Logging Signals Research Articles

Design and application of a Multi-function logging signal format adapter

Well logging, which can provide the dynamic geophysical parameters in the well that can offer scientific information, is being paid more attention by the oil industry. And the technology of combined logging can improve the efficiency and reduce the cost of well logging effectively, which had been confirmed in the past few years.In order to match with the different series of logging instruments which developed by different manufacturers, many logging instruments need to be upgraded by means of converting the interface signal format. A typical structure of combined logging system can be described as graph1: Combined Well logging system is composed of surface instrument part and down-hole apparatus part, and the communication between two parts relies on TCC-A(The down-hole telemetry unit) and TCM-A(The ground telemetry unit) through logging cable. Manchester encoding is widely used in the transition of logging cable due to its ease of time-sharing transmission and strong anti-interference capability .TCC-A communicate with the apparatuses (A,B,C) by means of down-hole bus such as DTB and CAN. In order to matching the different series of logging instruments which developed by different manufactures , we have to develop different kinds of signal format adapters used in as fellow four demands : ( 1) Matching a apparatus of Manchester input / output interface with a TCC-A of DTB bus; ( 2) Matching a apparatus of Manchester input / output interface with a TCC-A of CAN bus; ( 3) Matching a apparatus of DTB bus with a TCC-A of CAN bus; ( 4) Matching a apparatus of CAN bus with a TCC-A of DTB bus; In this paper, a multi-functional adapter is presented and realized, which has the functions of converting signal format between PCM and DTB, converting signal format between PCM and CAN, Design and application of a Multi-function logging signal format adapter Mingzhang Luo, Hongli Wang, Xiaodong Xiong, Bao Chen,Xiangping Wu

The Development of a Logging Signal Converter between PCM and DTB

A logging signal converter between PCM and DTB introduced in this paper , through which the PCM data transmission format of the well logging instrument can be matched with the EILOG05 logging system. The development of converter based on the scheme of FPGA for DTB bus timing control and SCM for software coding and decoding, which has been proved feasible by the experiments of Russian HIL matching with EILOG05 logging system.

Numerical modeling of the signal transmission by cables and electromagnetic coupling for logging while drilling

Lack of efficiency in transmitting logging signals has long been one of the crucial problems for the development of logging while drilling. This study aims to address this issue by using the advanced scheme for logging while drilling signal transmission, proposed by NovatekTM. The main points of the study focus on electromagnetic coupling between two adjacent pipes and the signal transmission in coaxial cables imbedded in drilling pipes. According to the axial symmetry of the electromagnetic coupling structure, the numerical mode matching is used to establish the numerical model for it. Through simulation analysis which is based on the numerical modeling of the electromagnetic coupling structure, we analyze how various parameters of the structure influence the coupling, obtain some significant conclusions, and optimize the coupling structure. The conclusions can be used to guide optimization design of coupling structure between the drill-pipe for signal transmission in logging while drilling. In addition, the rectangular transmission line whose characteristic impedance is 50 Ω is designed for the cable imbedded in the drill pipe, and the attenuation is calculated. Finally, simulation and experiment are performed for one unit of the pipeline. The results are in agreement with each other, thereby showing the good transmission performance.

Matching the Russian HIL with the EILOG06 Logging System

In order to match the Russian HIL with the EILOG06 logging system, a logging signal converter between PCM and CAN introduced in this paper, in which the functions of PCM3508 coding, decoding and CAN communication integrated in a micro-chip. The development of converter based on the scheme achieved good performance on small volume, lower power waster and high reliability, which had been proved feasible by the experiments of field experiment.

Reservoir information extraction using a fractional fourier transform and a smooth pseudo Wigner-Ville distribution

Currently, it is difficult for people to express signal information simultaneously in the time and frequency domains when analyzing acoustic logging signals using a simple-time or frequency-domain method. It is difficult to use a single type of time-frequency analysis method, which affects the feasibility of acoustic logging signal analysis. In order to solve these problems, in this paper, a fractional Fourier transform and smooth pseudo Wigner-Ville distribution (SPWD) were combined and used to analyze array acoustic logging signals. The time-frequency distribution of signals with the variation of orders of fractional Fourier transform was obtained, and the characteristics of the time-frequency distribution of different reservoirs under different orders were summarized. Because of the rotational characteristics of the fractional Fourier transform, the rotation speed of the cross terms was faster than those of primary waves, shear waves, Stoneley waves, and pseudo Rayleigh waves. By choosing different orders for different reservoirs according to the actual circumstances, the cross terms were separated from the four kinds of waves. In this manner, we could extract reservoir information by studying the characteristics of partial waves. Actual logging data showed that the method outlined in this paper greatly weakened cross-term interference and enhanced the ability to identify partial wave signals.

