The value of 3D seismic attributes for illuminating deep water deposits by seismic forward modeling of the Brushy Canyon formation

2003 ◽  
Author(s):  
Steven Clawson ◽  
Hai‐Zui Meng ◽  
Mark Sonnenfeld ◽  
Mike Uland ◽  
Safian Atan ◽  
...  
Keyword(s):  
Deep Water ◽  
Forward Modeling ◽  
Seismic Attributes ◽  
3D Seismic ◽  
Brushy Canyon Formation

The value of 3D seismic attributes for illuminating deep water deposits by seismic forward modeling of the Brushy Canyon formation

2002 ◽  
Author(s):  
Steven Clawson ◽  
Hai‐Zui Meng ◽  
Mark Sonnenfeld ◽  
Mike Uland ◽  
Safian Atan ◽  
...  
Keyword(s):  
Deep Water ◽  
Forward Modeling ◽  
Seismic Attributes ◽  
3D Seismic ◽  
Brushy Canyon Formation

Analysis of Oil-Volume Fluxes of Hydrocarbon-Seep Formations on the Green Canyon and Mississippi Canyon: A Study With 3D-Seismic Attributes in Combination With Satellite and Acoustic Data

2014 ◽  
Vol 17 (04) ◽  
pp. 430-435 ◽  
Author(s):  
Oscar Garcia-Pineda ◽  
Ian MacDonald ◽  
William Shedd
Keyword(s):  
Deep Water ◽  
Seismic Data ◽  
Seismic Attributes ◽  
3D Seismic ◽  
Sar Images ◽  
Acoustic Data ◽  
Oil Seeps ◽  
Mississippi Canyon ◽  
Geophysical Anomalies ◽  
3D Seismic Data

Summary Natural hydrocarbon seeps have an important role in the carbon cycle and in the Gulf of Mexico (GOM) ecosystem. The magnitude of these natural oil seeps was analyzed with 3D-seismic attributes in combination with satellite and acoustic data. Hydrocarbon seepage in the deep water of the GOM is associated with deep cutting faults, generated by vertical salt movement, that provide conduits for the upward migration of oil and gas. Seeps transform surface geology and generate prominent geophysical targets that can be identified in 3D-seismic data. Seafloor-amplitude anomalies in plain view correlate with the underlying fault systems. On the basis of 3D-seismic data, detailed mapping of the northern GOM has identified more than 24,000 geophysical anomalies across the basin. In addition to seismic data, synthetic-aperture-radar (SAR) images have proved to be a reliable tool for localizing natural seepage of oil. We used a texture-classifier neural-network algorithm (TCNNA) to process more than 1,200 SAR images collected over the GOM. We quantified more than 1,000 individual seep formations distributed along the outer continental shelf and in deep water. Comparison of the geophysical anomalies with the SAR oil-slick targets shows good general agreement between the distributions of the two indicators. However, there are far fewer active oil seeps than geophysical anomalies, probably because of timing constraints during the basin evolution. Studying the size of the oil slicks on the surface (normalized to weather conditions), we found that the average flux rate of oil (per seep) may be affected by the local change in the baroclinic and barotrophic pressures [e.g., warm core eddies (WCEs) and storms]. We found that oil slicks in the Mississippi Canyon (MC) protraction area tend to be more sensitive to pressure changes than Green Canyon (GC) protraction-area seeps.

scholarly journals Geomorphological Geometries and High-Resolution Seismic Sequence Stratigraphy of Malay Basin’s Fluvial Succession

2021 ◽  
Vol 11 (11) ◽  
pp. 5156
Author(s):  
Abd Al-Salam Al-Masgari ◽  
Mohamed Elsaadany ◽  
Numair A. Siddiqui ◽  
Abdul Halim Abdul Latiff ◽  
Azli Abu Bakar ◽  
...  
Keyword(s):  
High Resolution ◽  
Sequence Stratigraphy ◽  
Three Dimensional ◽  
Seismic Attributes ◽  
Seismic Facies ◽  
Seismic Sequence ◽  
3D Seismic ◽  
Seismic Sequences ◽  
Seismic Sequence Stratigraphy ◽  
Point Bars

This study identified the Pleistocene depositional succession of the group (A) (marine, estuarine, and fluvial depositional systems) of the Melor and Inas fields in the central Malay Basin from the seafloor to approximately −507 ms (522 m). During the last few years, hydrocarbon exploration in Malay Basin has moved to focus on stratigraphic traps, specifically those that existed with channel sands. These traps motivate carrying out this research to image and locate these kinds of traps. It can be difficult to determine if closely spaced-out channels and channel belts exist within several seismic sequences in map-view with proper seismic sequence geomorphic elements and stratigraphic surfaces seismic cross lines, or probably reinforce the auto-cyclic aggregational stacking of the avulsing rivers precisely. This analysis overcomes this challenge by combining well-log with three-dimensional (3D) seismic data to resolve the deposition stratigraphic discontinuities’ considerable resolution. Three-dimensional (3D) seismic volume and high-resolution two-dimensional (2D) seismic sections with several wells were utilized. A high-resolution seismic sequence stratigraphy framework of three main seismic sequences (3rd order), four Parasequences sets (4th order), and seven Parasequences (5th order) have been established. The time slice images at consecutive two-way times display single meandering channels ranging in width from 170 to 900 m. Moreover, other geomorphological elements have been perfectly imaged, elements such as interfluves, incised valleys, chute cutoff, point bars, and extinction surfaces, providing proof of rapid growth and transformation of deposits. The high-resolution 2D sections with Cosine of Phase seismic attributes have facilitated identifying the reflection terminations against the stratigraphic amplitude. Several continuous and discontinuous channels, fluvial point bars, and marine sediments through the sequence stratigraphic framework have been addressed. The whole series reveals that almost all fluvial systems lay in the valleys at each depositional sequence’s bottom bars. The degradational stacking patterns are characterized by the fluvial channels with no evidence of fluvial aggradation. Moreover, the aggradation stage is restricted to marine sedimentation incursions. The 3D description of these deposits permits distinguishing seismic facies of the abandoned mud channel and the sand point bar deposits. The continuous meandering channel, which is filled by muddy deposits, may function as horizontal muddy barriers or baffles that might isolate the reservoir body into separate storage containers. The 3rd, 4th, and 5th orders of the seismic sequences were established for the studied succession. The essential geomorphological elements have been imaged utilizing several seismic attributes.

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.

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