Use of the Rate-Transient Analysis, Porosity, and Permeability Core Analysis Method to Constrain Permeability Estimates from Mercury Injection Capillary Pressure Data

Summary The rate-transient analysis (RTA), porosity, and permeability (RTAPK) core analysis method was developed to replicate conditions under which wells completed in unconventional reservoirs are operated in the field; this allows RTAPK data to be analyzed using RTA methods. Multiple estimates of permeability and porosity can be obtained through analysis of RTA-derived flow regimes. Given its speed, dynamic range, and demonstrated consistency, RTAPK is used in this study to evaluate permeability correlations derived from mercury (Hg) injection capillary pressure (MICP) data, which are more commonly available. A suite of Permian Basin (Dean-Stark cleaned) core plug samples, representing a wide range of lithologies, rock fabrics, and textures, was analyzed with RTAPK using nitrogen (N2) gas. Samples were screened for further analysis based on RTAPK and MICP porosities. A new semi-automated, multisample RTAPK device was constructed to allow samples to be run in parallel. For each RTAPK test performed at 800 psi and 3,200 psi effective stress, flow regimes were identified and analyzed using RTA straightline analysis (SLA) methods to derive permeability and porosity. Results were corrected for gas slippage using the Klinkenberg reciprocal mean pressure plot. MICP measurements were performed on Soxhlet-cleaned end trims from the host RTAPK plugs. Each sample underwent a vacuum procedure (to 50 μmHg), followed by a low-pressure injection cycle (to 30 psia), which serves to provide mercury conformance around the bulk volume. After transferring to a high-pressure cell, mercury was injected (to a maximum pressure of 60,000 psia) to determine the pore throat distribution that contributes to permeability. The flow-regime sequence of transient linear flow (TLF) followed by boundary-dominated flow (BDF) was observed in most samples and test conditions. Some variations in this sequence were attributed to rock fabric, as verified by computed tomography (CT) scanning. Two (slip-corrected) permeability estimates were obtained from the square-root-of-time (SQRT) plot, one permeability estimate from the contacted fluid-in-place (CFIP) plot, and one from the flowing material balance (FMB) plot, for each test. The resultant permeabilities ranged from 10 nd to 100 μd. The high degree of consistency in the RTAPK permeabilities provides confidence in using this data set as a calibration reference for MICP-based correlations. Hence, multilinear regression (MLR) through machine learning was used to predict permeability from MICP-derived properties for comparison with RTAPK-derived permeability values. For the first time, MICP-derived permeability estimates are compared with RTAPK across a broad range of lithologies and permeabilities. The results demonstrate good agreement between the methods, providing greater confidence in permeability estimates for unconventional reservoirs obtained using RTAPK and MICP.