As muvh as 80% of the total pressure is consumed in lifting fluids from the reservoir to the surface. Therefore, this project carefully analyzes the variables that affect the performance of flowing wells.
Coupled with System Analysis, which offers a means of recognizing quickly the components of the production system that are restricting its performance, an empirical equation is proposed that can be used to predict the producing rate of naturally flowing wells. Consequently, the performance of wells can be monitored and improved significantly.
In this research, a base case was considered and data available was used to create a vertical lift model of well of interest using an industry standard production software. Different correlations were used to match the well test results. Correlations closest to the data, were considered for this analysis.
Some variables that affect flowrate were identified; theoretical and sensitivity analysis was carried out to see the effect of these variables using these correlations. Dimensionless analysis was carried out on these variables identified like fluid density, tubing diameter, and wellhead pressure by deriving the coefficient of these variables with respect to flowrate. These variables, with their respective flowrates, were trained in Artificial Neural Network to study these variables.
Results from these analyses distinguished some correlations from others based on their error histogram, performance plot, and training fit, and were retrieved from Artificial Neural Network.
|Commitee:||Dufreche, Stephen, Salehi, Saeed|
|School:||University of Louisiana at Lafayette|
|School Location:||United States -- Louisiana|
|Source:||MAI 54/06M(E), Masters Abstracts International|
|Keywords:||Artificial neural network, Correlation, Inflow performance, System analysis, Vertical lift performance|
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