Prediction of Equivalent Circulating Density from Drilling Parameters
DOI:
https://doi.org/10.29227/IM-2026-01-01-021Keywords:
equivalent circulating density, surface drilling parameters, artificial neural networks, oligocene formation, non-productive timeAbstract
Equivalent circulating density (ECD) is considered an essential and highly significant parameter during drilling operations because when ECD becomes excessively high, it directly reduces the rate of penetration, increases the likelihood of drill string sticking, and causes severe loss of circulation, particularly in drilling intervals with narrow mud weight windows. At the same time, it increases overall pressure loss in the wellbore and reduces the effective impact force capacity of the drill bit, thereby leading to greater non-productive time (NPT). Therefore, ECD measurement must ensure a very high level of accuracy to prevent operational problems, including critical well control incidents. In practical applications, however, direct measurement of ECD is extremely costly, while alternative mathematical prediction methods often provide relatively low accuracy, resulting in considerable risks during drilling operations. The accuracy of ECD predictions therefore remains questionable because incidents such as drill string sticking or severe loss of circulation are still observed during actual drilling. The main objective of this study is to apply advanced machine learning techniques to develop a robust ECD prediction model for wells drilled in the Oligocene Formation of the White Tiger field by relying only on surface drilling parameters without using expensive downhole measurements. This study proposes and evaluates an ECD prediction model based on artificial neural networks (ANN). Six key surface drilling parameters collected from Oligocene wells include rate of penetration (ROP), pump flow rate (Q), rotary speed (RPM), surface pump pressure (SPP), weight on bit (WOB), drill string torque (T), together with a wide range of ECD values. The ANN model was trained, tested, and validated to provide highly accurate predictions. The results demonstrated a strong and reliable overall predictive performance with a correlation coefficient (R) greater than 0.99. Consequently, the ANN-based ECD prediction model achieves consistently high accuracy and helps minimize risks in adjacent drilling operations by enabling timely adjustment of drilling parameters to maintain ECD below fracture pressure.
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