Improved Geological Inference from Seismic Data Using Composite, Colour-blended Seismic Attributes

2014 ◽  
Author(s):  
G.S. Paton ◽  
N.J. McArdle ◽  
J. Henderson
Keyword(s):  
Seismic Data ◽  
Seismic Attributes

scholarly journals Pitfalls and limitations in seismic attribute interpretation of tectonic features

2015 ◽  
Vol 3 (1) ◽  
pp. SB5-SB15 ◽  
Author(s):  
Kurt J. Marfurt ◽  
Tiago M. Alves
Keyword(s):  
Seismic Data ◽  
Seismic Attributes ◽  
Seismic Attribute ◽  
3D Data ◽  
Amplitude Data ◽  
Seismic Sections ◽  
Push Down ◽  
Data Coherence ◽  
Level Of Experience ◽  
Tectonic Features

Seismic attributes are routinely used to accelerate and quantify the interpretation of tectonic features in 3D seismic data. Coherence (or variance) cubes delineate the edges of megablocks and faulted strata, curvature delineates folds and flexures, while spectral components delineate lateral changes in thickness and lithology. Seismic attributes are at their best in extracting subtle and easy to overlook features on high-quality seismic data. However, seismic attributes can also exacerbate otherwise subtle effects such as acquisition footprint and velocity pull-up/push-down, as well as small processing and velocity errors in seismic imaging. As a result, the chance that an interpreter will suffer a pitfall is inversely proportional to his or her experience. Interpreters with a history of making conventional maps from vertical seismic sections will have previously encountered problems associated with acquisition, processing, and imaging. Because they know that attributes are a direct measure of the seismic amplitude data, they are not surprised that such attributes “accurately” represent these familiar errors. Less experienced interpreters may encounter these errors for the first time. Regardless of their level of experience, all interpreters are faced with increasingly larger seismic data volumes in which seismic attributes become valuable tools that aid in mapping and communicating geologic features of interest to their colleagues. In terms of attributes, structural pitfalls fall into two general categories: false structures due to seismic noise and processing errors including velocity pull-up/push-down due to lateral variations in the overburden and errors made in attribute computation by not accounting for structural dip. We evaluate these errors using 3D data volumes and find areas where present-day attributes do not provide the images we want.

Unsupervised machine learning using 3D seismic data applied to reservoir evaluation and rock type identification

2021 ◽  
pp. 1-69
Author(s):  
Marwa Hussein ◽  
Robert R. Stewart ◽  
Deborah Sacrey ◽  
Jonny Wu ◽  
Rajas Athale
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Rock Type ◽  
Seismic Attributes ◽  
Reservoir Quality ◽  
3D Seismic ◽  
Reservoir Properties ◽  
Unsupervised Machine Learning ◽  
Amplitude Data ◽  
3D Seismic Data

Net reservoir discrimination and rock type identification play vital roles in determining reservoir quality, distribution, and identification of stratigraphic baffles for optimizing drilling plans and economic petroleum recovery. Although it is challenging to discriminate small changes in reservoir properties or identify thin stratigraphic barriers below seismic resolution from conventional seismic amplitude data, we have found that seismic attributes aid in defining the reservoir architecture, properties, and stratigraphic baffles. However, analyzing numerous individual attributes is a time-consuming process and may have limitations for revealing small petrophysical changes within a reservoir. Using the Maui 3D seismic data acquired in offshore Taranaki Basin, New Zealand, we generate typical instantaneous and spectral decomposition seismic attributes that are sensitive to lithologic variations and changes in reservoir properties. Using the most common petrophysical and rock typing classification methods, the rock quality and heterogeneity of the C1 Sand reservoir are studied for four wells located within the 3D seismic volume. We find that integrating the geologic content of a combination of eight spectral instantaneous attribute volumes using an unsupervised machine-learning algorithm (self-organizing maps [SOMs]) results in a classification volume that can highlight reservoir distribution and identify stratigraphic baffles by correlating the SOM clusters with discrete net reservoir and flow-unit logs. We find that SOM classification of natural clusters of multiattribute samples in the attribute space is sensitive to subtle changes within the reservoir’s petrophysical properties. We find that SOM clusters appear to be more sensitive to porosity variations compared with lithologic changes within the reservoir. Thus, this method helps us to understand reservoir quality and heterogeneity in addition to illuminating thin reservoirs and stratigraphic baffles.

