Jianchao Cai

A Feature-Based Stochastic Permeability of Shale: Part 1—Validation and Two-Phase Permeability in a Utica Shale Sample

Estimate of permeability plays a crucial role in flow-based studies of fractured tight-rocks. It is well known that most of the flow through tight-rocks (e.g., shales) is controlled by permeable features (e.g., fractures, laminations, etc.), and there is negligible flow through the matrix. However, current approaches in the literature to model permeability of tight-rocks do not account for such features present within the rock ranging from micro-scale to field-scale. Current permeability modeling approach assumes a single continuum without considering the presence of permeable features within the matrix (e.g., micro-fractures) or outside the matrix (e.g., natural fractures). Although the laboratory-measured permeability implicitly captures discrete features present in that sample (e.g., fractures, laminations, micro-fractures), most of the permeability models proposed for shale do not account for these features. Fracture permeability in the literature is typically modeled using an ideal slit assumption; however, this highly overestimates its permeability because fractures in real medium are non-ideal in terms of their porosity and tortuosity, which affect their permeability. Additionally, the transition zone between fracture and matrix also affects the permeability of fracture. In this study, part of a two-part series, a new method to predict permeability of fractured shale by discretizing the medium into matrix (inorganic and organic) and fractures is presented. New analytical expressions of permeability are derived to account for non-ideal nature of porous medium and two-phase flow in fractures. Rock feature in each cell of the grid is identified as one of the three elements (organic matter, inorganic matter, or fracture), and permeability of that cell is estimated using a suitable analytical expression. This method allows estimating permeability at any scale of interest and more robustly than by a pure analytical approach. The proposed method is validated against local and global-scale measurements on three fractured samples from laboratory. Finally, the method is used to predict two-phase flow permeability of supercritical CO2 displacing water within a fracture in a Utica shale sample. The proposed two-phase flow permeability equations can be used as a quick analytical tool to predict relative permeability estimates of two-phase flow in fractured shale samples. In Part 2, the proposed method is used to estimate field-scale permeability through an optimization process that uses field-scale production and other readily available information.

A Feature-Based Stochastic Permeability of Shale: Part 2–Predicting Field-Scale Permeability

AbstractIn a recent numerical study, it was demonstrated that characterizing reservoir permeability in terms of rock’s quality, as observed in lab and field, is the most important step before implementing an enhanced oil recovery operation or drilling a new well in antight formation. In that study, it was shown that permeable features in shale-like organic matter (OM) and fractures were the only regions that allowed some reasonable movement of fluid, whereas inorganic matter (iOM) that occupies larger pore volume with significant saturation of hydrocarbons has extremely low permeability that did not allow any reasonable fluid movement to affect production. That study demonstrated the importance of characterizing reservoir heterogeneity in shale in order to economically exploit the shale resource. This study proposes a method to predict spatially heterogeneous field-scale permeability of shale in terms of natural fractures, and matrix (iOM and OM). The method developed in Part 1 is combined with a history-matching process that uses only readily available information from lab-scale and outcrop (information from geologists) to predict field-scale permeability. The method also ensures consistency between the underlying fracture distribution and optimally matched fracture lengths and their apertures, in addition to accounting for random distribution of fractures and their abundance. Optimized parameters of fracture distribution are used to generate multiple realizations of geological model, and the “best-fitting” (most-likely) permeability scenario is chosen by generating production response of each realization of the geological model and comparing them against the observed field production history. The novelty of the proposed to predict field-scale permeability is that it uses only readily available information while also ensuring consistency between the underlying fracture distribution and optimally matched fracture lengths and their apertures, in addition to accounting for random distribution of fractures and their abundance.