目前国内石油工程企业已通过自动化钻机、井场集控中心、钻井分析决策平台等技术初步构建形成了基于咨询模式的智能钻井决策系统,启动了从咨询模式向自主决策控制的升级。由于现有钻井机理模型与传统机器学习技术难以满足升级需求,文章...目前国内石油工程企业已通过自动化钻机、井场集控中心、钻井分析决策平台等技术初步构建形成了基于咨询模式的智能钻井决策系统,启动了从咨询模式向自主决策控制的升级。由于现有钻井机理模型与传统机器学习技术难以满足升级需求,文章基于AI FOR SCIENCE研究范式,以深度强化学习技术框架,面向钻井效率、风险与成本等优化场景,提出智能钻井大模型概念及构建方法。以智能钻井大模型为核心,设计了融合专家经验、机理模型、数据分析的多智能体协同决策的应用范式。通过技术架构设计与关键技术实验验证,该模型依托强化学习智能体的自适应学习、持续学习等能力,可适配复杂钻井环境并实现自主决策和全局优化,为智能钻井自主化升级提供了可行路径。展开更多
Lithology identificationwhile drilling technology can obtain rock information in real-time.However,traditional lithology identificationmodels often face limitations in feature extraction and adaptability to complex ge...Lithology identificationwhile drilling technology can obtain rock information in real-time.However,traditional lithology identificationmodels often face limitations in feature extraction and adaptability to complex geological conditions,limiting their accuracy in challenging environments.To address these challenges,a deep learning model for lithology identificationwhile drilling is proposed.The proposed model introduces a dual attention mechanism in the long short-term memory(LSTM)network,effectively enhancing the ability to capture spatial and channel dimension information.Subsequently,the crayfishoptimization algorithm(COA)is applied to optimize the model network structure,thereby enhancing its lithology identificationcapability.Laboratory test results demonstrate that the proposed model achieves 97.15%accuracy on the testing set,significantlyoutperforming the traditional support vector machine(SVM)method(81.77%).Field tests under actual drilling conditions demonstrate an average accuracy of 91.96%for the proposed model,representing a 14.31%improvement over the LSTM model alone.The proposed model demonstrates robust adaptability and generalization ability across diverse operational scenarios.This research offers reliable technical support for lithology identification while drilling.展开更多
文摘目前国内石油工程企业已通过自动化钻机、井场集控中心、钻井分析决策平台等技术初步构建形成了基于咨询模式的智能钻井决策系统,启动了从咨询模式向自主决策控制的升级。由于现有钻井机理模型与传统机器学习技术难以满足升级需求,文章基于AI FOR SCIENCE研究范式,以深度强化学习技术框架,面向钻井效率、风险与成本等优化场景,提出智能钻井大模型概念及构建方法。以智能钻井大模型为核心,设计了融合专家经验、机理模型、数据分析的多智能体协同决策的应用范式。通过技术架构设计与关键技术实验验证,该模型依托强化学习智能体的自适应学习、持续学习等能力,可适配复杂钻井环境并实现自主决策和全局优化,为智能钻井自主化升级提供了可行路径。
基金supported by the National Key Research and Development Program for Young Scientists,Chin(Grant No.2021YFC2900400)the Sichuan-Chongqing Science and Technology Innovation Cooperation Program Project,China(Grant No.2024TIAD-CYKJCXX0269)the National Natural Science Foundation of China,China(Grant No.52304123).
文摘Lithology identificationwhile drilling technology can obtain rock information in real-time.However,traditional lithology identificationmodels often face limitations in feature extraction and adaptability to complex geological conditions,limiting their accuracy in challenging environments.To address these challenges,a deep learning model for lithology identificationwhile drilling is proposed.The proposed model introduces a dual attention mechanism in the long short-term memory(LSTM)network,effectively enhancing the ability to capture spatial and channel dimension information.Subsequently,the crayfishoptimization algorithm(COA)is applied to optimize the model network structure,thereby enhancing its lithology identificationcapability.Laboratory test results demonstrate that the proposed model achieves 97.15%accuracy on the testing set,significantlyoutperforming the traditional support vector machine(SVM)method(81.77%).Field tests under actual drilling conditions demonstrate an average accuracy of 91.96%for the proposed model,representing a 14.31%improvement over the LSTM model alone.The proposed model demonstrates robust adaptability and generalization ability across diverse operational scenarios.This research offers reliable technical support for lithology identification while drilling.
