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Optimizing Drilling Operations with Azure SimOps

A cloud-based ML optimizer that predicts competitor fracking schedules, detects conflicts, and improves drilling efficiency.

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Situation

The drilling and completion teams of a leading Oil and Gas company frequently faced overlaps between drilling and fracking schedules, causing project delays, underutilization of equipment, and increased operational costs. As data volumes and field operations grew, manual schedule management through spreadsheets became inefficient and error-prone.

Problem

  • Lack of a centralized, automated system for visualizing and managing drilling operational schedules.
  • Absence of predictive capabilities to anticipate scheduling conflicts and competitor drilling activities.
  • High manual effort in reviewing and validating spreadsheets.
  • Inefficient collaboration across drilling and completion teams.

Solution

Mu Sigma developed cloud-based SimOps Schedule Optimizer hosted on Azure Web App Service to streamline schedule management and enable predictive conflict detection.

Key Components:

  • Data Layer: Azure Data Lake Storage (ADLS) and Azure SQL Database for secure, scalable data storage.
  • Processing & ML Layer: Azure Data Factory orchestrated data workflows; Azure Machine Learning deployed an RQFR model to predict competitor fracking dates and detect potential conflicts.
  • Application Layer: Web App Service using .NET, Angular, and Azure SignalR enabled real-time visualization and interaction.
  • Visualization Layer: Power BI dashboards delivered actionable insights on conflicts, competitor predictions, and operational alignment.
  • Monitoring & Automation: Azure Monitor and Application Insights enabled proactive issue tracking, while Azure DevOps automated CI/CD pipelines for seamless updates.
  • Collaboration & Governance: Integrated Microsoft Teams for alerts and decision-making, with planned SHAP-based model explainability for transparency.

Impact

  • Potential savings of ~$5M per conflict prevented through early detection and proactive scheduling.
  • Improved scheduling accuracy and reduced operational uncertainty.
  • 50%+ reduction in manual schedule reviewing effort.
  • Enhanced collaboration across drilling, completion, and operations teams.
  • Predictive visibility into competitor fracking schedules.
  • Scalable architecture supporting global deployment

Business Impact

  • $5M

    savings per conflict prevented

  • 50%+

    reduction in manual review effort

A cloud-native SimOps Optimizer that automates end-to-end drilling schedule planning. Using an RQFR model in Azure ML, it delivers real-time fracking forecasts and connects to ADLS, ADF, and App Service for dynamic, low-effort decision-making.

Let’s move from data to decisions together. Talk to us.