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DOI:10.1306/01092624010
Assessing rock physics models for TOC estimation in Lower Goru Formation: Case study on shale gas reservoir in Pakistan
Ghulam Mohyuddin Sohail1 , Muhammad Umair1 , Ahsan Mehmood1 , and Shahid Ejaz2
1 Department of Geological Engineering, University of Engineering & Technology, Lahore, Pakistan
2 Zeeruk International Pvt. Ltd.
Ahead of Print Abstract
Total Organic Carbon (TOC) is essential for evaluating shale gas reservoirs and estimating hydrocarbon reserves. Laboratory TOC measurements are accurate but time-consuming, costly, and often limited to specific depths. This study evaluates rock physics models for predicting TOC in the Lower Goru Formation, Lower Indus Basin, Pakistan, where core samples are unavailable. TOC data were derived from well cuttings to identify a well-log–based method producing TOC estimates comparable to laboratory results. Compressional and shear wave velocities from well logs were used, along with petrophysical and elastic properties from logs and literature. Individual models for TOC estimation, such as Kuster and Toksoz (KT) and self-consistent approximation (SCA), showed correlations between 55% and 75%. Combining KT and SCA models significantly improved TOC estimation, achieving correlations of 90% to 98% compared to laboratory data. TOC showed a negative correlation with compressional and shear wave velocities but no clear relation with pore geometries or shale porosity. The variability in pore geometries in shale reservoirs allows flexibility in selecting pore aspect ratios, defined as the ratio of the short to long axis of pores, ranging from 0 to 1 in this study. This variability supports the selection of appropriate aspect ratios for TOC estimation. Statistical analysis confirms the reliability of the combined models, though their applicability may vary with geological settings. This research addresses challenges in selecting input parameters for rock physics models with limited measured TOC and provides valuable insights into TOC estimation from wireline logs, enhancing the understanding of shale gas reservoir characterization globally.
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