This paper provides the technical details to develop a real-time deep learning model to detect and estimate the duration of downlinking sequences of Rotary Steerable Systems (RSS) based on a single measurement (standpipe pressure, SPP). Further analytics are derived based on the downlink recognition results together with other real-time log data (ROP, RPM, Torque, etc.) to drive directional drilling efficiency. Real-time RSS downlink recognition is treated as an image segmentation problem. The Deep Learning (DL) models were created using the dynamic U-Net concept and materialized with a pre-trained ResNet-34 as the underlying architecture. Transfer learning was used due to the limited number of training samples (≪ 100 downlinks per onshore well) to help with speed and accuracy. The SPP time series data was segmented based on stand of pipe drilled (one image per stand). This "image" was then fed into the model for downlink recognition. To further increase the accuracy, a second opinion mechanism was applied when the models were tested and deployed into the Real-Time Drilling (RTD) system. Using a dual model approach greatly reduced the number of false positives due to non-downlink pressure fluctuations causing "noise". The patterns of SPP and its rate of change (delta SPP) are quite different. They both have pros and cons for identifying the downlink, thus two independent models were built based on these two signals. The DL model A is trained based on the original SPP signal and the DL model B is trained based on delta SPP. A downlink is confirmed only when both models show positive results. Data of 10 onshore wells (2 rigs) drilled with RSS were segmented (8165 images in total) and labeled. There were 671 images with 795 downlinks and 7980 images without downlink. The five-fold cross-validation technique was used to identify the best model(s). The F1 score of blind test result was .991 (accuracy was ~99.82%, see Table 2). The relative error of duration estimation is 2.49%. The current rig fleet within the RTD system has a mix of drilling tool configurations - RSS and mud motors. To further validate the models’ robustness regarding drilling tools, additional tests were conducted using mud motor wells’ datasets from 21 rigs (25431 images without downlink). There were 3 false negatives from this extended test set, which resulted in a ~99.93% accuracy for the aggregated 31 wells dataset. These results suggest that the models are accurate, reliable and robust. The real-time DL solution presented in this paper enables operators to analyze RSS performance during and between downlinking events. This would allow drilling engineers and rig supervisors to make faster, more reliable data-driven decisions to optimize performance and directional control of the well path.

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.