Alexandre Campane Vidal

Segmentation of sandstone thin section images with separation of touching grains using optimum path forest operators

Abstract The segmentation of detrical sedimentary rock images is still a challenge for characterization of grain morphology in sedimentary petrography. We propose a fast and effective approach that first segments the grains from pore in sandstone thin section images and separates the touching grains automatically, and second lets the user to correct the misclassified grains with minimum interaction. The method is mostly based on the image foresting transform (IFT)—a tool for the design of image processing operators using optimum connectivity. The IFT interprets an image as a graph, whose nodes are the image pixels, the arcs are defined by an adjacency relation between pixels, and the paths are valued by a connectivity function. The IFT algorithm transforms the image graph into an optimum-path forest and distinct operators are designed by suitable choice of the IFT parameters and post-processing of the attributes of that forest. The solution involves a sequence of three IFT-based image operators for automatic segmentation and the interactive segmentation combines region- and boundary-based object delineation using two IFT operators. Tests with thin section images of two different sandstone samples have shown very satisfactory results, yielding r 2 and accuracy parameters of 0.8712 and 94.8% on average, respectively. Biases were the presence of the matrix and rock fragments.

Progress on empirical mode decomposition-based techniques and its impacts on seismic attribute analysis

AbstractSpectral decomposition plays a significant role in seismic data processing and is commonly used to generate seismic attributes that are useful for interpretation and reservoir characterization. Among several techniques that are applied to this finality, complete ensemble empirical mode decomposition (CEEMD) is an alternative procedure that has proven higher spectral-spatial resolution than the short-time Fourier transform or wavelet transform, thus offering potential in highlighting subtle geologic structures that might otherwise be overlooked. We have analyzed a recent development in CEEMD, which we call improved CEEMD (ICEEMD), and its impacts on seismic attribute analysis commonly used in the empirical mode decomposition framework. By replacing the estimation of modes by the estimation of local means, the mode mixing and the presence of noise in the modes are reduced. Application on a synthetic and real data reveals that ICEEMD improves the signal decomposition and the energy concentration in t...

Well log denoising and geological enhancement based on discrete wavelet transform and hybrid thresholding

Well logging is an important tool for the characterization of subsurface rocks, being commonly used in the study of reservoir geology. It is well known that signals obtained as responses from geological media contain noise that can affect their interpretation, and that wavelet transform is more suitable than the Fourier transform to denoise non-stationary signals, as the ones obtained from well logs. On the other hand, there are several parameters that must be considered when working with wavelet transform, such as the choice of the wavelet basis function (mother wavelet), the decomposition level and also the function and rules that “control” which and how the coefficients will be used for signal reconstruction. This study analyzes the process of denoising well log data by discrete wavelet transform. Since the well log data are usually used in lithological classification, we propose a method associated with the k-nearest neighbor classification algorithm to investigate how different combinations of parameters affect the output signals and its performance in the classification, thus making it a data driven process. We propose a new thresholding function that shows better results when compared with traditional ones. The potential of wavelet transform as a tool to aid geological interpretation is evidenced by the identification of important geological features of the Namorado Field, Campos Basin, Brazil.

Similarity attributes from differential resolution components

AbstractSeismic resolution plays an important role in the delineation of structural and stratigraphic features. The resolution improvement directly affects the seismic attributes and, consequently, the interpretation of a given feature. However, the broadband data do not necessarily provide the best insight for seismic attribute evaluation. Particularly, geologic discontinuities, such as karsts, faults, and fractures, can have different seismic expressions according to their intrinsic scales, and, therefore, they are better illuminated in a given frequency range. To extract dissimilar characteristics in different frequency bands, we have combined a recently developed spectral enhancement method based on differential resolution (DR) and similarity attributes. The DR algorithm is simultaneously used for frequency enhancement and acting as a pseudofilter, allowing us to compute similarity attributes at different frequency bands. The similarity computation follows the reflector dip of each DR subband and adju...

Delimitação do embasamento da Bacia de Taubaté

The aim of this study is to define the basement map of the Taubate Basin applying geostatistics to seismic, gravimetric and deep wells data. The study consisted of the interpretation of eleven seismic sections obtained in the central and northeastern portions of the basin. The altitude of the basement and the distribution of faults was determined based on these sections. New information was obtained from 79 wells located mainly in the regions of Sao Jose dos Campos and Jacarei. The method of kriging with an external drift was applied, using seismic and well data as the main variables and the gravimetric map as the secondary variable. The basement contour map obtained has a strong correlation with the main faults. It was possible to obtain a better resolution in the region of Sao Jose dos Campos and in the northeast area, where the vast majority of wells are located.

