Derivation of Seismic Attributes from Shallow Seismic Data

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.

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

Interpretation ◽

10.1190/int-2020-0108.1 ◽

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.

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Improving The Reliability of Velocity Analysis of Shallow Seismic Data

10.3997/2214-4609.202152103 ◽

2021 ◽

Author(s):

A.G. Yaroslavtsev ◽

M.V. Tarantin ◽

T.V. Baibakova

Keyword(s):

Seismic Data ◽

Velocity Analysis ◽

Shallow Seismic

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Prestack Kirchhoff depth migration of shallow seismic data

Geophysics ◽

10.1190/1.1444425 ◽

1998 ◽

Vol 63 (4) ◽

pp. 1241-1247 ◽

Cited By ~ 22

Author(s):

Linus Pasasa ◽

Friedemann Wenzel ◽

Ping Zhao

Keyword(s):

Waste Disposal ◽

Seismic Data ◽

Signal To Noise Ratio ◽

Disposal Site ◽

Model Estimation ◽

Waste Disposal Site ◽

Depth Migration ◽

Prestack Migration ◽

Shallow Seismic ◽

Kirchhoff Depth Migration

Prestack Kirchhoff depth migration is applied successfully to shallow seismic data from a waste disposal site near Arnstadt in Thuringia, Germany. The motivation behind this study was to locate an underground building buried in a waste disposal. The processing sequence of the prestack migration is simplified significantly as compared to standard common (CMP) data processing. It includes only two parts: (1) velocity‐depth‐model estimation and (2) prestack depth migration. In contrast to conventional CMP stacking, prestack migration does not require a separation of reflections and refractions in the shot data. It still provides an appropriate image. Our data example shows that a superior image can be achieved that would contain not just subtle improvements but a qualitative step forward in resolution and signal‐to‐noise ratio.

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

Journal of Petroleum Exploration and Production Technology ◽

10.1007/s13202-021-01286-z ◽

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.

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

Geophysics ◽

10.1190/geo2020-0829.1 ◽

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.

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