Stratal slicing, Part II: Real 3-D seismic data

Geophysics ◽  
1998 ◽  
Vol 63 (2) ◽  
pp. 514-522 ◽  
Author(s):  
Hongliu Zeng ◽  
Stephen C. Henry ◽  
John P. Riola
Keyword(s):  
Seismic Data ◽  
Large Degree ◽  
Original Data ◽  
Seismic Facies ◽  
Seismic Events ◽  
Depositional Facies ◽  
Time Model ◽  
Data Set ◽  
Geologic Time ◽  
Frequency Independent

Three‐dimensional seismic data from the Gulf of Mexico Tertiary section show a close dependence of seismic events on data frequency. While some events remain frequency independent, many events exhibit different occurrences with changing frequency and, therefore, are not parallel to geologic time surfaces. In the data set we have studied, observed maximum time transgression of seismic events is at least 120 ms traveltime on lower frequency sections. Severe interference in lower frequency data may produce false seismic facies characteristics and obscure the true stratigraphic relationships. This phenomenon has important implications for seismic interpretation, particularly for sequence stratigraphic studies. This time transgression problem is mitigated to a large degree by the stratal slicing technique discussed in Part I of this paper. Stratal slicing on a workstation is done by first tracking frequency‐independent, geologic‐time‐equivalent reference seismic events, then building a stratal time model and an amplitude stratal slice volume on the basis of linear interpolation functions between references. The new volumes have an x-, y-coordinate system the same as the original data, but a z-axis of relative geologic time. Stratal slicing is a useful new tool for basin analysis and reservoir delineation by making depositional facies mapping an easier task, especially in wedged depositional sequences. Examples show that the common depositional facies like fluvial channels, deltaic systems, and submarine turbidite deposits are often imaged from real 3-D data with relatively high lateral resolution.

Applications of Artificial Neural Networks for Seismic Facies Classification

2021 ◽  
Vol 73 (02) ◽  
pp. 68-69
Author(s):  
Chris Carpenter
Keyword(s):  
Neural Network ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Probabilistic Neural Network ◽  
Oil Field ◽  
Seismic Facies ◽  
Data Set ◽  
Full Field ◽  
Facies Classification

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 200577, “Applications of Artificial Neural Networks for Seismic Facies Classification: A Case Study From the Mid-Cretaceous Reservoir in a Supergiant Oil Field,” by Ali Al-Ali, Karl Stephen, SPE, and Asghar Shams, Heriot-Watt University, prepared for the 2020 SPE Europec featured at the 82nd EAGE Conference and Exhibition, originally scheduled to be held in Amsterdam, 1-3 December. The paper has not been peer reviewed. Facies classification using data from sources such as wells and outcrops cannot capture all reservoir characterization in the interwell region. Therefore, as an alternative approach, seismic facies classification schemes are applied to reduce the uncertainties in the reservoir model. In this study, a machine-learning neural network was introduced to predict the lithology required for building a full-field Earth model for carbonate reservoirs in southern Iraq. The work and the methodology provide a significant improvement in facies classification and reveal the capability of a probabilistic neural network technique. Introduction The use of machine learning in seismic facies classification has increased gradually during the past decade in the interpretation of 3D and 4D seismic volumes and reservoir characterization work flows. The complete paper provides a literature review regarding this topic. Previously, seismic reservoir characterization has revealed the heterogeneity of the Mishrif reservoir and its distribution in terms of the pore system and the structural model. However, the main objective of this work is to classify and predict the heterogeneous facies of the carbonate Mishrif reservoir in a giant oil field using a multilayer feed-forward network (MLFN) and a probabilistic neural network (PNN) in nonlinear facies classification techniques. A related objective was to find any domain-specific causal relationships among input and output variables. These two methods have been applied to classify and predict the presence of different facies in Mishrif reservoir rock types. Case Study Reservoir and Data Set Description. The West Qurna field is a giant, multibillion-barrel oil field in the southern Mesopotamian Basin with multiple carbonate and clastic reservoirs. The overall structure of the field is a north/south trending anticline steep on the western flank and gentle on the eastern flank. Many producing reservoirs developed in this oil field; however, the Mid- Cretaceous Mishrif reservoir is the main producing reservoir. The reservoir consists of thick carbonate strata (roughly 250 m) deposited on a shallow water platform adjacent to more-distal, deeper-water nonreservoir carbonate facies developing into three stratigraphic sequence units in the second order. Mishrif facies are characterized by a porosity greater than 20% and large permeability contrast from grainstones to microporosity (10-1000 md). The first full-field 3D seismic data set was achieved over 500 km2 during 2012 and 2013 in order to plan the development of all field reservoirs. A de-tailed description of the reservoir has been determined from well logs and core and seismic data. This study is mainly based on facies log (22 wells) and high-resolution 3D seismic volume to generate seismic attributes as the input data for the training of the neural network model. The model is used to evaluate lithofacies in wells without core data but with appropriate facies logs. Also, testing was carried out in parallel with the core data to verify the results of facies classification.

