Download or read book SPECTRAL DECOMPOSITION OF SEISMIC DATA USING SPARSE S TRANSFORM COMPARED TO CONVENTIONAL METHODS written by and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract : Spectral decomposition is a technique used to identify the component frequencies of a signal. Since seismic signals are nonstationary, spectral decomposition can be used as a tool for detecting spectral anomalies and interpreting geological features, making it valuable for identifying thin layers and potential hydrocarbon reservoirs. This research project presents the application of a newly developed spectral decomposition method known as the sparse S-transform (SST). This method calculates areas with the highest energy concentrations and eliminates areas containing unimportant features. It is based on inverse theory and uses an optimization problem to create sparse coefficients to prevent overfitting of data. This study performs two synthetic tests and a real seismic data example using the F3 North Sea dataset to compare the SST to conventional methods: short-time Fourier transform (STFT), continuous-wavelet transform (CWT), and S-transform (ST). Two synthetic wedge models were also created to demonstrate how thin layers are identified through spectral decomposition. The SST proves to generate solutions with superior time-frequency resolution and has the benefit of optimized window parameters. It also has advantages for interpreting geological features found in the F3 dataset, such as faults, sigmoidal bedding, thin layers, and unconformities.

Download or read book Seismic Data Decomposition Using Sparse Dictionary written by Rongchang Liu and published by . This book was released on 2019 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spectrum decomposition methods map 1D seismic trace into 2D plane of time and frequency. The decomposed frequency components of seismic trace are widely used to quantitatively predict the thickness of thin layers. The current popular time-spectrum analysis methods include the Short Time Fourier transform (STFT), Continuous Wavelet Transform (CWT), S-transform (ST), and Matching Pursuit (MP), among which MP is the most tolerant of window/scalar effect. However, the traditional wavelet library is a set of user defined wavelet which does not consider the seismic event interfering of thin layer. As a result, it is very difficult for MP based algorithms to obtain an accurate wavelet in each decomposition iteration for thin layer reservoirs. The improved MP (IMP) algorithm assumes that the seismic reflection response of thin layers can be simulated by the convolution between wavelet and a thin-layer cake model. The parameters of wavelet library of MP algorithm include wavelet type, the dominant frequency of wavelet, and the phase of the wavelet. The parameters of my new “wavelet” library include wavelet type, the dominant frequency of wavelet, the phase of the wavelet, and the time thickness of thin layer model. I applied IMP to three examples to demonstrate the effectiveness of IMP. The first example is a synthetic seismic trace generated using a layer-cake model. The second example is a synthetic seismic trace computed using well logs. The third example is the real seismic data. The first synthetic example indicates that the reflectivity set obtained using IMP accurately points out the interface of thin layers. The second synthetic example indicates that the impedance computed using reflectivity of IMP has higher correlation coefficient when compared to that of MP. The real seismic data example indicates that reflectivity set obtained using IMP can identify the top and base of thin layers whose two-way time thickness are great than T/5, where T is the period corresponding to the dominant frequency of seismic data.

Download or read book Modified S transform in Time frequency Analysis of Seismic Data and Its Application written by Duan Li and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The S-Transform (ST) is a frequency-dependent spectral decomposition method that truly localizes spectra in time and frequency. However, it exhibits poor temporal resolution at low frequencies, which can result in inaccurate localization of spectral anomalies and obscure features that have geological meanings. While prior literature developed various modified S-Transforms, called the Generalized ST, (GST) to tailor analyzing windows for particular applications, they do not specifically address the particular shortcoming as noted above. To improve the ST’s temporal resolution at low frequencies, this dissertation develops a new modification to the ST. The proposed algorithm (termed MST) replaces frequency in the ST’s normalized Gaussian window with a linear function of frequency. Through optimizing two constants in the linear frequency function, the modified window shortens its width and sharpens its waveform to improve time resolution at low frequencies without degrading frequency resolution while retaining the ST’s time and frequency resolution at higher frequencies. Further, the MST also conserves energy and preserves the spectral properties of the ST. Comparison analyses of synthetic and real seismic data using the ST, CWT, MST and GSTs show that the MST successfully accomplishes the above. By using optimized coefficients in its linear frequency function, the MST can be useful in hydrocarbon detection and geological interpretation.

