PREDICTION OF LOST CIRCULATION USING ARTIFICIAL INTELLIGENCE IN MAROUN OILFIELD

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MOAZZANI A.R.; NABAEI M.; GHADAMI JEGARLOEI S.

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Journal: JOURNAL OF TECHNICAL-ENGINEERING Year:2010 | Volume:3 | Issue:1 Page(s): 59-68

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Title

PREDICTION OF LOST CIRCULATION USING ARTIFICIAL INTELLIGENCE IN MAROUN OILFIELD

Author(s)

MOAZZANI A.R. | NABAEI M. | GHADAMI JEGARLOEI S. | Issue Writer Certificate

Keywords

ARTIFICIAL INTELLIGENCEQ2

ARTIFICIAL NEURAL NETWORKSQ2

LOST CIRCULATIONQ3

MAROUN OILFIELDQ3

DRILLING NON-PRODUCTIVE TIMEQ3

Abstract

Nowadays, huge human needs for energy force petroleum and drilling companies to drill deeper into the earth, which means spending more time on drilling and passing through numerous layers with different layer characteristics to tap the targeted oil. Since major part of a well cost depends on the duration of the drilling phase, an organized drilling program seems vital to save time and cost. Occurrence of different drilling problems like LOST CIRCULATION and pipe sticking may deviate the drilling operation from the schedule. Mechanical pipe sticking is likely to occur after complete loss. LOST CIRCULATION is one of the common drilling problems in the industry which impose heavy expenses on oil companies. This problem commences from beginning of the drilling and continues till putting the casing in place. Mud loss can occur in low fluxes up to complete loss; it can finally lead to well blowout or severe pipe stuck .Freeing the pipes may waste a week or even more time from the rig. Thus, having accurate information about returned fluid and recording mud loss rate can be a great help to prevent drilling problems from taking place. Drilling fluid loss is affected by different factors that make modeling of mud loss difficult from an analytical point of view. Thus, employing ARTIFICIAL NEURAL NETWORKS, the capability of which in simulation of complicated phenomena is proven, looks very effective. In this research, using drilling daily report of some wells in MAROUN OILFIELD (Southwest of Iran), attempts are directed to predict LOST CIRCULATION in different areas of this field. Network results in the prediction of drilling fluid loss show good compatibility with real data recorded in drilling daily reports.

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APA: Copy

MOAZZANI, A.R., NABAEI, M., & GHADAMI JEGARLOEI, S.. (2010). PREDICTION OF LOST CIRCULATION USING ARTIFICIAL INTELLIGENCE IN MAROUN OILFIELD. JOURNAL OF TECHNICAL-ENGINEERING, 3(1), 59-68. SID. https://sid.ir/paper/193943/en

Vancouver: Copy

MOAZZANI A.R., NABAEI M., GHADAMI JEGARLOEI S.. PREDICTION OF LOST CIRCULATION USING ARTIFICIAL INTELLIGENCE IN MAROUN OILFIELD. JOURNAL OF TECHNICAL-ENGINEERING[Internet]. 2010;3(1):59-68. Available from: https://sid.ir/paper/193943/en

IEEE: Copy

A.R. MOAZZANI, M. NABAEI, and S. GHADAMI JEGARLOEI, “PREDICTION OF LOST CIRCULATION USING ARTIFICIAL INTELLIGENCE IN MAROUN OILFIELD,” JOURNAL OF TECHNICAL-ENGINEERING, vol. 3, no. 1, pp. 59–68, 2010, [Online]. Available: https://sid.ir/paper/193943/en

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