Seismic characterization of a Mississippi Lime resource play in Osage County, Oklahoma, USA

2013 ◽  
Vol 1 (2) ◽  
pp. SB97-SB108 ◽  
Author(s):  
Benjamin L. Dowdell ◽  
J. Tim Kwiatkowski ◽  
Kurt J. Marfurt
Keyword(s):  
Seismic Data ◽  
Seismic Analysis ◽  
High Porosity ◽  
Fracture Density ◽  
Coherent Noise ◽  
High Fracture ◽  
Data Set ◽  
Horizontal Drilling ◽  
Prestack Inversion ◽  
Osage County

With the advent of horizontal drilling and hydraulic fracturing in the Midcontinent, USA, fields once thought to be exhausted are now experiencing renewed exploitation. However, traditional Midcontinent seismic analysis techniques no longer provide satisfactory reservoir characterization for these unconventional plays; new seismic analysis methods are needed to properly characterize these radically innovative play concepts. Time processing and filtering is applied to a raw 3D seismic data set from Osage County, Oklahoma, paying careful attention to velocity analysis, residual statics, and coherent noise filtering. The use of a robust prestack structure-oriented filter and spectral whitening greatly enhances the results. After prestack time migrating the data using a Kirchhoff algorithm, new velocities are picked. A final normal moveout correction is applied using the new velocities, followed by a final prestack structure-oriented filter and spectral whitening. Simultaneous prestack inversion uses the reprocessed and time-migrated seismic data as input, along with a well from within the bounds of the survey. With offsets out to 3048 m and a target depth of approximately 880 m, we can invert for density in addition to P- and S-impedance. Prestack inversion attributes are sensitive to lithology and porosity while surface seismic attributes such as coherence and curvature are sensitive to lateral changes in waveform and structure. We use these attributes in conjunction with interpreted horizontal image logs to identify zones of high porosity and high fracture density.

Applications of quantitative prestack seismic analysis to unconventional resource play characterization in the Permian/Delaware Basin

2019 ◽  
Vol 38 (2) ◽  
pp. 106-115 ◽  
Author(s):  
Phuong Hoang ◽  
Arcangelo Sena ◽  
Benjamin Lascaud
Keyword(s):  
Hydraulic Fracturing ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Seismic Analysis ◽  
Seismic Inversion ◽  
Reservoir Properties ◽  
Yield Attributes ◽  
Horizontal Drilling ◽  
Delaware Basin ◽  
Unconventional Resource

The characterization of shale plays involves an understanding of tectonic history, geologic settings, reservoir properties, and the in-situ stresses of the potential producing zones in the subsurface. The associated hydrocarbons are generally recovered by horizontal drilling and hydraulic fracturing. Historically, seismic data have been used mainly for structural interpretation of the shale reservoirs. A primary benefit of surface seismic has been the ability to locate and avoid drilling into shallow carbonate karsting zones, salt structures, and basement-related major faults which adversely affect the ability to drill and complete the well effectively. More recent advances in prestack seismic data analysis yield attributes that appear to be correlated to formation lithology, rock strength, and stress fields. From these, we may infer preferential drilling locations or sweet spots. Knowledge and proper utilization of these attributes may prove valuable in the optimization of drilling and completion activities. In recent years, geophysical data have played an increasing role in supporting well planning, hydraulic fracturing, well stacking, and spacing. We have implemented an integrated workflow combining prestack seismic inversion and multiattribute analysis, microseismic data, well-log data, and geologic modeling to demonstrate key applications of quantitative seismic analysis utilized in developing ConocoPhillips' acreage in the Delaware Basin located in Texas. These applications range from reservoir characterization to well planning/execution, stacking/spacing optimization, and saltwater disposal. We show that multidisciplinary technology integration is the key for success in unconventional play exploration and development.

