Jackson Tomski is a Staff Researcher with the Innovative Software & Research Team at Equity. Prior to Equity, he was a graduate research student at The University of Texas at Austin applying machine learning algorithms in aid of exploration geophysics and petroleum engineering-related problems. Jackson also did a summer internship as an earth scientist doing a data science project for Chevron within the Mid-Continent Business Unit (MCBU).
At Equity, he has performed mathematical modeling in Python for imaging bulges and dents, local thin areas, and abnormally high/low temperature in downstream tank and drums of different geometries. His current work for Equity encompasses continuous development and deployment of all xSight products that are being produced. He continues to contribute not only to the frontend/web app development but also to the machine learning and specifically the Bayes Net backend improvements and development.
Publications:
- Tomski, J. R. and Sen, M. K., “Enhanced artificial intelligence workflow for predicting production within the Bakken formation,” In S. S. Ganguli & V. P. Dimri (Eds.) Developments in Structural Geology and Tectonics (Vol. 6, pp. 83-139), Elsevier, 2023.
- Tomski, J. R., Sen, M. K., Hess, T. E., and Pyrcz, M. J., “Unconventional reservoir characterization by seismic inversion and machine learning of the Bakken Formation,” AAPG Bulletin, 106(11), 2203-2223, 2022.
- Tomski, J. R., “Unconventional reservoir parameter estimation by seismic inversion and machine learning of the Bakken Formation,” North Dakota, Diss, 2020.
