A workflow to skeletonize faults and stratigraphic features

Geophysics ◽  
2017 ◽  
Vol 82 (4) ◽  
pp. O57-O70 ◽  
Author(s):  
Jie Qi ◽  
Gabriel Machado ◽  
Kurt Marfurt
Keyword(s):  
Seismic Data ◽  
Fault Plane ◽  
Random Noise ◽  
Principal Component ◽  
Component Structure ◽  
Seismic Surveys ◽  
Seismic Amplitude ◽  
Amplitude Data ◽  
Seismic Reflectors

Improving the accuracy and completeness of subtle discontinuities in noisy seismic data is useful for mapping faults, fractures, unconformities, and stratigraphic edges. We have developed a workflow to improve the quality of coherence attributes. First, we apply principal component structure-oriented filtering to reject random noise and sharpen the lateral edges of seismic amplitude data. Next, we compute eigenstructure coherence, which highlights the stratigraphic and structural discontinuities. We apply a Laplacian of a Gaussian filter to the coherence attribute that sharpens the steeply dipping faults, attenuates the stratigraphic features parallel to the seismic reflectors, and skeletonizes the unconformity features subparallel to the reflectors. Finally, we skeletonize the filtered coherence attribute along with the fault plane. The filtered and skeletonized seismic coherence attribute highlights the geologic discontinuities more clearly and precisely. These discontinuous features can be color coded by their dipping orientation or as a suite of independent, azimuthally limited volumes, providing the interpreter a means of isolating fault sets that are either problematic or especially productive. We validate the effectiveness of our workflow by applying it to seismic surveys acquired from the Gulf of Mexico, USA, and the Great South Basin, New Zealand. The skeletonized result rejects noise and enhances discontinuities seen in the vertical and lateral directions. The corendering of the “fault” azimuth and the fault-dip magnitude exhibits the strengths of the discontinuities and their orientation. Finally, we compared our workflow with the results generated from the swarm intelligence and found our method to be better at tracking short faults and stratigraphic discontinuities.

Estimating tilted fracture weaknesses from azimuthal differences in seismic amplitude data

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. R135-R146
Author(s):  
Huaizhen Chen ◽  
Tiansheng Chen ◽  
Kristopher A. Innanen
Keyword(s):  
Reflection Coefficient ◽  
Tilt Angle ◽  
Seismic Data ◽  
Fractured Reservoirs ◽  
Transverse Isotropy ◽  
Elastic Response ◽  
Moderate Degree ◽  
Fractured Reservoir ◽  
Seismic Amplitude ◽  
Amplitude Data

Tilted transverse isotropy (TTI) provides a useful model for the elastic response of a medium containing aligned fractures with a symmetry axis oriented obliquely in the vertical and horizontal coordinate directions. Robust methods for determining the TTI properties of a medium from seismic observations to characterize fractures are sought. Azimuthal differencing of seismic amplitude data produces quantities that are particularly sensitive to TTI properties. Based on the linear slip fracture model, we express the TTI stiffness matrix in terms of the normal and tangential fracture weaknesses. Perturbing stiffness parameters to simulate an interface separating an isotropic medium and a TTI medium, we derive a linearized P-to-P reflection coefficient expression in which the influence of tilt angle and fracture weaknesses separately emerge. We formulate a Bayesian inversion approach in which amplitude differences between seismic data along two azimuths, interpreted in terms of the reflection coefficient approximation, are used to determine fracture weaknesses and tilt angle. Tests with simulated data confirm that the unknown parameter vector involving fracture weakness and tilted fracture weaknesses is stably estimated from seismic data containing a moderate degree of additive Gaussian noise. The inversion approach is applied to a field surface seismic data acquired over a fractured reservoir; from it, interpretable tilted fracture weaknesses, consistent with expected reservoir geology, are obtained. We determine that our inversion approach and the established inversion workflow can produce the properties of systems of tilted fractures stably using azimuthal seismic amplitude differences, which may add important information for characterization of fractured reservoirs.