Noise suppression in 2D and 3D seismic data with data-driven sifting algorithms

Geophysics ◽  
2019 ◽  
Vol 85 (1) ◽  
pp. V1-V10
Author(s):  
Julián L. Gómez ◽  
Danilo R. Velis ◽  
Juan I. Sabbione
Keyword(s):  
Seismic Data ◽  
Noise Suppression ◽  
Department Of Energy ◽  
Time Slice ◽  
3D Seismic ◽  
Coherent Noise ◽  
Data Set ◽  
Amplitude Data ◽  
3D Volume ◽  
2D And 3D

We have developed an empirical-mode decomposition (EMD) algorithm for effective suppression of random and coherent noise in 2D and 3D seismic amplitude data. Unlike other EMD-based methods for seismic data processing, our approach does not involve the time direction in the computation of the signal envelopes needed for the iterative sifting process. Instead, we apply the sifting algorithm spatially in the inline-crossline plane. At each time slice, we calculate the upper and lower signal envelopes by means of a filter whose length is adapted dynamically at each sifting iteration according to the spatial distribution of the extrema. The denoising of a 3D volume is achieved by removing the most oscillating modes of each time slice from the noisy data. We determine the performance of the algorithm by using three public-domain poststack field data sets: one 2D line of the well-known Alaska 2D data set, available from the US Geological Survey; a subset of the Penobscot 3D volume acquired offshore by the Nova Scotia Department of Energy, Canada; and a subset of the Stratton 3D land data from South Texas, available from the Bureau of Economic Geology at the University of Texas at Austin. The results indicate that random and coherent noise, such as footprint signatures, can be mitigated satisfactorily, enhancing the reflectors with negligible signal leakage in most cases. Our method, called empirical-mode filtering (EMF), yields improved results compared to other 2D and 3D techniques, such as [Formula: see text] EMD filter, [Formula: see text] deconvolution, and [Formula: see text]-[Formula: see text]-[Formula: see text] adaptive prediction filtering. EMF exploits the flexibility of EMD on seismic data and is presented as an efficient and easy-to-apply alternative for denoising seismic data with mild to moderate structural complexity.

Predictive removal of scattered noise

Geophysics ◽  
2006 ◽  
Vol 71 (2) ◽  
pp. V41-V49 ◽  
Author(s):  
Gérard C. Herman ◽  
Colin Perkins
Keyword(s):  
Mathematical Model ◽  
Wave Propagation ◽  
Surface Wave ◽  
Seismic Data ◽  
Coherent Noise ◽  
Data Set ◽  
Near Surface ◽  
Unique Data ◽  
Surface Wave Propagation ◽  
Petroleum Development

Land seismic data can be severely contaminated with coherent noise. We discuss a deterministic technique to predict and remove scattered coherent noise from land seismic data based on a mathematical model of near-surface wave propagation. We test the method on a unique data set recorded by Petroleum Development of Oman in the Qarn Alam area (with shots and receivers on the same grid), and we conclude that it effectively reduces scattered noise without smearing reflection energy.

A new Fourier azimuthal amplitude variation fracture characterization method: Case study in the Haynesville Shale

Geophysics ◽  
2018 ◽  
Vol 83 (1) ◽  
pp. WA101-WA120 ◽  
Author(s):  
Anthony Barone ◽  
Mrinal K. Sen
Keyword(s):  
Seismic Data ◽  
Large Scale ◽  
Rock Fracture ◽  
Crack Density ◽  
Fracture Density ◽  
Success Rates ◽  
High Fracture ◽  
Fracture Characterization ◽  
Free Data ◽  
Seismic Reflectivity

