Low parameter model to monitor bottom hole pressure in vertical multiphase flow in oil production wells

Abstract

The importance of the flow patterns through petroleum production wells proved for upstream experts to provide robust production schemes based on the knowledge about flow behavior. To provide accurate flow pattern distribution through production wells, accurate prediction/representation of bottom hole pressure (BHP) for determining pressure drop from bottom to surface play important and vital role. Nevertheless enormous efforts have been made to develop mechanistic approach, most of the mechanistic and conventional models or correlations unable to estimate or represent the BHP with high accuracy and low uncertainty. To defeat the mentioned hurdle and monitor BHP in vertical multiphase flow through petroleum production wells, inventive intelligent based solution like as least square support vector machine (LSSVM) method was utilized. The evolved first-break approach is examined by applying precise real field data illustrated in open previous surveys. thanks to the statistical criteria gained from the outcomes obtained from LSSVM approach, the proposed least support vector machine (LSSVM) model has high integrity and performance. Moreover, very low relative deviation between the model estimations and the relevant actual BHP data is figured out to be less than 6%. The output gained from LSSVM model are closed the BHP while other mechanistic models fails to predict BHP through petroleum production wells. Provided solutions of this study explicated that implies of LSSVM in monitoring bottom-hole pressure can indicate more accurate monitoring of the referred target which can lead to robust design with high level of reliability for oil and gas production operation facilities.

Keywords

Bottom hole pressure / Multiphase flow / Production well / Least square support vector machine / Genetic algorithm

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