Integrated seismic texture segmentation and cluster analysis applied to channel delineation and chert reservoir characterization

Geophysics ◽  
2011 ◽  
Vol 76 (5) ◽  
pp. P11-P21 ◽  
Author(s):  
Marcilio Castro de Matos ◽  
Malleswar (Moe) Yenugu ◽  
Sipuikinene Miguel Angelo ◽  
Kurt J. Marfurt
Keyword(s):  
Texture Analysis ◽  
Reservoir Characterization ◽  
Three Dimensional ◽  
Seismic Survey ◽  
Self Organizing Maps ◽  
Early Introduction ◽  
Statistical Measures ◽  
Seismic Waveform ◽  
Occurrence Matrix ◽  
Single Trace

In recent years, 3D volumetric attributes have gained wide acceptance by seismic interpreters. The early introduction of the single-trace complex trace attribute was quickly followed by seismic sequence attribute mapping workflows. Three-dimensional geometric attributes such as coherence and curvature are also widely used. Most of these attributes correspond to 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 and texture analysis techniques prove to be quite valuable in delineating more chaotic stratigraphy. In this paper, we coupled structure-oriented texture analysis based on the gray-level co-occurrence matrix with self-organizing maps clustering technology and applied it to classify seismic textures. By this way, we expect that our workflow should be more sensitive to lateral changes, rather than vertical changes, in reflectivity. We applied the methodology to a remote sensing image and to a 3D seismic survey acquired over Osage County, Oklahoma, USA. Our results indicate that our method can be used to delineate meandering channels as well as to characterize chert reservoirs.

Prestack waveform inversion of three-dimensional seismic data — An example from the Rock Springs Uplift, Wyoming, USA

Geophysics ◽  
2017 ◽  
Vol 82 (1) ◽  
pp. B1-B12 ◽  
Author(s):  
Josiane Pafeng ◽  
Subhashis Mallick ◽  
Hema Sharma
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Waveform Inversion ◽  
Three Dimensional ◽  
Seismic Inversion ◽  
Amplitude Variation ◽  
Amplitude Variation With Offset ◽  
Seismic Waveform ◽  
Rock Springs Uplift ◽  
Rock Springs

Applying seismic inversion to estimate subsurface elastic earth properties for reservoir characterization is a challenge in exploration seismology. In recent years, waveform-based seismic inversions have gained popularity, but due to high computational costs, their applications are limited, and amplitude-variation-with-offset/angle inversion is still the current state-of-the-art. We have developed a genetic-algorithm-based prestack seismic waveform inversion methodology. By parallelizing at multiple levels and assuming a locally 1D structure such that forward computation of wave equation synthetics is computationally efficient, this method is capable of inverting 3D prestack seismic data on parallel computers. Applying this inversion to a real prestack seismic data volume from the Rock Springs Uplift (RSU) located in Wyoming, USA, we determined that our method is capable of inverting the data in a reasonable runtime and producing much higher quality results than amplitude-variation-with-offset/angle inversion. Because the primary purpose for seismic data acquisition at the RSU was to characterize the subsurface for potential targets for carbon dioxide sequestration, we also identified and analyzed some potential primary and secondary storage formations and their associated sealing lithologies from our inversion results.

Reservoir characterization using seismic waveform and feedforword neural networks

Geophysics ◽  
2001 ◽  
Vol 66 (5) ◽  
pp. 1450-1456 ◽  
Author(s):  
P. An ◽  
W. M. Moon ◽  
F. Kalantzis
Keyword(s):  
Neural Networks ◽  
Reservoir Characterization ◽  
Random Noise ◽  
Feedforward Neural Networks ◽  
Oil Field ◽  
Seismic Survey ◽  
Coherent Noise ◽  
Reservoir Properties ◽  
Seismic Waveform ◽  
The Neural Networks

