TGS, Quantico Join for AI Seismic Inversion

September 17, 2019

TGS and Quantico Energy Solutions announced a technology collaboration to leverage their respective offerings in seismic data, AI-based well logs, and artificial intelligence (AI)-based seismic inversion.

TGS is a provider of multi-client geoscience and engineering data for exploration & production (E&P) companies. Quantico is a AI company focused on subsurface solutions for E&P companies.

The joint solution addresses the critical challenges in earth modeling workflows; specifically, insufficient seismic and log data, lengthy time until results, and difficulties mapping advanced geomechanical and petrophysical attributes.

TGS will leverage its industry leading data library of seismic and well log data in the key regions of oil and gas activity across the world. In addition, the collaboration will feature TGS's Analytics Ready LAS (ARLAS) solution.

Adding to the largest commercial digital log library in the world, ARLAS utilizes machine learning algorithms to predict missing curve responses in today's digital well log data.

According to a press release, TGS can successfully predict curve response in five key curve types: Gamma, Resistivity, Density, Neutron and Sonic. With accuracy rates well into the high 90% range for most curves, the data coverage and accuracy are unprecedented.

Quantico will leverage its QRes solution, which combines physics-based approaches with machine learning to map the subsurface within a fraction of the time as conventional inversion software.

QRes delivers earth model properties in significantly higher resolution than available today, and can deliver advanced attributes such as Porosity, Water Saturation, Compressive Strength and Wellbore Stability Curves that historically have been challenging to ascertain from seismic inversions.

By integrating QRes with TGS's data library and ARLAS solution, oil companies will have turnkey access to the most comprehensive geoscience data and technologically-advanced AI solutions to deliver maximum production with faster cycle times, the release said.



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