IIT Madras develops data analysis approach to detect petroleum underground

NEW DELHI: The researchers at Indian Institute of Technology Madras (IITM) have developed a data analysis approach to detect petroleum underground using a statistical approach that can characterize subsurface rock structure and detect petroleum and hydrocarbon reserves.

The researchers used this approach to analyse data obtained from seismic surveys from the North Assam region known for its petroleum reserve. They were able to get accurate information on the rock type distribution and the hydrocarbon saturation zones at depths of 2.3 kilometers.

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This research at IITM was led by department of ocean engineering, petroleum engineering programme, faculty, Rajesh R Nair. The findings of the research were published in the journal- NATURE Scientific Reports. The paper was co-authored by IIT Madras researchers M Nagendra Babu and D Venkatesh Ambati along with Nair.

In the research, the researchers also introduced an attribute named ‘Poisson impedance’ (PI). PI was used to identify the fluid content in the sandstone reservoir. Their findings also proved that ‘Poisson impedance’ (PI) was more effective in estimating hydrocarbon zone than conventional attributes.

Elaborating on the need for such research, IIT Madras, department of ocean engineering, petroleum engineering programme, faculty, Rajesh R Nair 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 complex well log and seismic data.”

Explaining the technical aspects of the study, he further added. “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. 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.”