Reservoir Sector Model Conditioning Using Numerical Well Testing
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Solution Overview
Problem
Existing methods for conditioning hydrocarbon reservoir sector models using numerical well testing are time-consuming and prone to human bias due to manual adjustments, failing to accurately capture reservoir heterogeneity and fluid properties.
Innovation Solution
A computer-implemented method that automates the conditioning of hydrocarbon reservoir sector models by using numerical well testing, involving gridding, upscaling, and parameter tuning to match well-test data, ensuring accurate representation of reservoir properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual adjustment of model parameters is used to match numerical models to well-test data, then the model can be conditioned to reflect actual reservoir behavior, but the process becomes time-consuming and prone to human errors and biases
Solution Approach 1:
The system enables self-service by implementing automated parameter adjustment through computer algorithms that independently match numerical models to well-test data without human intervention. The computer system automatically iterates through parameter adjustments, compares model responses with actual well-test data, and refines the reservoir sector model parameters to achieve accurate history matching, thereby eliminating manual conditioning while maintaining or improving accuracy.
Solution Approach 2:
The patent replaces the mechanical manual adjustment process with an automated computer-based system. Instead of human operators manually tweaking parameters, the system uses computational algorithms to automatically adjust model parameters, perform sensitivity analyses, and optimize the match between numerical models and well-test data, substituting human manual operations with automated computational mechanics.
2Manufacturing precision
If manual adjustment of model parameters is used to match numerical models to well-test data, then the model can be conditioned, but the process becomes highly subjective to human biases
Solution Approach 1:
The system eliminates human subjectivity by implementing self-service automation where computer algorithms objectively adjust parameters based solely on mathematical optimization criteria and well-test data. The automated system applies consistent, repeatable algorithms that are immune to human biases, ensuring that model conditioning results are purely objective and reproducible across different users and applications.
Solution Approach 2:
The system implements continuous feedback loops where the computer automatically compares model predictions with actual well-test data, quantifies the mismatch, and uses this feedback to iteratively refine parameters. This closed-loop feedback mechanism ensures objective conditioning by continuously optimizing the model based on measurable performance criteria rather than subjective human judgment.
3Productivity
If analytical models are used for well-test interpretation, then the results are easily obtained through pressure transient analysis, but the results are averaged over reservoir volume and lack details in reservoir heterogeneity, fluid and rock properties, and well geometry
Solution Approach 1:
The patent applies segmentation by dividing the reservoir into discrete sector models with detailed spatial heterogeneity rather than treating it as a homogeneous bulk. The numerical model segments the reservoir into grid cells that can represent different rock properties, fluid characteristics, and geological features, allowing detailed characterization of reservoir heterogeneity while maintaining computational efficiency through sector-based discretization.
Solution Approach 2:
The system implements local quality by assigning spatially varying properties to different regions of the reservoir sector model. Instead of using average reservoir properties throughout, the numerical model incorporates location-specific permeability, porosity, fluid saturation, and well geometry parameters that reflect actual local reservoir conditions, enabling accurate representation of heterogeneity at the sector scale.
Data Source
AI summary
Example methods and systems for conditioning hydrocarbon reservoir sector models using numerical well testing are disclosed. One example method includes obtaining well-test data of a well. A reservoir sector model of a sector of a reservoir is obtained, where the sector includes at least an area of the reservoir associated with the well-test data and the well. The reservoir sector model is conditioned by tuning parameters of the reservoir sector model to match a response of the conditioned reservoir sector model to the well-test data, where conditioning the reservoir sector model includes adjusting multiple properties in the reservoir sector model based on the well-test data and at least one of well logs, production logging surveys, or distributed pressure measurements associated with the reservoir. The conditioned reservoir sector model is provided to determine properties of the reservoir for robust reservoir simulation and efficient development of underlying hydrocarbon resource of the reservoir.