Schlumberger Information Solutions (SIS) has acquired the assets of Decision Team, an oil and gas software and consulting services firm based in Baden, Austria.
Decide!
Decision Team’s flagship ‘Decide!’ software provides ‘intelligent’ reservoir surveillance and production optimization. Decide captures, analyzes, conditions and transforms historical and real-time production data into ‘actionable operational decisions’.
Stundner
Decision Team MD Michael Stundner said, ‘Production engineers can leverage this immense volume of data while focusing on well and field-level problems. We look forward to integrating Decide with Schlumberger’s suite of production software to enable production optimization workflows such as simulation history matching.’
Goode
SIS president Peter Goode said, ‘The combination of SIS and Decision Team will provide a comprehensive set of petroleum engineering workflows for real-time production optimization and proactive reservoir management. Decide will be a catalyst for enhancing production and augments our real-time capabilities.’ Schlumberger told Oil IT Journal that Decision Team personnel will continue work on Decide within SIS.
Data volumes
Huge volumes of operational data present a challenge for today’s reservoir and production engineers. Decide transforms raw data into pertinent information and offers notification systems and ranking lists of underperforming wells. Automated event detection replaces routine field surveillance, resulting in ‘significant time saving’.
AI
Decide applies artificial intelligence (AI) decision analytics to reservoir and production engineering, generating useable information from such large volumes of data. Data mining techniques available include self organizing maps, multiple linear regression and neural nets. This analytical data mining support diagnostics and predictive modeling for activities such as optimizing field injection-production ratio, artificial lift performance and smart well control.
Read the book!
For more on the Decision Team approach see our review of the book ‘Oil and Gas Data Mining’ in Oil ITJ Vol. 9 N° 2.
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