We have evaluated a novel fracture characterization technique using azimuthal amplitude variations (AVAz) present in 3D seismic data, and we implemented it using synthetic and real seismic data targeting the Haynesville Shale. The method we evaluated overcomes many common AVAz limitations and differs from standard AVAz approaches in the following ways: (1) It was explicitly designed to model vertically fractured transverse isotropic (VFTI) media; (2) it can correctly resolve the fracture strike azimuth without a 90° ambiguity and uses a new magnitude-based method that is invariant to the sign of seismic reflectivity [Formula: see text]; and (3) it incorporates advanced inversion techniques to estimate a novel fracture density proxy that responds linearly to crack density. Our method is based on a newly derived relationship that relates seismic reflectivity directly to rock/fracture properties in VFTI media. We validated our method through rigorous testing on more than 400 synthetic seismic data sets. These synthetic tests indicate that our method excels at estimating fracture azimuth and fracture density from surface seismic data with overall success rates around 80%–85% for noisy data and 90%–95% for noise-free data. Applying our method to field data from the Haynesville Shale indicates that the dominant fracture set is oriented at approximately [Formula: see text] relative to geodetic north, i.e., rotated slightly counterclockwise of east–west. We assume a constant azimuth of 80° throughout our relatively small 20 square miles study area, and our method clearly identifies a general area with unusually high fracture density as well as several smaller subzones of dense fracturing. These smaller features appear to be connected by a pervasive large-scale fracture network covering the area with dominant features aligned at roughly parallel and perpendicular to our calculated fracture azimuth. Although we could not directly confirm these fracture characteristics, our results largely agree with previously published information about fracturing in our study area.

scholarly journals Seismic Imaging of Active and Ancient CO2 Pathways in the Little Grand Wash Fault

2021 ◽  
Author(s):  
Jonathan Yelton
Keyword(s):  
Seismic Velocity ◽  
Fluid Pressure ◽  
P Wave ◽  
Geochemical Data ◽  
Migration Behavior ◽  
High Porosity ◽  
Seismic Velocities ◽  
Fracture Density ◽  
High Fracture ◽  
Stress Changes

Understanding the migration behavior of carbon dioxide (CO2) during long-term geological storage is crucial to the success of carbon capture and sequestration technology. I explore p-wave and s-wave seismic properties across the Little Grand Wash fault in east-central Utah, a natural CO2 seep and analogue for a long-failed sequestration site. Travertines dated to at least 113,000 k.y. and geochemical surveys confirm both modern and ancient CO2 leakage along the fault. Outgassing is currently focused in damage zones where the total fluid pressure may reduce the minimum horizontal effective stress. Regional stress changes may be responsible for decadal- to millennial-scale changes in CO2 pathways. I identify subsurface geologic structure in the upper few hundred meters and relate surface CO2 outgassing zones to seismic reflection and first arrival tomography data. I tie my hammer seismic results to borehole logs, geology from outcrops, and geochemical data. I generate velocity tomograms that cross the fault zone and construct rock physics models. I identify high porosity and/or high fracture density zones from slow seismic velocity zones. These zones match mapped fault locations, are fully saturated, and are conduits for upward fluid/gas migration. Anomalously high seismic velocities at the fault are consistent with ancient CO2 flow pathways. Low CO2 flux regions show seismic velocities consistent with shallow unsaturated host rock. Studying the behavior of CO2 in this system can give insight of potential risks in future sequestration projects.

scholarly journals Coherent noise suppression by learning and analyzing the morphology of the data

Geophysics ◽  
2017 ◽  
Vol 82 (6) ◽  
pp. V397-V411 ◽  
Author(s):  
Pierre Turquais ◽  
Endrias G. Asgedom ◽  
Walter Söllner
Keyword(s):  
Seismic Data ◽  
Noise Suppression ◽  
Morphological Diversity ◽  
Seismic Signal ◽  
Coherent Noise ◽  
Data Set ◽  
Redundant Dictionary ◽  
Manual Search ◽  
Mechanical Noise ◽  
Learned Dictionary

We have developed a method for suppressing coherent noise from seismic data by using the morphological differences between the noise and the signal. This method consists of three steps: First, we applied a dictionary learning method on the data to extract a redundant dictionary in which the morphological diversity of the data is stored. Such a dictionary is a set of unit vectors called atoms that represent elementary patterns that are redundant in the data. Because the dictionary is learned on data contaminated by coherent noise, it is a mix of atoms representing signal patterns and atoms representing noise patterns. In the second step, we separate the noise atoms from the signal atoms using a statistical classification. Hence, the learned dictionary is divided into two subdictionaries: one describing the morphology of the noise and the other one describing the morphology of the signal. Finally, we separate the seismic signal and the coherent noise via morphological component analysis (MCA); it uses sparsity with respect to the two subdictionaries to identify the signal and the noise contributions in the mixture. Hence, the proposed method does not use prior information about the signal and the noise morphologies, but it entirely adapts to the signal and the noise of the data. It does not require a manual search for adequate transforms that may sparsify the signal and the noise, in contrast to existing MCA-based methods. We develop an application of the proposed method for removing the mechanical noise from a marine seismic data set. For mechanical noise that is coherent in space and time, the results show that our method provides better denoising in comparison with the standard FX-Decon, FX-Cadzow, and the curvelet-based denoising methods.

