Can fracture orientation and intensity be detected from seismic data? Woodford Formation, Anadarko Basin, Oklahoma investigation

2019 ◽  
Vol 38 (2) ◽  
pp. 144-150 ◽  
Author(s):  
Marianne Rauch-Davies ◽  
David Langton ◽  
Michael Bradshaw ◽  
Allon Bartana ◽  
Dan Kosloff ◽  
...  
Keyword(s):  
Seismic Data ◽  
Subsurface Layer ◽  
Azimuthal Velocity ◽  
Natural Fracture ◽  
Woodford Shale ◽  
Data Set ◽  
Anadarko Basin ◽  
Fracture Orientation ◽  
Anisotropic Elastic ◽  
Elastic Simulation

With readily available wide-azimuth, onshore, 3D seismic data, the search for attributes utilizing the azimuthal information is ongoing. Theoretically, in the presence of ordered fracturing, the seismic wavefront shape changes from spherical to nonspherical with the propagation velocity being faster parallel to the fracturing and slower perpendicular to the fracture direction. This concept has been adopted and is used to map fracture direction and density within unconventional reservoirs. More specifically, azimuthal variations in normal moveout velocity or migration velocity are often used to infer natural fracture orientation. Analyses of recent results have called into question whether azimuthal velocity linked to intrinsic azimuthal velocity variations can actually be detected from seismic data. By use of 3D orthorhombic anisotropic elastic simulation, we test whether fracture orientation and intensity can be detected from seismic data. We construct two subsurface models based on interpreted subsurface layer structure of the Anadarko Basin in Oklahoma. For the first model, the material parameters in the layers are constant vertically transverse isotropic (VTI) in all intervals. The second model was constructed the same way as the base model for all layers above the Woodford Shale Formation. For the shale layer, orthorhombic properties were introduced. In addition, a thicker wedge layer was added below the shale layer. Using the constructed model, synthetic seismic data were produced by means of 3D anisotropic elastic simulation resulting in two data sets: VTI and orthorhombic. The simulated data set was depth migrated using the VTI subsurface model. After migration, the residual moveouts on the migrated gathers were analyzed. The analysis of the depth-migrated model data indicates that for the typical layer thicknesses of the Woodford Shale layer in the Anadarko Basin, observed and modeled percentage of anisotropy and target depth, the effect of intrinsic anisotropy is too small to be detected in real seismic data.

A seismic to simulation unconventional workflow using automated fault-detection attributes

2017 ◽  
Vol 5 (3) ◽  
pp. SJ41-SJ48 ◽  
Author(s):  
Jesse Lomask ◽  
Luisalic Hernandez ◽  
Veronica Liceras ◽  
Amit Kumar ◽  
Anna Khadeeva
Keyword(s):  
Seismic Data ◽  
Spatial Information ◽  
Spatial Location ◽  
Statistical Information ◽  
Natural Fracture ◽  
Fracture Networks ◽  
Data Set ◽  
Fault Indicator ◽  
Well Data ◽  
Additional Constraints

Natural fracture networks (NFNs) are used in unconventional reservoir simulators to model pressure and saturation changes in fractured rocks. These fracture networks are often derived from well data or well data combined with a variety of seismic-derived attributes to provide spatial information away from the wells. In cases in which there is a correlation between faults and fractures, the use of a fault indicator can provide additional constraints on the spatial location of the natural fractures. We use a fault attribute based on fault-oriented semblance as a secondary conditioner for the generation of NFNs. In addition, the distribution of automatically extracted faults from the fault-oriented semblance is used to augment the well-derived statistics for natural fracture generation. Without the benefit of this automated fault-extraction solution, to manually extract the fault-statistical information from the seismic data would be prohibitively tedious and time consuming. Finally, we determine, on a 3D field unconventional data set, that the use of fault-oriented semblance results in simulations that are significantly more geologically reasonable.

