COMPRESSION OF OIL WELL LOG SIGNALS. Tom Ryen, Sven Ole Aase and John Håkon Husøy

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1 COMPRESSION OF OIL WELL LOG SIGNALS Tom Ryen, Sven Ole Aase and John Håkon Husøy Høgskolen i Stavanger Department of Electrical and Computer Engineering P. O. Box 557 Ullandhaug, N44 Stavanger, Norway Phone: , Fax: Tom.Ryen@tn.his.no ABSTRACT A data compression technique is presented for the compression of oil well log signals. An introduction to the Ultrasonic Doppler Velocimeter (UDV) is given. The compression system is based on subband coding (SBC), traditionally used for compressing speech and images. An SBC originally developed for other kinds of signals is employed and configured with respect to the signals from the UDV tool. Results from the compression of UDV signals are given. We conclude that the SBC in the present scheme can provide compression ratios of 3 without any important degradation of the velocity vector calculated from the UDV signal. A comparison between the configured SBC and standard coding techniques, such as CELP and MP3, shows that the SBC achieve better compression results.. INTRODUCTION In the oil well industry there is a need for transmitting large quantities of data via a narrow bandwidth channel between the production well and the surface. This article presents a study of this kind of data, more exactly signals from an Ultrasonic Doppler Velocimeter [] (UDV) tool developed by Maritime Well Service AS (MWS). Today the transmission from this tool uses analogue technology. The desire of including simultaneous logging of other tools require a conversion to digital transmission. Unfortunately, the required bandwidth is very high. Thus, the desire for signal compression is evident... The UDV tool Traditionally the flow in an oil well is measured with a spinner. Problems arise when measuring in horizontal wells. The spinner may indicate zero flow when the true flow patterns are composed of two flow components, each in opposite directions. Various velocity profiles in different heights in the tube may also appear and are caused by the differ horizontal oil well UDV tool HF??????? Figure : A schematic view of the UDV tool. ent flow media: Oil, gas or water. The UDV tool, see Figure, solves this problem. It has transponders producing high frequency (HF) and low frequency (LF) burst signals, respectively. The signals are emitted diagonally over the entire cross section of the tube. Moving particles in the flow reflect the signals, that are frequency shifted due to the Doppler effect []. Ultrasonic receivers pick up the reflected signals. From each channel the reflected signal is mixed with the emitted signal. The output signal is lowpass filtered and sampled with a sampling frequency of 4 khz. The transmission protocol Wellbus TM [3]isusedtotransmit the data from the tool to the surface. At the surface the samples are stored as Wave files. In a single file, the samples represent the frequency shift value from one of the receiver channels... Motivation for compressing UDV data The Wellbus TM protocol has an adjustable baud rate from.5 to kbit/s. The baud rate value for a specific measurement task depends on the wire length and its transmission quality. When measuring with several tools simultaneously, 8/3 of the total baud rate is dedicated to the UDV tool. The tool s sampling frequency is 4 khz and each sample is represented with bit. This necessitates compression ratios for the UDV data as presented in Table. From the ratio values in the table, we can infer that lossless coding [4] is unattainable. Thus, we seek an algorithm that achieve a high compression ratio at small sacrifice of reconstruction quality. LF

