MODELING TEXTURAL PARAMETERS OF A FORMATION WITH DOWNHOLE MEASUREMENTS
First Claim
1. A method, comprising:
- obtaining a dielectric measurement of a downhole formation;
processing the dielectric measurement via a trained machine learning system;
determining water saturation and at least one classification or textural parameter of the downhole formation, via the machine learning system, based at least in part on the dielectric measurement; and
assigning water saturation and at least one of the classification or the textural parameter to the downhole formation.
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Abstract
Embodiments include a method that includes obtaining a dielectric measurement of a portion of a downhole formation. The method also includes processing the dielectric measurement via a trained machine learning system. The method further includes determining at least one of a classification or a textural parameter simultaneously with formation water saturation of the downhole formation, via the machine learning system, based at least in part on the dielectric measurements. The machine learning system is based on pre-determined dataset from previous measurements or simulated results of synthetic cases. The method detangled the correlation between water saturation and formation texture through a frequency cascading training process based on sensitivity of complex dielectric spectrum with respect to desired parameters including water saturation and texture parameter. The method also includes assigning at least one of the classification or the textural parameter to the downhole formation.
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Citations
20 Claims
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1. A method, comprising:
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obtaining a dielectric measurement of a downhole formation; processing the dielectric measurement via a trained machine learning system; determining water saturation and at least one classification or textural parameter of the downhole formation, via the machine learning system, based at least in part on the dielectric measurement; and assigning water saturation and at least one of the classification or the textural parameter to the downhole formation. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A computing device, comprising:
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a microprocessor; and memory including instructions that, when executed by the microprocessor, cause the computing device to; generate a synthetic rock geometry, the synthetic rock geometry including at least one estimated parameter of a downhole formation; compute a textural parameter of the synthetic rock geometry; simulate a dielectric spectrum response of the synthetic rock geometry, based at least in part on the at least one estimated parameter; and determine a correspondence between at least one of the textural parameter or the dielectric response and the synthetic rock geometry. - View Dependent Claims (9, 10, 11, 12, 13, 14, 15)
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16. A system for conducting measurement operations, the system comprising:
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a dielectric measurement device forming at least a portion of a downhole tool string, the dielectric measurement device operable to generate measurement data for detecting a dielectric property of a formation; a microprocessor; and memory including instructions that, when executed by the microprocessor, cause the system to; receive the measurement data; process the measurement data, via a trained machine learning system; determine a classification of the formation, via the trained machine learning system, the classification being related to a likelihood of recoverability based on one or more textural properties of the formation; and assign the classification to the downhole formation. - View Dependent Claims (17, 18, 19, 20)
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Specification