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Automated Real-Time Torque-and-Drag Analysis Improves Drilling Performance

Significant progress has been made on physics-based torque-and-drag (T&D) models that can run either offline or in real time. Despite its numerous benefits, real-time T&D analysis is not prevalent because it requires merging real-time and contextual data of dissimilar frequency and quality, along with repeated calibration, the results of which are not easily accessible to the user. In this paper, the application of a real-time T&D model is demonstrated. The process of T&D analysis was automated, and the time and cost required to run physical models offline was reduced or, in some cases, eliminated.

Introduction

Traditional electronic drilling recorders (EDRs) are third-party systems that collect rig-sensor data. Major limitations in the operator’s ability to fully leverage the potential of this data exist, including issues with rig-sensor-measurement quality and rigsite data-aggregation methods, relatively slow data-sampling rates, and limited interoperability. To this end, the operator initiated a project to use drilling data better, focusing on improving data quality by building on previous work validating rig-sensor data.

Rig-Based T&D Advisory System

T&D Modeling. Onshore US operators are investing heavily in unconventional horizontal plays. In these wells, excessive T&D is a critical limiting factor in exposure to productive formations.

Predictive T&D computer models were developed as early as 1984. An initial model assumed that T&D are both caused by sliding friction on the wellbore, using the product of the normal force and friction coefficient to yield values for T&D. Later, the model was put in standard differential form and made to include the effects of mud pressure. These models (now termed soft-string) treat tubulars like a rope, ignoring bending moments and assuming continuous contact with the wellbore.

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 191426, “Implementation of a Fully Automated Real-Time Torque-and-Drag Model for Improving Drilling Performance: Case Study,” by Mojtaba Shahri, SPE, Timothy Wilson, Taylor Thetford, SPE, Brian Nelson, SPE, and Michael Behounek, SPE, Apache, and Adrian Ambrus, John D’Angelo, and Pradeepkumar Ashok, SPE, Intellicess, prepared for the 2018 SPE Annual Technical Conference and Exhibition, Dallas, 24–26 September. The paper has not been peer reviewed.
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Automated Real-Time Torque-and-Drag Analysis Improves Drilling Performance

01 February 2019

Volume: 71 | Issue: 2

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