摘要
Tight zones of the gas bearing Kangan and Dalan formations of the South Pars gas field contain a considerable amount of unswept gas due to their low porosity, low permeability and isolated pore types. The current study, integrates core data, rock elastic properties and 3D seismic attributes to delineate fight and low-reservoir-quality zones of the South Pars gas field. In the first step, the dynamic reservoir geomechanical parameters were calculated based on empirical relationships from well log data. The log-derived elastic moduli were validated with the available laboratory measurements of core data. Cross plots between estimated porosity and elastic parameters based on Young＇s modulus indicate that low porosity zone coincide with high values of Young＇s module. The results were validated with petro- graphic studies of the available thin sections. The core samples with low porosity and permeability are correlated with strong rocks with tight matrix frameworks and high elastic values. Subsequently, rock elastic properties including Young＇s modulus and Poisson＇s ratio along with porosity were estimated by using neural networks from a collection of 3D post-stack seismic attributes, such as acoustic impedance （ALl）, instantaneous phase of AI and apparent polarity. Distinguishing low reservoir quality areas in pay zones with unswept gas is then facilitated by locating low porosity and high elastic modulus values. An- hydrite zones are identified and eliminated as non-pay zones due to their characterization of zero porosi- ty and high Young modulus values. The methodology described has applications for unconventional re- servoirs more generally, because it is able to distinguish low porosity and permeability zones that are po- tentially productive from those unprospective zones with negligible reservoir quality.
Tight zones of the gas bearing Kangan and Dalan formations of the South Pars gas field contain a considerable amount of unswept gas due to their low porosity, low permeability and isolated pore types. The current study, integrates core data, rock elastic properties and 3D seismic attributes to delineate fight and low-reservoir-quality zones of the South Pars gas field. In the first step, the dynamic reservoir geomechanical parameters were calculated based on empirical relationships from well log data. The log-derived elastic moduli were validated with the available laboratory measurements of core data. Cross plots between estimated porosity and elastic parameters based on Young＇s modulus indicate that low porosity zone coincide with high values of Young＇s module. The results were validated with petro- graphic studies of the available thin sections. The core samples with low porosity and permeability are correlated with strong rocks with tight matrix frameworks and high elastic values. Subsequently, rock elastic properties including Young＇s modulus and Poisson＇s ratio along with porosity were estimated by using neural networks from a collection of 3D post-stack seismic attributes, such as acoustic impedance （ALl）, instantaneous phase of AI and apparent polarity. Distinguishing low reservoir quality areas in pay zones with unswept gas is then facilitated by locating low porosity and high elastic modulus values. An- hydrite zones are identified and eliminated as non-pay zones due to their characterization of zero porosi- ty and high Young modulus values. The methodology described has applications for unconventional re- servoirs more generally, because it is able to distinguish low porosity and permeability zones that are po- tentially productive from those unprospective zones with negligible reservoir quality.
