Hydrocarbon Reservoir Permeability Updates Through Pressure Grouping
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Solution Overview
Problem
Conventional history-matching techniques often result in localized permeability updates around individual wells, leading to unreliable predictions for future infill wells due to a lack of global permeability updates across the reservoir.
Innovation Solution
A divide and conquer approach is employed to group wells based on wellbore pressure similarity, forming pressure groupings that are used to generate a 3D model for region-based permeability updates, reducing the need for localized updates and improving well statistics simultaneously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If localized permeability updates are performed around individual wells, then well matches are improved, but global permeability updates are lost leading to unreliable infill well predictions
Solution Approach 1:
The reservoir is segmented into multiple pressure groupings based on pressure similarity, with each grouping representing a distinct pressure regime. This segmentation allows localized updates within each pressure grouping while maintaining global coherence, resolving the contradiction between localized well matches and global prediction reliability.
Solution Approach 2:
Multiple wells with similar pressure responses are merged into single pressure groupings that are updated simultaneously. This merging approach ensures that localized updates contribute to global permeability improvements, as all wells in a pressure grouping benefit from the same updated parameters, thereby improving both well matches and infill predictions.
2Measurement precision
If conventional history-matching is applied to individual wells, then localized updates are achieved, but the process is time-consuming and lacks efficiency
Solution Approach 1:
Multiple wells are merged into pressure groupings and updated simultaneously through a single history-matching process. This merging reduces the number of separate optimization runs required, significantly improving computational efficiency while maintaining high history-match quality across all wells in each pressure grouping.
Solution Approach 2:
The reservoir is divided into pressure groupings that can be updated independently but efficiently. This segmentation allows parallel processing of multiple pressure groupings, improving overall history-matching speed while ensuring each grouping receives appropriate localized updates based on its pressure characteristics.
3Measurement precision
If localized permeability updates are performed, then individual well statistics are improved, but global reservoir understanding is insufficient
Solution Approach 1:
Wells are merged into pressure groupings that reflect global pressure regimes. Updates to these groupings simultaneously improve individual well statistics and preserve global reservoir information, as the pressure groupings capture both localized well responses and broader reservoir-scale pressure behavior.
Solution Approach 2:
Pressure groupings serve multiple functions: they provide localized updates for individual wells while simultaneously representing global pressure regimes. This multi-functionality ensures that both well-level and reservoir-level information are maintained and improved through the history-matching process.
Data Source
AI summary
Techniques for updating hydrocarbon parameters include identifying well data associated with wells formed in subterranean formations of a hydrocarbon reservoir; determining a data density value for each well; assigning each well into a pressure grouping based on a wellbore pressure similarity of the well relative to an initial pattern well; generating a two-dimensional (2D) model of the hydrocarbon reservoir; converting the 2D model into a three-dimensional (3D) model of the hydrocarbon reservoir; and updating a permeability or a porosity associated with a grid cell of the 3D model.