A real-time drilling parameters optimization method for offshore large-scale cluster extended reach drilling based on intelligent optimization algorithm and machine learning

被引：9
|
作者：
Chen, Xuyue [1 ]
Du, Xu [1 ,2 ]
Weng, Chengkai [1 ]
Yang, Jin [1 ]
Gao, Deli [1 ]
Su, Dongyu [3 ]
Wang, Gan [1 ]
机构：
[1] China Univ Petr, MOE Key Lab Petr Engn, Beijing 102249, Peoples R China
[2] Beijing Geosci Ctr, Beijing 100084, Peoples R China
[3] Schlumberger Ltd, Scott, LA 70583 USA
来源：
OCEAN ENGINEERING | 2024年 / 291卷
基金：
中国国家自然科学基金;
关键词：
Offshore cluster wells; Extended reach drilling; Drilling parameters optimization; Rate of penetration; Machine learning; MECHANICAL SPECIFIC ENERGY; PENETRATION ROP; PETROLEUM; MODELS; WATER;
D O I：
10.1016/j.oceaneng.2023.116375
中图分类号：
U6 [水路运输]; P75 [海洋工程];
学科分类号：
0814 ; 081505 ; 0824 ; 082401 ;
摘要：
Offshore large-scale cluster extended reach wells (ERWs) are widely used to develop offshore oil & gas resources. Due to the complex downhole environments and complicated geological conditions, drilling parameters real-time optimization is challenging in offshore large-scale cluster ERWs drilling. In this paper, a real-time drilling parameters optimization method for offshore large-scale cluster extended reach drilling based on an intelligent optimization algorithm and machine learning (IOA-ML) is proposed. The method takes ROP as the objective function, establishes a ROP model based on long short-term memory (LSTM) neurons, and obtains drilling parameters optimization results asynchronously by combining the genetic algorithm, differential evolution algorithm, and particle swarm algorithm. The results show that ROP has been improved by 33.33% on average after the optimization by this real-time intelligent optimization method with an optimization time within 60s, which meets the requirements of real-time optimization of drilling parameters.
引用
收藏
页数：12
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