Field Development Optimization at Martin Linge Oil Reservoir

Field Development Optimization at Martin Linge Oil Reservoir

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An optimization framework is applied for well placement decision support at the Martin Linge oil reservoir. The optimization framework optimizes jointly for well placement and controls using derivative-free methods for well location, and a gradient-based routine for optimization of well control settings (e.g., bottom hole pressures). In this approach, well controls are optimized efficiently at each well location using gradients computed by an adjoint procedure. This pilot is a collaboration between NTNU IO Center and operator Total E&P Norge AS.
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Martin Linge Unit is located on the Norwegian Continental Shelf extending 75 km north of Frigg and 42 km west of Oseberg. Martin Linge Oil (MLO) is an independent oil reservoir within the Martin Linge Unit. Martin Linge Unit contains gas/condensate in Brent reservoirs and oil in Frigg sands, which was initially discovered more than 30 years ago. Total E&P Norge AS is operator with a share of 51%. Petoro and Statoil are partners with shares of 30% and 19%, respectively.

 
 
 

The joint approach for well placement and control optimization uses an outer-loop algorithm for well placement optimization coupled with an efficient inner-loop optimization of well controls. The joint approach is implemented in an optimization framework where well placement is solved using a derivative-free pattern-search method in a highly distributed environment. The embedded control optimization uses a gradient-based solver with derivatives computed by an adjoint procedure, and is performed at each well placement iteration.

 

For optimization purposes, the MLO reservoir model is run using Stanford’s General Purpose Research Simulator (ADGPRS). A collaborative environment between Industry Partner Total E&P and the IO Center Research Team was established to closely treat and discuss project stages such as problem and constraint definition, model validation, effective implementation of optimization framework, and adaptation of solutions to industry standard. Results will be submitted for publication in collaboration with Industry Partner.

 

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