Analysis using partial least-squares regression identifies reservoir types in tight sandstone gas, indicating unique cleanup strategies based on back-pressure effects.
Diagnosing water-phase damage remains challenging because routine petrophysical parameters do not capture capillary hysteresis and pressure-transmission effects. In this study, a standardized, auditable workflow was established to link laboratory descriptors to field-relevant cleanup. Full-curve mercury injection capillary pressure data were acquired and converted using consistent Washburn parameters, from which withdrawal efficiency was computed on the withdrawal branch. A pressure-transmission coefficient was evaluated under unified boundary conditions to complement permeability and porosity. After preprocessing and partial least-squares regression (PLSR) screening, MICP descriptors were clustered by k-means (k = 5) to obtain reservoir Types I–V. Regressions relating WE to permeability and flowback behavior were then used to assess engineering relevance. The results indicate that WE capture hysteretic trapping/back-pressure not contained in permeability or porosity and, when interpreted jointly with PTC, differentiates reservoir types by cleanup propensity. This framework provides a reproducible bridge from laboratory MICP hysteresis to field-scale flowback interpretation. Practical implications include prioritization of gas–wet wettability modification, low-surface-tension systems, and minimized early liquid loading for clusters exhibiting higher WE and lower PTC.
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Tian et al. (2025) studied this question.
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