A Novel Approach to Predict Permeability in Non-Cored Wells and Establish Reservoir Rock-Types in Complex Carbonate Reservoir Using Pore Geometry Structure (PGS): Integrating Petrophysical Properties and Diagenesis in Lower Cretaceous Carbonate Reser

Abstract The primary aim of our study was to introduce and validate the Pore Geometry Structure (PGS) approach for predicting permeability in non-cored wells and establishing reservoir rock types. To ensure consistency, this novel method was benchmarked against traditional core-derived permeability measurements and an existing machine learning method (K.mod module). PGS utilizes a constant cylindrical capillary pipes model as the dependent variable, integrating tortuosity and similarity exponent as independent variables to represent complexity. The study analyzed data from four cored wells, totaling nearly 1650 feet, which included 1185 routine core analysis (RCA) samples, 440 mercury injection capillary pressure (MICP) samples, and thin section data. These datasets were quality-checked and used to classify rock types by linking petrophysical groups (PGs) to geological features. Nine PGs were identified for limestone and six for dolomite facies based on MICP parameters, pore throat radius, and the porosity-permeability (phi/k) relationship. The phi/k data were plotted on a PGS diagram, establishing a relationship between pore geometry and structure, as delineated by different line gradients. This analysis resulted in eleven PGS-based rock types with distinct constants. The PGS rock-typing method demonstrated effective clustering, complementing PGs in defining diagenetic processes such as cementation and dissolution. Contingency analysis confirmed a strong correlation (70%) between PGs and PGS rock types. Furthermore, the PGS method's permeability predictions are closely aligned with core data, with 80-90% of predictions falling within the core-derived range. Compared to the K.mod method, the PGS approach exhibited a superior correlation in permeability prediction. This paper outlines a step-by-step process for independently calculating permeability predictions and defining rock types using the PGS approach in complex carbonate pore systems. Our study highlights the practical implications of the PGS approach in complex carbonate reservoirs. It significantly improves precision and reduces uncertainty in permeability predictions for non-cored wells. The PGS rock-typing method also offers an integrated workflow that links diagenetic processes to PG classification, thereby enhancing reservoir characterization.