The Indian Institute of Technology (IIT) Madras researchers have developed a statistical approach that can characterize subsurface rock structure and detect petroleum and hydrocarbon reserves.
The method proposed was successful in providing critical information on the rock type distribution and hydrocarbon saturation zones in 'Tipam formation' located in the Upper Assam basin.
According to the official release, the researchers used this approach to analyse data obtained from seismic surveys and well logs from the North Assam region known for its petroleum reserves.
They were able to get accurate information on the rock type distribution and the hydrocarbon saturation zones at such depth zones of 2.3 km.
This research was led by Professor Rajesh R Nair, Faculty, Petroleum Engineering programme, Department of Ocean Engineering, IIT Madras. The paper was co-authored by M Nagendra Babu and Dr Venkatesh Ambati researchers of IIT Madras along with Professor Rajesh R Nair.
Characterizing underground rock structures is a challenging task. Seismic survey methods and well-log data are used to understand the structure underneath the earth's surface. In a seismic survey, acoustic vibrations are sent through the ground.
As the waves hit various rock layers, they are reflected with different characteristics. The reflected waves are recorded and the underground rock structure is imaged using the reflection data. The well logs contain details of various layers of the earth seen when digging an oil well.
Since the discovery of the Digboi oilfield in Upper Assam more than 100 years ago, the Assam-Arakan has come to be characterized as a 'category-I' basin to denote that they have significant amounts of hydrocarbon reserves. Petroleum is found in the pore space of a hydrocarbon-bearing underground rock formations.
The identification of petroleum reservoirs in the oil-rich basins of Assam requires a survey of the rock structure of this region and the detection of hydrocarbon saturation zones in them.
Elaborating on the need for such research, Prof 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 the complex well log and seismic data."
Further, Prof Nair added, "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 well logs. These relationships help in estimating the petrophysical properties of the subsurface."
The team used their method to detect a hydrocarbon-saturated zone in the sandstone-based reservoir at the Tipam formation in the Upper Assam basin.
The researchers combined different statistical approaches to obtain the subsurface rock structure using data from seismic surveys and well logs.
Explaining the technical aspects of the study, Prof Rajesh Nair said, "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."
In the course of this work, the researchers also introduced a notable attribute named 'Poisson impedance' (PI) in their analysis. 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.
Nair added that India's mega offshore bidding process of 26 blocks for producing oil, and gas is presently ongoing and such new technologies for finding new discoveries will boost the oil and gas business enormously. For example on a thumb rule, a 0.07 incremental change in successful new technology will boost the oil and gas business by about 10 per cent.
( With inputs from ANI )
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