Robust vector median filtering with a structure-adaptive implementation

Geophysics ◽  
2020 ◽  
Vol 85 (5) ◽  
pp. V407-V414
Author(s):  
Yanghua Wang ◽  
Xiwu Liu ◽  
Fengxia Gao ◽  
Ying Rao
Keyword(s):  
Seismic Data ◽  
Impulse Noise ◽  
Reservoir Characterization ◽  
Median Filter ◽  
Original Data ◽  
Thin Layers ◽  
3D Seismic ◽  
Data Set ◽  
Vector Median ◽  
3D Seismic Data

The 3D seismic data in the prestack domain are contaminated by impulse noise. We have adopted a robust vector median filter (VMF) for attenuating the impulse noise from 3D seismic data cubes. The proposed filter has two attractive features. First, it is robust; the vector median that is the output of the filter not only has a minimum distance to all input data vectors, but it also has a high similarity to the original data vector. Second, it is structure adaptive; the filter is implemented following the local structure of coherent seismic events. The application of the robust and structure-adaptive VMF is demonstrated using an example data set acquired from an area with strong sedimentary rhythmites composed of steep-dipping thin layers. This robust filter significantly improves the signal-to-noise ratio of seismic data while preserving any discontinuity of reflections and maintaining the fidelity of amplitudes, which will facilitate the reservoir characterization that follows.

A statistical methodology for deriving reservoir properties from seismic data

Geophysics ◽  
1995 ◽  
Vol 60 (5) ◽  
pp. 1437-1450 ◽  
Author(s):  
Frédérique Fournier ◽  
Jean‐François Derain
Keyword(s):  
Seismic Data ◽  
Large Scale ◽  
Seismic Attributes ◽  
Seismic Facies ◽  
Reservoir Properties ◽  
Data Set ◽  
Analysis Technique ◽  
Seismic Facies Analysis ◽  
Statistical Relationships

The use of seismic data to better constrain the reservoir model between wells has become an important goal for seismic interpretation. We propose a methodology for deriving soft geologic information from seismic data and discuss its application through a case study in offshore Congo. The methodology combines seismic facies analysis and statistical calibration techniques applied to seismic attributes characterizing the traces at the reservoir level. We built statistical relationships between seismic attributes and reservoir properties from a calibration population consisting of wells and their adjacent traces. The correlation studies are based on the canonical correlation analysis technique, while the statistical model comes from a multivariate regression between the canonical seismic variables and the reservoir properties, whenever they are predictable. In the case study, we predicted estimates and associated uncertainties on the lithofacies thicknesses cumulated over the reservoir interval from the seismic information. We carried out a seismic facies identification and compared the geological prediction results in the cases of a calibration on the whole data set and a calibration done independently on the traces (and wells) related to each seismic facies. The later approach produces a significant improvement in the geological estimation from the seismic information, mainly because the large scale geological variations (and associated seismic ones) over the field can be accounted for.

Design and application of a towed land‐streamer system for cost‐effective 2-D and pseudo-3-D shallow seismic data acquisition

Geophysics ◽  
2001 ◽  
Vol 66 (2) ◽  
pp. 482-500 ◽  
Author(s):  
M. van der Veen ◽  
R. Spitzer ◽  
A. G. Green ◽  
P. Wild
Keyword(s):  
Data Acquisition ◽  
Cross Sections ◽  
Seismic Data ◽  
Simulated Data ◽  
Cost Effective ◽  
Original Data ◽  
Data Set ◽  
Rubber Sheet ◽  
Shallow Seismic ◽  
Seismic Data Acquisition

