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

• Published in: Journal of petroleum exploration and production technology 2020-10, Vol.10 (7), p.2785-2804
• Main Authors: Durrani, Muhammad Zahid Afzal, Talib, Maryam, Ali, Anwar, Sarosh, Bakhtawer, Naseem, Nasir
• Format: Article
• Language: English
• Subjects: Amplitude; Amplitudes; AVO attributes analysis; Carbonates; Classification; Coefficients; Density; Earth and Environmental Science; Earth Sciences; Energy Systems; Eocene; Fluid filters; Fluids; Geology; Geophysics; Industrial and Production Engineering; Industrial Chemistry/Chemical Engineering; Inspection; Limestone; Litho-facies classification; Lithology; Monitoring/Environmental Analysis; Neural networks; Offshore Engineering; Oil and gas fields; Original Paper-Exploration Geophysics; Petrophysical properties (clay volume and porosity); Porosity; Pre-stack simultaneous inversion; Probabilistic neural network (PNN); Probability theory; Properties; Properties (attributes); Reservoir characterization; Reservoirs; Sedimentary facies; Sedimentary rocks; Seismic data; Seismic surveys; Shale; Shales; Well data; Well logs
• ISSN: 2190-0558; 2190-0566
• DOI: 10.1007/s13202-020-00942-0

In this study a tight carbonate gas reservoir of early Eocene (S1 formation) is studied for litho-facies estimation and probabilistic estimation of reservoir properties prediction using quantitative geophysical approach from a mature gas field in the Middle Indus Basin, onshore Pakistan. Quantitative seismic reservoir characterization approach relied on well based litho-facies re-classification, Amplitude Variation with Offset (AVO) attributes analysis and Pre-Stack simultaneous inversion attributes constrained with customized well-log and seismic data (gathers) conditioning. Three main litho-facies (hydrocarbon bearing limestone, tight limestone and shale) are classified estimated based on the precise analysis of well data using petrophysical properties. AVO attributes (intercept and gradient) conveniently inspection for amplitude behavior (reflection coefficients) of the possible AVO (class I), fluids and lithology characteristics. Probable litho-facies (tight limestone and shale) are estimated using well based litho-facies classification and inverted seismic attributes ( p -impedance and density) from pre-stack simultaneous inversion in a Bayesian framework. Additionally, petrophysical properties (clay volume and porosity) are derived from probabilistic neural network approach using well logs and pre-stack inverted attributes (pimpedance and density) constrained with sample-based seismic attributes (instantaneous, windowed frequency, filters, derivatives, integrated and time).