Showing papers in "Geophysics in 2018"
TL;DR: This work proposes and implements a novel concept that bypasses these demanding steps, directly producing an accurate gridding or layered velocity model from shot gathers, and relies on training deep neural networks.
Abstract: Velocity-model building is a key step in hydrocarbon exploration. The main product of velocity-model building is an initial model of the subsurface that is subsequently used in seismic ima...
393 citations
TL;DR: This work has developed a method that uses the convolutional neural network to automatically detect and map fault zones using 3D seismic images in a similar fashion to the way done by interpreters, and clearly determined that the CNN-computed fault probability outperformed that obtained using the coherence technique in terms of exhibiting clearer discontinuities.
Abstract: Mapping fault planes using seismic images is a crucial and time-consuming step in hydrocarbon prospecting. Conventionally, this requires significant manual efforts that normally go through ...
206 citations
TL;DR: Synthetic and field results indicate the potential applications of DL in automatic attenuation of random noise (with unknown variance), linear noise, and multiples.
Abstract: Compared with traditional seismic noise attenuation algorithms that depend on signal models and their corresponding prior assumptions, removing noise with a deep neural network is trained b...
198 citations
TL;DR: In this paper, the quadratic Wasserstein metric is used to measure amplitude differences and global phase shifts, which helps to avoid cycle-skipping issues in full waveform inversion.
Abstract: Conventional full-waveform inversion (FWI) using the least-squares norm as a misfit function is known to suffer from cycle-skipping issues which increases the risk of computing a local rather than the global minimum of the misfit. The quadratic Wasserstein metric has been proved to have many ideal properties with regards to convexity and insensitivity to noise. When the observed and predicted seismic data are considered to be two density functions, the quadratic Wasserstein metric corresponds to the optimal cost of rearranging one density into the other, where the transportation cost is quadratic in distance. Unlike the least-squares norm, the quadratic Wasserstein metric measures not only amplitude differences but also global phase shifts, which helps to avoid cycle-skipping issues. We propose a new way of using the quadratic Wasserstein metric trace-by-trace in FWI and compare it to the global quadratic Wasserstein metric via the solution of the Monge-Ampere equation. We incorporate the quadratic Wasser...
188 citations
TL;DR: The intuition behind convolutional neural networks, a method revolutionizing the field of image analysis and pushing the state of the art, is looked into and considerations that must be made in order to make the method reliable are discussed.
Abstract: Deep-learning methods have proved successful recently for solving problems in image analysis and natural language processing. One of these methods, convolutional neural networks (CNNs), is...
154 citations
TL;DR: In this article, machine learning techniques are used to interpret seismic interpretations. But, they are subjective and often require significant time and expertise from the interpreter, which makes them difficult to use in practice.
Abstract: Seismic interpretations are, by definition, subjective and often require significant time and expertise from the interpreter. We are convinced that machine-learning techniques can help addr...
147 citations
TL;DR: In this paper, the most important goal of seismic stratigraphy studies is to interpret the elements of the seismic facies with respect to the geologic environment, and the results of the study are presented.
Abstract: One of the most important goals of seismic stratigraphy studies is to interpret the elements of the seismic facies with respect to the geologic environment. Prestack seismic data carry rich...
117 citations
TL;DR: This study applies geophysical inversion based on the least-squares method and artificial neural networks as a machine learning approach to solve reflectivity inversion using 2D synthetic data sets and 3D field data sets.
Abstract: Geophysical inversion and machine learning both provide solutions for inverse problems in which we estimate model parameters from observations. Geophysical inversions such as impedance inv...
100 citations
TL;DR: In this paper, new misfit functions for matching simulated and measured data have recently been introduced to enable the use of full waveform inversion in seismic imaging, which is a powerful computational tool for seismic imaging.
Abstract: Full-waveform inversion has evolved into a powerful computational tool in seismic imaging. New misfit functions for matching simulated and measured data have recently been introduced to avo...
96 citations
TL;DR: In this article, a velocity independent workflow for constructing a zero-offset reflection section that preserves most of the reflections and diffractions is presented, which constructs a migration image volume by prestack time migration (PSTM) using a series of constant-velocity models.
