Reservoir Data Uncertainty via Spatially Independent Subsets
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Solution Overview
Problem
Conventional methods fail to accurately determine the uncertainty of property distributions for spatially correlated reservoir data, as they assume independent measurements, which is not the case in petrophysical data, limiting accurate reservoir characterization and recovery forecasts.
Innovation Solution
A method using spatially independent subsets of data, generated through a variogram analysis, applies a bootstrap process to calculate property distribution uncertainty, ranking distributions with statistical parameters to characterize uncertainty effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional bootstrap methods are used to assess property distribution uncertainty, then the uncertainty can be calculated, but the results are inaccurate because the methods assume data independence which is violated in spatially correlated reservoir data
Solution Approach 1:
The patent divides the spatially correlated data into multiple spatially independent subsets using variogram analysis. Each subset is treated as an independent unit for bootstrap sampling, thereby resolving the contradiction between maintaining data independence for accurate uncertainty calculation and acknowledging the spatial correlation present in the original reservoir data.
2Reliability
If spatial bootstrap methods are used to account for spatial correlation, then data independence assumption is relaxed, but the methods only determine uncertainty of the mean and cannot determine uncertainty of the distribution itself
Solution Approach 1:
The patent segments the spatially correlated data into independent subsets, then applies bootstrap resampling to each subset to generate multiple bootstrap data sets. This segmentation approach enables the calculation of full distribution uncertainty while respecting spatial correlation structures.
Solution Approach 2:
The patent introduces spatially independent subsets as an intermediary between the original spatially correlated data and the bootstrap analysis. These subsets serve as a bridge that allows conventional bootstrap methods to be applied while accounting for spatial correlation through the subset selection process based on variogram analysis.
3Ease of manufacture
If all sample data are used directly in bootstrap analysis, then the calculation is simple, but the spatial correlation in the data leads to inaccurate uncertainty estimates
Solution Approach 1:
The patent automatically segments the data into spatially independent subsets using variogram analysis, maintaining the simplicity of the bootstrap process while improving accuracy. The segmentation is performed systematically based on spatial correlation structures rather than requiring complex manual intervention.
Solution Approach 2:
The variogram analysis automatically determines the optimal subset划分 based on the inherent spatial correlation structure of the data. The method self-adjusts to the data characteristics without requiring external intervention, maintaining ease of use while improving accuracy.
Data Source
AI summary
A system and a method, implemented on a computer, for calculating property distribution uncertainty of spatially correlated petrophysical data. The method includes inputting, into the computer, a sample petrophysical data comprising correlated data; applying, using the computer, a variogram to the sample petrophysical data to select a plurality of subsets of data, the subsets of data being substantially less correlated than the sample petrophysical data; and applying, using the computer, a bootstrap process on each of the plurality of subsets of data to obtain a plurality of bootstrap data sets from each of the plurality of subsets of data. The method further includes calculating data distributions for each of the obtained plurality of bootstrap data sets; ranking the data distributions by using a selected statistical parameter to obtain ranked data distributions; and characterizing the uncertainty based on the ranked data distributions.