Reservoir Uncertainty Characterization via Spatial Bootstrap
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Solution Overview
Problem
Conventional methods for characterizing reservoir formation evaluation uncertainty require accurate 'a priori' knowledge of input model parameters and assume independent measurements, leading to subjective estimates and costly, time-consuming iterations that may not yield accurate reserve estimates.
Innovation Solution
A computer-implemented method that accesses petrophysical reference data, derives an a-priori uncertainty distribution, and applies a spatial bootstrap process to generate multiple petrophysical model solutions within user-defined ranges, allowing for the derivation of posteriori distributions without requiring accurate 'a priori' knowledge of input model parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional Monte Carlo methods are used to estimate uncertainty from input model parameter ranges, then uncertainty estimates can be obtained, but the estimates become highly subjective and require accurate a priori knowledge of parameter ranges
Solution Approach 1:
The bootstrap method allows the data itself to provide uncertainty estimates without requiring external a priori knowledge. By resampling the actual measured data and recalculating model outputs, the method enables the data to self-characterize its own uncertainty, eliminating the need for subjective expert judgment about parameter ranges.
Solution Approach 2:
The method uses feedback from the actual data to refine uncertainty estimates. By iteratively resampling the data and comparing resulting model outputs against the original data, the system continuously adjusts and improves uncertainty characterization, allowing the process to learn from the data's actual variability rather than relying on pre-assumed ranges.
2Measurement precision
If conventional bootstrapping methods are used to assess uncertainty from data, then objective estimates can be obtained, but the method incorrectly assumes each property data collected is an independent measurement
Solution Approach 1:
The method segments the spatial domain by creating multiple subsamples from the actual data, then applies bootstrap resampling to each subsample independently. This segmentation allows the method to account for spatial correlation structure while still utilizing the power of bootstrap resampling for uncertainty estimation, effectively separating the correlation modeling from the uncertainty quantification steps.
Solution Approach 2:
The method changes the approach by using actual measured data values and their observed spatial correlations as the foundation for resampling, rather than assuming independence. By parameterizing the resampling process to reflect actual data structure, the method maintains objectivity while adapting to real-world correlated measurement conditions.
3Measurement precision
If conventional methods require multiple iterations to converge on accurate reserve estimates, then more accurate results can be achieved, but the process becomes expensive and time-consuming
Solution Approach 1:
The method performs preliminary action by pre-calculating and storing the actual measured data and its statistical properties before the main uncertainty estimation process. By preparing the data structure and correlation information in advance, the iterative resampling and model recalibration processes become significantly faster, reducing the time required for convergence without sacrificing accuracy.
Data Source
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AI summary
A method is provided that utilizes independent data spatial bootstrap to quantitatively derive P10, P50 and P90 reservoir property logs and zonal averages. The method utilizes at least a "baseline" dataset that is assumed to be correct, and determines the distribution of possible input parameter values that provide the most optimal solution to fit the log analysis to the core data. In one embodiment, independent data spatial bootstrap method can be applied to determine the uncertainty of porosity and saturation.