Download or read book Proceedings of the International Field Exploration and Development Conference 2021 written by Jia'en Lin and published by Springer Nature. This book was released on 2022-09-07 with total page 5829 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on reservoir surveillance and management, reservoir evaluation and dynamic description, reservoir production stimulation and EOR, ultra-tight reservoir, unconventional oil and gas resources technology, oil and gas well production testing, and geomechanics. This book is a compilation of selected papers from the 11th International Field Exploration and Development Conference (IFEDC 2021). The conference not only provides a platform to exchanges experience, but also promotes the development of scientific research in oil & gas exploration and production. The main audience for the work includes reservoir engineer, geological engineer, enterprise managers, senior engineers as well as professional students.

Download or read book Proceedings of the International Field Exploration and Development Conference 2022 written by Jia'en Lin and published by Springer Nature. This book was released on 2023-08-05 with total page 7600 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on reservoir surveillance and management, reservoir evaluation and dynamic description, reservoir production stimulation and EOR, ultra-tight reservoir, unconventional oil and gas resources technology, oil and gas well production testing, and geomechanics. This book is a compilation of selected papers from the 12th International Field Exploration and Development Conference (IFEDC 2022). The conference not only provides a platform to exchanges experience, but also promotes the development of scientific research in oil & gas exploration and production. The main audience for the work includes reservoir engineer, geological engineer, enterprise managers, senior engineers as well as professional students.

Download or read book Information Based Inversion and Processing with Applications written by T.J. Ulrych and published by Elsevier. This book was released on 2005-12-16 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information-Based Inversion and Processing with Applications examines different classical and modern aspects of geophysical data processing and inversion with emphasis on the processing of seismic records in applied seismology. Chapter 1 introduces basic concepts including: probability theory (expectation operator and ensemble statistics), elementary principles of parameter estimation, Fourier and z-transform essentials, and issues of orthogonality. In Chapter 2, the linear treatment of time series is provided. Particular attention is paid to Wold decomposition theorem and time series models (AR, MA, and ARMA) and their connection to seismic data analysis problems. Chapter 3 introduces concepts of Information theory and contains a synopsis of those topics that are used throughout the book. Examples are entropy, conditional entropy, Burg's maximum entropy spectral estimator, and mutual information. Chapter 4 provides a description of inverse problems first from a deterministic point of view, then from a probabilistic one. Chapter 5 deals with methods to improve the signal-to-noise ratio of seismic records. Concepts from previous chapters are put in practice for designing prediction error filters for noise attenuation and high-resolution Radon operators. Chapter 6 deals with the topic of deconvolution and the inversion of acoustic impedance. The first part discusses band-limited extrapolation assuming a known wavelet and considers the issue of wavelet estimation. The second part deals with sparse deconvolution using various 'entropy' type norms. Finally, Chapter 7 introduces recent topics of interest to the authors. The emphasis of this book is on applied seismology but researchers in the area of global seismology, and geophysical signal processing and inversion will find material that is relevant to the ubiquitous problem of estimating complex models from a limited number of noisy observations. Non-conventional approaches to data processing and inversion are presented Important problems in the area of seismic resolution enhancement are discussed Contains research material that could inspire graduate students and their supervisors to undertake new research directions in applied seismology and geophysical signal processing

