Bottom hole pressure estimation using hybridization neural networks and grey wolves optimization

2018年 4卷 第4期
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Menad Nait Amar Nourddine Zeraibi Kheireddine Redouane
An effective design and optimum production strategies of a well depend on the accurate prediction of its bottom hole pressure (BHP) which may be calculated or determined by several methods. However, it is not practical technically or economically to apply for a well test or to deploy a permanent pressure gauge in the bottom hole to predict the BHP. Consequently, several correlations and mechanistic models based on the known surface measurements have been developed. Unfortunately, all these tools (correlations & mechanistic models) are limited to some conditions and intervals of application. Therefore, establish a global model that ensures a large coverage of conditions with a reduced cost and high accuracy becomes a necessity.
Flowing bottom hole pressure (BHP); BHP correlations & mechanistic models; Artificial neural network; Neural network training; BP (back propagation); GWO; GA; PSO;
https://doi.org/10.1016/j.petlm.2018.03.013