An ant colony optimization framework has been compared and shown to be a viable alternative approach to other stochastic search algorithms. The algorithm has been tested for variety of different benchmark test functions involving constrained and unconstrained NLP, MILP, and MINLP optimization problems. This novel algorithm handles different types of continuous functions very well and can be successfully used for large-scale process optimization.
The Raageshwari field is situated within the RJ-ON-90/1 Contract Area. Of the seven producers which have been completed, six flow naturally and one is on artificial lift. A network model was created to verify the maximum producing potential of the field as well as identify any bottlenecks in the system. The model could then be used to evaluate de-bottlenecking options before going into full field implementation. The production potential of each well was categorized into three parameters: Reservoir Maximum Production Potential (RMPP) i.e. the maximum theoretical production that the reservoir can deliver at the sand-face, Well Maximum Production Potential (WMPP) i.e. the maximum production that well can deliver from sand-face to choke valve, and Plant Maximum Production Potential (PMPP) i.e. the maximum production that the surface facilities downstream of choke valve can handle. These values were generated using the Network Model and the lowest of these was termed as Lowest Maximum Production Potential (LMPP). Sensitivities were carried out in the model to identify constraints limiting the production potential of the field. The model was then used to predict the effect on production of removing these constraining factors. These predictions were then evaluated based on the cost to implement and their economic value. The study indicated that the field production can be increased by 20% with payback time of 7 to 10 days. This workflow for production optimization can be applied to similar marginal fields.
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