Download or read book Application of s transform in the spectral decomposition of seismic data written by and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Uma das principais etapas na exploração de petróleo é a definição de um modelo geológico que justifique a existência de uma acumulação de hidrocarbonetos. Ferramentas que possam aumentar o grau de precisão destemodelo são foco de constante estudo na indústria do petróleo. Neste contexto, recentemente, Partyka et al. apresentaram uma nova técnica que utiliza a decomposição espectral dos dados sísmicos para refinar o modelo geológicoem termos de definição de espessura de camadas. Nesta pesquisa, essa técnica é estudada e testada, e é proposta a utilização da transformada S, desenvolvida por Stockwell et al., para localizar as componentes de freqüência no domínio do tempo. Os testes realizados com dados sintéticos apontam o uso da técnica de Partyka et al. para fins mais qualitativos, já que, quando os modelos dos testes são perturbados, as análises quantitativas ficam comprometidas. A transformada S mostrou bons resultados na localização dascomponentes de freqüência no domínio do tempo; no entanto, ela acarreta a suavização do espectro de amplitudes. Ao final deste trabalho é apresentado um exemplo da utilização da técnica em dados reais tridimensionais.

Download or read book Advances and Applications of Distributed Optical Fiber Sensing DOFS in Multi scales Geoscience Problems written by Yibo Wang and published by Frontiers Media SA. This book was released on 2023-02-07 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Spectral Decomposition Using S transform for Hydrocarbon Detection and Filtering written by Zhao Zhang and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Spectral decomposition is a modern tool that utilizes seismic data to generate additional useful information in seismic exploration for hydrocarbon detection, lithology identification, stratigraphic interpretation, filtering and others. Different spectral decomposition methods with applications to seismic data were reported and investigated in past years. Many methods usually do not consider the non-stationary features of seismic data and, therefore, are not likely to give satisfactory results. S-transform developed in recent years is able to provide time-dependent frequency analysis while maintaining a direct relationship with the Fourier spectrum, a unique property that other methods of spectral decomposition may not have. In this thesis, I investigated the feasibility and efficiency of using S-transform for hydrocarbon detection and time-varying surface wave filtering. S-transform was first applied to two seismic data sets from a clastic reservoir in the North Sea and a deep carbonate reservoir in the Sichuan Basin, China. Results from both cases demonstrated that S-transform decomposition technique can detect hydrocarbon zones effectively and helps to build the relationships between lithology changes and high frequency variation and between hydrocarbon occurrence and low-frequency anomaly. However, its time resolution needs to be improved. In the second part of my thesis, I used S-transform to develop a novel Time-frequency-wave-number-domain (T-F-K) filtering method to separate surface wave from reflected waves in seismic records. The S-T-F-K filtering proposed here can be used to analyze surface waves on separate f-k panels at different times. The method was tested using hydrophone records of four-component seismic data acquired in the shallow-water Persian Gulf where the average water depth is about 10m and Scholte waves and other surfaces wave persistently strong. Results showed that this new S-T-F-K method is able to separate and sttenuate surface waves and to improve greatly the quality of seismic reflection signals that are otherwise completely concealed by the aliased surface waves.