Feedforward neural networks are used to estimate reservoir properties. The neural networks are trained with known reservoir properties from well log data and seismic waveforms at well locations. The trained neural networks are then applied to the whole seismic survey to generate a map of the predicted reservoir property. Both theoretical analysis and testing with synthetic models show that the neural networks are adaptive to coherent noise and that random noise in the training samples may increase the robustness of the trained neural networks. This approach was applied to a mature oil field to explore for Devonian reef‐edge oil by estimating the product of porosity and net pay thickness in northern Alberta, Canada. The resulting prediction map was used to select new well locations and design horizontal well trajectories. Four wells were drilled based on the prediction; all were successful. This increased production of the oil field by about 20%.

scholarly journals A substrate for brane shells from $$ T\overline{T} $$

2021 ◽  
Vol 2021 (5) ◽  
Author(s):  
Jeremias Aguilera-Damia ◽  
Louise M. Anderson ◽  
Evan Coleman
Keyword(s):  
Three Dimensional ◽  
Radial Position ◽  
Singular Behavior ◽  
Curvature Singularity ◽  
Closed Timelike Curves ◽  
Finite Radius ◽  
Linear Dilaton ◽  
Singularity Resolution ◽  
New Interpretation ◽  
Single Trace

Abstract A solvable current-current deformation of the worldsheet theory of strings on AdS3 has been recently conjectured to be dual to an irrelevant deformation of the spacetime orbifold CFT, commonly referred to as single-trace $$ T\overline{T} $$ T T ¯ . These deformations give rise to a family of bulk geometries which realize a non-trivial flow towards the UV. For a particular sign of this deformation, the corresponding three-dimensional geometry approaches AdS3 in the interior, but has a curvature singularity at finite radius, beyond which there are closed timelike curves. It has been suggested that this singularity is due to the presence of “negative branes,” which are exotic objects that generically change the metric signature. We propose an alternative UV-completion for geometries displaying a similar singular behavior by cutting and gluing to a regular background which approaches a linear dilaton vacuum in the UV. In the S-dual picture, a singularity resolution mechanism known as the enhançon induces this transition by the formation of a shell of D5-branes at a fixed radial position near the singularity. The solutions involving negative branes gain a new interpretation in this context.

scholarly journals Seismic characteristics of polygonal fault systems in the Great South Basin, New Zealand

2020 ◽  
Vol 12 (1) ◽  
pp. 851-865
Author(s):  
Sukonmeth Jitmahantakul ◽  
Piyaphong Chenrai ◽  
Pitsanupong Kanjanapayont ◽  
Waruntorn Kanitpanyacharoen
Keyword(s):  
Three Dimensional ◽  
Late Eocene ◽  
Seismic Survey ◽  
Fault System ◽  
Normal Faults ◽  
Tier 2 ◽  
South Basin ◽  
Fault Systems ◽  
Tier 1 ◽  
Polygonal Fault

AbstractA well-developed multi-tier polygonal fault system is located in the Great South Basin offshore New Zealand’s South Island. The system has been characterised using a high-quality three-dimensional seismic survey tied to available exploration boreholes using regional two-dimensional seismic data. In this study area, two polygonal fault intervals are identified and analysed, Tier 1 and Tier 2. Tier 1 coincides with the Tucker Cove Formation (Late Eocene) with small polygonal faults. Tier 2 is restricted to the Paleocene-to-Late Eocene interval with a great number of large faults. In map view, polygonal fault cells are outlined by a series of conjugate pairs of normal faults. The polygonal faults are demonstrated to be controlled by depositional facies, specifically offshore bathyal deposits characterised by fine-grained clays, marls and muds. Fault throw analysis is used to understand the propagation history of the polygonal faults in this area. Tier 1 and Tier 2 initiate at about Late Eocene and Early Eocene, respectively, based on their maximum fault throws. A set of three-dimensional fault throw images within Tier 2 shows that maximum fault throws of the inner polygonal fault cell occurs at the same age, while the outer polygonal fault cell exhibits maximum fault throws at shallower levels of different ages. The polygonal fault systems are believed to be related to the dewatering of sedimentary formation during the diagenesis process. Interpretation of the polygonal fault in this area is useful in assessing the migration pathway and seal ability of the Eocene mudstone sequence in the Great South Basin.