To reduce the field effort required for 2-D and 3-D shallow seismic surveying, we have developed a towed land‐streamer system. It was constructed with self‐orienting gimbal‐mounted geophones housed in heavy (1 kg) cylindrical casings, sturdy seismic cables with reinforced kevlar sheathing, robust waterproof connectors, and a reinforced rubber sheet that helped prevent cable snagging, maintained geophone alignment, and provided additional hold‐down weight for the geophones. Each cable had takeouts for 12 geophones at 1 m or 2 m intervals. By eliminating the need for manual geophone planting and cable laying, acquisition of 2-D profiles with this system proved to be 50–100% faster with 30–40% fewer personnel than conventional procedures. Costs of the land‐streamer system and total weight to be pulled could be minimized by employing nonuniform receiver configurations. Short receiver intervals (e.g., 1 m) at near offsets were necessary for identifying and mapping shallow (<50 m) reflections, whereas larger receiver intervals (e.g., 2 m) at far offsets were sufficient for imaging deeper (>50 m) reflections and estimating velocity‐depth functions. Our land‐streamer system has been tested successfully on a variety of recording surfaces (e.g., meadow, asphalt road, and compact gravel track). The heavy weight of the geophone casings and rubber sheet ensured good geophone‐to‐ground coupling. On the asphalt surface, a greater proportion of high‐frequency (above 300–350 Hz) energy was recorded by the land streamer than by standard baseplate‐mounted geophones. The land‐streamer system is a practical and efficient means for surveying in urbanized areas. Acquisition and processing of 3-D shallow seismic data with the land‐streamer system was simulated by appropriately decimating and reprocessing an existing 3-D shallow seismic data set. Average subsurface coverage of the original data was ∼50 fold, whereas that of the simulated data was ∼5 fold. The effort required to collect the simulated pseudo-3-D data set would have been approximately 7% of that needed for the original field campaign. Application of important data‐dependent processing procedures (e.g., refraction static corrections and velocity analyses) to the simulated data set produced surprisingly good results. Because receiver spacing along simulated cross‐lines (6 m) was double that along in‐lines (3 m), a pattern of high and low amplitudes was observed on cross‐sections and time slices at early traveltimes (⩽50 ms). At greater traveltimes, all major reflections could be identified and mapped on the land‐streamer data set. With this cost‐effective approach to pseudo-3-D seismic data acquisition, it is expected that shallow 3-D seismic reflection surveying will become attractive for a broader range of engineering and environmental applications.

Artificial immune-based self-organizing maps for seismic-facies analysis

Geophysics ◽  
2012 ◽  
Vol 77 (4) ◽  
pp. O45-O53 ◽  
Author(s):  
Puneet Saraswat ◽  
Mrinal K. Sen
Keyword(s):  
Immune System ◽  
Data Reduction ◽  
Seismic Data ◽  
Artificial Immune System ◽  
Facies Analysis ◽  
Seismic Facies ◽  
Artificial Immune ◽  
Self Organizing Maps ◽  
Data Set ◽  
Seismic Facies Analysis

Seismic facies, combined with well-log data and other seismic attributes such as coherency, curvature, and AVO, play an important role in subsurface geological studies, especially for identification of depositional structures. The effectiveness of any seismic facies analysis algorithm depends on whether or not it is driven by local geologic factors, the absence of which may lead to unrealistic information about subsurface geology, depositional environment, and lithology. This includes proper identification of number of classes or facies existing in the data set. We developed a hybrid waveform classification algorithm based on an artificial immune system and self-organizing maps (AI-SOM), that forms the class of unsupervised classification or automatic facies identification followed by facies map generation. The advantage of AI-SOM is that, unlike, a stand-alone SOM, it is more robust in the presence of noise in seismic data. Artificial immune system (AIS) is an excellent data reduction technique providing a compact representation of the training data; this is followed by clustering and identification of number of clusters in the data set. The reduced data set from AIS processing serves as an excellent input to SOM processing. Thus, facies maps generated from AI-SOM are less affected by noise and redundancy in the data set. We tested the effectiveness of our algorithm with application to an offshore 3D seismic volume from F3 block in the Netherlands. The results confirmed that we can better interpret an appropriate number of facies in the seismic data using the AI-SOM approach than with a conventional SOM. We also examined the powerful data-reduction capabilities of AIS and advantages the of AI-SOM over SOM when data under consideration were noisy and redundant.

