Delineation of geological elements from RGB color blending of seismic attribute volumes

2008 ◽  
Vol 27 (3) ◽  
pp. 342-350 ◽  
Author(s):  
Jonathan Henderson ◽  
Stephen J. Purves ◽  
Gaynor Fisher ◽  
Chris Leppard
Author(s):  
Jianhua Cao ◽  
Yang Yue ◽  
Kunyu Zhang ◽  
Jucheng Yang ◽  
Xiankun Zhang

KURVATEK ◽  
2017 ◽  
Vol 1 (2) ◽  
pp. 21-31
Author(s):  
Fatimah Miharno

ABSTRACT*Zefara* Field formation Baturaja on South Sumatra Basin is a reservoir carbonate and prospective gas. Data used in this research were 3D seismik data, well logs, and geological information. According to geological report known that hidrocarbon traps in research area were limestone lithological layer as stratigraphical trap and faulted anticline as structural trap. The study restricted in effort to make a hydrocarbon accumulation and a potential carbonate reservoir area maps with seismic attribute. All of the data used in this study are 3D seismic data set, well-log data and check-shot data. The result of the analysis are compared to the result derived from log data calculation as a control analysis. Hydrocarbon prospect area generated from seismic attribute and are divided into three compartments. The seismic attribute analysis using RMS amplitude method and instantaneous frequency is very effective to determine hydrocarbon accumulation in *Zefara* field, because low amplitude from Baturaja reservoir. Low amplitude hints low AI, determined high porosity and high hydrocarbon contact (HC).  Keyword: Baturaja Formation, RMS amplitude seismic attribute, instantaneous frequency seismic attribute


2007 ◽  
Author(s):  
Srinivasa Rao Narhari ◽  
Nikhil Banik ◽  
Sunil Kumar Singh ◽  
Talal Fahad Al-Adwani

2010 ◽  
Vol 29 (6) ◽  
pp. 95-102
Author(s):  
Jian GONG ◽  
Shumei XU ◽  
Yun MA ◽  
Jianguo YU ◽  
Jinduo WANG

2020 ◽  
Vol 11 (1) ◽  
pp. 219
Author(s):  
Jing Zeng ◽  
Alexey Stovas ◽  
Handong Huang ◽  
Lixia Ren ◽  
Tianlei Tang

Paleozoic marine shale gas resources in Southern China present broad prospects for exploration and development. However, previous research has mostly focused on the shale in the Sichuan Basin. The research target of this study is expanded to the Lower Silurian Longmaxi shale outside the Sichuan Basin. A prediction scheme of shale gas reservoirs through the frequency-dependent seismic attribute technology is developed to reduce drilling risks of shale gas related to complex geological structure and low exploration level. Extracting frequency-dependent seismic attribute is inseparable from spectral decomposition technology, whereby the matching pursuit algorithm is commonly used. However, frequency interference in MP results in an erroneous time-frequency (TF) spectrum and affects the accuracy of seismic attribute. Firstly, a novel spectral decomposition technology is proposed to minimize the effect of frequency interference by integrating the MP and the ensemble empirical mode decomposition (EEMD). Synthetic and real data tests indicate that the proposed spectral decomposition technology provides a TF spectrum with higher accuracy and resolution than traditional MP. Then, a seismic fluid mobility attribute, extracted from the post-stack seismic data through the proposed spectral decomposition technology, is applied to characterize the shale reservoirs. The application result indicates that the seismic fluid mobility attribute can describe the spatial distribution of shale gas reservoirs well without well control. Based on the seismic fluid mobility attribute section, we have learned that the shale gas enrich areas are located near the bottom of the Longmaxi Formation. The inverted velocity data are also introduced to further verify the reliability of seismic fluid mobility. Finally, the thickness map of gas-bearing shale reservoirs in the Longmaxi Formation is obtained by combining the seismic fluid mobility attribute with the inverted velocity data, and two favorable exploration areas are suggested by analyzing the thickness, structure, and burial depth. The present work can not only be used to evaluate shale gas resources in the early stage of exploration, but also help to design the landing point and trajectory of directional drilling in the development stage.


2011 ◽  
Author(s):  
Lifeng Liu ◽  
Sam Zandong Sun ◽  
Haiyang Wang ◽  
Haijun Yang ◽  
Jianfa Han ◽  
...  

2015 ◽  
Vol 3 (1) ◽  
pp. SB5-SB15 ◽  
Author(s):  
Kurt J. Marfurt ◽  
Tiago M. Alves

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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