Independent component spectral analysis

AbstractSpectral decomposition techniques can break down the broadband seismic records into a series of frequency components that are useful for seismic interpretation and reservoir characterization. However, it is laborious and time-consuming to analyze and to interpret each seismic frequency volume taking all the usable seismic bandwidth. In this context, we propose a multivariate technique based on independent component analysis (ICA) with the goal of choosing the spectral components that best represent the whole seismic spectrum while keeping the main geological information. The ICA-based method goes beyond the Gaussian assumption and takes advantage of higher order statistics to find a new set of variables that are independent of each other. The independence between two components is a more general statistical concept than the noncorrelation and, in principle, allows the extraction of more significant information from the data. We have tested four different contrast functions to estimate the independ...

Modelagem geoestatística 3D da impedância acústica para a caracterização do campo de namorado

The aim of this work is analyze the vertical seismic resolution of the turbidity reservoir of Namorado Field. In this work the seismic modeling was accomplished using the convolution method. The wavelet used was the Ricker type with dominant frequency of 20 hz, 35 hz and 50 hz. The results show that wavelet with frequencies of 35 hz and 50 hz have better seismic resolution than wavelets of 20 hz, however all frequencies delimit top and base of the reservoir. From the acoustic impedance model, obtained from the synthetic seismogram, was possible, knowing the correlation of this variable with reservoir rocks, determine the distribution of reservoir facies. For that was used the geostatistical analysis that still enabled the studies regarding to the scenarios analysis by means of the application of stochastic methods.

Maximum Autocorrelation Factors applied to electrofacies classification

A vast amount of data is obtained during the development of a petroleum field. Seismic data, well logs, core and production data, all contribute to a better reservoir characterization and modeling. Several methods of multivariate data analysis can be used to support its interpretation, helping in important tasks as the identification of lithological facies. The most used and widely known of those methods is Principal Component Analysis (PCA) which intends to reduce data dimension while keeping as much as possible of their variance. Data dimension reduction can also be performed with the method of Maximum Autocorrelation Factors (MAF) which seeks to keep the spatial autocorrelation in data. In this work both methods were applied to data from well logs of the Namorado field, testing their performances in the classification of electrofacies. Following data dimension reduction, supervised classification methods known as k-nearest neighbors (kNN) and weighted k-nearest neighbors (wk-NN) were applied, and the results obtained were compared by cross-validation. MAF showed to be more efficient than PCA in reducing data dimension, while keeping relevant information. The wk-NN performed a little better in classifying electrofacies than the usual k-NN. According to these results, the combination of MAF and wk-NN can be a valuable tool for classifying the facies of uncored wells from their logs.

A fast approach for unsupervised karst feature identification using GPU

Abstract Among the geological features, karst is the one that has received special attention in oil and gas exploration for being a strong indicator of the potential existence of hydrocarbon reservoirs. The integration of automatic pattern recognition methods and Graphics Processing Units (GPU) provides a powerful tool to help geological interpretation of seismic data. In order to provide insightful information for interpreters, this work investigates the usage of GPUs in addition to image segmentation by means of unsupervised classification for the identification of karst features in 3D seismic data. For this purpose, an implementation of the robust Self-Organizing Map for GPUs (SOM/GPU) is provided, and a comparison against a Central Processing Unit (CPU)-based SOM (SOM/CPU) is performed to assess the speeding-up provided by GPU. Experiments have shown promising results for geological interpretation using seismic data.

Learning to Classify Seismic Images with Deep Optimum-Path Forest

Due to the lack of labeled information, clustering techniques have been paramount in the last years once more. In this paper, inspired by the deep learning phenomenon, we presented a multi-scale approach to obtain more refined cluster representations of the Optimum-Path Forest (OPF) classifier, which has obtained promising results in a number of works in the literature. Here, we propose to fill a gap in OPF-based works by using a deep-driven representation of the feature space. Additionally, we validated the work in the context of high resolution seismic images aiming at petroleum exploration, as well as in general-purpose applications. Quantitative and qualitative analysis are conducted in order to assess the robustness of the proposed approach.