Geostatistical Secondary Data Generation via Clustering
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Solution Overview
Problem
Current geostatistical methods require separate experiments to generate secondary data, leading to increased time and costs, and inverse modeling techniques fail to preserve spatial correlation and primary data effectively.
Innovation Solution
A method that uses observed data to generate secondary data through distance-based clustering, selecting representative models, and iteratively refining models using geostatistics with spatial correlation and primary data to create more reliable models for uncertainty quantification and future performance prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate experiments or investigation procedures are conducted to acquire secondary data, then the reliability of geostatistical models is improved, but additional time and costs are incurred
Solution Approach 1:
The patent creates synthetic secondary data by copying and transforming existing primary data through geostatistical simulation techniques. Multiple realizations are generated that replicate the statistical properties and spatial correlations of the original data, providing reliable secondary data without conducting separate physical experiments or investigations
Solution Approach 2:
The system uses the existing primary data and observed data to generate its own secondary data through iterative geostatistical modeling. The model learns from the input data and automatically produces the required secondary data, eliminating the need for external experiments or additional data collection procedures
2Measurement precision
If inverse modeling techniques are used to improve models with observed data, then model accuracy is improved, but time and costs are greatly increased
Solution Approach 1:
The patent performs preliminary geostatistical simulation to generate multiple realizations before incorporating observed data. This preliminary modeling establishes the spatial correlation structure and statistical properties in advance, so that when observed data is integrated, the adjustment process is faster and more efficient than traditional inverse modeling
3Reliability
If inverse modeling techniques are used to incorporate observed data, then model reliability is improved, but spatial correlation data and primary data are not preserved
Solution Approach 1:
The patent merges observed data with primary data and spatial correlation information through a unified geostatistical framework. The secondary data generation process combines all three data sources, preserving the spatial correlation structure while incorporating observations, rather than replacing or losing any of the original data
Solution Approach 2:
The system uses an iterative feedback process where observed data is compared with model predictions, and the geostatistical parameters are adjusted accordingly. This feedback loop continuously refines the model while maintaining the spatial correlation structure and primary data integrity throughout the optimization process
Data Source
AI summary
A method of generating secondary data in geostatistics using observed data that: receiving prepared spatial correlation data, primary data, and observed data; generating initial models by performing a geostatistical technique using the spatial correlation data and the primary data; extracting a best representative model using the observed data from the initial models; and creating final models by converging candidate models. The initial models are created using geostatistics from the spatial correlation data and primary data, the representative models are determined using a distance-based clustering method, the best representative model is selected using the observed data, the candidate models near the best representative model are selected as final models depending on a convergence determination criterion, and uncertainty quantification and prediction of future performances may be conducted using the final models.