Use of Rate-Transient Analysis, Porosity, Permeability (RTAPK) Core Analysis Method to Constrain Permeability Estimates from MICP Data

Abstract The RTAPK (rate-transient analysis, porosity and permeability) 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. Due to this redundancy, speed, and dynamic range of the method, it is used herein to evaluate permeability correlations derived from mercury 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, were analyzed with RTAPK using N2 gas. Samples were screened for further analysis based on RTAPK and MICP porosities. A new semi-automated, multi-sample RTAPK device was constructed to allow samples to be run in parallel. For each RTAPK test performed at different effective stresses, flow regimes were identified and analyzed using RTA straight-line 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 50umHg), 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 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 nano-Darcy to 100 micro-Darcy. The high degree of consistency in the RTAPK permeabilities provides confidence in using this dataset as a calibration reference for MICP-based correlations. Hence, multi-linear 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 for a range of lithologies across a broad range of permeabilities. The results demonstrate good agreement between the methods, allowing for improved confidence in permeability estimates for unconventional reservoirs using RTAPK and MICP.