Publications

Carbonate reservoirs have been known to be a major source of hydrocarbons; it is well known that approximately 60% of the oil and 40% of the gas reserves in the world are found in carbonates, yet the understanding of the carbonate reservoir poses a significant challenge in exploration and exploitation. Presence of secondary porosity which differentiates it from clastic reservoirs brings its own set of complexity primarily owing to the poro-perm relationship. Carbonate fields in Bombay offshore (Western offshore of India) are no different. In order to understand the poro-perm complexity of one such field in Bombay offshore, the core samples obtained from wells went through Scanning Electron Microscopy (SEM). This study incorporates the results of experiments performed on core samples to determine the pore size distribution and the porosity of the samples at the given depth. The Self-Consistent Approximation (SCA) approach is applied on Scanning Electron Microscope (SEM) image data to determine the elastic properties of rocks and porosity partitioning of carbonate reservoir located in the western offshore region, India. The perquisites for this SCA modelling approach were sonic derived logs and SEM data extracted from core samples. The SEM images of cores from ten different depths and two different wells are analyzed by an algorithm to quantify the type of pores into cracks, inter-particle, and stiff defined by their aspect ratios. The sonic velocities were inverted using optimization technique for the entire depth range of the one well log. Machine learning algorithm was used to estimate the pore aspect ratio’s probability density. This study attempts to achieve porosity partitioning in carbonate reservoirs using SCA that will help in understanding the complex porosity system of these reservoirs and to develop a petro-physical and rock physics model to deduce the scalability of its different properties, not only in Bombay offshore but anywhere else.