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IIT Madras
New Delhi: Scientists at the Indian Institute of Technology (IIT) Madras have developed a statistical approach that can characterize the subsurface structure of rocks and detect oil and hydrocarbon reserves. The proposed method has successfully provided critical information on the rock type distribution and hydrocarbon saturation zones in the “Tipam Formation” in the Upper Assam Basin.
The researchers used this approach to analyze data obtained from seismic surveys and well logs from the North Assam region known for its oil reserves. They were able to get precise information on the distribution of rock type and zones of hydrocarbon saturation in the 2.3 km depth zones.
This research was led by Professor Rajesh R Nair, Faculty, Petroleum Engineering Programme, Department of Ocean Engineering, IIT Madras. This article was co-authored by M Nagendra Babu and Dr. Venkatesh Ambati researchers from IIT Madras along with Professor Rajesh R Nair.
Since the discovery of the Digboi oil field in Upper Assam more than 100 years ago, Assam-Arakan has come to be characterized as a “Category I” basin, indicating that they have significant hydrocarbon reserves. Oil is found in the pores of underground rock formations containing hydrocarbons. Identification of oil reservoirs in the oil basins of Assam requires exploration of the rock structure of the area and detection of hydrocarbon saturation zones therein.
The team used their method to detect a hydrocarbon-saturated zone in a sandstone-based reservoir in the Tipam Formation in the Upper Assam Basin. The researchers combined various statistical approaches to obtain the subsurface rock structure using data from seismic surveys and well logs.
Professor Rajesh Nair explained the technical aspects of the study: “Seismic inversion is a process commonly used to transform seismic reflection data into a quantitative description of reservoir rock properties. Our team used a 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 analysis tools such as target correlation coefficient analysis (TCCA), Poisson impedance inversion, and Bayesian classification to successfully retrieve the subsurface rock and soil structure of the region.
In the course of this work, the researchers also introduced a remarkable attribute called “Poisson Impedance” (PI) in their analysis. PI was used to identify the fluid content of the sandstone reservoir. Their findings also demonstrated that “Poisson Impedance” (PI) was more effective than conventional attributes in estimating the hydrocarbon zone.
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