The application of seismic attributes for reservoir characterization in Pre-Tertiary fractured basement, Vietnam-Malaysia offshore

2014 ◽  
Vol 2 (1) ◽  
pp. SA57-SA66 ◽  
Author(s):  
Nguyen Huy Ngoc ◽  
Sahalan B. Aziz ◽  
Nguyen Anh Duc
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Rock Type ◽  
Seismic Attributes ◽  
Basement Rock ◽  
Fracture Density ◽  
High Fracture ◽  
Offshore Area ◽  
Fracture Characteristics ◽  
Basement Reservoirs

The Pre-Tertiary fractured basement forms important hydrocarbon-bearing reservoirs in the Vietnam-Malaysia offshore area, and is being produced from such reservoirs in Vietnam where the authors have extensive working experiences for both clastics and fractured basement reservoirs and in both exploration and development phases. Due to their very small matrix porosity, the basement rocks become reservoirs only when they are strongly fractured. The quality of the fractured basement reservoirs depends on basement rock type, fracture density, and fracture characteristics including aperture, azimuth, dip, continuity, and fracture system intersection. Three-dimensional seismic data is applied widely to characterize these basement reservoirs. Based on results from applying many different seismic attributes to 3D seismic data from different Pre-Tertiary fractured basements in Vietnam and Malaysia, we demonstrate the utility of attributes in characterizing fractured basement reservoirs. Seismic attributes help predict the basement rock type and fracture characteristics from near top basement to deep inside basement. In the zone near the top of basement, the characteristics of fracture systems can be predicted by amplitude, coherence, curvature, and secondary derivative attributes. Deep inside the basement, relative acoustic impedance and its attributes have been successfully applied to predict the distribution of high fracture density, while dip and azimuth, ant-tracking, and gradient magnitude attributes have proven to be effective for predicting fracture characteristics. The accuracy of fracture characterization based on seismic attributes has been verified by drilling results.

An application of the Marchenko internal multiple elimination scheme formulated as a least-squares problem

Geophysics ◽  
2021 ◽  
pp. 1-70
Author(s):  
Rodrigo S. Santos ◽  
Daniel E. Revelo ◽  
Reynam C. Pestana ◽  
Victor Koehne ◽  
Diego F. Barrera ◽  
...  
Keyword(s):  
Least Squares ◽  
Seismic Data ◽  
Neumann Series ◽  
Convergence Criterion ◽  
Coherent Noise ◽  
Data Set ◽  
Least Squares Problem ◽  
Time Migration ◽  
Multiple Elimination ◽  
Seismic Images

Seismic images produced by migration of seismic data related to complex geologies, suchas pre-salt environments, are often contaminated by artifacts due to the presence of multipleinternal reflections. These reflections are created when the seismic wave is reflected morethan once in a source-receiver path and can be interpreted as the main coherent noise inseismic data. Several schemes have been developed to predict and subtract internal multiplereflections from measured data, such as the Marchenko multiple elimination (MME) scheme,which eliminates the referred events without requiring a subsurface model or an adaptivesubtraction approach. The MME scheme is data-driven, can remove or attenuate mostof these internal multiples, and was originally based on the Neumann series solution ofMarchenko’s projected equations. However, the Neumann series approximate solution isconditioned to a convergence criterion. In this work, we propose to formulate the MMEas a least-squares problem (LSMME) in such a way that it can provide an alternative thatavoids a convergence condition as required in the Neumann series approach. To demonstratethe LSMME scheme performance, we apply it to 2D numerical examples and compare theresults with those obtained by the conventional MME scheme. Additionally, we evaluatethe successful application of our method through the generation of in-depth seismic images,by applying the reverse-time migration (RTM) algorithm on the original data set and tothose obtained through MME and LSMME schemes. From the RTM results, we show thatthe application of both schemes on seismic data allows the construction of seismic imageswithout artifacts related to internal multiple events.