Download or read book Spectral Decomposition of Seismic Data Using Conventional U wavelets and U pseudo wavelets written by Dip Shankar Nanda and published by . This book was released on 2010 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Improving Accuracy and Efficiency of Seismic Data Analysis Using Deep Learning written by Harpreet Kaur (Ph. D.) and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ultimate goal of seismic data analysis is to retrieve high-resolution information about the subsurface structures. It comprises different steps such as data processing, model building, wave propagation, and imaging, etc. Increasing the resolution and fidelity of the different seismic data analysis tasks eventually leads to an improved understanding of fine-scale structural features. Conventional implementation of these techniques is computationally intensive and expensive, especially with large data sets. Recent advances in neural networks have provided an ability to produce a reasonable result to computationally intensive and time-consuming problems. Deep neural networks are capable of extracting complex nonlinear relationships among variables and have shown efficacy as compared to conventional statistical methods in different areas. A major bottleneck for seismic data analysis is the tradeoff between resolution and efficiency. I address some of these challenges by implementing neural network based frameworks. First, I implement a neural network based workflow for stable and efficient wave extrapolation. Conventionally, it is implemented by finite differences (FD), which have a low computational cost but for larger time-steps may suffer from dispersion artifacts and instabilities. On the other hand, recursive integral time extrapolation (RITE) methods, especially the low-rank extrapolation, which are mixed-domain space-wavenumber operators are designed to make time extrapolation stable and dispersion free in heterogeneous media for large time steps, even beyond the Nyquist limit. They have high spectral accuracy; however, they are expensive as compared to finite-difference extrapolation. The proposed framework overcomes the numerical dispersion of finite-difference wave extrapolation for larger time steps and provides stable and efficient wave extrapolation results equivalent to low-rank wave extrapolation at a significantly reduced cost. Second, I address wave-mode separation and wave-vector decomposition problem to separate a full elastic wavefield into different wavefields corresponding to their respective wave mode. Conventionally, wave mode separation in heterogeneous anisotropic media is done by solving the Christoffel equation in all phase directions for a given set of stiffness-tensor coefficients at each spatial location of the medium, which is a computationally expensive process. I circumvent the need to solve the Christoffel equation at each spatial location by implementing a deep neural network based framework. The proposed approach has high accuracy and efficiency for decoupling the elastic waves, which has been demonstrated using different models of increasing complexity. Third, I propose a hyper-parameter optimization (HPO) workflow for a deep learning framework to simulate boundary conditions for acoustic and elastic wave propagation. The conventional low-order implementation of ABCs and PMLs is challenging for strong anisotropic media. In the tilted transverse isotropic (TTI) case, instabilities may appear in layers with PMLs owing to exponentially increasing modes, which eventually degrades the reverse time migration output. The proposed approach is stable and simulates the effect of higher-order absorbing boundary conditions in strongly anisotropic media, especially TTI media, thus having a great potential for application in reverse time migration. Fourth, I implement a coherent noise attenuation framework, especially for ground-roll noise attenuation using deep learning. Accounting for non-stationary properties of seismic data and associated ground-roll noise, I create training labels using local-time frequency transform (LTF) and regularized non-stationary regression (RNR). The proposed approach automates the ground-roll attenuation process without requiring any manual input in picking the parameters for each shot gather other than in the training data. Lastly, I address the limitation of the iterative methods with conventional implementation for true amplitude imaging. I implement a workflow to correct migration amplitudes by estimating the inverse Hessian operator weights using a neural network based framework. To incorporate non-stationarity in the framework, I condition the input migrated image with different conditioners like the velocity model and source illumination. To correct for the remnant artifacts in the deep neural network (DNN) output, I perform iterative least-squares migration using neural network output as an initial model. The network output is close to the true model and therefore, with fewer iterations, a true-amplitude image with the improved resolution is obtained. The proposed method is robust in areas with poor illumination and can easily be generalized to more-complex cases such as viscoacoustic, elastic, and others. The proposed frameworks are numerically stable with high accuracy and efficiency and are, therefore, desirable for different seismic data analysis tasks. I use synthetic and field data examples of varying complexities in both 2D and 3D to test the practical application and accuracy of the proposed approaches

Download or read book Spectral Bandwidth Extension written by Chen Liang and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: There are valid and invalid post-processing methods to extend seismic bandwidth for resolution enhancement. Some methods attempt to invent high frequencies without a physical basis, while inversion-based methods extrapolate the spectra in reasonable ways. Frequency invention methods can extend the original seismic spectrum to desired spectral bandwidths. However, those spectral components they invent do not provide new effective information for enhancing resolution. Matching pursuit decomposition has been successfully applied to analyze the available spectrum of seismic data. Consequently, missing spectral components can be directly extrapolated from zero frequency all the way to the Nyquist frequency. Alternatively, the spectral information within the limited band can be modeled as an autoregressive process. Higher and lower frequencies outside the band can thus be predicted by designing a Wiener prediction filter. Spectral decomposition by matching pursuit on the band-limited seismic trace stabilizes the predictions to recover a broad-band reflectivity sequence. Further, continuous wavelet transform can be employed to spectrally decompose the band-limited signal into discrete sub-bands from which missing high and low frequencies could be extrapolated locally using multi-channel operators. Conventional sparse spike deconvolution attempts to retrieve a reflectivity sequence comprising isolated sparse delta functions, which may restore the missing part of the

