Characterization and probabilistic estimation of tight carbonate reservoir properties using quantitative geophysical app
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ORIGINAL PAPER-EXPLORATION GEOPHYSICS
Characterization and probabilistic estimation of tight carbonate reservoir properties using quantitative geophysical approach: a case study from a mature gas field in the Middle Indus Basin of Pakistan Muhammad Zahid Afzal Durrani1 · Maryam Talib1 · Anwar Ali1 · Bakhtawer Sarosh1 · Nasir Naseem1 Received: 10 December 2019 / Accepted: 22 June 2020 © The Author(s) 2020
Abstract In this study a tight carbonate gas reservoir of early Eocene (S1 formation) is studiedfor litho-facies estimation and probabilistic estimation of reservoir properties predictionusing quantitative geophysical approach from a mature gas field in the Middle IndusBasin, onshore Pakistan. Quantitative seismic reservoir characterization approachrelied on well based litho-facies re-classification, Amplitude Variation with Offset (AVO)attributes analysis and Pre-Stack simultaneous inversion attributes constrained withcustomized well-log and seismic data (gathers) conditioning. Three main litho-facies(hydrocarbon bearing limestone, tight limestone and shale) are classified estimatedbased on the precise analysis of well data using petrophysical properties. AVOattributes (intercept and gradient) conveniently inspection for amplitude behavior(reflection coefficients) of the possible AVO (class I), fluids and lithologycharacteristics. Probable litho-facies (tight limestone and shale) are estimated usingwell based litho-facies classification and inverted seismic attributes (p-impedance anddensity) from pre-stack simultaneous inversion in a Bayesian framework. Additionally,petrophysical properties (clay volume and porosity) are derived from probabilisticneural network approach using well logs and pre-stack inverted attributes (pimpedanceand density) constrained with sample-based seismic attributes(instantaneous, windowed frequency, filters, derivatives, integrated and time). Keywords Reservoir characterization · Litho-facies classification · AVO attributes analysis · Pre-stack simultaneous inversion · Petrophysical properties (clay volume and porosity) · Probabilistic neural network (PNN)
Introduction Carbonate rocks are considered as a major host rock for hydrocarbon reservoirs worldwide. It is approximated that 60% of the world’s oil and 40% of gas reserves are contained by carbonate reservoirs (Carrera et al. 2018). These reservoirs behave drastically different from siliciclastic reservoirs due to their depositional environment and complex diagenetic history (Anselmetti and Eberli 1993; Lucia 1995, 1999). Diagenetic (cementation, dissolution and dolomitization), differential compaction and faulting and solution collapse activities alter mineralogy and texture of rocks framework which ultimately cause reservoir rock to exhibit
* Muhammad Zahid Afzal Durrani [email protected] 1
Pakistan Petroleum Limited (PPL), 3rd Floor, PIDC House, Dr. Ziauddin Ahmed Road, Karachi, Pakistan
different petrophysical and elastic properties (Zhao et al. 2013; Eberli et al. 2003). Lucrative exploration and effective exploit
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