Abstract
An accurate prediction of well flowing bottom-hole pressure (FBHP) is highly needed in petroleum engineering applications such as for the field production optimization, cost per barrel of oil reduction, and quantification of workover remedial operations. A good number of empirical correlations and mechanistic models exist in the literature and are frequently used in oil industry to estimate FBHP. But majority of the empirical models were developed under a laboratory scale and are therefore inaccurate when scaled up for the field applications. The objective of this study is to present a new computational intelligence-based model to predict FBHP for a naturally flowing vertical well with multiphase flow. The present study shows that the accuracy of FBHP estimation using PSO-ANN is better than the conventional ANN model. A small average absolute percentage error of less than 2.1% is observed with the proposed model, while comparing the previous empirical correlations and mechanistic models on the same data gives more than 15% error. The new model is trained on a surface production data, which makes the prediction of FBHP in a real time. A group trend analysis tests were also carried out to assure that the proposed model is accurately capturing the underline physics behind the problem.
Highlights
Estimation of well bottom-hole pressure at any existing operating conditions is continuously needed in oil and gas wells to monitor fluid movements inside the wellbore and the nearby wellbore regions
The representative prediction of the pressure drop in a vertical well during the simultaneous multiphase flow of fluids is a well-known problem in the petroleum industry (Hagedorn and Brown 1965)
The need to properly estimate pressure drop in a vertical well is very necessary for the accurate forecast of production performances and for the appropriate well completions design and artificial-lift systems (Ansari et al 1994)
Summary
Estimation of well bottom-hole pressure at any existing operating conditions is continuously needed in oil and gas wells to monitor fluid movements inside the wellbore and the nearby wellbore regions. At pressure above the bubble point pressure of the liquid phase, at the bottom of the well, the flow is the single phase, i.e., oil phase only, but as oil moves up inside the vertical well, the hydrostatic pressure drop causes liberation of gases from the oil phase which resulted in the multiphase flow of oil and gas (Hagedorn and Brown 1965; Govier and Fogarasi 1975). Multiphase flow is a simultaneous flow of two or three phases such as oil, gas, and water which can start producing any time in the life of well (Beggs and Brill 1973). The representative prediction of the pressure drop in a vertical well during the simultaneous multiphase flow of fluids is a well-known problem in the petroleum industry (Hagedorn and Brown 1965). The need to properly estimate pressure drop in a vertical well is very necessary for the accurate forecast of production performances and for the appropriate well completions design and artificial-lift systems (Ansari et al 1994)
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