Published June - July 2026, Pg. 15-24

Section: Well drilling

UOT: 622.24:004.942:004.438

DOI: 10.37474/0365-8554/2026-06-07-15-24

Enhancing torque and drag modeling accuracy through Python-based workflow

G.V. Jabbarova PhD in Tech. Sc. - Azerbaijan State Oil and Industry University, “Geotechnological Problems of Oil, Gas and Chemistry” SRI

V.A. Iskandarov - Azerbaijan State Oil and Industry University

This paper presents a Python-based automated workflow developed to improve the accuracy of torque and drag (T&D) modeling during casing running operations in complex, high-pressure wells. Conventional approaches often rely on sparse, manually processed hook load measurements, resulting in limited friction factor calibration and increased operational risk. The proposed methodology leverages continuous mud logging data to extract pick-up (PU) and slack-off (SO) weights using advanced algorithms incorporating thresholding, rolling median filtering, and statistical outlier detection. The workflow consolidates raw data from multiple sources, performs automated cleaning, and generates high-resolution PU/SO profiles for interval-specific friction factor calibration.
Model validation against actual rig measurements demonstrated strong predictive performance, with Mean Absolute Percentage Error (MAPE) below 6 %, Root Mean Square Error (RMSE) within 11 tons, and coefficients of determination (R²) exceeding 0.96. These metrics confirm the robustness of the automated approach in accurately replicating field conditions. The case study highlights how calibrated friction factors significantly enhance T&D model reliability, reducing discrepancies between modeled and observed hook loads and mitigating risks such as overpull and stuck pipe.
By minimizing manual intervention and accelerating post-drilling analysis, this workflow enables precise friction factor calibration, optimizes casing running strategies, and supports safer well construction. The methodology represents a substantial advancement in T&D modeling practices and provides a foundation for real-time applications in future drilling projects.

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