The Numerical Modeling of the Signal Transmission by the Cables and the Coil Coupling for LWD

Lack of efficiency in transmitting logging signals from down-hole to the top-hole server has long been one of the crucial problems for the development of LWD. This study aims to address this issue by exploring into the advanced scheme for LWD signal transmission proposed by NovatekTM. The main focus of the study is on electromagnetic coupling between two adjacent pipes. The coupler is modeled numerically by NMM efficiently according to the axial symmetry of the coupler. Through simulation and analysis which are based on the numerical model of the coupler, various parameters of the coupler are discussed, and some significant conclusions are obtained. The conclusions can guide optimization design of the coupler between the drill-pipes for signal transmission in LWD. Furthermore, the experimental results verify the good transmission performance of the coupler which is designed in this paper.

Effects of electric-acoustic and acoustic-electric conversions of transducers on acoustic logging signal

Based on an acoustic logging transmission network and the engineering pattern of a sliding wave in acoustic logging, analysis and calculations have been performed in a study of the effects of the electric-acoustic and acoustic-electric conversions of the transducers on the acoustic logging signal. The results show that acoustic-electric conversion through the transducer can cause not only a serious disturbance in the signal amplitude, but also an apparent transmission delay. For engineering applications, the amplitude variation and transmission delay must be accounted for in a practical analysis of the acoustic logging signal in rocks. The results also show that with enhanced understanding and proper justification, the error caused by the acoustic-electric conversion can be significantly reduced in evaluation of the cement bond quality of a cased well, and the accuracy of rock porosity calculated using the measured acoustic velocity can be increased.

Application of Digital Phase Sensitive Detection for Electromagnetic Propagation Logging while Drilling

Intense electromagnetic interference while drilling prevents traditional analog phase sensitive detection (APSD) from correctly acquiring electromagnetic wave signal of logging while drilling (LWD). A digital phase sensitive detection (DPSD) utilizes high-performance floating-point DSP and FPGA to separately process real part and imaginary part of the acquired logging signal, changes traditional calculating method, improves processing speed and calculating precision. The programmable technique used can fully utilize logging information, simplify control logic, improve precision of timing control and measuring precision. Discussed are the principle, characteristics, implement of circuit and analysis of experimental data. Testing result shows that the DPSD may utilize logging information effectively and optimize the LWD system.

Corrections for downhole NMR logging

Nuclear magnetic resonance logging (NMR) is an open well logging method. Drilling mud resistivity, formation resistivity and sodium ions influence its radio frequency (RF) field strength and NMR logging signals. Research on these effects can provide an important basis for NMR logging data acquisition and interpretation. Three models, water-based drilling mud—water bearing formation, water-based drilling mud—oil bearing formation, oil-based drilling mud—water bearing formation, were studied by finite element method numerical simulation. The influences of drilling mud resistivity and formation resistivity on the NMR logging tool RF field and the influences of sodium ions on the NMR logging signals were simulated numerically. On the basis of analysis, RF field correction and sodium ion correction formulae were proposed and their application range was also discussed. The results indicate that when drilling mud resistivity and formation resistivity are 0.02 Ω·m and 0.2 Ω·m respectively, the attenuation index of centric NMR logging tool is 8.9% and 9.47% respectively. The RF field of an eccentric NMR logging tool is affected mainly by formation resistivity. When formation resistivity is 0.1 Ω·m, the attenuation index is 17.5%. For centric NMR logging tools, the signals coming from sodium ions can be up to 31.8% of total signal. Suggestions are proposed for further research into NMR logging tool correction method and response characteristics.

The Design of Well Logging Data Acquisition Based on FPGA

A novel kind of logging data acquisition is designed based on FPGA.In this paper, the hardware and software are described in detail. Experiment results show that the designed circuit and program are reasonable and reliable by the logging signal simulator, which can meet the logging requirement of oil field.