Stacking seismic data using local correlation

Geophysics ◽  
2009 ◽  
Vol 74 (3) ◽  
pp. V43-V48 ◽  
Author(s):  
Guochang Liu ◽  
Sergey Fomel ◽  
Long Jin ◽  
Xiaohong Chen
Keyword(s):  
Seismic Data ◽  
Field Data ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Image Point ◽  
Local Correlation ◽  
Seismic Profiles ◽  
Signal To Noise ◽  
Prestack Migration

Stacking plays an important role in improving signal-to-noise ratio and imaging quality of seismic data. However, for low-fold-coverage seismic profiles, the result of conventional stacking is not always satisfactory. To address this problem, we have developed a method of stacking in which we use local correlation as a weight for stacking common-midpoint gathers after NMO processing or common-image-point gathers after prestack migration. Application of the method to synthetic and field data showed that stacking using local correlation can be more effective in suppressing random noise and artifacts than other stacking methods.

Resolving subtle stratigraphic features using spectral ridges and phase residues

2013 ◽  
Vol 1 (1) ◽  
pp. SA93-SA108 ◽  
Author(s):  
Oswaldo Davogustto ◽  
Marcílio Castro de Matos ◽  
Carlos Cabarcas ◽  
Toan Dao ◽  
Kurt J. Marfurt
Keyword(s):  
Seismic Data ◽  
Thin Layers ◽  
Earth Model ◽  
Well Log ◽  
Anadarko Basin ◽  
Seismic Amplitude ◽  
West Texas ◽  
Amplitude Data ◽  
Using Data ◽  
Extract Information

Seismic 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 incised valleys seen on the well logs that are difficult to see on vertical slices through the original seismic amplitude.

A common-reflection-point gather random noise attenuation method based on the synchrosqueezing wavelet transform

2021 ◽  
pp. 1-81
Author(s):  
Xiaokai Wang ◽  
Zhizhou Huo ◽  
Dawei Liu ◽  
Weiwei Xu ◽  
Wenchao Chen
Keyword(s):  
Wavelet Transform ◽  
Frequency Domain ◽  
Seismic Data ◽  
Random Noise ◽  
Principal Component ◽  
Frequency Resolution ◽  
Reflection Point ◽  
Transform Method ◽  
Time Frequency ◽  
The Time Domain

Common-reflection-point (CRP) gather is one extensive-used prestack seismic data type. However, CRP suffers more noise than poststack seismic dataset. The events in the CRP gather are always flat, and the effective signals from neighboring traces in the CRP gather have similar forms not only in the time domain but also in the time-frequency domain. Therefore, we firstly use the synchrosqueezing wavelet transform (SSWT) to decompose seismic traces to the time-frequency domain, as the SSWT has better time-frequency resolution and reconstruction properties. Then we propose to use the similarity of neighboring traces to smooth and threshold the SSWT coefficients in the time-frequency domain. Finally, we used the modified SSWT coefficients to reconstruct the denoised traces for the CRP gather. Synthetic and field data examples show that our proposed method can effectively attenuate random noise with a better attenuation performance than the commonly-used principal component analysis, FX filter, and the continuous wavelet transform method.

Seismic fault attribute estimation using a local fault model

Geophysics ◽  
2019 ◽  
Vol 84 (4) ◽  
pp. O73-O80 ◽  
Author(s):  
Yihuai Lou ◽  
Bo Zhang ◽  
Ruiqi Wang ◽  
Tengfei Lin ◽  
Danping Cao
Keyword(s):  
Seismic Data ◽  
Fault Plane ◽  
3D Seismic ◽  
Seismic Amplitude ◽  
Seismic Fault ◽  
Analysis Window ◽  
Full Analysis ◽  
Fault Interpretation ◽  
Analysis Point ◽  
Staircase Artifacts

Faults in the subsurface can be an avenue of, or a barrier to, hydrocarbon flow and pressure communication. Manual interpretation of discontinuities on 3D seismic amplitude volume is the most common way to define faults within a reservoir. Unfortunately, 3D seismic fault interpretation can be a time-consuming and tedious task. Seismic attributes such as coherence help define faults, but suffer from “staircase” artifacts and nonfault-related stratigraphic discontinuities. We assume that each sample of the seismic data is located at a potential fault plane. The hypothesized fault divides the seismic data centered at the analysis sample into two subwindows. We then compute the coherence for the two subwindows and full analysis window. We repeat the process by rotating the hypothesized fault plane along a set of user-defined discrete fault dip and azimuth. We obtain almost the same coherence values for the subwindows and the full window if the analysis point is not located at a fault plane. The “best” fault plane results in maximum coherence for the subwindows and minimum coherence for the full window if the analysis point is located at a fault plane. To improve the continuity of the fault attributes, we finally smooth the fault probability attribute along the estimated fault plane. We illustrate the effectiveness of our workflow by applying it to a synthetic and two real seismic data. The results indicate that our workflow successfully produces a continuous fault attribute without staircase artifacts and stratigraphic discontinuities.

