Automated Hydrocarbon Reservoir Forecasting via Unified Static Dynamic Modeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The petroleum industry faces challenges in accurately forecasting hydrocarbon production from subterranean reservoirs due to incomplete or inaccurate reservoir characterizations, high capital expenses, and the complexity of integrating static and dynamic models, leading to potential financial losses and suboptimal recovery of hydrocarbon reserves.
Innovation Solution
A computer-implemented method and system that automates the forecasting of hydrocarbon production by integrating static and dynamic modeling modules through a control management module, generating offspring models iteratively until a performance objective is satisfied, thereby optimizing hydrocarbon recovery and mitigating model uncertainties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static and dynamic models are manually integrated through multiple disciplinary teams, then comprehensive reservoir characterization is achieved, but the process becomes extremely time-consuming and complex
Solution Approach 1:
The patent merges static and dynamic modeling processes into a single integrated system where both modeling operations are performed simultaneously on a unified reservoir model, eliminating the need for sequential manual integration by multiple teams and dramatically reducing processing time
Solution Approach 2:
The system employs a single reservoir model that serves multiple functions - it can be used for both static analysis and dynamic simulation, allowing the same model structure to support various modeling operations without requiring separate models for each discipline
2Reliability
If multiple realizations of geological models are created with quasi-random variations, then probabilistic reservoir characterization is improved, but model complexity and computational requirements increase
Solution Approach 1:
The system changes the approach from creating multiple complex geological model realizations to adjusting parameters within a single model framework, using parameter sampling and uncertainty analysis to achieve probabilistic characterization without the computational burden of multiple full model realizations
3Measurement precision
If detailed static and dynamic models are constructed with comprehensive data, then forecast accuracy is improved, but the cost and time for data collection and processing become prohibitive
Solution Approach 1:
The system applies partial action by focusing computational resources on the most critical model parameters and regions that have the greatest impact on forecast accuracy, rather than uniformly processing all available data, thereby achieving good forecast results with reduced data collection and processing costs
Data Source
AI summary
A system and method is taught to substantially automate forecasting for a hydrocarbon producing reservoir through integration of modeling module workflows. A control management module automatically generates static and dynamic offspring models, with static and dynamic modeling software, until a performance objective associated with the forecasting of the reservoir is satisfied. The performance objective can include an experimental design table to determine a sensitivity of a particular parameter or can be directed towards reservoir optimization, i.e., ultimate hydrocarbon recovery, net present value, reservoir percentage yield, reservoir fluid flow rate, or history matching error.