Oil Production Forecasting for Data-Driven Well Allocation
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Solution Overview
Problem
Oil and gas companies face challenges in optimizing production operations to maintain profit margins amidst volatile oil prices, requiring cost reduction and increased production efficiency.
Innovation Solution
A computer-implemented method and system utilizing machine learning and artificial intelligence to analyze historical production data, integrate subsurface and production data, and provide automated forecasting and decision support for optimizing oilfield operations, including sweet spot identification, production allocation, and well management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional production optimization methods are used, then operational simplicity is maintained, but production efficiency and decision-making accuracy deteriorate
Solution Approach 1:
The patent replaces traditional mechanical/manual production optimization methods with an automated machine learning system. The system uses algorithms to analyze historical production data, subsurface data, and operational data, substituting human decision-making processes with computational models that can process large datasets and provide optimized recommendations automatically.
Solution Approach 2:
The machine learning system is designed to autonomously analyze data, identify patterns, and generate optimization recommendations without requiring constant human intervention. The system serves itself by continuously learning from new data inputs and automatically updating its models to improve production efficiency over time.
2Measurement precision
If comprehensive data analysis is performed, then decision-making accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary analysis by pre-processing and organizing historical production data, subsurface data, and operational data before actual optimization needs arise. Machine learning models are trained in advance on comprehensive datasets, so when optimization decisions are needed, the system can quickly retrieve and apply pre-computed insights rather than analyzing all raw data from scratch.
Solution Approach 2:
The system creates simplified representations or copies of complex geological and production data through machine learning models. Instead of processing all original raw data repeatedly, the system uses trained models that capture essential patterns and relationships, allowing rapid analysis while maintaining decision-making accuracy.
3Ease of manufacture
If manual production optimization is used, then implementation simplicity is maintained, but cost reduction and efficiency improvement deteriorate
Solution Approach 1:
The patent introduces an intermediary machine learning system that bridges the gap between complex data analysis capabilities and simple implementation needs. The system acts as a mediator by processing complex subsurface and production data internally while presenting simplified recommendations and actionable insights to users, maintaining ease of implementation while achieving significant cost reduction and efficiency improvement.
4Reliability
If traditional forecasting methods are used, then system simplicity is maintained, but production uncertainty and forecast accuracy deteriorate
Solution Approach 1:
The patent replaces traditional mechanical forecasting methods based on simple trends or manual analysis with machine learning-based predictive models. These models analyze historical production data, subsurface characteristics, and operational parameters to generate more accurate forecasts of future production, reducing uncertainty while the system handles the computational complexity internally.
Data Source
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Figure 3A~3B
AI summary
A method for optimizing oil production includes receiving historical production data for one or more wells. A first model is generated based at least partially upon the historical production data. The production in the one or more wells is predicted based at least partially upon the first model. A second model is generated based at least partially on the predicted production. A production allocation is determined in the one or more wells to maximize production based at least partially upon the second model. The production allocation is then implemented.