Latest developments in seismic texture analysis for subsurface structure, facies, and reservoir characterization: A review

Geophysics ◽  
2011 ◽  
Vol 76 (2) ◽  
pp. W1-W13 ◽  
Author(s):  
Dengliang Gao
Keyword(s):  
Texture Analysis ◽  
Reservoir Characterization ◽  
Preliminary Test ◽  
Seismic Facies ◽  
Least Squares Regression ◽  
Reservoir Properties ◽  
Seismic Amplitude ◽  
Texture Model ◽  
Facies Classification ◽  
Analysis Window

In exploration geology and geophysics, seismic texture is still a developing concept that has not been sufficiently known, although quite a number of different algorithms have been published in the literature. This paper provides a review of the seismic texture concepts and methodologies, focusing on latest developments in seismic amplitude texture analysis, with particular reference to the gray level co-occurrence matrix (GLCM) and the texture model regression (TMR) methods. The GLCM method evaluates spatial arrangements of amplitude samples within an analysis window using a matrix (a two-dimensional histogram) of amplitude co-occurrence. The matrix is then transformed into a suite of texture attributes, such as homogeneity, contrast, and randomness, which provide the basis for seismic facies classification. The TMR method uses a texture model as reference to discriminate among seismic features based on a linear, least-squares regression analysis between the model and the data within an analysis window. By implementing customized texture model schemes, the TMR algorithm has the flexibility to characterize subsurface geology for different purposes. A texture model with a constant phase is effective at enhancing the visibility of seismic structural fabrics, a texture model with a variable phase is helpful for visualizing seismic facies, and a texture model with variable amplitude, frequency, and size is instrumental in calibrating seismic to reservoir properties. Preliminary test case studies in the very recent past have indicated that the latest developments in seismic texture analysis have added to the existing amplitude interpretation theories and methodologies. These and future developments in seismic texture theory and methodologies will hopefully lead to a better understanding of the geologic implications of the seismic texture concept and to an improved geologic interpretation of reflection seismic amplitude.

scholarly journals Measuring the Autocorrelation Function of Nanoscale Three-Dimensional Density Distribution in Individual Cells Using Scanning Transmission Electron Microscopy, Atomic Force Microscopy, and a New Deconvolution Algorithm

2017 ◽  
Vol 23 (3) ◽  
pp. 661-667 ◽  
Author(s):  
Yue Li ◽  
Di Zhang ◽  
Ilker Capoglu ◽  
Karl A. Hujsak ◽  
Dhwanil Damania ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Density Distribution ◽  
Scanning Transmission Electron Microscopy ◽  
Mass Density ◽  
Three Dimensional ◽  
Force Microscopy ◽  
Statistical Measures ◽  
Atomic Force ◽  
Transmission Electron ◽  
Scanning Transmission

AbstractEssentially all biological processes are highly dependent on the nanoscale architecture of the cellular components where these processes take place. Statistical measures, such as the autocorrelation function (ACF) of the three-dimensional (3D) mass–density distribution, are widely used to characterize cellular nanostructure. However, conventional methods of reconstruction of the deterministic 3D mass–density distribution, from which these statistical measures can be calculated, have been inadequate for thick biological structures, such as whole cells, due to the conflict between the need for nanoscale resolution and its inverse relationship with thickness after conventional tomographic reconstruction. To tackle the problem, we have developed a robust method to calculate the ACF of the 3D mass–density distribution without tomography. Assuming the biological mass distribution is isotropic, our method allows for accurate statistical characterization of the 3D mass–density distribution by ACF with two data sets: a single projection image by scanning transmission electron microscopy and a thickness map by atomic force microscopy. Here we present validation of the ACF reconstruction algorithm, as well as its application to calculate the statistics of the 3D distribution of mass–density in a region containing the nucleus of an entire mammalian cell. This method may provide important insights into architectural changes that accompany cellular processes.

