Application of Petrophysical Analysis, Rock Physics, Seismic Attributes, Seismic Inversion, Multi-attribute Analysis, and Probabilistic Neural Networks for Estimating Petrophysical Parameters for Source and Reservoir Rock Evaluations in the Lower Indus Basin, Pakistan

Abstract

Accurately characterizing reservoir petrophysical parameters and delineating lithofacies is challenging in heterogeneous formations. Traditional seismic interpretations may be uncertain, but probabilistic neural network (PNN) modeling and seismic inversion constrained by well log data have improved interpretation accuracy and reduced uncertainty in determining reservoir properties such as volume and distribution. It is necessary to determine reservoir assessment parameters precisely and conduct a thorough integrated study of promising blocks that hold paramount potential and will help reduce drilling risk and increase the recovery of oil and gas resources. This paper provides a comprehensive integrated approach to differentiate lithofacies within a gas-prone reservoir (Lower Goru Formation) and predict the potential for hydrocarbon resources in the Sinjhoro Block of Pakistan. This approach involves petrophysical analysis, rock physics, seismic attributes, seismic inversion, multi-attribute analysis, and PNN for estimating petrophysical parameters for source and reservoir rock evaluation. The trace-based 2D extracted attributes, such as pronounced root mean square amplitude anomalies within the Talhar Shale, indicate that hydrocarbon indicators are aligned with the seismic structure interpretation and are considered an appropriate tool for extracting information from poststack seismic data. The results obtained through an integrated approach effectively optimize lateral and vertical facies heterogeneities in target formations, enabling the precise prediction of reservoir parameter distributions. The petrophysical analysis results indicated the presence of gas sands in Basal Sands (hydrocarbon saturation = 53%) and Massive Sands (hydrocarbon saturation = 66%). The current findings demonstrate that the PNN method is the most accurate for estimating petrophysical parameters (volume of shale, total porosity, effective porosity, and water saturation), with a correlation of approximately 0.97–0.99, whereas multi-attribute regression analysis has a correlation of approximately 0.56–0.67. The well log analysis results revealed that the average total organic carbon content of the Talhar Shale in all the wells ranges 1.20–2.20%, its average porosity is 10–16%, its Poisson’s ratio is low (0.20–0.27), and its Young's modulus is high (05–08). Thus, the proposed methodology outlined in the current study has potential applicability in comparable geological settings across various basins in Pakistan and globally.