Quantitative lithostratigraphic interpretation of seismic data for characterization of the Unayzah Formation in central Saudi Arabia

Geophysics ◽  
2002 ◽  
Vol 67 (5) ◽  
pp. 1372-1381 ◽  
Author(s):  
Frédérique Fournier ◽  
Pierre‐Yves Déquirez ◽  
Costas G. Macrides ◽  
Marty Rademakers
Keyword(s):  
Pattern Recognition ◽  
Saudi Arabia ◽  
Seismic Data ◽  
Facies Analysis ◽  
Quality Criteria ◽  
Seismic Facies ◽  
Data Set ◽  
Good Convergence ◽  
Average Porosity ◽  
Seismic Facies Analysis

A lithostratigraphic interpretation of seismic data sets covering the Unayzah fluviatile formation in central Saudi Arabia has allowed us to map the sandstone distribution and to characterize the average porosity of the formation. First, sandstone distribution was predicted through seismic facies identification and interpretation with well information. Seismic facies analysis was performed with statistical pattern recognition applied to the portions of traces at the reservoir level, these traces being characterized by a series of seismic attributes. A good convergence of results from unsupervised and supervised pattern recognition was observed. This increases the confidence in the interpretation of sandprone facies. Second, using a statistical relationship between the reservoir average porosity defined at the wells and selected amplitudes of adjacent traces, the porosity was predicted all over the area covered by the seismic information. The model was based on a multivariate linear regression, showing satisfying quality criteria (correlation coefficient, residuals, etc.). The porosity variation predicted from the seismic data complements the sandstone distribution, derived from the seismic facies analysis. In particular, some areas where sandstones are predicted do not appear as porous as could have been suspected from their lithological content, perhaps as the result of diagenetic effects. Last, seismic facies analysis with pattern recognition applied to 2‐D exploratory lines, partly intersecting the 3‐D data set, led to the identification of potential prospects (Unayzah interval with a high sand–shale ratio).

scholarly journals Focal-time analysis: A new method for stratigraphic depth control of microseismicity and induced seismic events

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. KS173-KS182 ◽  
Author(s):  
Andrew Poulin ◽  
Ron Weir ◽  
David Eaton ◽  
Nadine Igonin ◽  
Yukuan Chen ◽  
...  
Keyword(s):  
Seismic Data ◽  
Seismic Events ◽  
3D Seismic ◽  
Time Analysis ◽  
High Quality ◽  
Data Set ◽  
Near Surface ◽  
Depth Control ◽  
Zero Offset ◽  
3D Seismic Data

Focal-time analysis is a straightforward data-driven method to obtain robust stratigraphic depth control for microseismicity or induced seismic events. The method eliminates the necessity to build an explicit, calibrated velocity model for hypocenter depth estimation, although it requires multicomponent 3D seismic data that are colocated with surface or near-surface microseismic observations. Event focal depths are initially expressed in terms of zero-offset focal time (two-way P-P reflection time) to facilitate registration and visualization with 3D seismic data. Application of the focal-time method requires (1) high-quality P- and S-wave time picks, which are extrapolated to zero offset and (2) registration of correlative P-P and P-S reflections to provide [Formula: see text] and [Formula: see text] time-depth control. We determine the utility of this method by applying it to a microseismic and induced-seismicity data set recorded with a shallow-borehole monitoring array in Alberta, Canada, combined with high-quality multicomponent surface seismic data. The calculated depth distribution of events is in good agreement with hypocenter locations obtained independently using a nonlinear global-search method. Our results reveal that individual event clusters have distinct depth distributions that can provide important clues about the mechanisms of fault activation.

Nonstatinary deconvolutive Radon transform

Geophysics ◽  
2021 ◽  
pp. 1-35
Author(s):  
Hojjat Haghshenas Lari ◽  
Ali Gholami
Keyword(s):  
Radon Transform ◽  
Seismic Data ◽  
Estimation Method ◽  
Original Data ◽  
Seismic Events ◽  
Seismic Data Processing ◽  
Convolution Operation ◽  
Inverse Transform ◽  
Time Space ◽  
Overlap Method

Different versions of the Radon transform (RT) are widely used in seismic data processing tofocus the recorded seismic events. Multiple separation, data interpolation, and noise attenuationare some of RT applications in seismic processing work-flows. Unfortunately, the conventional RTmethods cannot focus the events perfectly in the RT domain. This problem arises due to theblurring effects of the source wavelet and the nonstationary nature of the seismic data. Sometimes,the distortion results in a big difference between the original data and its inverse transform. Wepropose a nonstationary deconvolutive RT to handle these two issues. Our proposed algorithm takesadvantage of a nonstationary convolution technique. that builds on the concept of block convolutionand the overlap method, where the convolution operation is defined separately for overlapping blocks.Therefore, it allows the Radon basis function to take arbitrary shapes in time and space directions. Inaddition, we introduce a nonstationary wavelet estimation method to determine time-space-varyingwavelets. The wavelets and the Radon panel are estimated simultaneously and in an alternative way.Numerical examples demonstrate that our nonstationary deconvolutive RT method can significantlyimprove the sparsity of Radon panels. Hence, the inverse RT does not suffer from the distortioncaused by the unfocused seismic events.

