Understanding water uptake and drainage in shales has important implications for both hydrocarbon extraction and hydraulic fracturing fluid disposal. This study reports gravimetric water sorption isotherms and kinetics of water transport in shales. Moisture mass transport profiles during water uptake and drainage processes were numerically simulated. Quantitative parameters characterizing the water transport properties were calculated and their dependences on water saturation were analyzed. An approach was proposed to evaluate the permeability of shales using dynamic water sorption. The reliability of the estimated results was verified by the experimental values using gas permeability measurements.The apparent diffusion coefficients of water sorption on shales were found to be between 1.0 × 10−12 and 1.5 × 10−11 m2/s. The apparent diffusion coefficient first increases with water saturation and remains stable at a moderately saturated condition. However, this coefficient decreases for shales with high water saturation. Apparent diffusion coefficients for the sorption process are almost equal to those for the desorption process, except at the moderate saturation condition. Liquid water (including adsorbed water) contributes more than 80% to the water transport, whereas water vapor mainly contributes to shales with low water saturation. The liquid water permeability determined by water sorption is consistent with the crushed‐rock permeability measured by gas expansion. A further reasonable agreement is achieved between the analytical gas permeability, as a function of water saturation, and the experimental gas phase permeability. Water sorption kinetics provide an indirect method for assessing the water transport properties as a function of water saturation when direct measurements are not available.

This study presents both experimental and theoretical investigations about gas transport in shales. Gas apparent permeability coefficients and Klinkenberg slippage factors were determined on Longmaxi shales using He, Ar, N2, CH4, and CO2. Then, a model was developed to interpret the experimentally determined gas slippage factor, considering the effects of intrinsic permeability, porosity, tortuosity, and gas physical properties. The proposed model is verified by correlating Klinkenberg-corrected permeabilities and gas slippage factors of shales probed by He, Ar, N2, CH4, and CO2 at different confining pressures. The model can quantitatively describe the gas dependence of slippage factors (He > Ar > N2 > CH4 > CO2). According to the model presented, the slippage factor increases proportionally to the ratio of the characteristic gas parameter (C ) to tortuosity. The model also leads to a practicable approach to determine the effective tortuosity of tight rocks at in situ reservoir stress state. Effective tortuosity of shales determined using helium slippage measurements are far larger than the generally assumed values. Another advantage of the model is its ability to quantitatively account for the variation in permeability values at similar gas slippage and the counterintuitive reduction in gas slippage during compaction observed in previous experiments. The proposed model correctly matches a set of gas slippage measurements and provides insight into gas transport in tight porous medium.

Understanding the penetration and retention of fracturing water in geological systems is important for hydrocarbon extraction and fluid disposal during hydraulic fracturing. This paper explores the imbibition of fracturing water and its penetration profiles on shales from Sichuan Basin, China. Water imbibition experiments were performed on the collected shales with a variety of mineralogical compositions and pore structure characteristics. Sorptivity, quantitatively characterizing water imbibition capacity, was evaluated and its dependence on rock fabric and mineralogical compositions was examined. Then, a nonlinear diffusion model is presented to simulate the capillary flow during the water imbibition process according to the unsaturated flow theory. The solution of this model offers quantitative information about water penetration and distribution in shales. The water sorptivity of shales ranges from 0.1 to 1.8 × 10–6 m/s0.5. Water imbibed by shales is mainly along the shale lamination and bedding. The strong mineral alignment also contributes to sorptivity because of the preferential transport pathways. Shales with developed microfracture networks have higher sorptivity. Nevertheless, water penetration into shales is commonly less than 5 cm during the typical shut-in period after fracturing operations. The fracturing fluid loss is related to the development of microfracture networks and the fracture width. The complex fracture networks with a small fracture width result in low water recovery.

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Interpreters face two main challenges in seismic facies analysis. The first challenge is to define, or “label,” the facies of interest. The second challenge is to select a suite of attributes that can differentiate a target facies from the background reflectivity. Our key objective is to determine which seismic attributes can best differentiate one class of chaotic seismic facies from another using modern machine-learning technology. Although simple 1D histograms provide a list of candidate attributes, they do not provide insight into the optimum number or combination of attributes. To address this limitation, we have conducted an exhaustive search whereby we represent the target and background training facies by high-dimensional Gaussian mixture models (GMMs) for each potential attribute combination. The first step is to choose candidate attributes that may be able to differentiate chaotic mass-transport deposits and salt diapirs from the more conformal, coherent background reflectors. The second step is to draw polygons around the target and background facies to provide the labeled data to be represented by GMMs. Maximizing the distance between all GMM facies pairs provides the optimum number and combination of attributes. We use generative topographic mapping to represent the high-dimensional attribute data by a lower dimensional 2D manifold. Each labeled facies provides a probability density function on the manifold that can be compared to the probability density function of each voxel, providing the likelihood that a given voxel is a member of each of the facies. Our first example maps chaotic seismic facies associated with the development of salt diapirs and minibasins. Our second example successfully delineates karst collapse underlying a shale resource play from north Texas.