scholarly journals Characterization of reservoir sands using 3D seismic attributes in the coastal swamp area of Niger Delta Basin

2021 ◽  
Author(s):  
Oluwatoyin Khadijat Olaleye ◽  
Pius Adekunle Enikanselu ◽  
Michael Ayuk Ayuk
Keyword(s):  
Seismic Data ◽  
Niger Delta ◽  
Seismic Attributes ◽  
Effective Porosity ◽  
High Porosity ◽  
Hydrocarbon Accumulation ◽  
3D Seismic ◽  
Seismic Attribute ◽  
Niger Delta Basin

AbstractHydrocarbon accumulation and production within the Niger Delta Basin are controlled by varieties of geologic features guided by the depositional environment and tectonic history across the basin. In this study, multiple seismic attribute transforms were applied to three-dimensional (3D) seismic data obtained from “Reigh” Field, Onshore Niger Delta to delineate and characterize geologic features capable of harboring hydrocarbon and identifying hydrocarbon productivity areas within the field. Two (2) sand units were delineated from borehole log data and their corresponding horizons were mapped on seismic data, using appropriate check-shot data of the boreholes. Petrophysical summary of the sand units revealed that the area is characterized by high sand/shale ratio, effective porosity ranged from 16 to 36% and hydrocarbon saturation between 72 and 92%. By extracting attribute maps of coherence, instantaneous frequency, instantaneous amplitude and RMS amplitude, characterization of the sand units in terms of reservoir geomorphological features, facies distribution and hydrocarbon potential was achieved. Seismic attribute results revealed (1) characteristic patterns of varying frequency and amplitude areas, (2) major control of hydrocarbon accumulation being structural, in terms of fault, (3) prospective stratigraphic pinch-out, lenticular thick hydrocarbon sand, mounded sand deposit and barrier bar deposit. Seismic Attributes analysis together with seismic structural interpretation revealed prospective structurally high zones with high sand percentage, moderate thickness and high porosity anomaly at the center of the field. The integration of different seismic attribute transforms and results from the study has improved our understanding of mapped sand units and enhanced the delineation of drillable locations which are not recognized on conventional seismic interpretations.

Predicting the azimuth of natural fractures and in-situ horizontal stress: A case study from Sichuan Basin, China

Geophysics ◽  
2021 ◽  
pp. 1-97
Author(s):  
kai lin ◽  
Bo Zhang ◽  
Jianjun Zhang ◽  
Huijing Fang ◽  
Kefeng Xi ◽  
...  
Keyword(s):  
Hydraulic Fracturing ◽  
Seismic Data ◽  
Sichuan Basin ◽  
Horizontal Stress ◽  
Seismic Attributes ◽  
Seismic Events ◽  
Natural Fractures ◽  
Microseismic Data ◽  
Maximum Horizontal Stress

The azimuth of fractures and in-situ horizontal stress are important factors in planning horizontal wells and hydraulic fracturing for unconventional resources plays. The azimuth of natural fractures can be directly obtained by analyzing image logs. The azimuth of the maximum horizontal stress σH can be predicted by analyzing the induced fractures on image logs. The clustering of micro-seismic events can also be used to predict the azimuth of in-situ maximum horizontal stress. However, the azimuth of natural fractures and the in-situ maximum horizontal stress obtained from both image logs and micro-seismic events are limited to the wellbore locations. Wide azimuth seismic data provides an alternative way to predict the azimuth of natural fractures and maximum in-situ horizontal stress if the seismic attributes are properly calibrated with interpretations from well logs and microseismic data. To predict the azimuth of natural fractures and in-situ maximum horizontal stress, we focus our analysis on correlating the seismic attributes computed from pre-stack and post-stack seismic data with the interpreted azimuth obtained from image logs and microseismic data. The application indicates that the strike of the most positive principal curvature k1 can be used as an indicator for the azimuth of natural fractures within our study area. The azimuthal anisotropy of the dominant frequency component if offset vector title (OVT) seismic data can be used to predict the azimuth of maximum in-situ horizontal stress within our study area that is located the southern region of the Sichuan Basin, China. The predicted azimuths provide important information for the following well planning and hydraulic fracturing.

