Abstract
Cyclic noise can complicate interpretation of open-hole log data, cased-hole log data, and pressure transient data, especially when its amplitude becomes large relative to the primary measurement of interest. Sources of cyclic noise include the following: winch magnetism that affects the spontaneous potential measurement; cyclic (spiral or threaded) bore holes that cause variable standoff on bulk density, neutron porosity, and pad-type resistivity measurements; drill ship and floating platform vertical movement from wave action that can affect the absolute velocity of flow meter tools, reciprocating tool movement from logging-while-tractoring that affects the absolute velocity of flow meter tools; and tidal effects on long time pressure transient tests.
Depth (time) domain filtering of log data affected by cyclic noise is not suitable in most cases because the filter length required to treat the problem causes severe reduction in the vertical resolution of the filtered log measurement. Frequency domain filtering using fast Fourier transforms has been used in the past to eliminate random noise using low pass filters and to determine the vertical resolution of logging measurements. Frequency domain filtering using fast Fourier transforms is readily amenable to treating cycle noise using notch filter(s) centered around the frequency (or frequencies) of the cycle noise. This approach has been used in the electronics industry for decades. Although these techniques are simple to implement, they seldom are used for logging data because stable filter application usually requires interactive filter design.
This paper will review the methodology of applying fast Fourier transforms, designing frequency-domain filters, and applying them. Examples applying this approach to several of the types of cyclic noise listed above will be presented. Issues associated with non-zero mean cyclic noise such as is the case for log measurements made in spiral bore holes and its impact will be addressed.
