Fluid Optical Database Reconstruction via Neural Network Inversion
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Solution Overview
Problem
Existing fluid optical databases are limited by the number of fluid samples, particularly live oils, and lack diversity in geological distribution and compositions, making them incomplete and inconsistent, which hinders accurate reservoir fluid evaluation and exploration decisions.
Innovation Solution
A method using forward and inverse neural networks to integrate external fluid compositional data, validating and reconstructing an extended fluid optical database through optical sensor signal standardization and fluid spectra deconvolution, enabling improved cost-effectiveness and robustness in data reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If external databases with large amounts of diverse fluids are used to expand the database, then the quantity and diversity of fluid samples increase, but the data completeness and consistency deteriorate due to missing optical data and inconsistent measurement methodologies
Solution Approach 1:
The patent introduces an intermediary data processing system that acts as a mediator between external databases and the internal fluid optical database. This system applies data validation rules, consistency checks, and integration protocols to filter and standardize incoming data from external sources, ensuring that only high-quality, consistent data are incorporated while maintaining data reliability.
Solution Approach 2:
The patent implements feedback mechanisms through iterative validation and quality assessment processes. The system continuously monitors data quality metrics, identifies inconsistencies in external database entries, and applies corrective processing steps. This feedback loop ensures that data consistency is maintained while expanding the database with diverse fluid samples.
2Reliability
If laboratory experiments are conducted to expand the database with high-quality fluid samples, then the data quality and consistency improve, but the time and cost increase significantly
Solution Approach 1:
The patent merges multiple data sources including external databases, internal laboratory data, and public repositories into a unified fluid optical database. By combining these sources through automated integration processes, the system achieves comprehensive database expansion without requiring time-consuming laboratory experiments for every fluid sample, thus reducing time loss while maintaining data quality.
Solution Approach 2:
The patent creates validated copies of fluid optical data from external sources through automated data replication and validation processes. Instead of physically obtaining and testing every fluid sample in the laboratory, the system replicates and validates digital representations of fluid optical properties, significantly reducing the time required for database expansion while maintaining data reliability.
3Adaptability or versatility
If measurements from various laboratories are integrated into the database, then the diversity of fluid compositions increases, but the complexity of data processing increases due to inconsistent methodologies
Solution Approach 1:
The patent applies parameter transformation and standardization techniques to convert data from various measurement methodologies into a unified parameter set. The system identifies key optical parameters across different measurement techniques and transforms them into consistent standardized parameters, enabling integration of diverse fluid composition data while managing processing complexity through parameter harmonization.
Data Source
AI summary
A method includes receiving first material property data for a first material in one or more second materials, detecting material sensor data from at least one sensor, and applying an inverse model and a forward model to the first material property data to provide, at least in part, synthetic sensor measurement data for the one or more second materials.