Case study: Seismic resolution and reservoir characterization of thin sands using multiattribute analysis and bandwidth extension in the Daqing field, China

2020 ◽  
Vol 8 (1) ◽  
pp. T89-T102
Author(s):  
David Mora ◽  
John Castagna ◽  
Ramses Meza ◽  
Shumin Chen ◽  
Renqi Jiang
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Statistical Significance ◽  
Physical Property ◽  
Seismic Attributes ◽  
Oil Field ◽  
Thin Layers ◽  
Inversion Method ◽  
Bandwidth Extension ◽  
Multiattribute Analysis

The Daqing field, located in the Songliao Basin in northeastern China, is the largest oil field in China. Most production in the Daqing field comes from seismically thin sand bodies with thicknesses between 1 and 15 m. Thus, it is not usually possible to resolve Daqing reservoirs using only conventional seismic data. We have evaluated the effectiveness of seismic multiattribute analysis of bandwidth extended data in resolving and making inferences about these thin layers. Multiattribute analysis uses statistical methods or neural networks to find relationships between well data and seismic attributes to predict some physical property of the earth. This multiattribute analysis was applied separately to conventional seismic data and seismic data that were spectrally broadened using sparse-layer inversion because this inversion method usually increases the vertical resolution of the seismic. Porosity volumes were generated using target porosity logs and conventional seismic attributes, and isofrequency volumes were obtained by spectral decomposition. The resulting resolution, statistical significance, and accuracy in the determination of layer properties were higher for the predictions made using the spectrally broadened volume.

scholarly journals Geologic pattern recognition from seismic attributes: Principal component analysis and self-organizing maps

2015 ◽  
Vol 3 (4) ◽  
pp. SAE59-SAE83 ◽  
Author(s):  
Rocky Roden ◽  
Thomas Smith ◽  
Deborah Sacrey
Keyword(s):  
Principal Component Analysis ◽  
Seismic Data ◽  
Principal Component ◽  
Component Analysis ◽  
Seismic Attributes ◽  
Seismic Facies ◽  
Processing Unit ◽  
Large Set ◽  
Seismic Reflection Data ◽  
Self Organizing

Interpretation of seismic reflection data routinely involves powerful multiple-central-processing-unit computers, advanced visualization techniques, and generation of numerous seismic data types and attributes. Even with these technologies at the disposal of interpreters, there are additional techniques to derive even more useful information from our data. Over the last few years, there have been efforts to distill numerous seismic attributes into volumes that are easily evaluated for their geologic significance and improved seismic interpretation. Seismic attributes are any measurable property of seismic data. Commonly used categories of seismic attributes include instantaneous, geometric, amplitude accentuating, amplitude-variation with offset, spectral decomposition, and inversion. Principal component analysis (PCA), a linear quantitative technique, has proven to be an excellent approach for use in understanding which seismic attributes or combination of seismic attributes has interpretive significance. The PCA reduces a large set of seismic attributes to indicate variations in the data, which often relate to geologic features of interest. PCA, as a tool used in an interpretation workflow, can help to determine meaningful seismic attributes. In turn, these attributes are input to self-organizing-map (SOM) training. The SOM, a form of unsupervised neural networks, has proven to take many of these seismic attributes and produce meaningful and easily interpretable results. SOM analysis reveals the natural clustering and patterns in data and has been beneficial in defining stratigraphy, seismic facies, direct hydrocarbon indicator features, and aspects of shale plays, such as fault/fracture trends and sweet spots. With modern visualization capabilities and the application of 2D color maps, SOM routinely identifies meaningful geologic patterns. Recent work using SOM and PCA has revealed geologic features that were not previously identified or easily interpreted from the seismic data. The ultimate goal in this multiattribute analysis is to enable the geoscientist to produce a more accurate interpretation and reduce exploration and development risk.

