Data Analysis & Predictive Maintenance

Reduce equipment downtime and optimize production

Unexpected equipment failures cost thousands in lost production and emergency repairs. We analyze your historical production and maintenance data to predict failures before they happen and identify optimization opportunities.

Predict Equipment Failures

Machine learning models analyze historical data to predict pump, compressor, and separator failures weeks in advance

Reduce Downtime Costs

Schedule maintenance during planned shutdowns instead of emergency repairs at 2x-3x the cost

Optimize Production

Identify wells underperforming expectations and optimization opportunities to increase recovery

Data-Driven Decisions

Replace gut-feel decisions with quantitative analysis of your production and equipment data

Our Data Analysis Approach

Data Collection & Assessment

Gather historical production, maintenance, and equipment data from your existing systems, spreadsheets, and databases.

Pattern Analysis

Identify patterns in equipment failures, production declines, and operational anomalies using statistical analysis.

Model Development

Build machine learning models to predict failures and production issues based on historical patterns.

Validation & Testing

Test model accuracy against recent historical data to ensure predictions are reliable before deployment.

Dashboard Deployment

Deploy dashboards showing risk scores, predicted failure dates, and optimization recommendations.

Ongoing Monitoring

Continuously refine models as new data becomes available and operational conditions change.

01

Predictive Maintenance

Machine learning models predict pump, compressor, and separator failures based on historical maintenance and operational data.

02

Production Analytics

Identify underperforming wells, production decline patterns, and optimization opportunities using statistical analysis.

03

Custom Dashboards

Real-time production KPIs, equipment health scores, and predictive alerts consolidated into executive dashboards.

What Predictive Maintenance Looks Like

ESP Pump Failure Prediction

Predict electric submersible pump failures 2-4 weeks in advance based on current draw, vibration, temperature, and production patterns.

  • • Analyze 6-12 months of historical failures
  • • Identify leading indicators from sensor data
  • • Weekly risk score for each pump
  • • Alert when failure probability exceeds threshold

Production Decline Analysis

Identify wells declining faster than expected and quantify lost production value to prioritize workover decisions.

  • • Compare actual vs expected decline curves
  • • Quantify lost barrels per day
  • • Calculate NPV of intervention options
  • • Prioritize workovers by economic impact

Compressor Health Monitoring

Monitor gas lift compressor performance and predict maintenance needs before emergency shutdowns occur.

  • • Track pressure ratios and temperatures
  • • Detect anomalies from baseline performance
  • • Predict bearing and valve failures
  • • Schedule maintenance during planned shutdowns

Separator Performance Optimization

Optimize separator settings to maximize oil recovery and minimize emissions based on feed conditions.

  • • Analyze pressure/temperature combinations
  • • Identify optimal operating parameters
  • • Calculate potential production gains
  • • Track emissions impact of changes

Technologies & Methodologies

Technology Stack

  • Python & Pandas: Data extraction, cleaning, and transformation
  • scikit-learn: Machine learning model development and training
  • TensorFlow/PyTorch: Deep learning for complex pattern recognition
  • Custom Dashboards: Interactive visualizations and real-time monitoring
  • Azure ML / AWS SageMaker: Cloud-based model deployment
  • SQL & NoSQL databases: Data warehousing and storage

Data Science Methods

  • Time Series Analysis: Production trends and decline curves
  • Classification Models: Predict failure vs healthy equipment
  • Anomaly Detection: Identify unusual operational patterns
  • Regression Analysis: Quantify relationships between variables
  • Feature Engineering: Extract meaningful signals from raw data
  • Model Validation: Ensure predictions are accurate and reliable

Common Oil & Gas Challenges We Address

Unexpected Downtime

Equipment failures with no warning costing thousands in lost production

Reactive Maintenance

Emergency repairs at 2x-3x the cost of planned maintenance

Production Declines

Wells underperforming expectations without clear diagnosis

Data Scattered

Production and maintenance records in disconnected systems

No Early Warnings

No system to predict problems before they become failures

Limited Analysis

No time or expertise to analyze data for optimization opportunities

Expected Outcomes

Typical Results

  • • 30-50% reduction in unplanned equipment downtime
  • • 2-4 week advance notice of impending failures
  • • 20-40% reduction in emergency maintenance costs
  • • 5-15% production increase from optimization
  • • Identify 3-5 high-value workover candidates
  • • ROI within 6-12 months from reduced downtime

Timeline & Investment

  • Data collection: 1-2 weeks
  • Model development: 4-8 weeks
  • Deployment: 2-4 weeks
  • Typical investment: $35K-$100K
  • Expected ROI: 6-12 months
  • Ongoing: Model refinement as data grows

Ready to optimize your operations?

Schedule a consultation to identify improvement opportunities and develop a roadmap for streamlining your processes.

Schedule a Consultation