Oil Production Forecasting
Forecasts production using decline-curve methods and machine-learning experiments for practical planning and variance review.
Supports production planning with transparent scenarios that engineers and managers can compare.
The Challenge
Production planning needs forecasts that are transparent enough for engineers and flexible enough for changing field behaviour.
The Solution
Implemented forecasting workflows that combine domain methods with ML experiments for practical production analysis.
Key Features
- Forecast scenarios
- Decline curve analysis
- Model comparison
- Variance tracking
Business Impact
- Improves production planning conversations
- Supports target setting with comparable forecast scenarios
- Makes model tradeoffs visible instead of hidden
Lessons Learned
- Forecasting is a conversation between physics, data, and operational reality.
- Simple baselines are essential before adding advanced models.
Project Proof
Designed placeholder for architecture and evidence.
Real screenshots can be added later. For now, this proof layer documents the workflow, stack shape, and delivery logic behind the case study without inventing unavailable assets.
Architecture Summary
Implemented forecasting workflows that combine domain methods with ML experiments for practical production analysis.
Workflow
Frame the operational question
Prepare and validate source data
Forecast scenarios
Improves production planning conversations
Stack Grouping
BI & Analytics
Data & ML
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