基于BO-MGGP的机械钻速实时动态预测

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中图分类号：TE24文献标识码：ADOI：10.12473/CPM.202409023

Abstract： Current rate of penetration （ROP） prediction methods sufer from issues such as lack of accuracy， insufficient interpretabilityand inadequate dynamic predictioncapabilitywhile driling.Therefore，a real-time dynamic prediction model for ROP based on Bayesian Optimization-Multi-Gene Genetic Programming（BOMGGP）was proposed.First，a dataset was constructed from 13 637 logging data through outlier removal，data smoothing and data filtering，the variance inflation factor（VIF）was used as the feature evaluation index，and 7 parameters such as weight on bit（WOB）were selected as input features of the model. Second，based on the Multi-Gene Genetic Programming（MGGP）algorithm，the main factors afecting the ROP were combined to construct an explicit ROP expression.Finally，based on thereal-time drillng data stream，the Bayesian Optimization （BO）algorithm was used to dynamically update the model parameters to realize the inteligent prediction of ROP while drilling.The study results show that the MGGP based ROP prediction model exhibits advantages such as strong nonlinear fiting capability and high interpretability comparedwith traditional ROP mechanism equations and conventional ROP intellgent prediction models.After adopting the update strategy，the model achieves a determination coefficient R2 of 0. 95，mean absolute error Ea of 2.38m/h ，and mean absolute percentage error Ep of 7. 34% ，Compared with the un-updated model， ⋅R2 increased by 26.67 % ，while Ea and Ep decreased by 59.67 % and 64. 83% ，significantly enhancing its dynamic prediction capability while driling. The study conclusions provide new research methodologies and technical approaches for global collaborative optimization of drilling parameters.

Keywords： ROP;intelligent prediction；multi-gene genetic programming；bayesian optimization；determina tion coefficient;dynamic update

0 引言

机械钻速是指钻井过程中钻头在单位时间内的进尺，是评价钻井效率的关键指标之一。（剩余16434字）