HOUSTON, Sept. 17, 2019 /PRNewswire/ -- TGS, a leading provider of multi-client geoscience and engineering data for Exploration & Production companies, and Quantico Energy Solutions (Quantico), an artificial intelligence (AI) company focused on subsurface solutions for Exploration & Production companies, today announced a technology collaboration to leverage their respective offerings in seismic data, AI-based well logs, and AI-based seismic inversion. 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. 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.
"TGS continues to lead the way in the development, application and utilization of artificial intelligence within the energy sector by using innovative ways to solve problems such as crowdsourcing, SaltNet and ARLAS. Now we are taking the next step by partnering with Quantico to bring two AI products together, creating a next-generation solution that will help our clients accelerate their decision making and bolster performance," said Arvind Sharma, VP of Data & Analytics, TGS.
"The pace of innovation in subsurface AI and digitalization at E&P customers is accelerating at a speed that's been seldom experienced in our industry," said Barry Zhang, CEO of Quantico. "The joint inversion solution between TGS and Quantico exemplifies how collaboration between AI companies can unlock immediate, game changing advancement in reservoir characterization."
The collaboration between TGS and Quantico will allow E&P customers to maximize the value of geoscience data and enable faster decision making to stay time sensitive requirements of today's drilling and completion engineering workflows.
About Quantico Energy Solutions
Quantico Energy Solutions is an artificial intelligence company based in Houston, Texas. The company's focus is on providing the oil and gas industry with data-‐driven solutions that reduce cycle times and improve reservoir understanding. Today, Quantico's solutions are being adopted by the largest oil and service companies around the world. For more information, please visit www.quanticoenergy.com or contact email@example.com.
TGS provides multi-client geoscience data to oil and gas Exploration and Production companies worldwide. In addition to extensive global geophysical and geological data libraries that include multi-client seismic data, magnetic and gravity data, digital well logs, production data and directional surveys, TGS also offers advanced processing and imaging services, interpretation products, and data integration solutions. For more information, please visit TGS at www.tgs.com.
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