Petrophysical properties prediction from prestack seismic data using convolutional neural networks

Geophysics ◽  
2020 ◽  
Vol 85 (5) ◽  
pp. N41-N55
Author(s):  
Vishal Das ◽  
Tapan Mukerji
Keyword(s):  
Neural Networks ◽  
Correlation Coefficient ◽  
Elastic Properties ◽  
Convolutional Neural Networks ◽  
Seismic Data ◽  
High Porosity ◽  
Petrophysical Properties ◽  
Average Correlation ◽  
Data Set ◽  
End To End

We have built convolutional neural networks (CNNs) to obtain petrophysical properties in the depth domain from prestack seismic data in the time domain. We compare two workflows — end-to-end and cascaded CNNs. An end-to-end CNN, referred to as PetroNet, directly predicts petrophysical properties from prestack seismic data. Cascaded CNNs consist of two CNN architectures. The first network, referred to as ElasticNet, predicts elastic properties from prestack seismic data followed by a second network, referred to as ElasticPetroNet, that predicts petrophysical properties from elastic properties. Cascaded CNNs with more than twice the number of trainable parameters as compared to end-to-end CNN demonstrate similar prediction performance for a synthetic data set. The average correlation coefficient for test data between the true and predicted clay volume (approximately 0.7) is higher than the average correlation coefficient between the true and predicted porosity (approximately 0.6) for both networks. The cascaded workflow depends on the availability of elastic properties and is three times more computationally expensive than the end-to-end workflow for training. Coherence plots between the true and predicted values for both cases show that maximum coherence occurs for values of the inverse wavenumber greater than 15 m, which is approximately equal to 1/4 the source wavelength or λ/4. The network predictions have some coherence with the true values even at a resolution of 10 m, which is half of the variogram range used in simulating the spatial correlation of the petrophysical properties. The Monte Carlo dropout technique is used for approximate quantification of the uncertainty of the network predictions. An application of the end-to-end network for prediction of petrophysical properties is made with the Stybarrow field located in offshore Western Australia. The network makes good predictions of petrophysical properties at the well locations. The network is particularly successful in identifying the reservoir facies of interest with high porosity and low clay volume.

Ground-roll attenuation using generative adversarial networks

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. WA255-WA267 ◽  
Author(s):  
Yijun Yuan ◽  
Xu Si ◽  
Yue Zheng
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Coherent Noise ◽  
Traditional Methods ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Persistent Problem ◽  
Ground Roll

Ground roll is a persistent problem in land seismic data. This type of coherent noise often contaminates seismic signals and severely reduces the signal-to-noise ratio of seismic data. A variety of methods for addressing ground-roll attenuation have been developed. However, existing methods are limited, especially when using real land seismic data. For example, when ground roll and reflections overlap in the time or frequency domains, traditional methods cannot completely separate them and they often distort the signals during the suppression process. We have developed a generative adversarial network (GAN) to attenuate ground roll in seismic data. Unlike traditional methods for ground-roll attenuation dependent on various filters, the GAN method is based on a large training data set that includes pairs of data with and without ground roll. After training the neural network with the training data, the network can identify and filter out any noise in the data. To fulfill this purpose, the proposed method uses a generator and a discriminator. Through network training, the generator learns to create the data that can fool the discriminator, and the discriminator can then distinguish between the data produced by the generator and the training data. As a result of the competition between generators and discriminators, generators produce better images whereas discriminators accurately recognize targets. Tests on synthetic and real land seismic data show that the proposed method effectively reveals reflections masked by the ground roll and obtains better results in the attenuation of ground roll and in the preservation of signals compared to the three other methods.

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