Automated Drilling Fluid Characterization Enhances Real-Time Operational Problem Detection

This paper aims to highlight the relevance of high-quality real-time surface and downhole data to enhance the ability of digital twins to perform problem detection, optimization and effective automation of drilling activities. Special focus will be given to the integration of real-time density and rheology fluid data to hydraulic predictions. Two different systems to acquire online real-time density and rheology data were installed at two different offshore rigs operating for PETROBRAS. These data were used as continuous input for hydraulics analysis performed by a fully operational digital twin designed for drilling problem detection and optimization. The digital twin algorithms were modified to consider the input of this novel source of data. Rheology sensors provide usual field rheology readings, including gelation properties, at selected temperatures. Due to the automated experimental procedure, rheology data frequency is around 40 min. Density results, on the other hand, are provided every 3 minutes. Results highlight the impact of providing frequent fluid data in predicting downhole conditions and, consequently, the readiness of anticipating drilling problems, avoiding lost times and enhancing process optimization. Good correlation between online and lab data was observed. A discussion on the impact of the quality downhole pressure data on similar analysis is also discussed, indicating the need of improvement for this service. This article summarizes novel gains in the automatization of drilling operations, including real-time fluid data and cutting edge digital twins. This effort aims not only to increase operational performance, but also to minimize manpower exposed to risk in red zones. Additional development includes full drilling fluid online characterization and robotization of several additional tasks related to drilling operations.