Abstract: Summary We present a velocity independent workflow for constructing a zero-offset reflection section that preserves most of the reflections and diffractions This workflow constructs a migration image volume by prestack time migration (PSTM) using a series of constant-velocity models A deblurring filter for each constant-velocity model is applied to each time migration image to get a deblurred image volume In order to preserve all events in the image volume, each deblurred image panel is demigrated and then summed over the velocity axis The resulting demigration section is equivalent to a zero-offset reflection section Compared with the workflow without deblurring filter, the composite zero-offset reflection section has higher resolution A more accurate estimate of the velocity distribution can be obtained from this workflow using time-migration velocity analysis, which can then used as the velocity model to migrate the zero-offset section Numerical tests are used to validate the effectiveness of this method with 3D synthetic data
91 citations
TL;DR: This paper focuses on the interpretation of two important geologic structures, faults and salt domes, and summarizes interpretation workflows based on typical or advanced image-processing and machine-learning algorithms.
Abstract: As a process that identifies geologic structures of interest such as faults, salt domes, or elements of petroleum systems in general, seismic structural interpretation depends heavily on t...
TL;DR: Since the 1960s, the Permian Basin of west Texas and southeast New Mexico has experienced earthquakes that were possibly triggered by oil and gas activities In recent years, seismicity ha
Abstract: Since the 1960s, the Permian Basin of west Texas and southeast New Mexico has experienced earthquakes that were possibly triggered by oil and gas activities In recent years, seismicity ha
TL;DR: In this paper, the authors combined the 1D nonstationary seislet transform with empirical-mode decomposition (EMD) in the f-x domain, and the resulting representation showed remarkable sparsity.
Abstract: The seislet transform uses a prediction operator that is connected to the local slope or frequency of seismic events. We have combined the 1D nonstationary seislet transform with empirical-mode decomposition (EMD) in the f-x domain. We used EMD to decompose data into smoothly variable frequency components for the following 1D seislet transform. The resultant representation showed remarkable sparsity. We developed a detailed algorithm and used a field example to demonstrate the application of the new seislet transform for sparsity-promoting seismic data processing.
TL;DR: A novel strategy based on the interpretation of the seismic data in the graph space, which outperforms previously proposed optimal-transport-based misfit functions in terms of convexity and differentiability.
Abstract: Optimal transport distance has been recently promoted as a tool to measure the discrepancy between observed and seismic data within the full-waveform-inversion strategy. This high-resolutio...
TL;DR: In this article, the authors extended existing models to the finite fracture thickness case for P-waves propagating perpendicular to the fracture plane using the so-called branching function approach, and considered three types of fractures, namely, periodically and randomly-spaced planar fractures, as well as penny-shaped cracks.
Abstract: When a seismic wave travels through a fluid-saturated porous reservoir containing aligned fractures, it induces oscillatory fluid flow between the fractures and the embedding background medium. Although there are numerous theoretical models for quantifying the associated seismic attenuation and velocity dispersion, they rely on certain assumptions, such as infinitesimal fracture thickness and dilute fracture concentration, which usually do not hold in real reservoirs. The objective of this work is to overcome some of these limitations and, therefore, improve the applicability of the available theoretical models. To do so, we extend existing models to the finite fracture thickness case for P-waves propagating perpendicular to the fracture plane using the so-called branching function approach. We consider three types of fractures, namely, periodically- and randomly-spaced planar fractures, as well as penny-shaped cracks. The extended unified model is then tested by comparing with corresponding numerical sim...
TL;DR: In this paper, the amplitude compensation and phase correction were taken into account for attenuation compensation during seismic propagation in the reverse time migration (RTM) for attenuating media, and the phase correction was considered as well.
Abstract: Reverse time migration (RTM) for attenuating media should take amplitude compensation and phase correction into consideration. However, attenuation compensation during seismic propagation s...
TL;DR: Most seismic horizon extraction methods are based on seismic local reflection slopes that locally follow seismic structural features as discussed by the authors, however, these methods often fail to correctly track geodesic structural features.
Abstract: Most seismic horizon extraction methods are based on seismic local reflection slopes that locally follow seismic structural features. However, these methods often fail to correctly track ho...
TL;DR: An adaptive double-focusing method that is specifically designed for the field-data application of source-receiver Marchenko redatuming and is less sensitive to imperfections in the data and a sparse acquisition geometry than the MDD method.
