A novel clustering method is applied to well logs for improved rock type identification in hydrocarbon formations. For grouping the objects in the multi-dimensional data space, we propose a Most ...Frequent Value (MFV) based clustering technique applied to natural gamma ray, bulk density, sonic, photoelectric index, and resistivity logs. The MFV method is a robust estimator, which assists in finding the cluster centers more reliably than a more noise sensitive K-means clustering approach. The result of K-means cluster analysis highly depends on the choose of the initial centroids. To reduce the risk of inappropriately chosen starting values, we apply a histogram-based selection method to give the best position of the initial cluster centers. We assure the robustness of the solution by calculating the centroid as the MFV of the cluster elements and defining the overall deviation of cluster elements from the center by a weighted Euclidean (Steiner-) distance. The proposed workflow relies on a fully automated weighting of the cluster elements, which does not require a constraint on the statistical distribution of the observed variables. The processing of synthetic data shows high noise rejection capability and efficient cluster recognition even beside considerable amount of outlying and missing data; the accuracy is measured by the difference between the estimated and the exactly known distribution of cluster numbers. The clustering tool is first applied to single borehole data, then the procedure is extended to multi-well logging datasets to reconstruct the multi-dimensional spatial distributions of clusters revealing the lithological and petrophysical characteristics of the studied formations. A large in situ dataset acquired from several boreholes traversing Hungarian gas-bearing clastic reservoirs of Miocene age is analyzed. The accuracy of the field results is confirmed by core permeability measurements, independent well log analysis and a gradient metrics characterizing the noise rejection capability of the clustering method.
•Most frequent value based similarity metric in cluster analysis.•Histogram-based method for setting initial position of centroids.•Robust multi-dimensional cluster analysis using open-hole wireline logs.•Reservoir rock typing using multiple well logs in Hungarian gas field.
Matra Gravitational and Geophysical Laboratory (MGGL) was established near Gyöngyösoroszi, Hungary in 2015, in the cavern system of an unused ore mine. The laboratory is located 88 m below the ...surface, with the aim of measuring and analysing the advantages of the underground installation's third generation gravitational wave detectors. Specialized instruments have been installed to measure seismic, infrasound and electromagnetic noise, and the variation of the cosmic muon flux. In the preliminary (RUN-0) test period, March-August 2016, data collection was accomplished. In this paper we describe the research potential of the MGGL, list the installed equipment and summarize the experimental results of RUN-0. Here we report on the RUN-0 data, that prepares the systematic and synchronized data collection of the next run period.
► Thermal and mechanical cycling causes some changes in the hysteretic loops. ► After few cycles the stress–strain and strain-temperature response stabilize. ► In thermal cycling E increases and D ...decreases with increasing number of cycles. ► In mechanical cycling there is an opposite tendency.
Effect of thermal and mechanical cycling on β/β′ phase transformation in CuAl(11.5wt%)Ni(5.0wt%) single crystalline shape memory alloy was studied. The ε–σ and ξ–T hysteretic loops were investigated after different numbers of thermal and mechanical cycles (ε and ξ are the relative deformation and martensite fraction respectively, σ and T denote the stress and temperature). The ε–σ loops were determined at fixed temperature (373K). The ξ–T loops under zero stress were calculated from the DSC curves measured. The elastic and the dissipative energy contributions, following the procedure given in 1,2, were calculated as the function of the transformed fraction for both types of the hysteretic loops. Finally the dependence of the total elastic, E, and dissipative energy, D, (per one cycle) on the cycling number was calculated. In thermal cycling E increased by about 12J/mol, and D decreased by about 6J/mol. On the other hand for mechanical cycling E decreased by about 6J/mol and D increased by about 0.2J/mol.
The identification of lithology, fluid types, and total organic carbon content are of great priority in the exploration of unconventional hydrocarbons. As a new alternative, a further developed
...K-means
type clustering method is suggested for the evaluation of shale gas formations. The traditional approach of cluster analysis is mainly based on the use of the Euclidean distance for grouping the objects of multivariate observations into different clusters. The high sensitivity of the L
2
norm applied to non-Gaussian distributed measurement noises is well-known, which can be reduced by selecting a more suitable norm as distance metrics. To suppress the harmful effect of non-systematic errors and outlying data, the Most Frequent Value method as a robust statistical estimator is combined with the
K-means
clustering algorithm. The Cauchy-Steiner weights calculated by the Most Frequent Value procedure is applied to measure the weighted distance between the objects, which improves the performance of cluster analysis compared to the Euclidean norm. At the same time, the centroids are also calculated as a weighted average (using the Most Frequent Value method), instead of applying arithmetic mean. The suggested statistical method is tested using synthetic datasets as well as observed wireline logs, mud-logging data and core samples collected from the Barnett Shale Formation, USA. The synthetic experiment using extremely noisy well logs demonstrates that the newly developed robust clustering procedure is able to separate the geological-lithological units in hydrocarbon formations and provide additional information to standard well log analysis. It is also shown that the Cauchy-Steiner weighted cluster analysis is affected less by outliers, which allows a more efficient processing of poor-quality wireline logs and an improved evaluation of shale gas reservoirs.