Download or read book Advances and applications of artificial intelligence in geoscience and remote sensing written by Peng Zhenming and published by Frontiers Media SA. This book was released on 2023-08-30 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Seismic Geomorphology written by R. J. Davies and published by Geological Society of London. This book was released on 2007 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: We are poised to embark on a new era of discovery in the study of geomorphology. The discipline has a long and illustrious history, but in recent years an entirely new way of studying landscapes and seascapes has been developed. It involves the use of 3D seismic data. Just as CAT scans allow medical staff to view our anatomy in 3D, seismic data now allows Earth scientists to do what the early geomorphologists could only dream of - view tens and hundreds of square kilometres of the Earth's subsurface in 3D and therefore see for the first time how landscapes have evolved through time. This volume demonstrates how Earth scientists are starting to use this relatively new tool to study the dynamic evolution of a range of sedimentary environments.

Download or read book Geological CO2 Storage Characterization written by Ronald C. Surdam and published by Springer Science & Business Media. This book was released on 2013-12-12 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book investigates geological CO2 storage and its role in greenhouse gas emissions reduction, enhanced oil recovery, and environmentally responsible use of fossil fuels. Written for energy/environmental regulators at every level of government (federal, state, etc.), scientists/academics, representatives from the power and fossil energy sectors, NGOs, and other interested parties, this book uses the characterization of the Rock Springs Uplift site in Wyoming as an integrated case study to illustrate the application of geological CO2 storage science, principles, and theory in a real-world scenario.

Download or read book The Sparse Fourier Transform written by Haitham Hassanieh and published by Morgan & Claypool. This book was released on 2018-02-27 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Fourier transform is one of the most fundamental tools for computing the frequency representation of signals. It plays a central role in signal processing, communications, audio and video compression, medical imaging, genomics, astronomy, as well as many other areas. Because of its widespread use, fast algorithms for computing the Fourier transform can benefit a large number of applications. The fastest algorithm for computing the Fourier transform is the Fast Fourier Transform (FFT), which runs in near-linear time making it an indispensable tool for many applications. However, today, the runtime of the FFT algorithm is no longer fast enough especially for big data problems where each dataset can be few terabytes. Hence, faster algorithms that run in sublinear time, i.e., do not even sample all the data points, have become necessary. This book addresses the above problem by developing the Sparse Fourier Transform algorithms and building practical systems that use these algorithms to solve key problems in six different applications: wireless networks; mobile systems; computer graphics; medical imaging; biochemistry; and digital circuits. This is a revised version of the thesis that won the 2016 ACM Doctoral Dissertation Award.

Download or read book 3 D Seismic Interpretation written by M. Bacon and published by Cambridge University Press. This book was released on 2007-10-18 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: 3-D seismic data have become the key tool used in the petroleum industry to understand the subsurface. In addition to providing excellent structural images, the dense sampling of a 3-D survey makes it possible to map reservoir quality and the distribution of oil and gas. Topics covered in this book include basic structural interpretation and map-making; the use of 3-D visualisation methods; interpretation of seismic amplitudes, including their relation to rock and fluid properties; and the generation and use of AVO and acoustic impedance datasets. This new paperback edition includes an extra appendix presenting new material on novel acquisition design, pore pressure prediction from seismic velocity, elastic impedance inversion, and time lapse seismics. Written by professional geophysicists with many years' experience in the oil industry, the book is indispensable for geoscientists using 3-D seismic data, including graduate students and new entrants into the petroleum industry.