Abstract: We have developed an adaptive double-focusing method that is specifically designed for the field-data application of source-receiver Marchenko redatuming Typically, the single-focusing Marchenko method is combined with a multidimensional deconvolution (MDD) to achieve redatuming Our method replaces the MDD step by a second focusing step that naturally complements the single-focusing Marchenko method Instead of performing the MDD method with the directionally decomposed Green's functions that result from single-focusing, we now use the retrieved upgoing Green's function and the retrieved downgoing focusing function to obtain a redatumed reflection response in the physical medium Consequently, we only remove the strongest overburden effects instead of removing all of the overburden effects However, the gain is a robust method that is less sensitive to imperfections in the data and a sparse acquisition geometry than the MDD method In addition, it is computationally much cheaper, more straightforward to implement, and it can be parallelized over pairs of focal points, which makes it suitable for application to large data volumes We evaluate the successful application of our method to 2D field data of the Santos Basin
TL;DR: Schoenball et al. as mentioned in this paper analyzed the evolution of seismic activity in the Guthrie-Langston sequence in central Oklahoma in greater detail and found that seismic activity has reactivated a network of at least 12 subvertical faults in an area less than 10 km across.
Abstract: Author(s): Schoenball, M; Walsh, FR; Weingarten, M; Ellsworth, WL | Abstract: Large-scale wastewater disposal has led to a fast-paced reawakening of faults in the Oklahoma/Kansas region. High-resolution earthquake relocations show that the inventory of ancient basement faults in the study region differs from results of seismic surveys and geologic mapping focused on the sedimentary cover. We analyze the evolution of seismic activity in the Guthrie-Langston sequence in central Oklahoma in greater detail. Here, seismic activity has reactivated a network of at least 12 subvertical faults in an area less than 10 km across. Recorded activity began in late 2013, peaked about six months later, and includes two M 4 earthquakes. These earthquakes characteristically occur at about 4 km depth below the top of the basement and do not reach the sedimentary cover. The sequence shows a radial growth pattern despite being no closer than 10 km to significant wastewater disposal activity. Hydrologic modeling suggests that activity initiated with a time lag of several years relative to early injection activity. Once initiated, earthquake interactions contribute to the propagation of seismicity along the reactivated faults. As a result, the spatiotemporal evolution of the seismicity mimics a diffusive pattern that is typically thought to be associated with injection activity. Analysis of the fault slip potential shows that most faults are critically stressed in the contemporary stress field. Activity on some faults, for which we find low slip probability, suggests a significant contribution of geomechanical heterogeneities to the reawakening of these ancient basement faults.
TL;DR: In this article, the authors used the machine learning algorithm Random Forests (RF) to classify the lithology of an underexplored area adjacent to the historically significant Junction gold mine, using geophysical and remotesensing data.
Abstract: The Eastern Goldfields of Western Australia is one of the world’s premier gold-producing regions; however, large areas of prospective bedrock are under cover and lack detailed lithologic mapping. Away from the near-mine environment, exploration for new gold prospects requires mapping geology using the limited data available with robust estimates of uncertainty. We used the machine learning algorithm Random Forests (RF) to classify the lithology of an underexplored area adjacent to the historically significant Junction gold mine, using geophysical and remotesensing data, with no geochemical sampling available at this reconnaissance stage. Using a sparse training sample, 1.6% of the total ground area, we produce a refined lithologic map. The classification is stable, despite including parts of the study area with later intrusions and variable cover depth, and it preserves the stratigraphic units defined in the training data. We assess the uncertainty associated with this new RF classification using information entropy, identifying those areas of the refined map that are most likely to be incorrectly classified. We find that information entropy correlates well with inaccuracy, providing a mechanism for explorers to direct future expenditure toward areas most likely to be incorrectly mapped or geologically complex. We conclude that the method can be an effective additional tool available to geoscientists in a greenfield, orogenic gold setting when confronted with limited data. We determine that the method could be used either to substantially improve an existing map, or produce a new map, taking sparse observations as a starting point. It can be implemented in similar situations (with limited outcrop information and no geochemical data) as an objective, data-driven alternative to conventional interpretation with the additional value of quantifying uncertainty.
TL;DR: A recent refinement to a deep-learning fault identification process is demonstrated that improves the continuity and compactness of predicted fault planes in areas where faults intersect.
Abstract: Deep learning is arguably one of the most important innovations in artificial intelligence in recent times. It allows for computational solutions to problems that are not easily characteri...
TL;DR: In this paper, the joint inversion of seismic data for elastic and petrophysical properties is an inverse problem with a nonunique solution, and several factors that impact the accuracy of the resul...
