Case Study
Predicting Equipment Failure with Machine Learning
Validated ML feasibility and provided clear insights on data readiness and next steps
Client aimed to enhance operational efficiency by integrating advanced AI tools
- A leading hydraulic fracturing service provider aimed to enhance operational efficiency by integrating advanced AI tools within field operations.
- The company processes over 1 TB of data monthly from active wells, making it essential to harness this data effectively to prevent disruptions. However, they faced challenges in applying AI and machine learning technologies to streamline real-time decision-making in the field.
- The client partnered with Valent to validate AI could improve efficiency through a series of POCs applied within field operations.
Developed AI models to detect pressure anomalies, preventing failures
- Delivering AI-powered insights in operational reports.
- Develop machine learning models that could be deployed to detect pressure anomalies at well sites, helping to prevent equipment failure
Validated ML feasibility and provided clear insights on data readiness and next steps
- Confirmed the feasibility and effectiveness of deploying ML models in field operations, enabling real-time detection of potential pressure spikes.
- Demonstrated the full capabilities and identified key caveats for leveraging AI-powered reporting both in field operations.
- Developed an automated ML pipeline to train, validate, and generate reports on model performance.
- Labeled thousands of data records to train machine learning (ML) models effectively.
- Successfully integrated, expanded, and modeled compressed job site data into the Microsoft Fabric ecosystem.
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