We study the general phenomenon of random organization using a vortex system. When a periodic shear with a small shear amplitude dinp is applied to many-particle (vortex) assemblies with a random ...distribution, the particles (vortices) gradually self-organize to avoid future collisions and transform into an organized configuration. This is detected from the time-evolution of the voltage V ( t ) (average velocity) that increases towards a steady-state value. From the subsequent readout measurements of V ( t ) using various shear amplitudes, we find that the information of the input shear amplitude dinp is memorized in the configuration of the vortex distributions in the transient as well as the steady state, and that it is readable. We also find that the transient vortex configuration formed during random organization is not microscopically homogeneous but consists of disordered and organized regions.
Abstract
In this paper, we introduced an efficient inversion method for Hilbert transform calculation which can be able to eliminate the outlier noise. The Most Frequent Value method (MFV) developed ...by Steiner merged with an inversion-based Fourier transform to introduce a powerful Fourier transform. The Fourier transform process (IRLS-FT) ability to noise overthrow efficiency and refusal to outliers make it an applicable method in the field of seismic data processing. In the first part of the study, we introduced the Hilbert transform stand on a efficient inversion, after that as an example we obtain the absolute value of the analytical signal which can be used as an attribute gauge. The method depends on a dual inversion, first we obtain the Fourier spectrum of the time signal via inversion, after that, the spectrum calculated via transformation of Hilbert transforms into time range using a efficient inversion. Steiner Weights is used later and calculated using the Iterative Reweighting Least Squares (IRLS) method (efficient inverse Fourier transform). Hermite functions in a series expansion are used to discretize the spectrum of the signal in time. These expansion coefficients are the unknowns in this case. The test procedure was made on a Ricker wavelet signal loaded with Cauchy distribution noise to test the new Hilbert transform. The method shows very good resistance to outlier noises better than the conventional (DFT) method.
When a small periodic shear is applied to randomly distributed vortices, they progressively transform into an organized configuration, which is called random organization or dynamic ordering. By ...contrast, when the vortices with a moderately organized configuration are driven by a small dc force over a random substrate, they are gradually pinned by random pinning sites and finally reach disordered plastic flow, which is indicative of dynamic pinning or dynamic disordering. From the time-dependent voltage (i.e. average velocity), we find that random organization caused by the ac drive is suppressed with an increase in the dc drive superimposed with the ac one, and finally vanishes as the dc voltage becomes equal to the amplitude of the ac voltage in the steady state, where the vortices move in the forward direction only. The steady-state vortex configuration formed with the superimposed ac and dc drives is, in general, not uniform microscopically but comprises organized and disordered regions.
In our previous paper (Dobróka et al. Acta Geod Geophys Hung 47(2):185–196,
2012
) we proposed a new robust algorithm for the inversion-based Fourier transformation. It was presented that the Fourier ...transform and its variants responds very sensitively to any little measurement noise affected an input data set. The continuous Fourier spectra are assumed as a series expansion with the scaled Hermite functions. The expansion coefficients are determined by solving an over-determined inverse problem. Here, we use the new Steiner’s weights (previously called the weights of most frequent values or abbreviated as MFV), where the scale parameter can be determined in an internal iteration process. This method results a very efficient robust inversion method in which we calculate the Steiner weights from iteration to iteration into an IRLS procedure. The new method using the Steiner’s weights is also numerically tested by using synthetic data.
The Kozeny–Carman equation has achieved widespread use as a standard model for estimating hydraulic conductivity of aquifers. An empirically modified form applicable in shallow formations called ...Csókás’ formula is discussed, which is based on the relation between the effective grain-size and formation factor of freshwater-bearing unconsolidated sediments. The method gives a continuous estimate of hydraulic conductivity along a borehole by using electric and nuclear logging measurements without the need of grain-size data. In the first step, synthetic well-logging data sets of different noise levels are generated from an exactly known petrophysical model to test the noise sensitivity of the Csókás’ method and to assess the degree of correlation between the results of Csókás’ and Kozeny–Carman model. In the next step, borehole logs acquired from Hungarian sites are processed to make a comparison between the Csókás’ formula and the Kozeny–Carman equation including grain-size data measured on rock samples. The hydraulic conductivity logs derived separately from the Csókás’ and Kozeny–Carman formulae show reliable interpretation results, which are also validated by the Hazen’s formula and statistical factor analysis. The fundamental goal of Professor Csókás’ research was to derive some useful hydraulic parameters solely from well-logging observations. This idea may be of importance today since the input parameters can be determined more accurately by advanced measurement techniques. Hence, the Csókás’ formula may inspire the hydrogeophysicists to make further developments for a more efficient exploration of groundwater resources.
When a periodic shear force with a small amplitude dinp is applied to vortex assemblies having a random distribution, the vortices gradually self-organize to avoid future collisions and transform ...into an organized configuration. We showed recently that this random-organization or dynamic-ordering process can be detected from the time-evolution of voltage V(t) that increases to a steady-state voltage. We also showed from the subsequent readout experiment of V(t) using various ac amplitudes d that the transient vortex configuration during random organization is not microscopically homogeneous but consists of the disordered and organized regions. In this work, we develop an alternative readout method using a dc drive. It is found that the dc method gives the same results as obtained from the ac one, which further supports our view of the coexistence regions. It is expected that both methods will be applied complementarily to detect the vortex configuration over a wide range of disorder.
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