AI-Driven Real-Time H₂S Monitoring and Risk Mitigation During Drilling in Southern Iraq Fields Using Fuzzy ART Unsupervised Learning

Abstract

Drilling in formations with hydrogen sulfide (H₂S) in southern Iraq poses environmental and safety problems. To create more sophisticated, intelligent monitoring systems, this study implemented an AI-driven, real-time methodology to monitor and mitigate the risks associated with H₂S. The system was developed using a self-learning AI agent trained using field data from 21 wells with 41 H₂S incidents.

The AI agent applies fuzzy adaptive resonance theory (ART)-based unsupervised learning to check the alert levels, predict H₂S spikes, and recommend the optimal adjustment for the mud pH to counterbalance the dissolved H₂S in the drilling fluids. Unlike static rule-based systems, this approach is based on adaptive thresholds that improve with learning and combines the alarm protocols adopted by Health, Safety, and Environment (HSE) with the proposed tresholds that reflect the formation data. This process enabled more rapid responses to fluctuations in gas levels due to earlier problem detection plus more accurate assessments of the crew’s cumulative exposure.

This AI-based technology delivered a strong and adaptable approach, with several advantages. It provides an accurate estimate of the amount of H2S in each formation, reduces the operational risk, and allows the team access to safer, data-driven decisions when drilling in H₂S bearing formations.