Selection & Optimization of Artificial Lift Using Delphi, TOPSIS, and SAW Methods for Natural Flow Oil Wells at HAS Field

Hafis Syafrudin Anshari, Dedy Kristanto, Dyah Rini Ratnaningsih, Dimas Suryo Wicaksono

Abstract


ABSTRACT

The HAS field started producing oil and gas in 2004 until 2018, production wells in HAS experienced a decrease in oil production from 45,000 bopd to 6,190 bopd and an increase in water production from 0 bwpd to 20,463 bwpd. The decline in production occurs due to increased water production. The decrease in production was caused by a decrease in reservoir pressure, causing a larger water cut. Therefore, it is necessary to optimize production wells by considering the things mentioned above.

The artificial lift selection method used is the Delphi method which has 21 screening parameters combined with the Topsis method; helps facilitate Decision-Making from various complex alternatives, by conducting comprehensive comparisons between each alternative and using the Simple Additive Weighting (SAW) method; known as the weighted addition method. The selection of the Artificial Lift in the HAS Field was carried out based on the reservoir parameters, production, well construction, and the economic factors of the artificial lift used. The artificial lift method that will be used in the HAS Field is the Electrical Submersible Pump (ESP). Based on the results of the selection of an artificial lift with a combined method of Delphi, Topsis, and SAW,

Efforts to increase production with an electric submersible pump (ESP) are carried out by optimizing the number of stages and setting the frequency to a value of 45 Hz. Further optimization is carried out by gradually changing the pump frequency up to a maximum of 60 Hz, without changing the pump type. From the results of the economic analysis in the HAS Field, it was found that the most economical scenario was to use an ESP pump with the highest NPV@10 % value than the other scenarios, namely 434.85 MUS$.

Keywords :Artificial Lift Conversion; Delphi; Topsis; SAW; Optimization


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DOI: https://doi.org/10.31315/jpgt.v3i1.6852

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