Parametric Indices for Geological Data Analogy Identification
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Solution Overview
Problem
Current methods for identifying data similarity in geological characterization of reservoirs rely on arbitrary decisions and limited parameterization, making them inefficient and not fully utilizing available data, which hinders productivity and economic benefits.
Innovation Solution
A method that calculates parametric indices such as Knowledge Well Index (KWI), Knowledge Quality Index (KQI), and Knowledge Analogy Index (KAI) to evaluate data samples and determine data analogy, allowing for user-defined criteria and improved comparison of data populations across different fields.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional arbitrary decisions by geologists or limited parameterization analyzes are used to identify data similarity, then the method is simple to implement, but the productivity in identifying analogous occurrences is low and data utilization is insufficient
Solution Approach 1:
The patent transforms the identification process from arbitrary qualitative decisions to quantitative parameter-based analysis. It introduces multiple parametric indices (KWI, KQI, KAI) that systematically evaluate data similarity through calculated metrics rather than subjective judgment, thereby increasing productivity while maintaining manageable complexity through standardized parameter sets
Solution Approach 2:
The patent introduces parametric indices as intermediary elements between raw geological data and similarity identification conclusions. These indices (KWI for well information completeness, KQI for data quality, KAI for analogy degree) serve as mediators that systematically process and evaluate data characteristics, enabling more efficient and comprehensive data utilization without requiring complex direct comparison methods
2Measurement precision
If comprehensive data analysis with multiple parametric indices is performed, then the data utilization and identification accuracy are improved, but the complexity of the analysis method increases
Solution Approach 1:
The patent segments the comprehensive data analysis into distinct modular components, each represented by a specific parametric index. The Knowledge Well Index (KWI) evaluates well information completeness, the Knowledge Quality Index (KQI) assesses data quality, and the Knowledge Analogy Index (KAI) measures analogy degree. This segmentation allows each aspect to be analyzed independently with dedicated metrics, improving measurement precision while keeping individual analysis modules manageable in complexity
Solution Approach 2:
The patent creates a universal multi-functional analysis framework where the same parametric index structure (KWI, KQI, KAI) can be applied across different geological data sets and exploration scenarios. This universal approach enables consistent precision measurement across diverse applications without requiring separate complex analysis methods for each case, thereby improving overall identification accuracy while maintaining methodological simplicity
Data Source
AI summary
The present invention proposes a method for identifying data similarity, specifically applied to the execution of exploratory projects and geological characterization of reservoirs.The invention evaluates different samples of a data population according to groupers defined by users and analyzes the data according to the population analyses, returning parametric indices that allow the comparison of groupers with definition of data analogy.The invention provides greater productivity in identifying analogous occurrences by data and brings economic benefits by ensuring greater use of available data.