Churning seismic attributes with principal component analysis

2014 ◽  
Author(s):  
Satinder Chopra* ◽  
Kurt J. Marfurt
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Seismic Attributes

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.

Applying principal component analysis to seismic attributes for interpretation of evaporite facies: Lower Triassic Jialingjiang Formation, Sichuan Basin, China

2017 ◽  
Vol 5 (4) ◽  
pp. T461-T475 ◽  
Author(s):  
Suyun Hu ◽  
Wenzhi Zhao ◽  
Zhaohui Xu ◽  
Hongliu Zeng ◽  
Qilong Fu ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Gamma Ray ◽  
Principal Component ◽  
Lower Triassic ◽  
Component Analysis ◽  
Seismic Attributes ◽  
Hydrocarbon Exploration ◽  
Seismic Survey ◽  
Seismic Attribute ◽  
Siliciclastic Rocks

In China and elsewhere, it is important to predict different lithologies and lithofacies for hydrocarbon exploration in a mixed evaporite-carbonate-siliciclastic system. The lower section of the second member of the Jialingjiang Formation (T1j2L) is mainly composed of anhydrite, dolostone, limestone, and siliciclastic rocks, providing a rare opportunity to reconstruct detailed facies in a [Formula: see text] 3D seismic survey with 31 wells. Wireline logs (sonic, density, and gamma ray) calibrated by core analysis are essential in distinguishing anhydrite, siliciclastics, and carbonates. Although different lithologies are characterized by different acoustic impedance (AI), with certain overlapping, it is still difficult to predict lithology by any single seismic attribute because of the limited seismic resolution in a thinly interbedded formation of multiple lithologies. In our study, principal component analysis (PCA) was applied to extract lithologic information from selected seismic attributes; the first two principal components were used to predict the content of anhydrite, siliciclastics, and carbonates. Content maps of anhydrite, siliciclastics, and carbonates — created by mixing the represented color — were used to reconstruct lithofacies of the T1j2L submember. It is quite difficult, even with the PCA approach, to uniquely resolve the three lithologies due to the overlapped AI and the limited resolution of the seismic data. However, the workflow that we evaluated dramatically improved the prediction accuracy of lithology and lithofacies. Facies transition during the deposition of the T1j2L submember in the study area was inferred from a paleo-uplift in the southwest to a restricted lagoon and then to an open marine setting in the northeast.

scholarly journals Fracture network Based on Principal Component Analysis and Neural Network– A case study in the Malay Basin

2018 ◽  
Vol 7 (3.32) ◽  
pp. 62
Author(s):  
Shamsuddin A.A.S. ◽  
D Ghosh
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Fracture Network ◽  
Component Analysis ◽  
Seismic Attributes ◽  
Geological Features ◽  
Geological Complexity ◽  
Production Areas

Finding oil in the fractured basement rock in South East Asia has been a goal for several decades, but remains a challenge in terms of exploration/production areas in the Malay Basin due to geological complexity including fracture. Thus, the purpose of this study is to delineate fracture network based on the geometrical attributes in order to have better fracture understanding. In this study, the top of the basement acts as the key surface incorporated with the combination of geometrical seismic attributes analysis. The analysis started with data conditioning and seismic interpretation of the key surface. The final steps were conducted by using geometrical seismic attributes, principal component analysis and neural network. Principal component analysis of these four seismic attributes is able to delineate the contribution of each attributes based on eigenvalue with the PC0: 1.3450 (33.63%), PC1:1.0556 (26.39%), PC2:0.9270 (23.17%) and PC3:0.6724 (16.81%). While neural network contributes four main results (i) PC0 (ii) PC0 and PC1 (iii) PC0, PC1 and PC2 (iv) PC0, PC1, PC 2 and PC3. Fracture networks were able to be delineated and geological features that might be overlooked were able to be captured and can be used to guide the fracture network inside the fractured basement.  

Fault detection using principal component analysis of seismic attributes in the Bakken Formation, Williston Basin, North Dakota, USA

2017 ◽  
Vol 5 (3) ◽  
pp. T361-T372 ◽  
Author(s):  
Ismot Jahan ◽  
John Castagna ◽  
Michael Murphy ◽  
M. Amin Kayali
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Principal Component ◽  
Component Analysis ◽  
Seismic Attributes ◽  
Bakken Formation ◽  
Seismic Fault ◽  
Three Forks Formation ◽  
Three Forks ◽  
Borehole Image

Seismic fault detection using principal component analysis (PCA) is an effective method for interpreting fault distribution and orientations in the Bakken Formation. The PCA fault attribute indicates significantly different, and geologically more plausible, 3D fault distributions than conventional seismic attributes, such as curvature. The PCA fault attribute has identified different fault patterns in the Upper, Middle, and Lower Bakken members and the Three Forks Formation. Two distinct fault trends in approximately 40°–50° northeast–southwest and 50°–60° northwest–southeast directions are observed in the Bakken Formation in the study area, and they are apparent on the strike and dip attributes derived from the PCA fault attribute. Fault cuts interpreted from missing well-log sections correlate well with the PCA fault attribute. Seismically derived fault orientations correlate with borehole image log data in the horizontal wells. Crossing conjugate faults observed on the fault dip attribute may result in the widening of the faulted area and localized thinning of the rock sequence where the faults intersect, and this could potentially enhance permeability along the fault strike.

A German version of the Intermittent Claudication Questionnaire (ICQ): cultural adaptation and validation

VASA ◽  
2012 ◽  
Vol 41 (5) ◽  
pp. 333-342 ◽  
Author(s):  
Kirchberger ◽  
Finger ◽  
Müller-Bühl
Keyword(s):  
Principal Component Analysis ◽  
Intermittent Claudication ◽  
Completion Time ◽  
Short Form ◽  
Principal Component ◽  
Component Analysis ◽  
German Version ◽  
Average Completion Time ◽  
Sf 36 ◽  
Related Quality

Background: The Intermittent Claudication Questionnaire (ICQ) is a short questionnaire for the assessment of health-related quality of life (HRQOL) in patients with intermittent claudication (IC). The objective of this study was to translate the ICQ into German and to investigate the psychometric properties of the German ICQ version in patients with IC. Patients and methods: The original English version was translated using a forward-backward method. The resulting German version was reviewed by the author of the original version and an experienced clinician. Finally, it was tested for clarity with 5 German patients with IC. A sample of 81 patients were administered the German ICQ. The sample consisted of 58.0 % male patients with a median age of 71 years and a median IC duration of 36 months. Test of feasibility included completeness of questionnaires, completion time, and ratings of clarity, length and relevance. Reliability was assessed through a retest in 13 patients at 14 days, and analysis of Cronbach’s alpha for internal consistency. Construct validity was investigated using principal component analysis. Concurrent validity was assessed by correlating the ICQ scores with the Short Form 36 Health Survey (SF-36) as well as clinical measures. Results: The ICQ was completely filled in by 73 subjects (90.1 %) with an average completion time of 6.3 minutes. Cronbach’s alpha coefficient reached 0.75. Intra-class correlation for test-retest reliability was r = 0.88. Principal component analysis resulted in a 3 factor solution. The first factor explained 51.5 of the total variation and all items had loadings of at least 0.65 on it. The ICQ was significantly associated with the SF-36 and treadmill-walking distances whereas no association was found for resting ABPI. Conclusions: The German version of the ICQ demonstrated good feasibility, satisfactory reliability and good validity. Responsiveness should be investigated in further validation studies.

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