Abstract

Our studies of the underlying fundamental gas recovery mechanisms from shale gas are motivated by expectations of increasing role of shale gas in national energy portfolios worldwide. We use pore-scale analysis of reservoir shale samples to identify critical parameters to be employed in a gas flow model used to evaluate well production data. We exploit a number of 3D imaging technologies to study the complexity of shale pore structure: from low-resolution x-ray computed tomography (CT) to focused ion beam and scanning electron microscopy (FIB/SEM). We observe that heterogeneity is present at all scales. The CT data show fractures, thin layers, and density heterogeneity. The nanometer-scale resolution FIB/SEM images show that various mineral inclusions, clays, and organic matter are dispersed within a few-hundred micron-cubed volume. Samples from different regions differ sharply in the shape, size and distribution of pores, solid grains, and the presence of organic matter. Although the samples have clearly distinguishable signatures related to the regions of origin, extremely low permeability is a common feature. This and other pore-scale observations suggest a bounded-stimulated-domain model of a horizontal well within fractured shale that accounts for both compression and adsorption gas storage. Using the method of integral relations, we obtain an exact analytical formula approximating the solution to the pseudo-pressure diffusion equation. This formula makes fast and simple evaluation of well production possible without resorting to complex computations. It defines a decline curve, which predicts two phases of production. During the early stage, the production rate declines with reciprocal square root of time, whereas later the rate declines exponentially. The model has been verified by successful matching monthly production data from a number of shale gas wells collected over several years of operation. With appropriate scaling, the data from multiple wells collapse on a single type curve. Pore-scale image analysis and the mesoscale model suggest a dimensionless adsorption storage factor (ASF) to characterize the relative contributions of compression and adsorption gas storage.

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