Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
Dielectric logging can be used to quantify water saturation, water resistivity and textural properties of formation. Application of this log is especially useful in situations where the resistivity of water is unknown or it varies considerably at different depth. This logging is based on the contrast between the dispersive dielectric behavior of dry matrix, hydrocarbon and water as elements present in the rock. As such the real and imaginary parts of dielectric dispersion at various frequencies are used to infer the petro-physical properties of formation. The wide frequency of operation, large dynamic range of response and numerous parameters involved make the interpretation of data challenging especially in the presence of noisy environment. This paper takes a look at a method to define quality control criteria for dielectric logging. As any other measurements the quality of recorded data should be judged based on the acceptable range of variations for inferred parameters as well as related confidence levels. These would lead to an acceptable noise level in the recorded data. Having access to the aforementioned quality control criteria can be very useful for example when the tool response is measured in a predetermined environment and it is compared with expected values or when the response of symmetrically positioned transmitters are compared assuming that tool is facing a homogeneous medium. In this paper a petro-physically driven quality control method is presented for dielectric logging. It uses a probabilistic Bayesian inference method in obtaining the required accuracy level for dielectric tool response. The criterion is defined in a way that guarantees the inverted petro-physical parameters will be within a predefined range around the actual values with a required confidence level. The application of method is shown through few examples.
Dielectric logging can be used to quantify water saturation, water resistivity and textural properties of formation. Application of this log is especially useful in situations where the resistivity of water is unknown or it varies considerably at different depth. This logging is based on the contrast between the dispersive dielectric behavior of dry matrix, hydrocarbon and water as elements present in the rock. As such the real and imaginary parts of dielectric dispersion at various frequencies are used to infer the petro-physical properties of formation. The wide frequency of operation, large dynamic range of response and numerous parameters involved make the interpretation of data challenging especially in the presence of noisy environment. This paper takes a look at a method to define quality control criteria for dielectric logging. As any other measurements the quality of recorded data should be judged based on the acceptable range of variations for inferred parameters as well as related confidence levels. These would lead to an acceptable noise level in the recorded data. Having access to the aforementioned quality control criteria can be very useful for example when the tool response is measured in a predetermined environment and it is compared with expected values or when the response of symmetrically positioned transmitters are compared assuming that tool is facing a homogeneous medium. In this paper a petro-physically driven quality control method is presented for dielectric logging. It uses a probabilistic Bayesian inference method in obtaining the required accuracy level for dielectric tool response. The criterion is defined in a way that guarantees the inverted petro-physical parameters will be within a predefined range around the actual values with a required confidence level. The application of method is shown through few examples.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.