2 Baud rate Compression ratio.5 kbit/s 7 5 kbit/s 34 5 kbit/s 7 Table : Necessary compression ratio at some actual baud rates. input data code control bits quantizer analysis uniform run length Huffman filter bank encoder encoder... code control bits Huffman run length inverse syntesis quantizer decoder decoder filter bank output data 3 x 5 Figure 3: A schematic view of the Subband coder..5 power density normalized frequency Figure : The power spectral density for the samples in h.wav. normalized autocorrelation autocorrelation lags normalized autocorrelation autocorrelation lags The power spectral density of a typical channel signal, Figure, shows that most of the energy in the signal is concentrated in the lower frequency range. This indicate that use of a Subband Coder [4, 5] (SBC) would be an effective device for compressing such signals with a minimum of loss. Figure 4: The autocorrelation of subband no. and subband no. with the input signal h. The total number of subbands in the filter bank is Outline of the paper In section we consider the architecture of the SBC, and the configuration of an SBC with respect to the UDV signals. In section 3 the results from the subband coding of several UDV signals are given. Here, we present the generation of the spectrogram and the velocity curve. In this section we show how the accuracy of the velocity depends on the compression. In section 4 we present the results of using standard coding techniques to compress the UDV data. Finally, Section 5 summarizes and concludes the paper.. THE SUBBAND CODER In Figure 3 we see the building blocks of the SBC used in this work. The uniform filter bank consist of twoband Quadrature Mirror Filter (QMF) banks [5] in a tree structure. A uniform quantizer is used. The coder part consist of a runlength coder followed by an entropy coder. An SBC program originally developed for compressing ECG signals is employed []. This program contains option parameters used to find the best SBC configuration to ensure optimal bitrate/snr trade off. As a quality measurement for the reconstructed signal, the signaltonoise ratio (SNR) is used. It is defined by x SNR = log e [db]; () where x is the variance for the input signal and e is the variance of the difference between the input and the output signal. Extensive experiments, details can be found in [7], show that the following parameter selection for the SBC is optimal: The filter bank consists of 3 subbands. The IIR filter f [8] is used in the twoband QMF banks. Use of this filter gives much the same compression results as use of a Johnston FIRfilter [9], but the IIR filter have a much lower computational complexity. If there exist correlation between the samples in a subband, a higher coding gain could be obtained by further decorrelation of the signal. In Figure 4 an estimate of the autocorrelation for the two lower subbands of a representative signal is shown. The correlation between neighbor samples is low. This indicates that further decorrelation is not necessary. Thus, a uniform scalar quantizer [4] is used. A Huffman coder [, ] is optimal to achieve entropy coding of the samples from the runlength coder.

3 original wavefile SNR? original spectrogram visual inspection? original velocity curve visual inspection SNR v? reconstructed reconstructed reconstructed wavefile spectrogram velocity curve Compression ratio SNR v x = %.9 db x = % 8.8 db x =: %. db Table : Compression results for the example signal SerieAl4 at three different quantization step sizes. Figure 5: Methods for quality measurements between the original and the reconstructed values. 3. COMPRESSION RESULTS FROM SUBBAND CODING From the frequency shifted values stored in the wavefile, we can calculate a spectrogram. It tells how the energy is distributed in a timefrequency domain. From the energy distribution in the spectrogram, an algorithm produce the velocity curve, which tells us the velocity of the flow at a specific height in the oil tube. A typical greyscale spectrogram with the computed velocity curve, is shown in Figure 7. The velocity [m/s] is directly proportional to the frequency [Hz]. There are several methods to make a comparison between the original and the reconstructed values from subband coding. In Figure 5 the methods used in this work is presented. The signaltonoise ratio for the velocity curve, SNR v is given by v SNR v = log [db]; () e where v is the variance to the original velocity curve and is the variance to the difference between the original and e the reconstructed velocity curves. The mean error percentage error for the velocity curve,, isgivenby N, X e i = %; (3) i= v i where e i is the absolute difference between the sample in the original velocity curve, v i, and the corresponding reconstructed sample. N is the total amount of samples in the velocity curve. 3.. The wavefile To see the effect of the compression on the wavefile samples, the compression ratio versus SNR plot would be an informative view. In Figure, samples from 4 exam ple wavefiles are compressed and reconstructed. The compression ratio is regulated by adjusting the quantization step size () for the uniform quantizer in the SBC. For the three subfigures, is x =5, x = and (c) x =:3, respectively, where x is the standard deviation of the current input signal. We can see that the SNR falls when increasing, but the compression ratio increases. We also discover that the variation in the compression result for the signals used increases with. 3.. The spectrogram The accuracy of the reconstructed spectrogram and specially the reconstructed velocity curve are of vital importance. In Figure 7 we can see the influence to the spectrogram when compressing at a ratio of The energy in the spectrum gets more concentrated around the high energy frequencies. In spite of that, the changes to the velocity curve are small. This indicates that just an insignificant amount of important data is lost The velocity curve To watch the influence of the compression on the velocity curve, we can take a look at Figure 8. Here, the original velocity curve is shown in solid and the reconstructed curve in dashed for all the three subfigures. The example signal SerieAl4 is representative for the most of the UDV signals. The two first reconstructions have an acceptable accuracy from a visual point of view. The last one represents a marginal case. Table presents the compression ratio, and SNR v values from the three different curves shown in Figure 8. From the figure and the table, we can conclude that compression ratios up to 3 4 would result in a mean percentage error below %. This is an acceptable tolerance in our application. 4. A COMPARISON WITH STANDARD CODING TECHNIQUES Since the UDV signals have a bandwidth inside the audio spectrum, we will try to use a standard speech coder and a

4 4 SerieA SerieB SerieC SNR [db] compression ratio H velocity [m/s] 3 frequency [Hz].5 4 SerieA SerieB SerieC 5 5 time [s] c = m/s nfft = 5 SNR [db] kvant\ compression ratio 5 4 H velocity [m/s] 3 frequency [Hz] 4 SerieA SerieB SerieC.5 SNR [db] time [s] c = m/s nfft = compression ratio (c) Figure 7: The spectrogram and the velocity curve for h. is the original and is the reconstructed when = x =. Figure : The compression ratio versus SNR for channels from measurement A, B, C and D. The quantizer steps () are x =5, x = and (c) x =:3.

