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.

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 Evaluation of seismic attributes for reservoir characterization over Edi field, Niger delta, Nigeria using 3d seismic data

2020 ◽  
Vol 8 (2) ◽  
pp. 168
Author(s):  
Nyeneime O. Etuk ◽  
Mfoniso U. Aka ◽  
Okechukwu A. Agbasi ◽  
Johnson C. Ibuot
Keyword(s):  
Seismic Data ◽  
Niger Delta ◽  
Reservoir Characterization ◽  
Seismic Attributes ◽  
3D Seismic ◽  
Root Mean Square Amplitude ◽  
Phase Gradient ◽  
Major Fault ◽  
Seismic Sections ◽  
3D Seismic Data

Seismic attributes were evaluated over Edi field, offshore Western Niger Delta, Nigeria, via 3D seismic data. Manual mappings of the horizons and faults on the in-lines and cross-lines of the seismic sections were done. Various attributes were calculated and out put on four horizons corresponding to the well markers at different formations within the well were identified. The four horizons identified, which includes: H1, H2, H3 and H4 were mapped and interpreted across the field. The operational agenda was thru picking given faults segments on the in–line of seismic volume. A total of five faults coded as F1, F2, F3, F4 and F5, F1 and F5 were the major fault and were observed as extending through the field. Structural and horizon mappings were used to generate time structure maps. The maps showed the various positions and orientations of the faults. Different attributes which include: root mean square amplitude, instantaneous phase, gradient magnitude and chaos were run on the 3D seismic data. The amplitude and incline magnitude maps indicate direct hydrocarbon on the horizon maps; this is very important in the drilling of wells because it shows areas where hydrocarbons are present in the subsurface. The seismic attributes revealed information, which was not readily apparent in the raw seismic data.   

Identification of polygonal faulting from legacy 3D seismic data in vintage Gulf of Mexico data using seismic attributes

2021 ◽  
pp. 1-17
Author(s):  
Karen M. Leopoldino Oliveira ◽  
Heather Bedle ◽  
Karelia La Marca Molina
Keyword(s):  
Seismic Data ◽  
Seismic Attributes ◽  
Fault System ◽  
3D Seismic ◽  
Seismic Attribute ◽  
Variable Amplitude ◽  
Attribute Analysis ◽  
Amplitude Data ◽  
3D Seismic Data ◽  
Polygonal Fault

We analyzed a 1991 3D seismic data located offshore Florida and applied seismic attribute analysis to identify geological structures. Initially, the seismic data appears to have a high signal-to-noise-ratio, being of an older vintage of quality, and appears to reveal variable amplitude subparallel horizons. Additional geophysical analysis, including seismic attribute analysis, reveals that the data has excessive denoising, and that the continuous features are actually a network of polygonal faults. The polygonal faults were identified in two tiers using variance, curvature, dip magnitude, and dip azimuth seismic attributes. Inline and crossline sections show continuous reflectors with a noisy appearance, where the polygonal faults are suppressed. In the variance time slices, the polygonal fault system forms a complex network that is not clearly imaged in the seismic amplitude data. The patterns of polygonal fault systems in this legacy dataset are compared to more recently acquired 3D seismic data from Australia and New Zealand. It is relevant to emphasize the importance of seismic attribute analysis to improve accuracy of interpretations, and also to not dismiss older seismic data that has low accurate imaging, as the variable amplitude subparallel horizons might have a geologic origin.

Discrete 3D fracture network extraction and characterization from 3D seismic data — A case study at Teapot Dome

2015 ◽  
Vol 3 (3) ◽  
pp. ST29-ST41 ◽  
Author(s):  
Manoj Vallikkat Thachaparambil
Keyword(s):  
Seismic Data ◽  
Seismic Attributes ◽  
3D Seismic ◽  
Spectral Edge ◽  
Teapot Dome ◽  
Fracture Data ◽  
Discontinuity Surfaces ◽  
Seismic Discontinuity ◽  
3D Seismic Data ◽  
Tensleep Formation

Three-dimensional discrete fracture networks (DFNs) extracted from the seismic data of the Tensleep Formation at Teapot Dome successfully matched 1D fracture data from multiple boreholes within the area. The extraction process used four seismic attributes, i.e., variance, chaos, curvature, and spectral edge, and their multiple realizations to define seismic discontinuities that could potentially represent fractures within the Tensleep Formation. All of the potential fracture attributes were further enhanced using a fracture-tracking attribute for better extraction and analysis of seismic discontinuity surfaces and their network properties. A state-of-the-art discontinuity surface extraction and characterization workflow uniformly extracted and interactively characterized the seismic discontinuity surfaces and networks that correlate with borehole fracture data. Among the attributes, a fracture-tracking attribute cube created out of the high-resolution spectral-edge attribute provided the best match with the borehole fracture data from the Tensleep Formation. Therefore, the extracted discontinuity planes were classified as fractures and then characterized. The extracted fracture population also matched earlier published records of faults and fractures at Teapot Dome. Unlike the conventional method, which uses 1D borehole fracture data as primary input and 3D seismic data as a guide volume during DFN modeling, I used 3D seismic attributes as the primary data and the 1D borehole fracture data only for quality control. I also evaluated the power of converting seismic fracture attribute volumes into discrete surfaces and networks for effective correlation with 1D fracture logs from boreholes.

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