造斜率的准确预测是进行井眼轨迹调控的基础,直接影响定向井钻井效率,但由于井下力学行为的复杂性,传统预测方法存在一定限制,难以实现精确预测。为此,提出了一种力学-智能模型融合的造斜率预测方法。利用力学模型计算钻头侧向力、钻头...造斜率的准确预测是进行井眼轨迹调控的基础,直接影响定向井钻井效率,但由于井下力学行为的复杂性,传统预测方法存在一定限制,难以实现精确预测。为此,提出了一种力学-智能模型融合的造斜率预测方法。利用力学模型计算钻头侧向力、钻头转角和极限造斜率并作为主控因素,通过自动化机器学习框架联合其他参数进行拟合预测,从而取代传统方法反演经验系数的过程,使其充分发挥力学模型宏观规律描述准确和智能模型非线性拟合能力强的优势。利用新疆玛湖区块14口井数据进行训练和测试。结果显示,融合力学参数后,模型造斜率最大误差、均方根误差和平均绝对误差分别下降了17%、12%和8%,其中均方根误差和平均绝对误差均小于每30 m 1.00°,表明该方法能够有效提升造斜率预测精度,尤其在造斜率急剧变化的井段表现出更优的预测性能。研究结果可为造斜率的准确预测提供新的思路,同时也可为井眼轨迹的精确调控提供一定的技术支撑。展开更多
Current successes in artificial intelligence domain have revitalized interest in spacecraft pursuit-evasion game,which is an interception problem with a non-cooperative maneuvering target.The paper presents an automat...Current successes in artificial intelligence domain have revitalized interest in spacecraft pursuit-evasion game,which is an interception problem with a non-cooperative maneuvering target.The paper presents an automated machine learning(AutoML)based method to generate optimal trajectories in long-distance scenarios.Compared with conventional deep neural network(DNN)methods,the proposed method dramatically reduces the reliance on manual intervention and machine learning expertise.Firstly,based on differential game theory and costate normalization technique,the trajectory optimization problem is formulated under the assumption of continuous thrust.Secondly,the AutoML technique based on sequential model-based optimization(SMBO)framework is introduced to automate DNN design in deep learning process.If recommended DNN architecture exists,the tree-structured Parzen estimator(TPE)is used,otherwise the efficient neural architecture search(NAS)with network morphism is used.Thus,a novel trajectory optimization method with high computational efficiency is achieved.Finally,numerical results demonstrate the feasibility and efficiency of the proposed method.展开更多
文摘造斜率的准确预测是进行井眼轨迹调控的基础,直接影响定向井钻井效率,但由于井下力学行为的复杂性,传统预测方法存在一定限制,难以实现精确预测。为此,提出了一种力学-智能模型融合的造斜率预测方法。利用力学模型计算钻头侧向力、钻头转角和极限造斜率并作为主控因素,通过自动化机器学习框架联合其他参数进行拟合预测,从而取代传统方法反演经验系数的过程,使其充分发挥力学模型宏观规律描述准确和智能模型非线性拟合能力强的优势。利用新疆玛湖区块14口井数据进行训练和测试。结果显示,融合力学参数后,模型造斜率最大误差、均方根误差和平均绝对误差分别下降了17%、12%和8%,其中均方根误差和平均绝对误差均小于每30 m 1.00°,表明该方法能够有效提升造斜率预测精度,尤其在造斜率急剧变化的井段表现出更优的预测性能。研究结果可为造斜率的准确预测提供新的思路,同时也可为井眼轨迹的精确调控提供一定的技术支撑。
基金supported by the National Defense Science and Technology Innovation program(18-163-15-LZ-001-004-13).
文摘Current successes in artificial intelligence domain have revitalized interest in spacecraft pursuit-evasion game,which is an interception problem with a non-cooperative maneuvering target.The paper presents an automated machine learning(AutoML)based method to generate optimal trajectories in long-distance scenarios.Compared with conventional deep neural network(DNN)methods,the proposed method dramatically reduces the reliance on manual intervention and machine learning expertise.Firstly,based on differential game theory and costate normalization technique,the trajectory optimization problem is formulated under the assumption of continuous thrust.Secondly,the AutoML technique based on sequential model-based optimization(SMBO)framework is introduced to automate DNN design in deep learning process.If recommended DNN architecture exists,the tree-structured Parzen estimator(TPE)is used,otherwise the efficient neural architecture search(NAS)with network morphism is used.Thus,a novel trajectory optimization method with high computational efficiency is achieved.Finally,numerical results demonstrate the feasibility and efficiency of the proposed method.