5 Figure 9: A comparison between MP3 coding and subband coding at the results of the velocity curve standard audio coder to achieve compression to our UDV data (c) Figure 8: Velocity curve for SerieAl4. is x =5, x = and (c) x =:3, respectively. The solid curve is the original velocity, the dashed curve is the reconstructed one. The standard speech coder CELP, Code Excited Linear Prediction [4], is based on an adaptive vector quantisation technique. With a demo program produced by Analogical Systems [], a mean percentage error at % was obtained in the velocity curve for the example signal h. This is nearly the same as for the SBC. But, the difference in compression ratio is considerable. The SBC had a ratio of 49.5 when the CELP reached 9.3. This indicates that CELP coding is not a good compression technique for the UDV data. ISOMPEG Audio Layer3, MP3, is a standard for audio compression. The algorithm is built up by a subband coder combined with a psychoacoustic model to exploit the human ear s characteristics [3] in a compression point of view. The MP3 coder used in this experiment was taken from [4]. We will take a look at the results in the velocity curve from the compression with MP3. A comparison between MP3 and the SBC in Figure 9 shows that the reconstruction quality from the SBC is much better then from the MP3 coding. This is in spite of the fact that the compression ratio for the SBC was 3. against the MP3 coder s.

6 5. SUMMARY AND CONCLUSIONS In this paper a new compression application has been presented. The subband coder has shown to be a good algorithm for compressing signals from the UDV tool. A compression ratio of 3 is obtainable at a mean percentage error in the range of 5 to % on the velocity curve for most of the UDV signals. This is within an acceptable tolerance for this kind of application. From the motivation in section. we can see that baud rates below 5 kbit/s would require a compression ratio higher than 34. At these compression ratios the reconstruction quality will depend on the signal characteristic. There is no guarantee to get an acceptable quality in the velocity curve at these compression rates. However, we can conclude that transmitting UDV data at baud rates of 5 kbit/s or higher would be obtainable with an acceptable error tolerance for the most of the UDV signals. We have also shown that a straight forward use of wellknown and popular compression algorithms, in this case CELP and MP3, not necessarily fit well to all kind of signals. [9] J. D. Johnston, A filter family designed for use in quadrature mirror filter banks, in Proc. Int. Conf. Acoust. Speech, Signal Proc., (Denver, CO), pp. 9 94, IEEE, 98. [] J. Anderson and S. Mohan, Source and Channel Coding: An Algorithmic Approach. Boston: Kluwer Academic Publishers, 99. [] W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical recipes in C: The art of scientific computing. New York, NY: Cambridge University Press, nd ed., 99. [] Analogical Systems, ASCELP Demonstration Program, ( March 999. [3] S. J. Solari, Digital video and audio compression.new York: McGrawHill Companies, 997. [4] Tord Jansson, Blade s MP3 Encoder Manual, ( December REFERENCES [] Maritime Well Service AS, Ultrasonic Doppler Velocimeter Brochure, ( November 999. [] J. Johnsrud, Grunnbok i teleteknikk. Oslo: Universitetsforlaget, 98 (in Norwegian). [3] Maritime Well Service AS, Advanced Production Logging System Brochure, ( November 999. [4] A. Gersho and R. M. Gray, Vector Quantization and Signal Compression. Boston: Kluwer Academic Publishers, 99. [5] J. G. Proakis and D. M. Manolakis, Digital Signal Processing: Principles, Algorithms and Applications. New Jersey: Prentice Hall Inc., 3rd ed., 99. [] T. Gjerde, Komprimering av EKG og EEG signaler ved hjelp av delbåndskoding, Master s thesis, Rogaland University Center, 993 (in Norwegian). [7] T. Ryen, Komprimering av loggedata fra oljebrønn, Master s thesis, Høgskolen i Stavanger, 999 (in Norwegian). [8] J. H. Husøy, Subband Coding of Still Images and Video. PhD thesis, Norwegian Institute of Technology, Jan. 99.