In oil and gas industries, drilling is a complex and critical operation which require constant and accurate real-time monitoring. To this aim, real-time models are required to provide an overview of the drilling operations when direct and reliable measurements are not available. Given the harsh operating environment, sensor reliability and calibration are critical issues and bad data quality is a typical problem which affects the accuracy of the model. As a result, the driller may be misled about the down-hole situation or receive conflicting claims about operating conditions. This paper presents two approaches based on the use of artificial intelligence to improve monitoring of drilling processes in terms of reduced uncertainty and increased confidence. The first exploits the aggregation of the opinion of different experts within a so-called ensemble approach; the second is based on a so-called grey-box approach which combines a physical model and artificial intelligence. The two approaches are applied to the problem of predicting the bottom-hole pressure during a managed pressure drilling operation to demonstrate the improved accuracy and robustness.

You can access this article if you purchase or spend a download.