Through-Casing Resistivity Logging Signal Acquisition and Processing Techniques

Weak signal detection is the key technique in developing through-casing resistivity logging tool. In this paper, ultra-low-noise preamplifier, oversampling method, sampling integration and sampling average method, digital phase-sensitive detection technique are applied in detecting logging signals and 30nV is achieved. The indoor calibration test and field experiment of through-casing resistivity logging model machine with those weak signal detection techniques were carried out. The result showed that the measurement range of formation resistivity is 0~200 Ω.m.

Exact Lithologic Boundary Detection Based on Wavelet Transform Analysis and Real-Time Investigation of Facies Discontinuities Using Drilling Data

Exact detection of lithologic boundaries is one of the main challenges in exploration, drilling operations, and geology. Investigation of facies discontinuities has been performed using petrophysical data regarding sharp changes along the wellbore. Due to the fact that recorded well logging signals contain lots of high-frequency waves (noise), detection of the layer boundaries comes with some uncertainties that should be eliminated by denoising those signals. Wavelet transform analysis is a good approach to denoise the signals and its ability has been proven in several studies. In this study, implementation of wavelet transform analysis resulted in an innovative approach for exact differentiation of neighborhood lithologic units. Detection of boundaries between different layers, especially the ones in the vicinity of the reservoir during drilling operations, is one of the crucial issues in petroleum well engineering. This purpose is usually achieved by cutting analysis and geological maps, which are not accurate enough and may cause substantial problems. Unconfined rock compressive strength can be considered as an accurate criterion to detect geological boundaries. In this study, an artificial neural network (ANN) model is developed that can predict the unconfined rock compressive strength of formations being drilled by importing 10 drilling parameters as inputs. Because rock strength will experience sudden changes while entering the next layer, it can be used as a key parameter to determine boundaries.

Error, reproducibility and sensitivity: a pipeline for data processing of Agilent oligonucleotide expression arrays

BackgroundExpression microarrays are increasingly used to obtain large scale transcriptomic information on a wide range of biological samples. Nevertheless, there is still much debate on the best ways to process data, to design experiments and analyse the output. Furthermore, many of the more sophisticated mathematical approaches to data analysis in the literature remain inaccessible to much of the biological research community. In this study we examine ways of extracting and analysing a large data set obtained using the Agilent long oligonucleotide transcriptomics platform, applied to a set of human macrophage and dendritic cell samples.ResultsWe describe and validate a series of data extraction, transformation and normalisation steps which are implemented via a new R function. Analysis of replicate normalised reference data demonstrate that intrarray variability is small (only around 2% of the mean log signal), while interarray variability from replicate array measurements has a standard deviation (SD) of around 0.5 log2 units ( 6% of mean). The common practise of working with ratios of Cy5/Cy3 signal offers little further improvement in terms of reducing error. Comparison to expression data obtained using Arabidopsis samples demonstrates that the large number of genes in each sample showing a low level of transcription reflect the real complexity of the cellular transcriptome. Multidimensional scaling is used to show that the processed data identifies an underlying structure which reflect some of the key biological variables which define the data set. This structure is robust, allowing reliable comparison of samples collected over a number of years and collected by a variety of operators.ConclusionsThis study outlines a robust and easily implemented pipeline for extracting, transforming normalising and visualising transcriptomic array data from Agilent expression platform. The analysis is used to obtain quantitative estimates of the SD arising from experimental (non biological) intra- and interarray variability, and for a lower threshold for determining whether an individual gene is expressed. The study provides a reliable basis for further more extensive studies of the systems biology of eukaryotic cells.

