A New Approach for Optimization of Drilling Parameters Using Artificial Neural Network, Simulated Annealing Algorithm and Mechanical Specific Energy

سال انتشار: 1396
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 588

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شناسه ملی سند علمی:

CHCONF04_161

تاریخ نمایه سازی: 8 آذر 1396

چکیده مقاله:

Drilling in an effective and cost efficient manner is critical to the successful development of an oilfield. Mechanical Specific Energy (MSE) is one of the most important factors affecting the success of adrilling operation. In this study we optimized drilling parameters based on minimum mechanical specificenergy. The MSE model for the formation is constructed by combining the rate of penetration (ROP) modeland MSE equation. This model can be used for prediction of MSE based on drilling parameters in theformation during drilling. For calculation of MSE, the ROP should be predicted. Because of complexdownhole conditions there is no acceptable universal model that accurately describes ROP. The ROP modelis constructed using artificial neural network (ANN). Drilling parameters such as weight on bit, rotaryspeed, flow rate, mud weight and true vertical depth are considered as input parameters and rate ofpenetration is the output of ANN. The goal is to find drilling parameters that minimize mechanical specificenergy during drilling operation. Simulated Annealing Algorithm (SAA) is used for optimizing theparameters of proposed MSE model. Results show that a combination of artificial neural network,Simulated Annealing Algorithm and mechanical specific energy can be used for optimization of drillingparameters and determining the optimal drilling rate.

نویسندگان

Hossein Yavari

M.Sc. Student of Petroleum Department of Amirkabir University of Technology, Tehran, Iran

Rasoul Khosravanian

Assistant Professor of Petroleum Department, Amirkabir University of Technology, Tehran, Iran

Mohammad Fazaelizadeh

Ph.D. degree in drilling engineering, University of Calgary, Calgary, Canada

Vahab Hassani

Head of Bit Operation & Drilling Optimization, Dana Energy, Tehran, Iran