4D Guided History Matched Models for Reservoir Simulation
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Solution Overview
Problem
Traditional history matching methods for subsurface reservoirs are time-consuming and often result in inconsistent models due to limited scalar parameters and large volumes of historical data, especially in fields with hundreds of wells, leading to inaccurate predictive simulations.
Innovation Solution
A 4D guided history matching system that uses a network of saturation sensors and a model processing hub to compare reservoir simulation models with 4D saturation models, calculating updated permeability distribution data through a permeability distribution transform function to create a consistent surface and subsurface match.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional history matching approach is used with limited scalar parameters, then the model can be updated relatively easily, but the accuracy and consistency of the model deteriorates significantly
Solution Approach 1:
The patent transforms the history matching process from adjusting scalar parameters to adjusting the entire permeability distribution field. By changing from limited scalar parameters to full field permeability updates guided by 4D saturation data, the system achieves both ease of operation through automated transform functions and high measurement precision through comprehensive parameter adjustment across the entire reservoir model.
2Productivity
If traditional history matching approach is used with limited scalar parameters, then the computational time is reduced, but the time required for multiple iterations increases significantly
Solution Approach 1:
The patent applies preliminary action by using 4D saturation data from saturation sensors to pre-determine the target permeability distribution before running the history matching simulation. The permeability distribution transform function is prepared in advance to automatically adjust permeability based on saturation differences, eliminating the need for multiple iterative trials and significantly reducing the time required to achieve an accurate history match.
Solution Approach 2:
The system implements feedback by continuously comparing simulated saturation data with actual saturation sensor measurements and using the differences to automatically adjust permeability distribution through the transform function. This closed-loop feedback mechanism eliminates the need for multiple manual iterations, as the system self-corrects based on real-time saturation data, thereby reducing total history matching time while maintaining computational efficiency.
3Quantity of substance
If traditional history matching approach is used with surface level production data only, then the data requirements are minimized, but the consistency with subsurface measured data deteriorates
Solution Approach 1:
The patent introduces 4D saturation data from subsurface saturation sensors as an intermediary between surface production data and the reservoir model. This intermediary layer provides direct subsurface saturation measurements that mediate the connection between observed production and model predictions, enabling simultaneous honoring of both surface and subsurface data and ensuring model consistency across all measurement levels.
Solution Approach 2:
The system transitions from two-dimensional surface level data to three-dimensional subsurface saturation data by incorporating 4D saturation measurements from multiple depths and times. This dimensional expansion allows the model to simultaneously match surface production data and subsurface saturation data, resolving the inconsistency problem by adding the temporal and spatial dimensions necessary to bridge both data sources.
4Device complexity
If traditional history matching approach is used with limited parameters, then the model complexity is reduced, but the ability to handle large volumes of historical data deteriorates
Solution Approach 1:
The patent changes the approach from adjusting a limited number of scalar parameters to adjusting the complete permeability distribution field. This parameter transformation enables the model to handle large volumes of historical data from hundreds of wells by distributing the adjustment across the entire field rather than concentrating it in few parameters, thereby maintaining model simplicity while accommodating comprehensive data volumes.
Data Source
AI summary
A system for creating a 4D guided history matched model may include a network of saturation sensors and a model processing hub. The saturation sensors may identify water production rates. The model processing hub may be in communication with the network of saturation sensors and may include a reservoir simulation model and a processor. The processor of the model processing hub may build a 4D saturation model, compare the reservoir simulation model and the 4D saturation model to generate a saturation Δ, calculate updated permeability distribution data, and update the reservoir simulation model with the updated permeability distribution data to create the 4D guided history matched model. A method of creating a 4D guided history matched model may include comparing a reservoir simulation model and a 4D saturation model, calculating updated permeability distribution data, and updating the reservoir simulation model to create the 4D guided history matched model.