Information Systems
Research on the situation analysis and decision-making system for oil and gas resource based on artificial intelligence technology
The paper addresses the complexity of situation analysis for oil and gas resources amidst global energy transition, climate change, and geopolitical shifts. It argues that conventional methods are insufficient and highlights the potential of AI. The research examines current AI applications in oil and gas exploration, development, production, and market analysis, acknowledging their limitations. It then proposes an AI-based system framework, integrating data pre-processing, feature extraction, machine learning, intelligent prediction, and decision-making for comprehensive, efficient, and accurate analysis of hydrocarbon resource situations.
Executive Impact
The global energy sector faces unprecedented challenges, demanding more efficient and intelligent systems for resource management. This AI-driven approach offers a pathway to optimized exploration, reduced costs, enhanced production efficiency, and informed strategic decisions, directly addressing the limitations of traditional analytical methods in a rapidly evolving market.
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Improvement in reservoir prediction accuracy using simplified CNN.
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Reduction in production prediction error using RNN for oil well optimization.
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Faster anomaly detection in production processes.
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Information Systems
AI's Role in Seismic Interpretation
Accuracy Boost AI significantly improves the interpretation accuracy of seismic data for identifying geological structures and reservoir properties, leading to more precise drilling locations.AI-Powered Oil & Gas Decision System Flow
Data Acquisition & Pre-processing
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Feature Extraction & Model Training
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Intelligent Prediction & Analysis
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Decision Support & Optimization
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Real-time Monitoring & Feedback
| Feature | Traditional Methods | AI-Driven System |
|---|---|---|
| Data Volume Handling | Limited to manageable datasets; struggles with big data. |
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| Prediction Accuracy | Relies on empirical judgments; often less precise. |
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| Speed of Analysis | Time-consuming manual processes. |
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| Adaptability | Slow to adapt to new data patterns or market shifts. |
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Enhanced Production Optimization
A major oil field implemented an AI-driven production optimization system. By utilizing RNNs to analyze simulated production data, the system achieved a 10% reduction in prediction error for oil well output. This led to more accurate adjustments in production rates and optimized water injection schemes, significantly improving overall field efficiency and resource recovery.
Challenge: Inefficient resource extraction due to imprecise production forecasting.
Solution: Deployment of AI-powered RNN models for real-time production data analysis and predictive optimization.
Result: Improved production efficiency, optimized resource allocation, and a 10% reduction in prediction error.
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Your AI Implementation Roadmap
A structured approach to integrating AI for maximum impact in your organization.
Phase 1: Data Infrastructure & Integration
Establish a unified data platform, integrate heterogeneous data sources (geological, geophysical, production, market data), and set data standards.
Phase 2: AI Platform Development & Model Training
Develop the AI platform with GPU optimization, train machine learning and deep learning models for exploration, development, and market analysis.
Phase 3: Business Application & Integration
Integrate AI capabilities into business processes for resource evaluation, market analysis, and strategic planning. Deploy intelligent Q&A and reasoning systems.
Phase 4: Real-time Monitoring & Continuous Optimization
Implement real-time monitoring of production data, enable intelligent diagnosis of issues, and continuously optimize production and market strategies.
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