Marcílio Castro de Matos

Unsupervised seismic facies analysis using wavelet transform and self-organizing maps

Unsupervised seismic facies analysis provides an effective way to estimate reservoir properties by combining different seismic attributes through pattern recognition algorithms. However, without consistent geological information, parameters such as the number of facies and even the input seismic attributes are usually chosen in an empirical way. In this context, we propose two new semiautomatic alternative methods. In the first one, we use the clustering of the Kohonen self-organizing maps (SOMs) as a new way to build seismic facies maps and to estimate the number of seismic facies. In the second method, we use wavelet transforms to identify seismic trace singularities in each geologically oriented segment, and then we build the seismic facies map using the clustering of the SOM. We tested both methods using synthetic and real seismic data from the Namorado deepwater giant oilfield in Campos Basin, offshore Brazil. The results confirm that we can estimate the appropriate number of seismic facies through the clustering of the SOM. We also showed that we can improve the seismic facies analysis by using trace singularities detected by the wavelet transform technique. This workflow presents the advantage of being less sensitive to horizon interpretation errors, thus resulting in an improved seismic facies analysis.

Latent space modeling of seismic data: An overview

Modeling of seismic data takes two forms: those based on physical or geological (phenomenological) models and those that are data-driven (probabilistic) models. In the phenomenological approach, physical and geologic models are tied to seismic data either through geologic analogs or principles of structural deformation and sedimentary deposition. The results are then compared to the observed data, and the model is iterated as necessary to improve agreement. In contrast, probabilistic modeling looks at patterns in the data. The data could include raw seismic observations or seismic attributes. Probabilities can then be assigned to observations or potential observations; however, many common techniques such as neural networks and clustering do not explicitly take this step.

Inverse continuous wavelet transform “Deconvolution”

Most deconvolution algorithms try to transform the seismic wavelet into spikes by designing inverse filters that attempts to remove an estimated seismic wavelet from seismic data. Considering that seismic trace singularities are associated with acoustic impedance contrasts, and can be characterized by wavelet transform modulus maxima lines (WTMML), we show how to improve seismic resolution by using the wavelet transform. Specifically, we apply complex Morlet continuous wavelet transform (CWT) to each seismic trace and compute the WTMML‟s. Then, we reconstruct the seismic trace with the inverse continuous wavelet transform (ICWT) from the computed WTMML‟s with a slightly different complex Morlet wavelet than that used in the forward CWT. As the reconstruction process preserves amplitude and phase along different scales, or frequencies, the result resembles a deconvolution process. Using synthetic and real seismic data we show the effectiveness of the methodology on detecting seismic events associated with acoustic impedance changes.

Wavelet transform filtering in the 1D and 2D for ground roll suppression

Among the various types of noise found in seismic land acquisition there is the one produced by surface waves. This noise is called ground roll, and it can be defined as a group of events that contaminates seismic data by forming a high dip (low velocity) and high amplitude cone, which can dominate near-surface events on the seismic records. We present in this paper a technique based on the 2D wavelet transform to remove the ground roll. The method was tested with real data and the results were superior to the ones obtained with the 1D wavelet transform.

Detecting stratigraphic discontinuities using time-frequency seismic phase residues

Spectral decomposition is a proven, powerful means of identifying strong amplitude anomalies at specific frequencies that are otherwise buried in the broadband response. Most publications focus on using spectral magnitude instead of phase components to identify lateral changes in stratigraphy, wavefield attenuation from the quality factor Q, and unconformities between geologic formations. Although seismic acquisition and processing preserve phase very well, little has been published about interpreting the phase components resulting from spectral decomposition. Morlet complex wavelet transform phase residues can improve seismic spectral decomposition interpretation by detecting the phase discontinuities in the joint time-frequency spectral phase component. Phase singularities can be associated with geologic features, and work with phase residues can improve interpretation of the Anadarko basin Red Fork channels of Oklahoma, U.S.A.

Wavelet transform Teager-Kaiser energy applied to a carbonate field in Brazil

Spectral decomposition has prov-en a powerful means to identify strong amplitude anomalies at specific frequencies that are otherwise buried in the broadband response. Partyka et al. (1999) showed that the seismic spectrum response from a short time window depends on the acoustic properties and thickness of the layers spanned by the window. They applied this idea to good quality marine data to delineate thin channels in Tertiary sediments in the Gulf of Mexico. They also applied spectral decomposition to moderate quality land data to delineate incised channels in Paleozoic rocks in the U.S. midcontinent. Since then, spectral decomposition has been applied to reservoir characterization, hydrocarbon detection, and stratigraphic analysis.

Resolving subtle stratigraphic features using spectral ridges and phase residues

AbstractSeismic interpretation is dependent on the quality and resolution of seismic data. Unfortunately, seismic amplitude data are often insufficient for detailed sequence stratigraphy interpretation. We reviewed a method to derive high-resolution seismic attributes based upon complex continuous wavelet transform pseudodeconvolution (PD) and phase-residue techniques. The PD method is based upon an assumption of a blocky earth model that allowed us to increase the frequency content of seismic data that, for our data, better matched the well log control. The phase-residue technique allowed us to extract information not only from thin layers but also from interference patterns such as unconformities from the seismic amplitude data. Using data from a West Texas carbonate environment, we found out how PD can be used not only to improve the seismic well ties but also to provide sharper sequence terminations. Using data from an Anadarko Basin clastic environment, we discovered how phase residues delineate inci...

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...

Seismic interpretation of self-organizing maps using 2D color displays

Classification without supervision of patterns into groups is formally called clustering. Depending on the application area these patterns are called data lists, observations or vectors. For exploration geophysicists, these patterns are usually associated with seismic attributes, seismic waveforms or seismic facies. The main objective of this paper is to show how one of the most popular clustering algorithms - Kohonen self-organizing maps, can be applied to enhance seismic interpretation analysis associated with one and two-dimensional colormaps.

Integrated seismic texture segmentation and clustering analysis to improved delineation of reservoir geometry

Summary In recent years, 3D volumetric attributes have gained wide acceptance by geosciences interpreters. The early introduction of single-trace complex trace attributes was quickly followed by seismic sequence attribute mapping workflows. 3D geometric attributes such as coherence and curvature are also widely used. Most of these attributes correspond to a very simple easy-to-understand measures of a waveform or surface morphology. However, not all geologic features can be so easily quantified. For this reason, simple statistical measures of the seismic waveform such as RMS amplitude prove to be quite valuable in delineating more chaotic stratigraphy. In this paper, we show how modern texture analysis based on the gray-level co-occurrence matrix, when coupled with recent developments in self-organizing maps clustering technology, extends such statistical measures to delineate features that geoscientists can see, but not easily describe.