Unlocking thin sand potential: a data-driven approach to reservoir characterization and pore pressure mapping

The gradual decline of production of the major fields of the Indus Basin has influenced the field pressure. Therefore, the prospects need to be assessed by the petro-elastic relationship. This study highlights the value-addition for identifying gas pockets within the E-Sand of the Lower Goru Formation through estimating and populating petro-elastic properties. A probabilistic neural network (PNN) was employed to develop the relationship of pore pressure to the inverted properties. The outcomes will help identify the high-pressure areas in accordance with the petro-elastic relationship for optimizing production strategy. In order to evaluate the petro-elastic relationship, stochastic seismic inversion was employed to holistically capture the reservoir’s variability. Pore pressure volumetric was estimated from inverted attributes using a PNN, a non-linear algorithm that was more suited to heterogeneous reservoirs. The main objective of the research is achieved via enhanced resolution of elastic properties attained through the integration of stochastic inversion and PNN processes. The high-resolution elastic properties including P-impedance, S-impedance, and Vp/Vs ratio can delineate the heterogeneities more profoundly. The petrophysical volumes obtained via enhanced inverted properties combined with pore-pressure through PNN illuminated the potential facies and the correlations in predicting the properties. The procedure provides a linkage of elastic, petrophysical, and geomechanical properties that is helpful for professionals covering a broad field of the petroleum sector. The developed workflow can be followed globally where problems occur regarding heterogeneities and early depletion.