Home / Current Issue / Paper 1724177
AI-Driven Non-Productive Time Reduction in Saudi Drilling Operations: Predictive Detection of Wellbore Instability, Stuck Pipe and Downhole Equipment Failures
Subject area: Science,Engineering and Technology · Area of research: AI Prediction of Drilling Non-Productive Time
DOI: 10.64388/IREV10I4-1724177
Abstract
Drilling non-productive time arises when mechanical dysfunction, unstable formations and failing equipment interrupt planned progress. Predictive analytics could shorten the interval between an emerging precursor and a suitable intervention, yet model discrimination is not equivalent to verified operational savings. This critical review examines published research from 2020 to 2025 on stuck-pipe precursors, geomechanical instability, drilling-fluid loss, harmful vibrations and downhole component degradation, with Saudi Arabian operating conditions as the principal application context. Evidence is compared through signal availability, physical plausibility, prediction horizon, dataset separation and intervention feasibility. The analysis identifies complementary rather than interchangeable functions for geomechanical models, temporal anomaly detectors, vibration classifiers and component-health estimators. It proposes a risk-gated monitoring architecture in which sensor quality checks, uncertainty estimates and mechanistic screening precede actionable recommendations. A distinction is maintained between laboratory findings, historical-well validations and prospective reductions in rig downtime. The resulting framework prioritises interpretable early warnings, crew authority, reliable telemetry and post-deployment evaluation. Its principal contribution is an integrated decision pathway connecting the forecast event, a proportionate operational response and measurable avoided disruption rather than a single universal prediction score.
Keywords
non-productive time; drilling analytics; stuck pipe; geomechanics; tool reliability; Saudi Arabia; early warning
How to cite this paper
@article{1724177,
author = {Qamar ur zaman},
title = {AI-Driven Non-Productive Time Reduction in Saudi Drilling Operations: Predictive Detection of Wellbore Instability, Stuck Pipe and Downhole Equipment Failures},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {4},
pages = {3078-3089},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1724177.pdf},
abstract = {Drilling non-productive time arises when mechanical dysfunction, unstable formations and failing equipment interrupt planned progress. Predictive analytics could shorten the interval between an emerging precursor and a suitable intervention, yet model discrimination is not equivalent to verified operational savings. This critical review examines published research from 2020 to 2025 on stuck-pipe precursors, geomechanical instability, drilling-fluid loss, harmful vibrations and downhole component degradation, with Saudi Arabian operating conditions as the principal application context. Evidence is compared through signal availability, physical plausibility, prediction horizon, dataset separation and intervention feasibility. The analysis identifies complementary rather than interchangeable functions for geomechanical models, temporal anomaly detectors, vibration classifiers and component-health estimators. It proposes a risk-gated monitoring architecture in which sensor quality checks, uncertainty estimates and mechanistic screening precede actionable recommendations. A distinction is maintained between laboratory findings, historical-well validations and prospective reductions in rig downtime. The resulting framework prioritises interpretable early warnings, crew authority, reliable telemetry and post-deployment evaluation. Its principal contribution is an integrated decision pathway connecting the forecast event, a proportionate operational response and measurable avoided disruption rather than a single universal prediction score.},
keywords = {non-productive time; drilling analytics; stuck pipe; geomechanics; tool reliability; Saudi Arabia; early warning},
month = {October},
doi = {https://doi.org/10.64388/IREV10I4-1724177}
}