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Digital Transformation Strategy Enables Automated Real-Time Torque-and-Drag Modeling


 The automated real-time torque and drag (RT-T&D) analysis compares real-time measurements to evergreen models to monitor and manage downhole wellbore friction, improving drilling performance and safety. Enabling RT-T&D modeling with contextual well data, rig-state detection, and RT-interval event filters poses significant challenges. To address these challenges, this paper presents a solution that integrates a physics-based T&D stiff/soft string model with a real-time drilling (RTD) analytics system using a custom built ETL-Translator and digital transformation applications to automate the T&D modeling workflow.
 The overall RT-T&D solution consists of four parts (see Figure 1): the digital transformation apps/ETL-Translator, the T&D model API, pre-existing data infrastructure, and the RTD analytics system. The pre-existing data infrastructure and workflows have time lag (updated daily) and data gap issues which are not acceptable for the RTD analytics system. To overcome this obstacle, digital transformation applications (PWP Digitizer and DPAT) were designed to fit into the drilling team's workflow, enabling automated digitization of contextual well data during normal engineering processes. The PWP Digitizer is a data pipeline that fits within the existing Planned Well Path (PWP) approval workflow and digitizes the PWP automatically. The Drilling Program Automation Tool (DPAT) is an application that automates the drilling program preparation process and digitizes related well information (BHAs, casing program, etc.) automatically. The commercial T&D model is deployed as a REST API and serves any applications for T&D modeling via a Translator. The current use cases are the RTD analytics system inferencing the T&D REST API via the Translator for real-time analysis, as well as a desktop application for pre-job/post-job analysis. The automated Extract, Transform and Load (ETL) module was developed to interact with various databases, pulling and storing all data needed for the model. The Translator module is designed to communicate with the T&D model via a REST API, and act as a coordinator to link all elements together. The RT-T&D modeling workflow executes as follows: once a real-time directional survey arrives in the RTD system, the RTD system will send the necessary data to the Translator and trigger a T&D model calculation. The Translator maps all inputs for the commercial model using a lookup table, prepares a JSON payload for the T&D model API, and, finally, returns the status to the RTD system. Once the calculation is ready, the RTD system will request the results so that they can be pushed to the RTD user interface.
 This automated RT-T&D workflow is plan to be integrated into the RTD analytics system to serve Delaware Basin operations. Business value is derived from both reduced time and cost to generate and analyze modeled and actual torque and drag data as well as operational risk reduction during the drilling process. The RT-T&D workflow can accommodate any commercial or proprietary T&D model without impacting the overall architecture. Drilling engineers can leverage the T&D workflow online or offline to perform recalculations, comparative analyses, and friction calibrations, enabling optimized operations, pre-job planning, and post-job analysis. The digital transformation apps not only digitize and aggregate contextual well data for RTD system in an automated and sustainable way, but they also streamline the drilling team's workflow by reducing manual data aggregation tasks during the drilling program preparation process, thereby saving engineering hours.
 This paper proposes an automated and sustainable digital transformation solution to address a common well data digitization issue, which enables automated RT-T&D modeling. The presented solution architecture is not limited to RT-T&D modeling; it lays the foundation for any real-time physics-based modeling, including real-time bottom-hole assembly (BHA) and equivalent circulating density (ECD) modeling. After the proof of concept of the RT-T&D modeling workflows, more physics-based models will be integrated into the RTD analytics system for real-time analysis using the same architecture. End user endorsement is the key to success for any digital transformation solution. This paper shares the lessons learned in obtaining the end users’ buy-in for use of digital transformation apps: instead of imposing new workflows on end users, these apps are designed to fit within end users’ existing workflows, streamlining and optimizing the processes.