Predictive coherence

2015 ◽  
Vol 3 (4) ◽  
pp. SAE1-SAE7 ◽  
Author(s):  
Parvaneh Karimi ◽  
Sergey Fomel ◽  
Lesli Wood ◽  
Dallas Dunlap
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Original Data ◽  
Data Set ◽  
Additional Information ◽  
Reference Trace ◽  
Analysis Window ◽  
Spatial Window ◽  
The Relationship ◽  
Fault Information

Detection and interpretation of fault systems and stratigraphic features and the relationship between them are crucial for seismic interpretation and reservoir characterization. To provide better interpretation insight and to be able to extract overlooked features out of seismic data volumes, we have developed a new attribute that detects faults and other discontinuities while handling local nonstationary variations across them. First, we used predictive painting to form a structural prediction of seismic events from neighboring traces (left and right neighboring traces in 2D and neighboring traces in all directions around a reference trace in 3D) according to the local structural slopes. Then, we computed prediction residuals by subtracting each prediction from the original data, and we found the smallest prediction-error interval for each point that best represented discontinuity information at that point. The extracted fault information changed with location (spatially and temporally), and it was nonstationary. Conventional coherence measures operate on a spatial window of neighboring traces and a temporal (vertical) analysis window of samples above and below the analysis point, and they can hardly cope with nonstationarity in fault information. In contrast, in our method, neither temporal nor spatial windows were involved in coherence computation, which allowed us to honor nonstationary changes of fault information and to achieve high resolution in the vertical and lateral directions. To assess the performance of the proposed attribute, we compared it with the conventional coherence attribute over the same data set. The comparison demonstrated the effectiveness of discontinuity detection using predictive coherence and showed its value in extracting additional information from seismic data.

Estimating frequency-dependent attenuation quality factor values from prestack surface seismic data

Geophysics ◽  
2017 ◽  
Vol 82 (1) ◽  
pp. O11-O22 ◽  
Author(s):  
James Beckwith ◽  
Roger Clark ◽  
Linda Hodgson
Keyword(s):  
Quality Factor ◽  
Seismic Data ◽  
Synthetic Data ◽  
Rock Physics ◽  
Analytic Form ◽  
Ocean Bottom ◽  
Data Set ◽  
Frequency Dependent ◽  
Seismic Quality Factor ◽  
Frequency Independent

The intrinsic seismic quality factor [Formula: see text] is known from poroelastic rock-physics theory to be frequency dependent, even within typical bandwidths of individual surface- and borehole-based surveys in which measurement methods usually deliver frequency-independent [Formula: see text]. Thus, measuring frequency-dependent [Formula: see text] instead offers better characterization of seismic properties and moreover a potential step toward estimating permeability directly from seismic data. Therefore, we have introduced a method to measure frequency-dependent [Formula: see text] from pairs of reflections in prestack [Formula: see text]-[Formula: see text] domain surface seismic data — a data type that, unlike a vertical seismic profile, offers useful areal coverage. Although, in principle, any analytic form with a manageable number of parameters could be prescribed, the frequency dependence of [Formula: see text] is modeled as a power law, [Formula: see text]. Inversion is done with a simple grid search over coefficient ([Formula: see text]) and exponent [Formula: see text], seeking a minimum [Formula: see text]-norm. We have found, using a numerical experiment and a synthetic data set, that it is robust and also accurate down to a signal-to-noise ratio of approximately 0.65. Then, [Formula: see text] is estimated for some 955 [Formula: see text] superbins of a 3D prestack ocean bottom cable data set over the Kinnoull field, central North Sea. All combinations of eight prominent reflections between Top Miocene and Base Cretaceous were treated together to give some 21,000 frequency-dependent and (for comparison) constant-[Formula: see text] results. The median coefficient ([Formula: see text]) and exponent [Formula: see text] were 0.0074 and 0.06, respectively, with sharply peaked distributions (excess kurtosis [Formula: see text]). Outlier, strongly frequency-dependent results, given by [Formula: see text], coincide with low-frequency “shadows” under amplitude anomalies, adversely affecting the spectra of reflections. The inferred frequency-dependent [Formula: see text] at 32.5 Hz, the center of the available bandwidth, is not statistically different from the frequency-independent [Formula: see text], 181 with a standard error from the distribution of one, derived from the same data. Hence for this data set, a constant-[Formula: see text] assumption would in fact be adequate. However, our method has the ability to measure stable estimates of [Formula: see text].

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