Stuck pipe averter (on-premises)
Challenge
- Stuck pipe is one of the biggest contributors to drilling non-productive time.
- Anomaly-based real-time data predictions remain unreliable and prone to false alarms.
- Limited effectiveness of machine learning (ML) models due to the uniqueness of different stuck pipe events.
- Heavy reliance on subject matter expert (SME) domain experience and intuition can lead to human biases in decision-making.
Solution
An on-premises AI solution that proactively manages stuck pipe risks by combining inference with domain expertise.
- Transparent explanations: Provides clear, human-friendly causal explanations of contributing factors for better understanding and trust.
- Localized risk models: Adapts predictions to specific geological and operational contexts.
- Objective decision support: Delivers quantified risk estimates to enable data-driven decisions, supported by SMEs.
- Customizable framework: Allows field-specific fine-tuning for proactive drilling risk management.
Results
Improved decision-making workflow: Enables traceable, objective, and data-driven decisions during drilling operations.
Reduction in stuck pipe events: Decreases operational disruptions and reduces well construction time.
Enhanced risk management: Provides actionable insights for proactive mitigation of drilling hazards.
Operational efficiency: Saves time and costs by preventing non-productive time (NPT) associated with stuck pipe incidents.
Human-friendly diagnostics: Offers powerful diagnostic tools with transparent causal explanations for better collaboration between engineers and decision-makers.
