Wellbore Data Anonymization for Confidential Sharing
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Solution Overview
Problem
Downhole exploration and production efforts face challenges in anonymizing data collected from wellbore operations to protect confidential and proprietary information, which hinders the sharing and analysis of this data without compromising sensitive information.
Innovation Solution
The techniques involve anonymizing data through shuffling, normalization, and non-dimensionalization to remove identifiable information, using encryption methods like parallelized block chaining or stream ciphers, and aggregate signature extraction schemes, allowing for secure data sharing and analysis while maintaining confidentiality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is shared and analyzed across wellbores, then productivity and decision-making quality improve, but confidential and proprietary information may be compromised
Solution Approach 1:
The patent extracts and removes identifiable information from downhole data through anonymization techniques. Specifically, it removes wellbore identifiers, location data, and proprietary parameters while retaining the technical characteristics needed for analysis. This allows data to be shared for productivity improvement without exposing confidential information.
Solution Approach 2:
The patent introduces anonymized data as an intermediary between the original data source and the analysis system. This intermediary form preserves the analytical value of the data while eliminating direct exposure of proprietary information. The anonymization process acts as a mediator that enables data sharing while protecting confidentiality.
2Loss of information
If data is anonymized through shuffling, normalization, and non-dimensionalization, then confidentiality is protected, but data complexity and processing requirements increase
Solution Approach 1:
The patent applies anonymization techniques (shuffling, normalization, non-dimensionalization) as preliminary actions before data sharing. By pre-processing the data to remove identifiable information, the system reduces the complexity of protecting confidential information during subsequent analysis and sharing operations.
Solution Approach 2:
The patent transforms data parameters through normalization and non-dimensionalization, changing the scale and units of measurement while preserving the underlying relationships. This parameter transformation enables confidentiality protection without requiring complex encryption or obfuscation methods.
3Measurement precision
If anonymized data models are aggregated across multiple wellbores, then analysis quality and decision-making improve, but the risk of re-identification increases
Solution Approach 1:
The patent merges anonymized data from multiple wellbores into aggregated models. By combining data from multiple sources, the system improves analysis accuracy through increased statistical power and broader coverage, while the aggregation itself provides an additional layer of protection against re-identification of individual wellbores.
Solution Approach 2:
The patent applies excessive anonymization measures by implementing multiple anonymization techniques (shuffling, normalization, non-dimensionalization) simultaneously. This excessive approach ensures that even when data is aggregated from multiple wellbores, the probability of re-identification remains negligibly low, while still maintaining sufficient data quality for accurate analysis.
Data Source
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AI summary
Examples of techniques for anonymizing data are disclosed. In one example implementation according to aspects of the present disclosure, a computer-implemented method includes receiving, by a processing device, raw data from a wellbore operation. The raw data can be associated with depths. The method further includes anonymizing, by the processing device, the raw data to convert the raw data to anonymized data. One or more techniques can be implemented to anonymize the data, such as shuffling the raw data, normalizing the raw data, and/or non-dimensionalizing the raw data. The method further includes analyzing, by the processing device, the anonymized data. The method further includes performing an action at the wellbore operation based at least in part on the analysis of the anonymized data.