Addressing artifacts in PP-PS registration prior to performing joint impedance inversion

2020 ◽  
Vol 39 (1) ◽  
pp. 47-52
Author(s):  
Satinder Chopra ◽  
Ritesh Kumar Sharma
Keyword(s):  
Seismic Data ◽  
The United States ◽  
Synthetic Seismograms ◽  
Data Sets ◽  
Well Log ◽  
Data Set ◽  
Anadarko Basin ◽  
Matching Process ◽  
Impedance Inversion ◽  
Integrated Interpretation

Multicomponent seismic data analysis enhances confidence in interpretation by providing mode-converted PS data for imaging the subsurface. Integrated interpretation of PP and PS data begins with the identification of reflections corresponding to similar geologic events on both data sets. This identification is accomplished by carrying out well-log correlation through the generation of PP and PS synthetic seismograms. There are a few issues associated with the approach. One of the issues is that PS data have lower resolution than PP data. This presents difficulties in the correlation of equivalent reflection events on both data sets. Even if few consistent horizons are tracked, the horizon-matching process introduces artifacts on the PS data mapped in PP time. In this paper, we elaborate on such challenges with a data set from the Anadarko Basin in the United States. We then propose a novel workflow to address the challenges.

Regularization and datuming of seismic data by weighted, damped least squares

Geophysics ◽  
2006 ◽  
Vol 71 (5) ◽  
pp. U67-U76 ◽  
Author(s):  
Robert J. Ferguson
Keyword(s):  
Least Squares ◽  
Seismic Data ◽  
Synthetic Data ◽  
Real Data ◽  
Structural Data ◽  
Irregular Sampling ◽  
Data Set ◽  
Near Surface ◽  
Damped Least Squares ◽  
Wavefield Extrapolation

The possibility of improving regularization/datuming of seismic data is investigated by treating wavefield extrapolation as an inversion problem. Weighted, damped least squares is then used to produce the regularized/datumed wavefield. Regularization/datuming is extremely costly because of computing the Hessian, so an efficient approximation is introduced. Approximation is achieved by computing a limited number of diagonals in the operators involved. Real and synthetic data examples demonstrate the utility of this approach. For synthetic data, regularization/datuming is demonstrated for large extrapolation distances using a highly irregular recording array. Without approximation, regularization/datuming returns a regularized wavefield with reduced operator artifacts when compared to a nonregularizing method such as generalized phase shift plus interpolation (PSPI). Approximate regularization/datuming returns a regularized wavefield for approximately two orders of magnitude less in cost; but it is dip limited, though in a controllable way, compared to the full method. The Foothills structural data set, a freely available data set from the Rocky Mountains of Canada, demonstrates application to real data. The data have highly irregular sampling along the shot coordinate, and they suffer from significant near-surface effects. Approximate regularization/datuming returns common receiver data that are superior in appearance compared to conventional datuming.

Fracture mapping from azimuthal velocity analysis using 3D surface seismic data

1996 ◽  
Author(s):  
Dennis Corrigan ◽  
Robert Withers ◽  
Jim Darnall ◽  
Tracey Skopinski
Keyword(s):  
Seismic Data ◽  
Azimuthal Velocity ◽  
Velocity Analysis ◽  
3D Surface ◽  
Fracture Mapping

Stochastic inversion of pressure and saturation changes from time-lapse AVO data

Geophysics ◽  
2006 ◽  
Vol 71 (5) ◽  
pp. C81-C92 ◽  
Author(s):  
Helene Hafslund Veire ◽  
Hilde Grude Borgos ◽  
Martin Landrø
Keyword(s):  
Stochastic Model ◽  
Seismic Data ◽  
Synthetic Data ◽  
Time Lapse ◽  
Bayesian Framework ◽  
Reservoir Modeling ◽  
Data Set ◽  
The North ◽  
Fluid Saturation ◽  
Likelihood Model

