Interpretable Lost Circulation Analysis: Labeled, Identified, and Analyzed Lost Circulation in Drilling Operations

Abstract

Summary Lost circulation (LC) is a serious problem in drilling operations, as it increases nonproductive time and costs. It can occur due to various complex factors, such as geological parameters, drilling fluid properties, and operational drilling parameters, either individually or in combination. Therefore, studying the types, influencing factors, and causes of LC is crucial for effectively improving prevention and plugging techniques. Currently, the expert diagnosis of LC types relies heavily on the experience and judgment of experts, which may lead to inconsistencies and biases. Additionally, difficulties in obtaining data or missing important data can affect the efficiency and timeliness of diagnosis. Traditional physical modeling methods struggle to analyze 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 LC. First, we use Gaussian mixture model (GMM) clustering to analyze the LC characteristics of regional case data, efficiently and accurately labeling 296 LC events. Second, we establish the relationship between geological features, drilling fluid properties, operational drilling parameters, and LC types using the XGBoost algorithm. This enables timely identification of LC types during drilling operations using real-time data, with a precision greater than 85%. Finally, we use interpretable machine learning techniques to conduct a comprehensive quantitative analysis of influencing factors based on the established XGBoost model, providing a clear explanation for the identification model. This enables drilling engineers to gain deeper insights into the factors influencing LC events. 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 using real-time data, and conducting quantitative analysis of the factors and causes of LC. This approach addresses the limitations of traditional methods and offers valuable insights for drilling engineers.