One of the simplest ways to increase production from a well is to optimize the pumping unit. A properly sized and configured pumping unit lifts the maximum barrels of fluid that the well is capable of producing while minimizing the wear on the rods and pump. Optimization is usually done through the use of software such as RODSTAR or ECHOMETER on a well-by-well basis. The user must input all the well parameters by hand, causing it to be a long and tedious process. This makes overall field-wide optimization extremely time-consuming for fields in which there are many wells. Moreover, the software determines theoretically the most optimal unit configuration for the well and may recommend a unit that is not readily available in the area.

This paper describes a new approach for field wide pumping unit optimization. The methodology described here uses a neuro-system consisting of a neural network and an intelligent swapping procedure to find the optimum pumping unit placement for the field. Since field data is used in the model, only pumping units readily available in the field are used. This method is suitable for any field with pumping wells, especially those fields in which there are many wells, thus allowing the selection of a confident data set.

Criteria and constraints are set to select the wells that are currently sufficiently optimized. These wells are used as the model data to train a confident neural network. Optimum pumping unit sizes are then predicted for those wells that are considered to be non-optimized. Finally, an intelligent swapping procedure is invoked to swap over- and under-sized units, thus providing field wide optimization.

The final goal of this study is to provide a tool that allows engineers to set acceptable, realistic criteria to optimize pumping unit size for each well and pumping unit placement on a field-wide basis. The paper presents an example of methodology applicability to a Chevron-operated oil field in California. The proposed procedure provides confident results, great flexibility, and fast optimization.

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