Equivalent circulating density (ECD) denotes the density of drilling mud during circulation within a well. It is determined by integrating the equivalent static density with the pressure loss attributable to friction between the flowing mud and the geological formation. The effective management of ECD is imperative during drilling operations, as it plays a critical role in preventing kicks and minimising mud losses. Mud ECD has undergone extensive investigation through laboratory experiments, field measurements, and predictive modelling. Nevertheless, a comprehensive review of the various predictive models associated with ECD remains absent. The objective of this study is to review and critique existing correlations for estimating ECD. To accomplish this, a thorough bibliometric analysis was performed, focusing on peer-reviewed journals, mud manuals, and oil and gas conference papers. For the sake of clarity, existing models were categorized into tables, with their principal features highlighted. A critique of each model was subsequently provided. In total, 45 models related to ECD were identified, reviewed, and critiqued. The findings reveal that over 44% of the models are based on machine learning (ML), 27% are analytical models, 16% are regression based models, and 13% are simulator-related. Although there is no universally accepted model for ECD, there is an observable trend towards the utilization of ML algorithms for ECD estimation due to their predictive capabilities. However, the interpretability of these ML-based models remains a significant concern. This review serves as a comprehensive source of information on ECD for both readers and industry practitioners. Additionally, it directs researchers towards areas requiring further exploration and aids drilling professionals in selecting appropriate ECD models.
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号