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.
Realtime Rate of Penetration Optimization Using the Shuffled Frog Leaping Algorithm
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.
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Yi, P., Kumar, A., and Samuel, R. (October 21, 2014). "Realtime Rate of Penetration Optimization Using the Shuffled Frog Leaping Algorithm." ASME. J. Energy Resour. Technol. May 2015; 137(3): 032902. https://doi.org/10.1115/1.4028696
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