Abstract: The joint inversion of seismic data for elastic and petrophysical properties is an inverse problem with a nonunique solution. There are several factors that impact the accuracy of the resul...
TL;DR: A joint probabilistic inversion methodology for the prediction of petrophysical and elastic properties and lithology/fluid classes that combined statistical rock physics and Bayesian seismic inversion was developed in this article.
Abstract: Seismic reservoir characterization focuses on the prediction of reservoir properties based on the available geophysical and petrophysical data. The inverse problem generally includes continuous properties, such as petrophysical and elastic attributes, and discrete properties, such as lithology/fluid classes. We have developed a joint probabilistic inversion methodology for the prediction of petrophysical and elastic properties and lithology/fluid classes that combined statistical rock physics and Bayesian seismic inversion. The elastic attributes depend on continuous petrophysical variables, such as porosity and clay content, and discrete lithology/fluid classes, through a nonlinear rock-physics relationship together. The seismic model relates the elastic attributes, such as velocities and density, to their seismic response (reflectivity, traveltime, and amplitudes). The advantage of our integrated approach is that the inversion method accounts for the uncertainty associated to each step of the mo...
TL;DR: Modeling indicates that low frequencies and wide offsets may be the key to success when building velocity models using FWI, and just how low and how wide that may be required for FWI succe...
Abstract: Subsalt imaging has been a long-term challenge for the oil and gas industry. The substantial progress made in data acquisition and imaging since the late 1990s has made some subsalt imagin...
TL;DR: In this paper, a multiparameter deblurring filter that approximates the Hessian inverse was proposed to reduce the footprint noise, balance the amplitude and increase the resolution of the elastic migration images.
Abstract: Summary We present a multiparameter deblurring filter that approximates the Hessian inverse. This filter considers the coupling between different parameters by using stationary local filters to approximate the submatrices of the Hessian inverse for the same and different types of parameters. Numerical tests with elastic migration and inversion show that the multiparameter deblurring filter not only reduces the footprint noise, balances the amplitude and increases the resolution of the elastic migration images, but also mitigates the crosstalk artifacts. When used as a preconditioner, it also accelerates the convergence rate for elastic inversion.
TL;DR: In this paper, a robust and fully data-driven workflow for prestack diffraction separation based on wavefront attributes, which are determined using the common-reflection surface (CRS) method, is presented.
Abstract: Diffraction imaging can lead to high-resolution characterization of small-scale subsurface structures. A key step of diffraction imaging and tomography is diffraction separation and enhancement, especially in the full prestack data volume. We have considered point diffractors and developed a robust and fully data-driven workflow for prestack diffraction separation based on wavefront attributes, which are determined using the common-reflection-surface (CRS) method. In the first of two steps, we apply a zero-offset-based extrapolation operator for prestack diffraction separation, which combines the robustness and stability of the zero-offset CRS processing with enhanced resolution and improved illumination of the finite-offset CRS processing. In the second step, when the finite-offset diffracted events are separated, we apply a diffraction-based time migration velocity model building that provides high-quality diffraction velocity spectra. Applications of the new workflow to 2D/3D complex synthetic ...
TL;DR: This paper proposes a criterion based on Monte Carlo for the intelligent reduction of training sets, and shows that a machine learning method based on support vector regression for seismic data intelligent interpolation can fully utilize large data as training data.
Abstract: Acquisition technology advances, as well as the exploration of geologically complex areas, are pushing the quantity of data to be analyzed into the “big-data” era. In our related work, we f...
TL;DR: An optimal surface-voting method to enhance a fault attribute image so that the noisy features are suppressed whereas the fault features become cleaner and more continuous, which makes the method highly efficient.
Abstract: Numerous types of fault attributes have been proposed to detect faults by measuring reflection continuities or discontinuities. However, these attributes can be sensitive to other seismic d...
TL;DR: The potential of collocated measurements of 6C data (3C of translational and 3C of rotational motion) has been demonstrated in global seismology using high-sensit... as discussed by the authors.
Abstract: Over the past few decades, the potential of collocated measurements of 6C data (3C of translational and 3C of rotational motion) has been demonstrated in global seismology using high-sensit...
TL;DR: In this article, a joint inversion of seismic data for the simultaneous estimation of facies and reservoir properties, such as porosity, mineralogy, and saturation, is presented.
Abstract: I have developed a joint inversion of seismic data for the simultaneous estimation of facies and reservoir properties, such as porosity, mineralogy, and saturation. The inversion method is ...