Data‑Driven Approach in Drilling and Well Engineering Management to Enhance Productivity, Safety, and Cost Control

The increasing complexity of drilling operations and the growing demand for safer, more efficient, and cost‑effective well construction have accelerated the adoption of data‑driven technologies in drilling and well engineering management. Traditionally, drilling decisions relied heavily on empirical experience and limited real‑time data interpretation, which often led to operational uncertainties, safety risks, and inefficient resource utilization. With the rapid advancement of digital technologies, large volumes of operational data can now be collected, processed, and analyzed to support more informed and proactive decision‑making. The main objective of this study is to review and analyze the role of data‑driven approaches in improving productivity, operational safety, and cost control in drilling and well engineering management. This study adopts a comprehensive literature review methodology by analyzing recent academic and industry research on data analytics, machine learning, real‑time monitoring systems, predictive maintenance, and decision‑support systems applied in drilling operations. The review synthesizes findings from multiple studies to identify the key technologies, analytical techniques, and practical applications that contribute to improved drilling performance and operational reliability. The findings indicate that the integration of data‑driven technologies significantly enhances drilling efficiency by optimizing critical parameters such as rate of penetration, bit selection, and well trajectory planning. In addition, real‑time monitoring systems and artificial intelligence–based anomaly detection tools improve safety by enabling early identification of abnormal drilling events such as kicks, losses, and equipment malfunctions. Predictive maintenance models further contribute to operational reliability by forecasting equipment failures and reducing non‑productive time. Furthermore, decision‑support systems that integrate geological, operational, and economic data help engineers make more accurate and timely operational decisions. Overall, the study demonstrates that data‑driven approaches play a crucial role in transforming drilling and well engineering management from reactive operations toward predictive and intelligent systems, ultimately leading to improved productivity, enhanced safety, and more effective cost management in modern drilling operations.

کلیدواژه ها:

Data‑driven drilling ، real‑time monitoring ، predictive maintenance ، well engineering ، decision‑support systems