Unified Petroleum Modeling Workflow for Prediction Accuracy
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Solution Overview
Problem
Current numerical models in the oil and gas industry face challenges in providing accurate predictions due to uncertainties, leading to increased exploration and production costs, as they often rely on historical data without clear methodologies for integration and are updated independently, neglecting interdependencies between processes.
Innovation Solution
A data-driven workflow is introduced that connects various processes involved in petroleum exploration and production, using a global objective function to minimize mismatches between simulation results and observed data, accounting for dependencies and uncertainties through a unified input/output format and iterative optimization processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current process-based numerical models are used independently for petroleum exploration and production, then each model can be developed and maintained separately, but the predictions include major uncertainties and do not account for interdependencies between processes
Solution Approach 1:
The patent combines multiple independent process-based numerical models into a unified integrated modeling system. The system merges source rock modeling, reservoir modeling, production engineering, and recovery process models into a single coherent framework that shares common data structures and calculation engines, thereby improving prediction reliability while managing complexity through systematic integration
Solution Approach 2:
The integrated modeling system is designed with universal components that can perform multiple functions. A single modeling platform handles diverse petroleum engineering tasks including exploration, production optimization, and recovery enhancement, eliminating the need for separate specialized models and reducing overall system complexity
2Adaptability or versatility
If multiple independent numerical models are used for different petroleum processes, then each model can be optimized for its specific process, but the overall system lacks coordination and produces inconsistent predictions
Solution Approach 1:
The integrated modeling system is segmented into distinct modular components for source rock analysis, reservoir modeling, production engineering, and recovery processes. Each module maintains its specialized optimization while communicating through standardized interfaces, ensuring both process-specific adaptability and overall system consistency
Solution Approach 2:
The system implements feedback mechanisms where prediction results from one model are fed back into the integrated system to adjust and coordinate with other process models. This ensures that predictions across different petroleum processes are consistent and mutually reinforcing rather than contradictory
3Quantity of substance
If historical data is used without clear integration methodology, then data availability is maintained, but the incorporation of new data and models is inefficient and uncertain
Solution Approach 1:
The system performs preliminary organization and standardization of historical data before integration into the modeling framework. Data are pre-processed, validated, and structured according to unified schemas, enabling efficient incorporation of new data and models without repeated processing overhead and improving overall integration productivity
Data Source
AI summary
A global objective function is initialized to an initial value. A particular model simulation process is executed using prepared input data. A mismatch value is computed by using a local function to compare an output of the particular model simulation process to corresponding input data for the particular model simulation process. Model objects associated with the particular model simulation process are sent to another model simulation process. An optimization process is executed to predict new values for input data to reduce the computed mismatch value.