Hydrocarbon production from unconventional resources and the use of reservoir stimulation techniques, such as hydraulic fracturing, has grown explosively over the last decade. However, concerns have arisen that reservoir stimulation creates significant environmental threats through the creation of permeable pathways connecting the stimulated reservoir with shallower freshwater aquifers, thus resulting in the contamination of potable groundwater by escaping hydrocarbons or other reservoir fluids. This study investigates, by numerical simulation, gas and water transport between a shallow tight-gas reservoir and a shallower overlying freshwater aquifer following hydraulic fracturing operations, if such a connecting pathway has been created. We focus on two general failure scenarios: (1) communication between the reservoir and aquifer via a connecting fracture or fault and (2) communication via a deteriorated, preexisting nearby well. We conclude that the key factors driving short-term transport of gas include high permeability for the connecting pathway and the overall volume of the connecting feature. Production from the reservoir is likely to mitigate release through reduction of available free gas and lowering of reservoir pressure, and not producing may increase the potential for release. We also find that hydrostatic tight-gas reservoirs are unlikely to act as a continuing source of migrating gas, as gas contained within the newly formed hydraulic fracture is the primary source for potential contamination. Such incidents of gas escape are likely to be limited in duration and scope for hydrostatic reservoirs. Reliable field and laboratory data must be acquired to constrain the factors and determine the likelihood of these outcomes.Key Points:Short-term leakage fractured reservoirs requires high-permeability pathways
Production strategy affects the likelihood and magnitude of gas release
Gas release is likely short-term, without additional driving forces
Abstract. It has become increasingly common for researchers to utilize methods that identify weather features in climate models. There is an increasing recognition that the uncertainty associated with choice of detection method may affect our scientific understanding. For example, results from the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) indicate that there are a broad range of plausible atmospheric river (AR) detectors and that scientific results can depend on the algorithm used. There are similar examples from the literature on extratropical cyclones and tropical cyclones. It is therefore imperative to develop detection techniques that explicitly quantify the uncertainty associated with the detection of events. We seek to answer the following question: given a “plausible” AR detector, how does uncertainty in the detector quantitatively impact scientific results? We develop a large dataset of global AR counts, manually identified by a set of eight researchers with expertise in atmospheric science, which we use to constrain parameters in a novel AR detection method. We use a Bayesian framework to sample from the set of AR detector parameters that yield AR counts similar to the expert database of AR counts; this yields a set of “plausible” AR detectors from which we can assess quantitative uncertainty. This probabilistic AR detector has been implemented in the Toolkit for Extreme Climate Analysis (TECA), which allows for efficient processing of petabyte-scale datasets. We apply the TECA Bayesian AR Detector, TECA-BARD v1.0.1, to the MERRA-2 reanalysis and show that the sign of the correlation between global AR count and El Niño–Southern Oscillation depends on the set of parameters used.
We present an unsplit method for the time-dependent compressible Navier-Stokes equations in two and three dimensions. We use a conservative, second-order Godunov algorithm. We use a Cartesian grid, embedded boundary method to resolve complex boundaries. We solve for viscous and conductive terms with a second-order semiimplicit algorithm. We demonstrate second-order accuracy in solutions of smooth problems in smooth geometries and demonstrate robust behavior for strongly discontinuous initial conditions in complex geometries.
We present a new parallel algorithm for generating consistent Voronoi diagrams from distributed input data for the purposes of simulation and visualization. The algorithm functions by building upon any serial Voronoi tessellation algorithm. The output of such a serial tessellator is used to determine the connectivity of the distributed domains without any assumptions about how points are distributed across those domains, and then in turn to build the portion of the global tessellation local to each domain using information from that domains neighbors. The result is a generalized methodology for adding distributed capabilities to serial tessellation packages. Results from several two-dimensional tests are presented, including strong and weak scaling of its current implementation.
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