Improved reservoir knowledge is key to extracting additional value from existing oil and gas assets. However, given the uncertainty in the subsurface, it is always a question if our current development strategy is the most robust choice, or if there are alternatives that can further increase the value of our field. This paper presents a novel solution that enables the asset team to answer these questions in a new way. Furthermore, the solution helps teams quickly identify and screen new opportunities that ultimately increase both subsurface understanding and the value of the field. The solution combines a quasi- Newton gradient based numerical optimization scheme with a stochastic simplex approximate gradient (StoSAG) algorithm. Because the algorithm is non-intrusive with respect to the fluid flow simulator, we can directly apply the solution on any flow optimization problem without the need to access the simulator source code. The solution is implemented using a microservice architecture that allows for efficient scaling and deployment either on cloud-based or internal systems. We demonstrate the proposed solution on a field containing 11 oil producers and 7 water injectors by optimizing the water injection and oil production rates. The machine learning algorithm allows us to quickly explore different drainage strategies, given the current understanding and associated uncertainties of the reservoir.

Specifically, the software solution suggests that 6 of the 18 pre-defined well targets are high risk and/or of little value. Running a second development scenario where we do not drill these six wells reduces the investment cost of this field by 163 MUSD and increases the expected net present value per well of the field by 48 percent. Compared with the reactive control drainage strategy approach, we increase the expected net present value of the field by 9.0 %, while simultaneously lowering the associated risk.

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