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A decentralized operating performance assessment for geological drilling process via multi-block total projection to latent structures and Bayesian inference
Journal of Process Control ( IF 4.2 ) Pub Date : 2022-07-19 , DOI: 10.1016/j.jprocont.2022.07.005
Haipeng Fan , Min Wu , Xuzhi Lai , Sheng Du , Wanke Yu , Chengda Lu

Geological drilling process is an industrial process that contains a lot of variables, and the relationships and characters among them are also complex. Accordingly, implementing operating performance assessment by conventional methods has always been a problem that operators are dedicated to solving. In fact, geological drilling process is a complex industrial process involving multiple systems and stratigraphic uncertainties, which makes assessing its operating performance difficult. A decentralized operating performance assessment based on multi-block total projection to latent structures (T-PLS) and Bayesian inference is proposed for the geological drilling process. Utilizing the variational trends of the detection variables, the most related variables can be grouped in the same block, and the process capability index determines the operating performance grade. This is followed by a T-PLS algorithm-based operating performance assessment model and Bayesian inference. Each sub-block’s assessment results are integrated to achieve a comprehensive performance assessment. Last but not least, drill data show that the proposed method is effective and superior. The proposed method provides better accuracy and generalizability in assessing drilling performance. The novelty of the assessment scheme involves that a decentralized framework is proposed for operating performance assessment by identifying normal operating conditions first and then constructing the multi-block T-PLS-based monitoring model on the local sub-blocks.



中文翻译:

通过对潜在结构的多区块总投影和贝叶斯推理对地质钻井过程的分散作业性能评估

地质钻探过程是一个包含大量变量的工业过程,它们之间的关系和特征也很复杂。因此,通过传统方法进行运营绩效评估一直是运营商致力于解决的问题。事实上,地质钻井过程是一个复杂的工业过程,涉及多个系统和地层的不确定性,这使得评估其作业性能变得困难。针对地质钻井过程提出了一种基于多区块总投影到潜在结构(T-PLS)和贝叶斯推理的分散作业性能评估。利用检测变量的变化趋势,可以将最相关的变量分组在同一个块中,工艺能力指标决定操作性能等级。随后是基于 T-PLS 算法的操作性能评估模型和贝叶斯推理。综合各子块的考核结果,实现综合绩效考核。最后但并非最不重要的一点是,钻探数据表明所提出的方法是有效且优越的。所提出的方法在评估钻井性能方面提供了更好的准确性和普遍性。该评估方案的新颖之处在于,通过首先识别正常运行条件,然后在本地子块上构建基于多块 T-PLS 的监控模型,提出了一个分散的运行性能评估框架。随后是基于 T-PLS 算法的操作性能评估模型和贝叶斯推理。综合各子块的考核结果,实现综合绩效考核。最后但并非最不重要的一点是,钻探数据表明所提出的方法是有效且优越的。所提出的方法在评估钻井性能方面提供了更好的准确性和普遍性。该评估方案的新颖之处在于,通过首先识别正常运行条件,然后在本地子块上构建基于多块 T-PLS 的监控模型,提出了一个分散的运行性能评估框架。随后是基于 T-PLS 算法的操作性能评估模型和贝叶斯推理。综合各子块的考核结果,实现综合绩效考核。最后但并非最不重要的一点是,钻探数据表明所提出的方法是有效且优越的。所提出的方法在评估钻井性能方面提供了更好的准确性和普遍性。该评估方案的新颖之处在于,通过首先识别正常运行条件,然后在本地子块上构建基于多块 T-PLS 的监控模型,提出了一个分散的运行性能评估框架。钻探数据表明,该方法有效且优越。所提出的方法在评估钻井性能方面提供了更好的准确性和普遍性。该评估方案的新颖之处在于,通过首先识别正常运行条件,然后在本地子块上构建基于多块 T-PLS 的监控模型,提出了一个分散的运行性能评估框架。钻探数据表明,该方法有效且优越。所提出的方法在评估钻井性能方面提供了更好的准确性和普遍性。该评估方案的新颖之处在于,通过首先识别正常运行条件,然后在本地子块上构建基于多块 T-PLS 的监控模型,提出了一个分散的运行性能评估框架。

更新日期:2022-07-19
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