This paper presents an efficient production optimization scheme for an oil reservoir undergoing water injection by optimizing the production rate for each well. In this approach, an adaptive version of simulated annealing (ASA) is used in two steps. The optimization variables updating in the first stage is associated with a coarse grid model. In the second step, the fine grid model is used to provide more details in final solution search. The proposed method is formulated as a constrained optimization problem defining a desired objective function and a set of existing field/facility constraints. The use of polytope in the ASA ensures the best solution in each iteration. The objective function is based on net present value (NPV). The initial oil production rates for each well come from capacity and property of each well. The coarse grid block model is generated based on average horizon permeability. The proposed optimization workflow was implemented for a field sector model. The results showed that the improved rates optimize the total oil production. The optimization of oil production rates and total water injection rate leads to increase in the total oil production from 315.616 MSm3 (our initial guess) to 440.184 MSm3, and the recovery factor is increased to 26.37%; however, the initial rates are much higher than the optimized rates. Beside this, the recovery factor of optimized production schedule with optimized total injection rate is 3.26% larger than the initial production schedule with optimized total water injection rate.
An Efficient Workflow for Production Allocation During Water Flooding
Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received April 30, 2016; final manuscript received August 24, 2016; published online October 10, 2016. Assoc. Editor: Egidio Marotta.
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Azamipour, V., Assareh, M., Dehghani, M. R., and Mittermeir, G. M. (October 10, 2016). "An Efficient Workflow for Production Allocation During Water Flooding." ASME. J. Energy Resour. Technol. May 2017; 139(3): 032902. https://doi.org/10.1115/1.4034808
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