Data Maturity Assessment for Hydrocarbon Exploration
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Solution Overview
Problem
Hydrocarbon exploration and production activities face challenges due to varying levels of data quality, governance, availability, and security, which are not effectively addressed by existing methods, leading to high risks and costs.
Innovation Solution
A system and method for assessing data maturity by categorizing data types into key categories, developing unified data models, and using a scoring concept to measure quality, governance, availability, and security based on predefined key performance indicators, providing a comprehensive dashboard for monitoring and improving data maturity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data from multiple sources with different quality levels is used for hydrocarbon exploration decisions, then the quantity and diversity of data increases, but the reliability and consistency of decision-making deteriorates
Solution Approach 1:
The patent segments data into distinct categories (seismic data, well data, production data, geological data) with specific maturity assessments for each type. This segmentation allows tailored quality controls and maturity evaluations for each data category, ensuring that diverse data sources are systematically managed according to their specific characteristics and requirements.
Solution Approach 2:
The patent introduces a data maturity scoring system that transforms qualitative data quality assessments into quantitative parameters (maturity scores). This parameter change enables objective comparison and tracking of data quality across different sources and types, converting the heterogeneous nature of exploration data into a unified measurable framework that supports consistent decision-making.
2Reliability
If comprehensive data quality assessment frameworks are implemented across all data types, then the quality and reliability of data improves, but the complexity of data management processes increases
Solution Approach 1:
The patent implements a universal data maturity assessment framework that can be applied across all data types (seismic, well, production, geological) through a common scoring methodology. This universal approach consolidates multiple assessment processes into a single multi-functional system, reducing overall complexity while maintaining comprehensive quality evaluation across diverse data sources.
Solution Approach 2:
By transforming complex qualitative quality assessments into simplified quantitative maturity scores, the patent reduces the complexity of data management. The scoring system provides a straightforward metric that can be easily tracked, compared, and used for decision-making, replacing cumbersome detailed quality evaluation processes with an efficient parameter-based approach.
3Measurement precision
If separate assessment methods are used for different data quality dimensions (quality, governance, security), then the precision of individual assessments improves, but the overall efficiency and comprehensiveness of assessment deteriorates
Solution Approach 1:
The patent merges separate assessment dimensions (data quality, governance, security, availability) into a unified data maturity model that evaluates all aspects simultaneously. This combination maintains the precision of individual assessments by incorporating specific metrics for each dimension while improving overall efficiency through an integrated assessment process that produces a comprehensive maturity score.
Solution Approach 2:
The unified data maturity model serves multiple assessment functions simultaneously, evaluating quality, governance, security, and availability through a single framework. This multi-functional approach maintains measurement precision for each dimension while significantly improving productivity by eliminating the need for separate assessment processes.
Data Source
AI summary
A method for assessing the maturity of data includes cataloging types of the data; developing key performance indicators for each data type; developing quality, governance, availability, and security rules for each data type; updating a database storing the data; and assessing quality, governance, availability, and security of the data based on the rules for each data type.