3D interpolation of irregular data with a POCS algorithm

Geophysics ◽  
2006 ◽  
Vol 71 (6) ◽  
pp. E91-E97 ◽  
Author(s):  
Ray Abma ◽  
Nurul Kabir
Keyword(s):  
Seismic Data ◽  
Convex Sets ◽  
Fourier Transforms ◽  
Higher Dimensions ◽  
Seismic Surveys ◽  
Projection Onto Convex Sets ◽  
Irregular Data ◽  
Restoration Method ◽  
The Cost

Seismic surveys generally have irregular areas where data cannot be acquired. These data should often be interpolated. A projection onto convex sets (POCS) algorithm using Fourier transforms allows interpolation of irregularly populated grids of seismic data with a simple iterative method that produces high-quality results. The original 2D image restoration method, the Gerchberg-Saxton algorithm, is extended easily to higher dimensions, and the 3D version of the process used here produces much better interpolations than typical 2D methods. The only parameter that makes a substantial difference in the results is the number of iterations used, and this number can be overestimated without degrading the quality of the results. This simplicity is a significant advantage because it relieves the user of extensive parameter testing. Although the cost of the algorithm is several times the cost of typical 2D methods, the method is easily parallelized and still completely practical.

scholarly journals Application of Artificial Neural Network in seismic reservoir characterization: a case study from Offshore Nile Delta

2021 ◽  
Author(s):  
Adel Othman ◽  
Mohamed Fathy ◽  
Islam A. Mohamed
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Nile Delta ◽  
Reservoir Properties ◽  
Seismic Amplitude ◽  
Inversion Methods ◽  
Amplitude Data ◽  
Seismic Reservoir

AbstractThe Prediction of the reservoir characteristics from seismic amplitude data is a main challenge. Especially in the Nile Delta Basin, where the subsurface geology is complex and the reservoirs are highly heterogeneous. Modern seismic reservoir characterization methodologies are spanning around attributes analysis, deterministic and stochastic inversion methods, Amplitude Variation with Offset (AVO) interpretations, and stack rotations. These methodologies proved good outcomes in detecting the gas sand reservoirs and quantifying the reservoir properties. However, when the pre-stack seismic data is not available, most of the AVO-related inversion methods cannot be implemented. Moreover, there is no direct link between the seismic amplitude data and most of the reservoir properties, such as hydrocarbon saturation, many assumptions are imbedded and the results are questionable. Application of Artificial Neural Network (ANN) algorithms to predict the reservoir characteristics is a new emerging trend. The main advantage of the ANN algorithm over the other seismic reservoir characterization methodologies is the ability to build nonlinear relationships between the petrophysical logs and seismic data. Hence, it can be used to predict various reservoir properties in a 3D space with a reasonable amount of accuracy. We implemented the ANN method on the Sequoia gas field, Offshore Nile Delta, to predict the reservoir petrophysical properties from the seismic amplitude data. The chosen algorithm was the Probabilistic Neural Network (PNN). One well was kept apart from the analysis and used later as blind quality control to test the results.

scholarly journals Multi-Component Structure of Roland-Morris Disability Questionnaire (RDQ), a Lumbago-Specific QOL (Quality of Life) Measure

2011 ◽  
Vol 2 ◽  
pp. JCM.S6224
Author(s):  
K. Yoh ◽  
M. Yoshizawa ◽  
A. Kuwabara ◽  
K. Tanaka
Keyword(s):  
Quality Of Life ◽  
Bodily Pain ◽  
Principal Component ◽  
Roland Morris Disability Questionnaire ◽  
Social Aspect ◽  
Component Structure ◽  
Life Measure ◽  
Disability Questionnaire ◽  
Disease Specific

