Quantitative characterization of unconventional (tight) hydrocarbon reservoir by integrating rock physics analysis and seismic inversion: a case study from the Lower Indus Basin of Pakistan

The paper demonstrates a successful application of Bayesian classification method to accurately predict petrophysical properties and lithofacies classification in the deep unconventional (tight gas) hydrocarbon resource potential of early Cretaceous in the Lower Indus Basin of Pakistan. To explore the true potential for exploration and development phases, we quantitatively characterized the tight gas reservoir based on an integrated methodology using the Bayesian approach constraint with rock physics analysis which utilized deterministic petrophysical results from a well information to extract the desired lithofacies at seismic scale. The employed methodology relied on stepwise sequential integration of all available data through petrophysical, rock physics analysis and seismic inversion technique. Simultaneous inversion approach is used to invert elastic properties for reservoir interpretation. Seismic-based petrophysical properties are predicted using regression analysis by establishing a functional relationship between well logs for Sembar formation. The rock physics template (acoustic impedance versus Vs/ Vs ratio) model helped to differentiate lithological units of sand and shale in the well. Three lithofacies (HC sands, shale and shalier sand) are properly classified in rock physics template, and their probabilities are accurately defined using Bayes’ theorem. Finally, estimated lithofacies and hydrocarbon probability map from the Bayesian approach are meticulously validated from well data. The quantitative seismic reservoir characterization study provided important support for the unconventional prospect evaluation and hydrocarbon reserve estimations necessary to delineate unexplored parts which could prove helpful in effectively planning for the horizontal well placement and optimal reservoir development.

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Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).