Effects of pressure and fluid saturation can have the same degree of impact on seismic amplitudes and differential traveltimes in the reservoir interval; thus, they are often inseparable by analysis of a single stacked seismic data set. In such cases, time-lapse AVO analysis offers an opportunity to discriminate between the two effects. We quantify the uncertainty in estimations to utilize information about pressure- and saturation-related changes in reservoir modeling and simulation. One way of analyzing uncertainties is to formulate the problem in a Bayesian framework. Here, the solution of the problem will be represented by a probability density function (PDF), providing estimations of uncertainties as well as direct estimations of the properties. A stochastic model for estimation of pressure and saturation changes from time-lapse seismic AVO data is investigated within a Bayesian framework. Well-known rock physical relationships are used to set up a prior stochastic model. PP reflection coefficient differences are used to establish a likelihood model for linking reservoir variables and time-lapse seismic data. The methodology incorporates correlation between different variables of the model as well as spatial dependencies for each of the variables. In addition, information about possible bottlenecks causing large uncertainties in the estimations can be identified through sensitivity analysis of the system. The method has been tested on 1D synthetic data and on field time-lapse seismic AVO data from the Gullfaks Field in the North Sea.

Application of waveform tomography to a crooked-line 2D land seismic data set

Geophysics ◽  
2015 ◽  
Vol 80 (5) ◽  
pp. B115-B129 ◽  
Author(s):  
Rie Kamei ◽  
Takayuki Miyoshi ◽  
R. Gerhard Pratt ◽  
Mamoru Takanashi ◽  
Shogo Masaya
Keyword(s):  
Seismic Data ◽  
Data Set ◽  
Waveform Tomography

Stochastic discrete fracture network modeling in shale reservoirs via integration of seismic attributes and petrophysical data

2021 ◽  
pp. 1-50
Author(s):  
Yongchae Cho
Keyword(s):  
Seismic Data ◽  
Stochastic Approach ◽  
Fracture Network ◽  
Natural Fracture ◽  
Well Logs ◽  
Fracture Model ◽  
Well Log ◽  
Discrete Fracture ◽  
Discrete Fracture Network Modeling ◽  
Fracture Network Modeling

The prediction of natural fracture networks and their geomechanical properties remains a challenge for unconventional reservoir characterization. Since natural fractures are highly heterogeneous and sub-seismic scale, integrating petrophysical data (i.e., cores, well logs) with seismic data is important for building a reliable natural fracture model. Therefore, I introduce an integrated and stochastic approach for discrete fracture network modeling with field data demonstration. In the proposed method, I first perform a seismic attribute analysis to highlight the discontinuity in the seismic data. Then, I extrapolate the well log data which includes localized but high-confidence information. By using the fracture intensity model including both seismic and well logs, I build the final natural fracture model which can be used as a background model for the subsequent geomechanical analysis such as simulation of hydraulic fractures propagation. As a result, the proposed workflow combining multiscale data in a stochastic approach constructs a reliable natural fracture model. I validate the constructed fracture distribution by its good agreement with the well log data.

High-resolution stratigraphic characterization of natural fracture attributes in the Woodford Shale, Arbuckle Wilderness and US-77D Outcrops, Murray County, Oklahoma

2018 ◽  
Vol 6 (1) ◽  
pp. SC29-SC41 ◽  
Author(s):  
Sayantan Ghosh ◽  
John N. Hooker ◽  
Caleb P. Bontempi ◽  
Roger M. Slatt
Keyword(s):  
Natural Fracture ◽  
Fracture Aperture ◽  
Aperture Size ◽  
Fracture Density ◽  
Area Formula ◽  
Woodford Shale ◽  
Fracture Spacing ◽  
Spacing Ratio ◽  
Fracture Intensity ◽  
Bed Thickness