Lumbago is one of the most prevalent symptoms in patients with osteoporotic vertebral fracture. Roland-Morris Disability Questionnaire (RDQ) is a quality of life (QOL) questionnaire targeted for evaluating lumbago. Although total score is the usual way of analysis, we have tried to make more use of it by subscale analysis. Forty-four osteoporotic patients were evaluated for their QOL using RDQ and SF-8; a widely accepted generic (non disease-specific) QOL questionnaire. Subscales and summary scores of SF-8 were significantly lower than Japanese norm. Patients with fracture had significantly lower scores including RDQ. Multiple regression analysis has shown that total score of RDQ was significantly contributed by bodily pain as well as other subscales of SF-8. Principal component analysis has revealed that RDQ consists of two components representing general, and mental or social aspect of lumbago. Defining the component structure and determining the procedure to obtain the subscales would make the most use of RDQ, and contribute to the better evaluation of patients with lumbago.

Impact of sedimentary facies on machine learning of acoustic impedance from seismic data: lessons from a geologically realistic 3D model

2021 ◽  
pp. 1-52
Author(s):  
Hongliu Zeng ◽  
Yawen He ◽  
Leo Zeng
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Random Noise ◽  
Sedimentary Facies ◽  
Rock Properties ◽  
Prediction Errors ◽  
Case Scenario ◽  
Data Set ◽  
Single Well

We have developed a new machine-learning (ML) workflow that uses random forest (RF) regression to predict sedimentary-rock properties from stacked and migrated 3D seismic data. The training, validation, and testing are performed with 40 features extracted from a geologically realistic 46 × 66-trace model built in the Miocene Powderhorn Field in South Texas. We focus on the responses of the RF model to sedimentary facies and the strategies adopted to achieve better prediction with various data conditions. We apply explained variation (R2) and root-mean-square (rms) prediction errors to map the relationship between the quality of prediction and the sedimentary facies. In the single-well model, R2 and rms error maps highly resemble sand-percentage maps, or lithofacies maps, showing the facies control on the quality of the ML model. We observe that training with a small well data set (1–10 wells) leads to low and unstable test scores (R2 = 0.2–0.7). The R2 score increases and stabilizes with more (as many as 1000) training wells (R2 = 0.7–0.9), realizing the effective correction of facies bias. The stratigraphic and spatial features are useful and should be used. Weak to moderate random noise (−20 to −15 dB) slightly lowers the training score (R2 < 0.05) and should not be a major concern. Sparse well-supported models can outperform linear regression and model-based inversion and can be useful if caution is exercised. In the best-case scenario (500 wells), the predicted model largely duplicates the true model with a significant improvement in accuracy (R2 = 0.85) and stability. Such results can be applied in most, if not all, exploration and production practices.

Coherence attribute applications on seismic data in various guises — Part 1

2018 ◽  
Vol 6 (3) ◽  
pp. T521-T529 ◽  
Author(s):  
Satinder Chopra ◽  
Kurt J. Marfurt
Keyword(s):  
Seismic Data ◽  
Added Value ◽  
Karst Collapse ◽  
Additional Information ◽  
Seismic Amplitude ◽  
Input Amplitude ◽  
Amplitude Data ◽  
Decomposition Procedure ◽  
Band Spectra ◽  
Definition Of

The iconic coherence attribute is very useful for imaging geologic features such as faults, deltas, submarine canyons, karst collapse, mass-transport complexes, and more. In addition to its preconditioning, the interpretation of discrete stratigraphic features on seismic data is also limited by its bandwidth, where in general the data with higher bandwidth yield crisper features than data with lower bandwidth. Some form of spectral balancing applied to the seismic amplitude data can help in achieving such an objective so that coherence run on spectrally balanced seismic data yields a better definition of the geologic features of interest. The quality of the generated coherence attribute is also dependent in part on the algorithm used for its computation. In the eigenstructure decomposition procedure for coherence computation, spectral balancing equalizes each contribution to the covariance matrix, and thus it yields crisper features on coherence displays. There are other ways to modify the spectrum of the input data in addition to simple spectral balancing, including the amplitude-volume technique, taking the derivative of the input amplitude, spectral bluing, and thin-bed spectral inversion. We compare some of these techniques, and show their added value in seismic interpretation, which forms part of the more elaborate exercise that we had carried out. In other work, we discuss how different spectral components derived from the input seismic data allow interpretation of different scales of discontinuities, what additional information is provided by coherence computed from narrow band spectra, and the different ways to integrate them.

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