Using relative geologic time to constrain CNN-based seismic interpretation and property estimation

Geophysics ◽  
2021 ◽  
pp. 1-44
Author(s):  
Aria Abubakar ◽  
Haibin Di ◽  
Zhun Li
Keyword(s):  
Large Scale ◽  
Three Dimensional ◽  
Seismic Interpretation ◽  
Seismic Survey ◽  
Geologic Time ◽  
Seismic Amplitude ◽  
Property Estimation ◽  
Constrained Learning ◽  
Seismic Amplitudes ◽  
Multiplicative Regularization

Three-dimensional seismic interpretation and property estimation is essential to subsurface mapping and characterization, in which machine learning, particularly supervised convolutional neural network (CNN) has been extensively implemented for improved efficiency and accuracy in the past years. In most seismic applications, however, the amount of available expert annotations is often limited, which raises the risk of overfitting a CNN particularly when only seismic amplitudes are used for learning. In such a case, the trained CNN would have poor generalization capability, causing the interpretation and property results of obvious artifacts, limited lateral consistency and thus restricted application to following interpretation/modeling procedures. This study proposes addressing such an issue by using relative geologic time (RGT), which explicitly preserves the large-scale continuity of seismic patterns, to constrain a seismic interpretation and/or property estimation CNN. Such constrained learning is enforced in twofold: (1) from the perspective of input, the RGT is used as an additional feature channel besides seismic amplitude; and more innovatively (2) the CNN has two output branches, with one for matching the target interpretation or properties and the other for reconstructing the RGT. In addition is the use of multiplicative regularization to facilitate the simultaneous minimization of the target-matching loss and the RGT-reconstruction loss. The performance of such an RGT-constrained CNN is validated by two examples, including facies identification in the Parihaka dataset and property estimation in the F3 Netherlands dataset. Compared to those purely from seismic amplitudes, both the facies and property predictions with using the proposed RGT constraint demonstrate significantly reduced artifacts and improved lateral consistency throughout a seismic survey.

scholarly journals FORMING THE STACK OF TEXTURE FEATURES FOR LIVER ULTRASOUND IMAGES CLASSIFICATION

2020 ◽  
Vol 3 (4) ◽  
pp. 240-251
Author(s):  
Dmitro Yuriiovych Hrishko ◽  
Ievgen Arnoldovich Nastenko ◽  
Maksym Oleksandrovych Honcharuk ◽  
Volodymyr Anatoliyovich Pavlov
Keyword(s):  
Random Forest ◽  
Texture Analysis ◽  
Binary Classification ◽  
Texture Features ◽  
Normal Liver ◽  
Group Method ◽  
Ultrasound Images ◽  
Liver Ultrasound ◽  
Random Forest Models ◽  
Occurrence Matrix

This article discusses the use of texture analysis methods to obtain informative features that describe the texture of liver ultrasound images. In total, 317 liver ultrasound images were analyzed, which were provided by the Institute of Nuclear Medicine and Radiation Diagnostics of NAMS of Ukraine. The images were taken by three different sensors (convex, linear, and linear sensor in increased signal level mode). Both images of patients with a normal liver condition and patients with specific liver disease (there were diseases such as: autoimmune hepatitis, Wilson's disease, hepatitis B and C, steatosis, and cirrhosis) were present in the database. Texture analysis was used for “Feature Construction”, which resulted in more than a hundred different informative features that made up a common stack. Among them, there are such features as: three authors’ patented features derived from the grey level co-occurrence matrix; features, obtained with the help of spatial sweep method (working by the principle of group method of data handling), which was applied to ultrasound images; statistical features, calculated on the images, brought to one scale with the help of differential horizontal and vertical matrices, which are proposed by the authors; greyscale pairs ensembles (found using the genetic algorithm), which identify liver pathology on images, transformed with the help of horizontal and vertical differentiations, in the best possible way. The resulting trait stack was used to solve the problem of binary classification (“norma-pathology”) of ultrasound liver images. A Machine Learning method, namely “Random Forest”, was used for this purpose. Before the classification, in order to obtain objective results, the total samples were divided into training (70 %), testing (20 %), and examining (10 %). The result was the best three Random Forest models separately for each sensor, which gave the following recognition rates: 93.4 % for the convex sensor, 92.9 % for the linear sensor, and 92 % for the reinforced linear sensor

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