Machine Learning for Fluid prediction (post-stack seismic data, Innovation Norway grant 2020/521538)
In May 2020, PSS-Geo with partners (CaMa Geo, AGGS AS, and Lime Petroleum Norway AS) was awarded an Innovation Norway grant to extend the current research project of Rune Inversion (Artificial Intelligence-based seismic inversion algorithm) for fluid prediction. The produced inverted volumes of P-wave velocity, Density, P-impedance, and their algebraical extension to Volume of Clay and Porosity, created a unique possibility to predict a type of fluid using ML algorithms.
Today, PSS-Geo is the holder of the unique dataset of seismic and well logs that gives the company a tremendous advantage in ML algorithms development for geo exploration.:
Seismic merge that is suitable for inversion (seamless, phase-controlled, with true amplitudes preservation)
Inverted seismic post-stack cube of 62 000km2: P-wave velocity, Density, P-impedance, Volume of Clay and Porosity
Accurate CPI database with a unique "fresh-water" logs estimation (300 wells)
Competence in Rock Physics, Seismic Processing, and Data science
If you are interested in joining the project, do not hesitate to contact us firstname.lastname@example.org
The project participants have flexible possibilities to receive ML code, product (currently a prediction over the entire Norwegian Sea Shelf), and a product over the chosen area).
The basic principal of the algorithm can be investigated
in YouTube session of Fluxgate Technologies NG GeoPython Week: https://www.youtube.com/watch?v=YRr2ZM5bPzE (3hours)
The test area data (16 000km2), wells and the Python codes can be downloaded for free
The first result: dark blue - HC, yellow - brine.
Fluid prediction from seismic data using different machine learning methods. V. Kalashnikova, E. Karaseva, R. Øverås, T.R. Sharafutdinov. 2021. 7th EAGE Conference Tumen' - Extended Abstract (in rus).
Field examples. Ærfugl and Marulk gas/condensate reservoirs in the Norwegian sea.
Post-stack seismic data was inverted for P-wave velocity and Density using the Rune Inversion algorithm. The volume of clay was computed using Gyllenhamar's formula (from P-wave and Density), and porosity was computed using a standard approach. The produced volumes were used as recognition datasets, while 7 random wells' logs were used for learning. The results of the Machine Learning for fluid prediction are shown in Section 1 and Section 2.