Phase Velocity Processing For Acoustic Logging-While-Drilling Full Waveform Data
This paper presents the principle and demonstrates the advantages of using a Phase Velocity Processing technique (PVP) applied to the full wave acoustic data recorded by a logging-while-drilling tool. Phase Velocity Processing is particularly well suited for handling the characteristics of LWD data that may degrade the results of Semblance method. Among them are: large tool-mode content, high signal attenuation across the receiver array, and strong road noise. Another advantage of applying PVP is its high vertical resolution: due to the common receiver mode of operation vertical resolution of the calculated slowness is equal to the offset between adjacent receivers. By comparison, the Semblance method delivers results averaged across the entire receiver array span. The PVP algorithm employs series of Fast Fourier Transform procedures to (a) filter the data using Heisenberg’s method, (b) define time domain distribution of the phase arrivals using a Hilbert transform, and finally (c) perform the Phase Velocity analysis. Large number of phase arrival points provides efficient mechanism to calculate standard deviation of the results, quality indicators are added to show how well the algorithm was able to calculate both compressional and shear slownesses. Processing package includes filters operating in the frequency, time, slowness or space domains to minimize competing acoustic modes, especially the tool arrivals that are frequently present in LWD waveform data. Examples demonstrate improvement of the slowness calculation performed with the PVP over the results delivered by the Semblance method. The results obtained in attenuating formations, across the bed boundaries, and in thin beds are presented. The ability of the new filtering technique to identify and suppress the tool mode arrivals is also demonstrated. INTRODUCTION The acoustic logging-while-drilling tools that were introduced six years ago (Minear et. al., 1995, Heysse et. al., 1996, Minear et. al., 1996) are by necessity full waveform recording devices. The drilling noise and the requirement for rigidity in an LWD environment prevented the use of a simple threshold technique as employed in the older wireline acoustic tools. The semblance method was the most commonly utilized processing technique at the time. It was implemented in the form of embedded firmware for downhole processing and as the surface system application for memory waveform data analysis. Semblance processing has a number of drawbacks, such as its requirement for constant amplitude and velocity across the receiver array, the smoothing of the waveforms across the receiver span, the ability to mask defective data collected by an under performing tool, and not in the least its numerical complexity and therefore long execution time. We introduce here the Phase Velocity Processing algorithm implemented to process full waveform data recorded with a loggingwhile-drilling acoustic tool. ACOUSTIC LWD WAVEFORM AND LWD TOOL ARCHITECTURE The very nature of acoustic measurements performed under drilling conditions has necessitated the recording of full waveform data. This has complicated not only the construction of the acoustic LWD tools but also the downhole processing, as realtime sonic slowness calculations are required. A constant presence in LWD waveforms is tool mode, defined as the signal traveling from the transmitters to the receivers, either directly in the tool body or deflected by the borehole wall. The isolator sections in acoustic LWD tools are not perfect due to the fact that the LWD devices, contrary to wireline units, need to be absolutely rigid: the collar strength cannot be compromised by the introduction of the electronics and isolators. Tool mode signal is
Hilbert-semblance method for acoustic logging data processing
Abstract We suggest a method of multi-element low-frequency acoustic logging data processing which combines advantages of the semblance and phase velocity processing (PVP) techniques, namely, the interference resistance in the former and better accuracy in the latter. The new method is based on point (windowless) semblance by the Hilbert transformation. The introduced concept of phase semblance makes it possible to estimate the signal distribution semblance and the phase. Also suggested are an algorithm for preliminary estimation of formation S-wave slowness from signal group power variations and improved forms of processed data imaging.
PRECISION FARMING APPROACHES TO SMALL-FARM AGRICULTRUE
Precision farming implies a management strategy to increase productivity and economic returns with a reduced impact on the environment. It is based on the application of information technology to a description of variability in the field, variable-rate operations and the decisionmaking system. There are three technology levels and three strategies in the development of precision farming. Precision farming practices can be used on small farms as well as big ones, and they play a core role in rural development programs which are integrated with industry. A real-time soil spectrophotometer was developed to describe soil variability in farmers' fields, to be used in precision farming.