The method was successful in providing important information on rock type distribution and hydrocarbon saturation zones in the 'Tipam Formation' located in the Upper Assam Basin with a depth of 2.3 km.
According to an official release, a group of researchers from IIT Madras has developed a statistical approach that can characterize the subsurface rock structure and locate petroleum and hydrocarbon reserves.
The research was led by Professor Rajesh R Nair, Faculty, Petroleum Engineering Programme, Department of Ocean Engineering, IIT Madras. The paper was co-authored by researchers M Nagendra Babu and Dr Venkatesh Ambati from IIT Madras along with Professor Rajesh R Nair.
As reported by ANI, the method was successful in providing important information on rock type distribution and hydrocarbon saturation zones in the 'Tipam Formation' located in the Upper Assam Basin at a depth of 2.3 km.
They used this approach to analyze data obtained from seismic surveys and logs of wells known to have petroleum reserves from the North Assam region.
Identification of petroleum reservoirs in the oil rich basins of Assam requires survey of the rock structure of the region and locating hydrocarbon saturation zones in them.
Professor Rajesh Nair elaborated on the need for such research and said, "The challenge of imaging underground structures arises from the low resolution of seismic images and the difficulty in correlating data from well-logs and seismic surveys."
"Our team at IIT Madras has developed a methodology to predict hydrocarbon zones from complex well log and seismic data," he said.
“The characterization of subsurface structures to locate oil-bearing rocks involves the use of data analytics methods that establish statistical relationships between seismic data and petrophysical data obtained from well logs. These relationships reflect the petrophysical properties of the subsurface help estimate. Added on.
The team combined a variety of statistical approaches to derive subsurface rock composition using data from seismic surveys and well logs. Professor Nair further explained the technical aspects of the study.
"Seismic inversion is a process commonly used to convert seismic reflection data into a quantitative rock-property description of a reservoir. Our team used a type of seismic inversion called 'simultaneous prestack seismic inversion. ' (SPSI). This analysis provided the spatial distribution of petrophysical properties in the seismic image," he said.
He further added, “Our team then combined this with other data analytics tools such as target correlation coefficient analysis (TCCA), Poisson impedance inversion, and Bayesian classification to successfully derive the underground rock and soil composition of the region.”
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