Fluid Coding and Hydraulic Zone Determination via Sliding Regression
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Solution Overview
Problem
Current methods for determining the characteristics and locations of underground fluids are time-consuming and expensive, often resulting in noisy or uncertain data points, which complicates the interpretation of formation pressure tests and hydraulic zone analysis.
Innovation Solution
A system and method that conduct a series of formation pressure tests, process the results to quantify uncertainty, and use sliding regressions to filter out bad measurements, combine relevant data to form fluid codes, and determine hydraulic zones by calculating fluid boundaries, thereby providing a comprehensive display for reservoir development and monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple formation pressure tests are conducted to improve fluid characterization accuracy, then measurement precision improves, but the time and cost of interpretation increases
Solution Approach 1:
The system performs self-interpretation by automatically analyzing formation pressure test data through sliding regression analysis and uncertainty quantification. The processor independently determines fluid codes, hydraulic zones, and fluid boundaries without requiring manual operator interpretation, thus maintaining high measurement precision while eliminating time-consuming manual analysis
Solution Approach 2:
The patent replaces the manual mechanical interpretation process with an automated computational system. The processor uses algorithms to perform sliding regressions, calculate uncertainties, and determine fluid boundaries automatically, substituting human operator analysis with machine-based data processing that maintains accuracy while significantly reducing interpretation time
2Measurement precision
If more formation tests are taken to reduce uncertainty, then measurement precision improves, but device complexity and cost increase
Solution Approach 1:
The system applies sliding regression analysis with weighted uncertainty factors to selectively process data points. Rather than requiring all possible formation tests, the method uses weighted regressions that give appropriate emphasis to reliable measurements while filtering out noisy data, achieving sufficient precision without the need for excessive testing or overly complex processing systems
Solution Approach 2:
The patent transforms the raw pressure and depth measurements into fluid codes through systematic parameter changes. By converting continuous pressure-depth data into discrete fluid code classifications based on regression slopes and uncertainty thresholds, the system simplifies the processing requirements while maintaining data reliability
3Measurement precision
If manual interpretation is used to analyze formation pressure data, then measurement precision can be maintained, but productivity decreases
Solution Approach 1:
The automated system performs self-interpretation of formation pressure data by independently executing sliding regression analyses, calculating uncertainties, determining fluid codes, and identifying hydraulic zones. This self-service capability maintains interpretation accuracy while dramatically increasing productivity by eliminating the need for manual operator analysis
Solution Approach 2:
The system enables continuous automated interpretation of formation pressure data as it becomes available. Rather than requiring periodic manual analysis, the processor continuously applies sliding regressions and uncertainty quantification to new measurements, maintaining accurate fluid characterization while enabling ongoing reservoir monitoring and management without productivity interruptions
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach automates the fluid coding and hydraulic zone determination process, reducing uncertainty and improving the accuracy of fluid characterization, leading to more efficient and cost-effective reservoir management.
Implementation Method 1
the slope of a regression between identified fluid codes may be illustrative of the density of the fluid of the fluid code. To determine the regression(s), the processing system may quantify the uncertainty associated with the formation pressure tests
Data Source
AI summary
Systems, methods, and media for processing formation pressure test data are provided. The method includes determining using a processor, a plurality of regressions for measurements of the formation pressure test data, and determining that two or more of the plurality of regressions represent a fluid code. The method also includes combining the two or more of the plurality of regressions representing the fluid code to generate a first fluid-type regression, and combining two or more other ones of the plurality of regressions representing a second fluid code to generate a second fluid-type regression. The method further includes determining that the first fluid-type regression and the second fluid-type regression are in a first hydraulic zone, and calculating a location of a boundary between the first fluid-type regression and the second fluid-type regression by extrapolating the first and second fluid-type regressions to a point of intersection therebetween.