcontinent and is traditionally viewed as an infinite capacity aquifer. Thousands of wells annually dispose hundreds of millions of barrels of wastewater into the aquifer across Kansas and Oklahoma, but direct links between injection and recent increases in seismicity have been hindered by a lack of pressure data for the Arbuckle Group. Here we present a newly compiled data set for 49 wells across Kansas that provides a unique perspective on the aquifer's performance over two decades. Statistical analysis of falloff test pressures, static fluid levels, and injection volumes shows that Arbuckle pressures and fluid levels are rising, recently at faster rates, likely associated with increased wastewater injection. The new data also suggest that the pressure diffusion, the primary driver of induced seismicity, can reach distances up to 25 km from an injection point and is connected to static fluid level rises. The compiled dataset explains the recent surge in midcontinent seismicity. The data set also suggests that the Arbuckle has finite storage capacity and that wastewater disposal across parts of the midcontinent may soon require alternatives.

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Identifying attractive candidate reservoirs for producing geothermal energy requires predictive models. In this work, inspectional analysis and statistical modeling are used to create simple predictive models for a line drive design. Inspectional analysis on the partial differential equations governing this design yields a minimum number of fifteen dimensionless groups required to describe the physics of the system. These dimensionless groups are explained and confirmed using models with similar dimensionless groups but different dimensional parameters. This study models dimensionless production temperature and thermal recovery factor as the responses of a numerical model. These responses are obtained by a Box-Behnken experimental design. An uncertainty plot is used to segment the dimensionless time and develop a model for each segment. The important dimensionless numbers for each segment of the dimensionless time are identified using the Boosting method. These selected numbers are used in the regression models. The developed models are reduced to have a minimum number of predictors and interactions. The reduced final models are then presented and assessed using testing runs. Finally, applications of these models are offered. The presented workflow is generic and can be used to translate the output of a numerical simulator into simple predictive models in other research areas involving numerical simulation.

Reduced-order modeling (ROM) is a novel approach in all realms of computational science including reservoir simulation. Among various ROM methods, trajectory piecewise linearization (TPWL) is evolving for reservoir engineering applications. Previous investigations reflect promising future for incorporating TPWL into the next generations of enhanced reservoir simulators. In this work, we employ this method to examine the claimed efficiency, robustness and accuracy of it as a surrogate simulator. The self-construction of the used simulator gives us the opportunity to explore this method and to examine previous assertions on the subject. The efficiency of TPWL is primarily due to direct calculation of new saturation and pressure states using a linearized expansion around previously simulated states instead of traditionally solving the flow equations. For further efficiency and reduction of the required memory, TPWL method needs to accompany a space reducing scheme, through which the captured dynamic of the reservoir is projected into a lower-order space. The projection matrix is conventionally constructed through proper orthogonal decomposition (POD) of converged time stepping solutions known as 'snapshots' which are obtained during a serious of preprocessing runs called 'training' runs. In this work, we apply TPWL method to a hypothetical three-dimensional heterogeneous reservoir consisting of a compressible rock type. We assume an inverted five-spot production-injection pattern and present the results for a two-phase (oil-water) reservoir model under water flooding scenario, in which the injection well is controlled by injection rate. Achieved results demonstrate that use of TPWL leads to significantly faster simulation compared to high fidelity model. We achieved speedup of a factor of 120 while preserving accuracy and reliability of the results. This study suggests that TPWL methodology will be particularly attractive when many solutions of similar simulation models with different well settings are required for history matching or optimization problems. Future research should focus to assess the applicability of TPWL to conditions with strongly compressible flow or capillary pressure effects.