Abstract

Pore geometry plays an important role in the elastic response of carbonate rocks. Diagenetic processes in carbonate sediments generate a range of pore-type distributions. Hence, the petroelastic modeling (PEM) of carbonate rocks is more complex than for clastics. Petrophysical properties connect to elastic properties through PEM or, in general terms, rock-physics modeling. Pore types cause variation in P-wave velocity — up to 40% for a given porosity. A variety of pore types with different aspect ratios such as vuggy, moldic, interparticle, intraparticle, fracture, and crack makes the porosity-velocity relationship complex, and empirical models fail to handle it properly. We propose a new, easy-to-implement approach for PEM of carbonate rocks that leads to more accurate elastic properties estimation. It offers a novel PEM method that reduces the number of defined parameters and equations. In it, the Xu-Payne rock-physics modeling equations are replaced with an extended pore-space stiffness equation. Instead of including a pore's aspect ratio as is done when using the Xu-Payne inclusion model formulation, in our proposed technique only the appropriate value of pore-space stiffness for each pore type is considered, together with the corresponding volume fraction of pore types. However, parameters are optimized by calibrating the estimated elastic properties with corresponding information from well-log measurements. This inclusion model yields acceptable predictions of elastic properties at wells that do not have measured elastic logs. The method was tested using well data from a carbonate reservoir in Central Luconia, offshore Sarawak, Malaysia. Here, one well has a complete suite of log data needed to calibrate the model. The calibrated model was then used to predict the missing shear velocity log in the other well. Next, simultaneous elastic seismic inversion was performed on 3D seismic data covering the area of the carbonate reservoir, and elastic property volumes (acoustic impedance and VP/VS ratio) were estimated. From these results, a posterior probability distribution of stiff pore types was determined, which validated the outcome of this approach using a blind test.

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