摘要
Robotics&Machine Learning Daily News的一位新闻记者兼工作人员新闻编辑每日新闻-机器学习的新数据在一份新的报告中呈现。根据NewsRx记者从中华人民共和国北京发回的新闻报道，Research称：“(LC)井漏是钻井作业中的一个严重问题，因为它增加了非生产时间和成本。它可能是由各种复杂因素引起的，例如地质参数、钻井液性质和钻井作业参数S，无论是单独的还是联合的。”我们的新闻编辑引用了中国石油大学的一篇研究报告：“因此，研究LC的类型、影响因素和成因，对于有效地改进预防和封堵技术至关重要，目前LC类型的专家诊断严重依赖于专家的经验和判断，这可能导致不一致和偏差。”摘要:数据获取的困难或重要数据缺失会影响诊断的效率和及时性。传统的物理建模方法难以分析复杂因素的相关性,而传统的机器学习技术的解释能力有限。本文提出了一种可解释的漏失分析(ILCA)框架,为分析漏失提供了一种新的方法。摘要:利用高斯混合模型(GMM)聚类分析了区域实例数据的LC特征,高效、准确地标注296个LC类型。其次,利用XGBoo st算法建立了地质特征、钻井液性质、钻井操作参数与LC类型之间的关系,从而在钻井作业中利用实时数据及时识别LC类型。最后，在建立的XGBoo ST模型的基础上，利用可解释性机器学习技术对影响因素进行了综合定量分析，为识别模型提供了清晰的解释，使钻井工程师对影响LC EV的因素有了更深入的了解。提出的ILCA框架能够根据区域病例数据有效地标记LC类型，利用实时数据及时识别LC类型，并对LC的影响因素和原因进行定量分析。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Machine Learning are pre sented in a new report. According to news reporting originating from Beijing, Pe ople’s Republic of China, by NewsRx correspondents, research stated, “Lost circu lation (LC) is a serious problem in drilling operations, as it increases nonprod uctive time and costs. It can occur due to various complex factors, such as geol ogical parameters, drilling fluid properties, and operational drilling parameter s, either individually or in combination.” Our news editors obtained a quote from the research from the China University of Petroleum, “Therefore, studying the types, influencing factors, and causes of L C is crucial for effectively improving prevention and plugging techniques. Curre ntly, the expert diagnosis of LC types relies heavily on the experience and judg ment of experts, which may lead to inconsistencies and biases. Additionally, dif ficulties in obtaining data or missing important data can affect the efficiency and timeliness of diagnosis. Traditional physical modeling methods struggle to a nalyze complex factor correlations, and conventional machine learning techniques have limited interpretability. In this paper, we propose an interpretable lost circulation analysis (ILCA) framework that provides a new method for analyzing L C. First, we use Gaussian mixture model (GMM) clustering to analyze the LC chara cteristics of regional case data, efficiently and accurately labeling 296 LC eve nts. Second, we establish the relationship between geological features, drilling fluid properties, operational drilling parameters, and LC types using the XGBoo st algorithm. This enables timely identification of LC types during drilling ope rations using real - time data, with a precision greater than 85 %. Finally, we use interpretable machine learning techniques to conduct a comprehen sive quantitative analysis of influencing factors based on the established XGBoo st model, providing a clear explanation for the identification model. This enabl es drilling engineers to gain deeper insights into the factors influencing LC ev ents. In summary, the proposed ILCA framework is capable of efficiently labeling LC types based on regional case data, identifying LC types in a timely manner u sing real - time data, and conducting quantitative analysis of the factors and c auses of LC.”
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