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The future of the upstream industry lies in the adoption of innovative geoscience digitalization solutions to optimize operations. Machine learning applications for geoscience data have been in use for more than 25 years but have recently become critical due to massive growth in the amount of petrotechnical data being acquired. As machine learning evolves, it will play an increasingly visible role in analyzing surface and subsurface data.

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In this article, legacy 3D seismic data from the KG deep water basin underwent broadband reprocessing using state-of-the-art-software.

The latest advancements in seismic processing and imaging, such as geostatistical interval velocity derivation and full-azimuth imaging, have improved many geologically complex and structurally deeper sediments.

High-quality subsurface images are critical for uncovering geological and geophysical aspects of the subsurface. Much vintage data was acquired in areas where no new surveys are available for a variety of reasons, so that reprocessing existing data may be the only option. The reprocessing results discussed in this paper have tremendously improved image quality and increased the interpreter’s confidence by resolving depth issues, fault positioning and reservoir connectivity.

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Surface waves can generate coherent noise, known as ground roll, in seismic surveys. Ground roll can significantly degrade data quality.

Learn how AspenTech processing and imaging software enabled more accurate reservoir characterization of the KG basin off the coast of India. Sharper subsurface images provided a more detailed description of the reservoir and enhanced data interpretability, while the use of advanced broadband processing enabled the extraction of additional information from legacy data, with no need for further costly acquisition. Better mapping of gas hydrate deposits led to the identification of a site for pilot production drilling.

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