Developing an Integrated Real-Time Drilling Ecosystem to Provide a One-Stop Solution for Drilling Monitoring and Optimization

Abstract

The paper provides a technical overview of an operator's Real-Time Drilling (RTD) ecosystem currently developed and deployed to all US Onshore and Deepwater Gulf of Mexico rigs. It also shares best practices with the industry through the journey of building the RTD solution: first designing and building the initial analytics system, then addressing significant challenges the system faces (these challenges should be common in drilling industry, especially for operators), next enhancing the system from lessons learned, and lastly, finalizing a fully integrated and functional ecosystem to provide a one-stop solution to end users. The RTD ecosystem consists of four subsystems as shown in architecture Figure 1. (I) The StreamBase RTD streaming system, which is the backbone of the ecosystem. It takes the real-time streaming log data as well as other contextual well data (for example, OpenWells), processes it through analytical models, generates results, and delivers them to the web-based user interface; (II) The analytics models, which include the Machine Learning (ML)/Deep Learning (DL) models, the physics-based models and the stream analytical/statistical models; (III) The digital transformation solution, which wasdesigned to address contextual well data digitization issues to enable real-time physics-based modeling. Contextual well data like bottom hole assemblies (BHAs) and casing programs are challenging to aggregate and deliver to models, as this data is often stored in locations across multiple systems and in various formats. The digital transformation applications are designed to fit into the drilling teams' workflows and collect this information during the course of normal engineering processes, enhancing both the engineering workflow and the data collection process; (IV) the cloud based ML pipeline, which streamlines the original ML workflows, as well as establishes an anomaly detection and re-training mechanism for ML models in production. Figure 1 RTD ecosystem architecture All of these subsystems are fully integrated and interact with each other to function as one system, providing a one-stop solution for real-time drilling optimization and monitoring. This RTD ecosystem has become a powerful decision support tool for the drilling operations team. While it was a significant effort, the long term operational and engineering benefits to operators designing such a real-time drilling analytics ecosystem far outweighs the cost and provides a solid foundation to continue pushing the historical limitations of drilling workflow and operational efficiency during this period of rapid digital transformation in the industry.