scholarly journals Integration of Seismic Anisotropy and Reservoir Performance Data for Characterization of Naturally Fractured Reservoirs Using Discrete Feature Network Models

2005 ◽  
Vol 8 (02) ◽  
pp. 132-142 ◽  
Author(s):  
Robert Will ◽  
Rosalind A. Archer ◽  
William S. Dershowitz
Keyword(s):  
Seismic Data ◽  
Elastic Anisotropy ◽  
Fractured Reservoirs ◽  
Seismic Attributes ◽  
Performance Data ◽  
Naturally Fractured Reservoirs ◽  
Fracture System ◽  
Reservoir Performance ◽  
Naturally Fractured ◽  
System Characteristics

Summary This paper proposes a method for quantitative integration of seismic(elastic) anisotropy attributes with reservoir-performance data as an aid in characterizing systems of natural fractures in hydrocarbon reservoirs. This method is demonstrated through application to history matching of reservoir performance using synthetic test cases. Discrete-feature-network (DFN) modeling is a powerful tool for developing fieldwide stochastic realizations of fracture networks in petroleum reservoirs. Such models are typically well conditioned in the vicinity of the wellbore through incorporation of core data, borehole imagery, and pressure-transient data. Model uncertainty generally increases with distance from the borehole. Three-dimensional seismic data provide uncalibrated information throughout the interwell space. Some elementary seismic attributes such as horizon curvature and impedance anomalies have been used to guide estimates of fracture trend and intensity (fracture area per unit volume) in DFN modeling through geostatistical calibration with borehole and other data. However, these attributes often provide only weak statistical correlation with fracture-system characteristics. The presence of a system of natural fractures in a reservoir induces elastic anisotropy that can be observed in seismic data. Elastic attributes such as azimuthally dependent normal move out velocity (ANMO), reflection amplitude vs. azimuth (AVAZ), and shear-wave birefringence can be inverted from 3D-seismicdata. Anisotropic elastic theory provides physical relationships among these attributes and fracture-system properties such as trend and intensity. Effective-elastic-media models allow forward modeling of elastic properties for fractured media. A technique has been developed in which both reservoir-performance data and seismic anisotropic attributes are used in an objective function for gradient-based optimization of selected fracture-system parameters. The proposed integration method involves parallel workflows for effective elastic and effective permeability media modeling from an initial DFN estimate of the fracture system. The objective function is minimized through systematic updates of selected fracture-population parameters. Synthetic data cases show that3D-seismic attributes contribute significantly to the reduction of ambiguity in estimates of fracture-system characteristics in the interwell rock mass. The method will benefit enhanced-oil-recovery (EOR) program planning and management, optimization of horizontal-well trajectory and completion design, and borehole-stability studies. Introduction Anisotropy and heterogeneity in reservoir permeability present challenges during the development of hydrocarbon reserves in naturally fractured reservoirs. Predicting primary reservoir performance, planning development drilling or EOR programs, completion design, and facilities design all require accurate estimates of reservoir properties and the predictions of future reservoir behavior computed from such estimates. Over the history of naturally-fractured-reservoir development, many methods have been used to characterize fracture systems and their effect on fluid flow in the reservoir. These include the use of geologic surface-outcrop analogs; core, single-well, and multiwell pressure-transient analysis; borehole-imaging logs; and surface and borehole seismic observations. To date, efforts to integrate seismic data into the workflow for characterization of naturally fractured reservoirs have been focused on the use of post-stack data. CDP stacking of seismic data takes advantage of redundancy in seismic data sets for the attenuation of noise. Unfortunately, CDP stacking also eliminates valuable information about spatial and orientational variations in the data. Such variations are often related to fracture-system characteristics. CDP-stacked seismic data are typically used to define the main structural elements of the reservoir. Fracture density has been correlated successfully with horizon curvature determined from seismic horizons. Seismic attributes frequently can be correlated with reservoir properties such as shale fraction, which often correlates with fracture-population statistics. Acoustic impedance computed from seismic data frequently exhibits dim spots in the presence of fractures.

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