摘要：The rate of penetration (ROP) is a crucial metric for assessing drilling efficiency. However, the ROP prediction models based on conventional machine learning have high accuracy but poor interpretability, and the offline models make it difficult to capture the dynamic impact of formation and drilling condition changes on ROP. In this paper, a real-time prediction method for ROP using multi-gene genetic programming (MGGP) algorithm is proposed. First, outliers and non-drilling conditions data are eliminated. Through the multicollinearity analysis, seven mud logging variables, weight on bit, rotary speed, torque, inlet flow, inlet density, standpipe pressure, and mud pit volume, are taken as input variables and filtered. Then the explicit expression between variables and ROP is constructed based on the MGGP algorithm, and the variables sensitivity is analyzed. Finally, a real-time update framework is proposed to capture the dynamic trend of ROP, with a coefficient of determination of 0.89 and a mean absolute percentage error of 5.44%. This study verifies the effectiveness of the MGGP algorithm in the prediction of ROP and improves the interpretability of the model, which provides a basis for complex geological drilling process.

关键字：Rate of penetration; multi-gene genetic programming; explicit expression; real-time update

ISSN号：无

卷、期、页：PROCEEDINGS OF ASME 2024 43RD INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE AND ARCTIC ENGINEERING, OMAE2024, VOL 8

发表日期：2024-06-09

期刊分区（SCI为中科院分区）：无

收录情况：CPCI-S（科技会议录索引）,国际学术会议论文集

发表期刊名称：PROCEEDINGS OF ASME 2024 43RD INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE AND ARCTIC ENGINEERING, OMAE2024, VOL 8

通讯作者：马宝东,Liu, Zihao

第一作者：祝兆鹏,宋先知,张诚恺

论文类型：会议论文

论文概要：马宝东,祝兆鹏,宋先知,张诚恺,Liu, Zihao，REAL-TIME PREDICTION OF RATE OF PENETRATION USING MULTI-GENE GENETIC PROGRAMMING，PROCEEDINGS OF ASME 2024 43RD INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE AND ARCTIC ENGINEERING, OMAE2024, VOL 8，2024，PROCEEDINGS OF ASME 2024 43RD INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE AND ARCTIC ENGINEERING, OMAE2024, VOL 8

论文题目：REAL-TIME PREDICTION OF RATE OF PENETRATION USING MULTI-GENE GENETIC PROGRAMMING