Natural fracture aperture-size, spacing, and stratigraphic variation in fracture density are factors determining the fluid-flow capacity of low-permeability formations. In this study, several facies were identified in a Woodford Shale complete section. The section was divided into four broad stratigraphic zones based on interbedding of similar facies. Average thicknesses and percentages of brittle and ductile beds in each stratigraphic foot were recorded. Also, five fracture sets were identified. These sets were split into two groups based on their trace exposures. Fracture linear intensity (number of fractures normalized to the scanline length [[Formula: see text]]) values were quantified for brittle and ductile beds. Individual fracture intensity-bed thickness linear equations were derived. These equations, along with the average bed thickness and percentage of brittle and ductile lithologies in each stratigraphic foot, were used to construct a fracture areal density (number of fracture traces normalized to the trace exposure area [[Formula: see text]]) profile. Finally, the fracture opening-displacement size variations, clustering tendencies, and fracture saturation were quantified. Fracture intensity-bed thickness equations predict approximately 1.5–3 times more fractures in the brittle beds compared with ductile beds at any given bed thickness. Parts of zone 2 and almost entire zone 3, located in the upper and middle Woodford, respectively, have high fracture densities and are situated within relatively organic-rich (high-GR) intervals. These intervals may be suitable horizontal well landing targets. All observed fracture cement exhibit a lack of crack-seal texture. Characteristic aperture-size distributions exist, with most apertures in the 0.05–1 mm (0.00016–0.0032 ft) range. In the chert beds, fracture cement is primarily bitumen or silica or both. Fractures in dolomite beds primarily have calcite cement. The average fracture spacing indices (i.e., bed thickness-fracture spacing ratio) in brittle and ductile beds were determined to be 2 and 1.2, respectively. Uniform fracture spacing was observed along all scanlines in the studied beds.

Sparse reflectivity inversion for nonstationary seismic data with surface-related multiples: Numerical and field-data experiments

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. R199-R217 ◽  
Author(s):  
Xintao Chai ◽  
Shangxu Wang ◽  
Genyang Tang
Keyword(s):  
Seismic Data ◽  
Resolution Enhancement ◽  
Synthetic Data ◽  
Data Sets ◽  
Data Set ◽  
Anelastic Attenuation ◽  
Seismic Resolution ◽  
Text Filtering ◽  
The Stability ◽  
Reflectivity Inversion

Seismic data are nonstationary due to subsurface anelastic attenuation and dispersion effects. These effects, also referred to as the earth’s [Formula: see text]-filtering effects, can diminish seismic resolution. We previously developed a method of nonstationary sparse reflectivity inversion (NSRI) for resolution enhancement, which avoids the intrinsic instability associated with inverse [Formula: see text] filtering and generates superior [Formula: see text] compensation results. Applying NSRI to data sets that contain multiples (addressing surface-related multiples only) requires a demultiple preprocessing step because NSRI cannot distinguish primaries from multiples and will treat them as interference convolved with incorrect [Formula: see text] values. However, multiples contain information about subsurface properties. To use information carried by multiples, with the feedback model and NSRI theory, we adapt NSRI to the context of nonstationary seismic data with surface-related multiples. Consequently, not only are the benefits of NSRI (e.g., circumventing the intrinsic instability associated with inverse [Formula: see text] filtering) extended, but also multiples are considered. Our method is limited to be a 1D implementation. Theoretical and numerical analyses verify that given a wavelet, the input [Formula: see text] values primarily affect the inverted reflectivities and exert little effect on the estimated multiples; i.e., multiple estimation need not consider [Formula: see text] filtering effects explicitly. However, there are benefits for NSRI considering multiples. The periodicity and amplitude of the multiples imply the position of the reflectivities and amplitude of the wavelet. Multiples assist in overcoming scaling and shifting ambiguities of conventional problems in which multiples are not considered. Experiments using a 1D algorithm on a synthetic data set, the publicly available Pluto 1.5 data set, and a marine data set support the aforementioned findings and reveal the stability, capabilities, and limitations of the proposed method.

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