IIT Madras researchers have developed a statistical approach to detect petroleum and hydrocarbon reserves.

IIT Madras researchers have developed a statistical approach to detect petroleum and hydrocarbon reserves.

 

According to the official release, a group of researchers at IIT Madras have developed a statistical approach to detect petroleum and hydrocarbon reserves. 

 

Professor Rajesh R Nair, Faculty, Petroleum Engineering program, Department of Ocean Engineering, IIT Madras led the research. 

 

The method was successful in providing critical information on the rock type distribution and hydrocarbon saturation zones at such depth zones of 2.3 km in ‘Tipam formation’ located in the Upper Assam basin, as per ANI reports.

 

Professor Rajesh Nair elaborated on the need for such research and said, “The challenge to imaging underground structures arises from the low resolution of the seismic images and the difficulty in correlating the data from well-log and seismic surveys.”

 

“Our team at IIT Madras has developed a methodology for predicting the hydrocarbon zones from the complex well log and seismic data,” he said, as quoted by ANI. 

 

“The characterization of subsurface structures for the detection of oil-bearing rocks involves the use of data analytics methods that establish statistical relationships between seismic data and petrophysical data obtained from good logs. These relationships help in estimating the petrophysical properties of the subsurface,” he further added.

 

Professor Nair further explained the technical aspects of the study. 

 

“Seismic inversion is a process that is commonly used to transform the 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 stated.

 

He further said, “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 obtain the underground rock and soil structure of the region.”

 

 

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