Geobody Continuity in Geological Models via Multiple Point Statistics
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Solution Overview
Problem
Multiple Point Statistics (MPS) simulation struggles to effectively capture long-range patterns and continuities in geological models without incurring significant CPU and RAM costs, leading to limitations in reproducing realistic geobody shapes and conditioning to multiple types of data.
Innovation Solution
A novel regular-random hybrid simulation path is introduced, where a regular path is used on the coarsest level of multiple grids to capture large-scale continuity and a random path is employed at lower levels to explore variability, reducing the number of multi-grid levels in the vertical direction and making the regular path follow the minor geobody continuity direction for improved hard data conditioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Shape
If MPS simulation uses traditional random path on all grid levels, then computational simplicity is maintained, but geobody continuity and large-scale pattern reproduction are poor
Solution Approach 1:
The simulation process is segmented into different grid levels (coarsest, intermediate, finest), with each level using an appropriate simulation path strategy. The coarsest level uses regular path to establish large-scale continuity, while lower levels use random path to maintain stochastic properties, thus resolving the contradiction between continuity and efficiency.
Solution Approach 2:
Different simulation path qualities are applied to different regions of the parameter space. Regular path (deterministic) is applied where large-scale continuity is needed (coarsest grid level), while random path (stochastic) is applied where local variability is needed (lower grid levels), achieving both continuity and computational efficiency.
2Productivity
If MPS simulation reduces number of multi-grid levels, then computational cost is reduced, but ability to capture multi-scale patterns is weakened
Solution Approach 1:
Large-scale patterns are established in advance at the coarsest grid level using regular simulation path before proceeding to finer levels. This preliminary action ensures that multi-scale patterns are captured efficiently with fewer grid levels, reducing computational cost while maintaining pattern reproduction accuracy.
3Shape
If regular path is used on coarsest grid level, then large-scale continuity is improved, but hard data conditioning may be compromised
Solution Approach 1:
The simulation path strategy is dynamically adjusted based on grid level. Regular path is used at coarsest level for continuity, while random path is used at lower levels to properly condition hard data, achieving both large-scale continuity and accurate data conditioning through adaptive methodology.
Data Source
AI summary
The present disclosure describes a method that improves the long-range geobody continuity in Multiple Point Statistical methods, wherein the coarsest multi-grid level cells are simulated in a regular path, and the subsequent level cells are simulated in a random path as usual. The method is general and is applicable to different cases: such as hard data conditioning, soft data conditioning, non-stationarity modeling, 2 or more than 2 types of facies modeling, and 2D and 3D modeling. The method is particularly useful in reservoir modeling, especially for the channelized systems, but can be generally applied to other geological environments.