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ADIPEC 2026 - Abu Dhabi

November 2-5, 2026 | Platform demonstrations and strategic networking

Schedule Meeting

AI Model Training & Data Intelligence

Surgical AI development demonstration for fuel-integrity use cases with minimal data requirements

Models Demonstrated

3

Pilot-ready in demonstration

Average Accuracy

88.6%

Across all models

Training Data Volume

High-volume

High-quality labeled data

Business Impact

MX$23.49B

Addressable loss-prevention opportunity

Fuel Theft Detection Model

Advanced anomaly detection for identifying suspicious fuel flow patterns

Training
87.3%
Accuracy
Precision
89.0%
Recall
84.0%

Business Impact

theft prevention:modeled scenario
detection time:< 5 minutes (target)
accuracy improvement:designed for high accuracy
Training Progress120/200 epochs

Fuel Efficiency Optimization Model

Real-time optimization of fuel flow, storage, and distribution parameters

Production
92.1%
Accuracy
Precision
94.0%
Recall
90.0%

Business Impact

efficiency gain:illustrative scenario
cost savings:illustrative scenario
response time:< 30 seconds (target)
Training Progress350/350 epochs

Predictive Maintenance Model

Predict equipment failures before they occur using sensor data and maintenance history

Deployed
89.7%
Accuracy
Precision
91.0%
Recall
88.0%

Business Impact

downtime reduction:illustrative scenario
maintenance savings:illustrative scenario
prediction horizon:2-8 weeks advance notice (target)
Training Progress280/300 epochs

Fuel Demand Forecasting Model

Predict fuel demand patterns to optimize inventory and distribution

Testing
85.4%
Accuracy
Precision
87.0%
Recall
84.0%

Business Impact

inventory optimization:illustrative scenario
demand accuracy:designed for high accuracy
planning horizon:30-90 days
Training Progress95/150 epochs

Strategic Data Expansion

Volumetric System Integration

Designed to ingest real-time feeds from SCADA and mandated volumetric systems

PLANNING
Data Volume: high-volume
Timeline: 6-8 weeks
Investment: MX$450K setup + MX$50K monthly
Value: High - Direct operational data
Data Sources:
SCADA systemsFlow metersTank levelsGPS tracking

IoT Sensor Network Expansion

Deploy additional sensors across critical fuel infrastructure

PLANNING
Data Volume: 850GB daily
Timeline: 12-16 weeks
Investment: MX$1.2M hardware + MX$80K monthly
Value: Very High - Real-time monitoring
Data Sources:
Pressure sensorsFlow rate monitorsQuality sensorsEnvironmental monitors

Synthetic Data Generation

Generate synthetic training data for rare event scenarios

ACTIVE
Data Volume: 500GB generated
Timeline: 4-6 weeks
Investment: MX$120K development
Value: Medium - Rare event coverage
Data Sources:
GAN networksPhysics simulatorsMonte Carlo methodsExpert systems

Partner Data Sharing

Aggregate anonymized data from partner contractors

NEGOTIATING
Data Volume: 1.1TB daily
Timeline: 8-12 weeks
Investment: Revenue sharing model
Value: High - Industry-wide patterns
Data Sources:
Contractor operationsMaintenance logsPerformance metricsIncident reports

🎯 Surgical AI Training Strategy

Maximum value extraction from minimal data through strategic partnerships and targeted model development.

Partner Data Access

Leverage partner relationships to access high-quality operational data without major infrastructure investment.

Transfer Learning

Use pre-trained models and adapt them to PEMEX-specific use cases, reducing training time by 70%.

Active Learning

Intelligently select the most valuable data points for labeling, maximizing model performance per data point.