Wavelet Denoising of Well Logs and its Geological Performance

Well logs play a very important role in exploration and even exploitation of energy resources, but they usually contain kinds of noises which affect the results of the geological interpretation of them. It is common knowledge that wavelet transform does better than Fourier transform in noise removal and suppression of such non-stationary signals as logging signals. However, there are variable choices of the parameters such as the wavelet basis (mother wavelet function), the thresholding rule and the decomposition level etc. in denoising with the wavelet transform. In this paper, the wavelet denoising theory and steps are briefly introduced first, and then lots of numerical experiments on real well logs were done by the authors with different combination of the parameters and the denoising effect analyzed by comparison of the differences between the pre-denoising and post-denoising signals with difference value calculation and frequency spectral analysis. The experiment results show that the wavelet basis ‘sym8’, the soft threshold rule ‘heursure’ and 5-level decomposition are outstanding in the wavelet denoising of well logging data. Furthermore, we took the AC (acoustic logs) well logging data of a certain borehole in Jiyang Depression, Shandong province of North China, for a case study to check the combination of the parameters settled above. It is found that the denoised acoustic logging signal outperforms the original one in revealing the geological information of gas bearing layers. So, we believe that the wavelet transform can do an excellent job in the denoising of well logs on condition that the related parameters are set properly. Also, the authors assume that it would be of bright prospect to extract and reveal some more geological information such as stratigraphic sequences, sedimentary facies and reservoir properties etc. with reasonable denoising process of different kinds of logging data at certain scales.

A Hybrid Monte Carlo Method Based Artificial Neural Networks Approach for Rock Boundaries Identification: A Case Study from the KTB Bore Hole

Identification of rock boundaries and structural features from well log response is a fundamental problem in geological field studies. However, in a complex geologic situation, such as in the presence of crystalline rocks where metamorphisms lead to facies changes, it is not easy to discern accurate information from well log data using conventional artificial neural network (ANN) methods. Moreover inferences drawn by such methods are also found to be ambiguous because of the strong overlapping of well log signals, which are generally tainted with deceptive noise. Here, we have developed an alternative ANN approach based on Bayesian statistics using the concept of Hybrid Monte Carlo (HMC)/Markov Chain Monte Carlo (MCMC) inversion scheme for modeling the German Continental Deep Drilling Program (KTB) well log data. MCMC algorithm draws an independent and identically distributed (i.i.d) sample by Markov Chain simulation technique from posterior probability distribution using the principle of statistical mechanics in Hamiltonian dynamics. In this algorithm, each trajectory is updated by approximating the Hamiltonian differential equations through a leapfrog discrimination scheme. We examined the stability and efficiency of the HMC-based approach on “noisy” data assorted with different levels of colored noise. We also perform uncertainty analysis by estimating standard deviation (STD) error map of a posteriori covariance matrix at the network output of three types of lithofacies over the entire length of the litho section of KTB. Our analyses demonstrate that the HMC-based approach renders robust means for classification of complex lithofacies successions from the KTB borehole noisy signals, and hence may provide a useful guide for understanding the crustal inhomogeneity and structural discontinuity in many other tectonically critical and complex regions.

The filtering characteristics of HHT and its application in acoustic log waveform signal processing

Array acoustic logging plays an important role in formation evaluation. Its data is a non-linear and non-stationary signal and array acoustic logging signals have time-varying spectrum characteristics. Traditional filtering methods are inadequate. We introduce a Hilbert-Huang transform (HHT) which makes full preservation of the non-linear and non-stationary characteristics and has great advantages in the acoustic signal filtering. Using the empirical mode decomposition (EMD) method, the acoustic log waveforms can be decomposed into a finite and often small number of intrinsic mode functions (IMF). The results of applying HHT to real array acoustic logging signal filtering and de-noising are presented to illustrate the efficiency and power of this new method.

Novel approach to select genes from RMA normalized microarray data using functional hearing tests in aging mice

Presbycusis – age-related hearing loss – is the number one communicative disorder and one of the top three chronic medical condition of our aged population. High-throughput technologies potentially can be used to identify differentially expressed genes that may be better diagnostic and therapeutic targets for sensory and neural disorders. Here we analyzed gene expression for a set of GABA receptors in the cochlea of aging CBA mice using the Affymetrix GeneChip MOE430A. Functional phenotypic hearing measures were made, including auditory brainstem response (ABR) thresholds and distortion-product otoacoustic emission (DPOAE) amplitudes (four age groups). Four specific criteria were used to assess gene expression changes from RMA normalized microarray data (40 replicates). Linear regression models were used to fit the neurophysiological hearing measurements to probe-set expression profiles. These data were first subjected to one-way ANOVA, and then linear regression was performed. In addition, the log signal ratio was converted to fold change, and selected gene expression changes were confirmed by relative real-time PCR. Major findings: expression of GABA-A receptor subunit α6 was upregulated with age and hearing loss, whereas subunit α1 was repressed. In addition, GABA-A receptor associated protein like-1 and GABA-A receptor associated protein like-2 were strongly downregulated with age and hearing impairment. Lastly, gene expression measures were correlated with pathway/network relationships relevant to the inner ear using Pathway Architect, to identify key pathways consistent with the gene expression changes observed.

