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Research Papers: Petroleum Engineering

Realtime Rate of Penetration Optimization Using the Shuffled Frog Leaping Algorithm

[+] Author and Article Information
Ping Yi

University of Houston,
5000 Gulf Freeway Bldg. 9,
Houston, TX 77204-0945
e-mail: pingyi1129@gmail.com

Aniket Kumar

Halliburton,
10200 Bellaire Blvd.,
Houston, TX 77072
e-mail: aniket.aniket@halliburton.com

Robello Samuel

Halliburton Fellow
Halliburton,
10200 Bellaire Blvd.,
Houston, TX 77072
e-mail: robello.samuel@halliburton.com

This paper was previously published at the 2014 SPE Intelligent Energy Conference and Exhibition held in Utrecht, The Netherlands on 1–3 April, 2014.

Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received April 9, 2014; final manuscript received July 7, 2014; published online October 21, 2014. Editor: Hameed Metghalchi.

J. Energy Resour. Technol 137(3), 032902 (Oct 21, 2014) (7 pages) Paper No: JERT-14-1103; doi: 10.1115/1.4028696 History: Received April 09, 2014; Revised July 07, 2014

The increasing complexities of wellbore geometry imply an increasing well cost. It has become more important than ever to achieve an increased rate of penetration (ROP) and, thus, reduced cost per foot. To achieve maximum ROP, an optimization of drilling parameters is required as the well is drilled. While there are different optimization techniques, there is no acceptable universal mathematical model that achieves maximum ROP accurately. Usually, conventional mathematical optimization techniques fail to accurately predict optimal parameters owing to the complex nature of downhole conditions. To account for these uncertainties, evolutionary-based algorithms can be used instead of mathematical optimizations. To arrive at the optimum drilling parameters efficiently and quickly, the metaheuristic evolutionary algorithm, called the “shuffled frog leaping algorithm,” (SFLA) is used in this paper. It is a type of rising swarm-intelligence optimizer that can optimize additional objectives, such as minimizing hydromechanical specific energy. In this paper, realtime gamma ray data are used to compute values of rock strength and bit–tooth wear. Variables used are weight on bit (WOB), bit rotation (N), and flow rate (Q). Each variable represents a frog. The value of each frog is derived based on the ROP models used individually or simultaneously through iteration. This optimizer lets each frog (WOB, N, and Q) jump to the best value (ROP) automatically, thus arriving at the near optimal solution. The method is also efficient in computing optimum drilling parameters for different formations in real time. The paper presents field examples to predict and estimate the parameters and compares them to the actual realtime data.

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References

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Figures

Grahic Jump Location
Fig. 2

Memetic algorithm flow chart in local search

Grahic Jump Location
Fig. 4

Relative abrasiveness

Grahic Jump Location
Fig. 5

Wear function with depth

Grahic Jump Location
Fig. 6

Comparisons between actual WOB and optimum WOB

Grahic Jump Location
Fig. 7

Comparisons between actual RPM and optimum RPM

Grahic Jump Location
Fig. 8

Comparisons between actual flow rate and optimum flow rate

Grahic Jump Location
Fig. 9

Comparisons between calculated ROP using real-time data and optimum ROP

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