This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 199670, “Digital Transformation Strategy Enables Automated Real-Time Torque-and-Drag Modeling,” by Dingzhou Cao, Occidental Petroleum; Don Hender, SPE, IPCOS; and Sam Ariabod, Apex Systems, et al., prepared for the 2020 IADC/SPE International Drilling Conference, Galveston, Texas, 3-5 March. The paper has not been peer reviewed.

Automated real-time torque-and-drag (RT-T&D) analysis compares real-time measurements with 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. The complete 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 extract, transform, and load (ETL) translator and digital-transformation applications to automate the T&D modeling work flow.

Methodology

A T&D representational state transfer (REST) application program interface (API) was integrated with an RTD analytics system capable of receiving and processing both real-time (hookload, torque, and rig-state) and digitized (drillstring and casing components, trajectory profiles, and mud-property) well data across multiple platforms. This strategy consists of four parts:

Digital transformation apps, ETL, and translator

Physics-based stiff/soft string T&D model API

Pre-existing data infrastructure

RTD analytics system

The data-flow architecture reveals a flexible design in the sense that it can accommodate different types of T&D models or any other physics-based REST API models (e.g., drillstring buckling or drilling hydraulics) and can be accessed offline for prejob/post-job planning. Drilling engineers can also leverage the RTD systems’ historical database to perform recalculations, comparative analysis, and friction calibrations. The RT-T&D model also can be deployed in a cloud environment to ensure that horizontal scalability is achieved.

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