Signal detection in power-law noise: effect of spectrum exponents

Many natural backgrounds have approximately isotropic power spectra of the power-law form, P(f)=K/f(beta), where f is radial frequency. For natural scenes and mammograms, the values of the exponent, beta, range from 1.5 to 3.5. The ideal observer model predicts that for signals with certain properties and backgrounds that can be treated as random noise, a plot of log (contrast threshold) versus log (signal size) will be linear with slope, m, given by: m=(beta-2)/2. This plot is referred to as a contrast-detail (CD) diagram. It is interesting that this predicts a detection threshold that is independent of signal size for beta equal to 2. We present two-alternative forced-choice (2AFC) detection results for human and channelized model observers of a simple signal in filtered noise with exponents from 1.5 to 3.5. The CD diagram results are in good agreement with the prediction of this equation.

Effects of Globin mRNA Reduction Methods on Gene Expression Profiles from Whole Blood

Excessive globin mRNA in whole blood RNA decreases transcript detection sensitivity and increases signal variation on microarrays. Hence, methods based on peptide nucleic acid inhibitory oligos and biotinylated DNA capture oligos have been developed to reduce globin mRNA. However, there is limited information about the effects of these two methods on gene expression profiles. Thus, we systematically compared the facility and effects of the two globin reduction methods on profile measurements from Jurkat cell line RNA with or without spiked globin mRNA and human blood RNA isolated using PAXgene collection tubes. We showed that the methods were efficient at increasing the sensitivity of transcript detection without loss of specificity, but neither method could recover a profile equivalent to that of an identical RNA sample without globin mRNA excesses. The capture oligo method had slightly better transcript detection sensitivity for cell line RNA, lowered signal variation for PAXgene RNA, and more similar profiles to controls than the inhibitory method. However, the capture method required larger amounts of initial high-quality RNA to yield sufficient cRNA amounts, and its procedures were more complex and time consuming than the inhibitory method. These results inform the selection of methods suitable for multicenter surveillance of gene expression profiles.

Solvation, transduction and independent component analysis for pattern recognition in SAW electronic nose

This paper revisits the fundamental basis for signal generation in polymer coated SAW vapor sensors and applies the independent component analysis (ICA) for feature extraction from the SAW sensor array data to explore whether the independent components could represent analyte-specific solvation parameters and whether they could form the feature vector for reliable pattern classification. Thermodynamic partitioning of analytes between vapor and polymer phases is treated as independent contributions from different solvation mechanisms, each associated with characteristic ‘environment swap’ energy. The overall equilibrium partition coefficient of an analyte is modeled as product of partial partition coefficients associated with different solvation mechanisms. The polymer films on SAW devices are treated to be acoustically thin. The theory of signal generation accounts for effects from both the mass as well as the viscoelastic loadings. It explains the signal amplification factor due to viscoelastic effects, and models the sensor signal to be proportional to the equilibrium partition coefficient. Thus, the logarithmic signal becomes a linear combination of the partial free energies associated with various solvation mechanisms. A linear-solvation-energy relationship (LSER) like factorization is assumed for the partial free energies where the latter are expressed as product of analyte and complimentary polymer associated solvation parameters. The problem of sensor array signal analysis is then treated as a blind source separation problem with the analyte solvation parameters being the independent sources, the polymer solvation parameters being the mixing weights and the log(signals) being the measured variables. The FastICA algorithm with Gram-Schmidt orthogonalization is applied to determine independent components. The principal component analysis (PCA) is done as pre-processing step for ICA. An experimental SAW sensor array data available in the literature [Rose-Pehrsson et al., Anal. Chem. 60 (1988) 2801–2811] is used to seek validation for our approach, and to examine the role of ICA in SAW sensor array signal processing. In brief, the paper establishes a direct relationship between the independent components and the analyte solvation parameters, and presents ICA as an effective method for feature extraction for pattern recognition in SAW electronic noses.