Bauxite orebody demarcating and virtual mining for mining optimization within an underground bauxite seam, Southwest China

Wang, Shaofeng; Li, Xibing

doi:10.1007/s12665-018-7404-8

Cited by 2 publications

(1 citation statement)

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“…Combined with the strong global search ability of genetic algorithm and the characteristics of high local optimization accuracy of the pattern search method, the optimal drilling parameter combination is found. Compared with the conventional design method of trial calculations and optimization schemes based on experience, the new method is more efficient and can directly determine the optimal scheme under given conditions [2,3].…”

Section: Introductionmentioning

confidence: 99%

Prediction and Optimization of Stability Parameters of Borehole Sensor for Deep Water Drilling Based on Genetic Algorithm

Huang¹,

Xia

Cai

et al. 2022

Journal of Sensors

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In order to study the prediction and optimization of borehole stability parameters in deepwater drilling based on genetic algorithm. First, a genetic hybrid algorithm based on pattern search is proposed. Then, based on the adaptive genetic algorithm, the evolutionary population is searched for patterns, which makes the hybrid algorithm not only has a strong global search ability but also improves the local optimization accuracy. Finally, the unit footage cost in the drilling process is taken as the objective function, and the algorithm is verified by taking the drilling in Karamay area as an example. The calculation results show that if the bit wear reaches 0.8-0.9 and then the bit is pulled out, the utilization rate of the bit can be increased, the design efficiency and accuracy can be improved, and the drilling cost can be reduced. The wear amount of the optimized bit is higher than that of the actual bit. Increasing the utilization rate of the bit can reduce the cost of drilling meters to a certain extent and improve the economic benefits of drilling. The objective function and constraint conditions for the optimization of drilling parameters are determined, and the algorithm is verified with the drilling data of Karamay Oilfield. The results show that the algorithm improves the stability and speed of iterative convergence and improves the reliability of data analysis results. Based on the regional three-dimensional formation rock parameter data volume, the optimization method can be used to optimize the drilling parameters before drilling and provide a basis for formulating the drilling design scheme.

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Section: Introductionmentioning

confidence: 99%