基于遗传算法的往返概率优化预测系统的开发

Seung Soo Lee, J. Seo, Kwang Yeom Kim, H. Shin
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引用次数: 0

摘要

在深钻工程中,由于钻头的磨损,往返活动是离散的。由于钻进深度越深,更换钻头的时间越长,往复钻进对钻进性能影响很大。因此,为了对钻井项目进行可行性分析和有效管理,需要有可靠的往返预测技术。Lee et al.(2013)开发了TOSA(往返发生模拟算法),可以分析钻头在各个磨损状态下的往返发生深度和时间。然而,TOSA的缺点是随着仿真段数的增加,仿真次数呈指数增长,仿真时间长。本研究利用遗传算法开发了基于TOSA的往返性能预测模块,并在短时间内进行了仿真,验证了仿真结果。
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Development of an Optimized Prediction System of Round Trip Occurrence using Genetic Algorithm
Round trip activity occurs discretely due to the abrasion of drill bit in the deep drilling project. Round trip has great impact on the drilling performance because it takes more time to change a drill bit as the depth goes deeper. Therefore, a reliable prediction technology of the round trip should be secured for feasibility analysis and effective management of the drilling project. Lee et al. (2013) developed the TOSA (round trip occurrence simulation algorithm) which can analyze the depth and timing of round trip occurrence at each abrasion state of bit. However, TOSA has weakness that it takes long time for simulation because the number of simulation increase exponentially as increasing the number of simulation section. This study developed the TOSA based round trip performance prediction module using genetic algorithm for simulating in a short time and verified simulation results.
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