Electrical noise model for detection circuitry of an NMR-based formation evaluation Tool
Author(s)Maison, Julie Laure K
Electrical noise model for detection circuitry of an Nuclear Magnetic Resonance-based formation evaluation Tool
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Elfar Adalsteinsson and Brian Boling.
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The RF signals received from Nuclear Magnetic Resonance (NMR) measurements in logging while drilling NMR instruments are often of the same amplitude as the noise generated by the instruments. Designers of these devices are thus usually faced with the challenging task of improving the sensitivity of the measurement process either by reducing the noise generated by the system or by boosting the signal relative to the electrical noise. For NMR equipment used in earth formation evaluation, this is rendered more difficult by the measurement geometry and noise of the samples under consideration. Schlumberger's proVISION logging-while-drilling tool is one such NMR device. It makes use of the technique of NMR to evaluate the porosity of the earth's rock formations. Although the tool boosts the signal-to-noise ratio (SNR) to a level sufficient for productivity analysis, SNR improvement is a continuing goal to improve signal quality and provide better results to help optimize the drilling process. The objective of this thesis is to model the electrical noise in the detection path of the NMR signal of the proVISION tool. Intrinsic and extrinsic noise sources contributing to the overall electrical noise in the acquisition path prior to digital processing of the detected signal are accounted for by this model. The results of this analysis provide the necessary data for further SNR improvements in the system.
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 41).
DepartmentMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.