scholarly journals Geometrical Seismic Attribute for Fault Identifiction on Canada Exploration Field

2017 ◽  
Vol 17 (2) ◽  
pp. 91
Author(s):  
Reni Agustiani ◽  
Puguh Hiskiawan ◽  
Rano Rano
Keyword(s):  
Seismic Data ◽  
Data Interpretation ◽  
East Sea ◽  
3D Seismic ◽  
Seismic Attribute ◽  
Data Volume ◽  
Major Fault ◽  
Western Side ◽  
Sonic Logs ◽  
3D Seismic Data

It has been performed data interpretation of 3D seismic data and drilling field exploration wellsBasin Nova ScotiaKanada to know structure fault on the field Missisauga Formation. Seismic dataused is 601 inline, crossline 482, and the data used drilling wells are two wells which there is a loggamma ray, sonic logs and log RHOB. Interpretation is done the analysis of the map in thestructure of time and analysis of seismic attribute maps based on the geometrical attribute serves todetermine their structure or structural faults of the data volume 3D. Based on the time structuremap well known that first well is in the region heights and second wells is in low region. Based oninterpretation of the map attributes known three faults are two major fault and one minor fault.Two faults are in the East Sea drilling wells and a small fracture that was on its western side. Thethree fults are directed from Northwest to the Southeast. Fault is expected to serve as ahydrocarbon trap in the area that will be accumulated in drilling wells.Keywords: geometrical attribute, Seismic data, drilling wells, time structure map.

Using AI/ML to Explore & Develop Quickly and Efficiently

2021 ◽  
Author(s):  
Anthony Aming
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Seismic Data ◽  
3D Seismic ◽  
Structure Amplitude ◽  
Zero Crossing ◽  
Petroleum Systems ◽  
Exploration And Production ◽  
Data Volume ◽  
3D Seismic Data

Abstract See how application of a fully trained Artificial Intelligence (AI) / Machine Learning (ML) technology applied to 3D seismic data volumes delivers an unbiased data driven assessment of entire volumes or corporate seismic data libraries quickly. Whether the analysis is undertaken using onsite hardware or a cloud based mega cluster, this automated approach provides unparalleled insights for the interpretation and prospectivity analysis of any dataset. The Artificial Intelligence (AI) / Machine Learning (ML) technology uses unsupervised genetics algorithms to create families of waveforms, called GeoPopulations, that are used to derive Amplitude, Structure (time or depth depending on the input 3D seismic volume) and the new seismic Fitness attribute. We will show how Fitness is used to interpret paleo geomorphology and facies maps for every peak, trough and zero crossing of the 3D seismic volume. Using the Structure, Amplitude and Fitness attribute maps created for every peak, trough and zero crossing the Exploration and Production (E&P) team can evaluate and mitigate Geological and Geophysical (G&G) risks and uncertainty associated with their petroleum systems quickly using the entire 3D seismic data volume.

Application of 3D seismic attributes to optimize the placement of horizontal wells within a tight gas sand reservoir, Ordos Basin, China

Geophysics ◽  
2016 ◽  
Vol 81 (3) ◽  
pp. B77-B86 ◽  
Author(s):  
Zhiguo Wang ◽  
Jinghuai Gao ◽  
Xiaolan Lei ◽  
Xiaojie Cui ◽  
Daxing Wang
Keyword(s):  
Seismic Data ◽  
Ordos Basin ◽  
Horizontal Wells ◽  
Tight Gas ◽  
3D Seismic ◽  
Seismic Attribute ◽  
Horizontal Drilling ◽  
The Ordos Basin ◽  
Gas Bearing ◽  
3D Seismic Data

The Lower Permian Xiashihezi Formation in the Ordos Basin, China, is a quartz-sandstone reservoir with low porosity and low permeability. We have acquired 3D seismic data and well data from 18 vertical and four horizontal wells to indicate the potential of seismic attribute analyses in locating seismic sweet spots for lateral placement of horizontal wells. Using the analytic wavelet transform with a Morse wavelet, the integration of high tuning spectral components, high sweetness and high spectral attenuation helped us to estimate the distribution of gas-bearing tight sands in the Xiashihezi Formation. Our results revealed that the principal target of horizontal drilling and production was gas-bearing massive point bars in the braided river delta setting of the Ordos Basin. The integrated workflow of the seismic attribute analysis contributes to the optimal horizontal well planning by mining and exposing critical geological information of a tight gas sand reservoir from within 3D seismic data.

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.   

A comparison of classification techniques for seismic facies recognition

2015 ◽  
Vol 3 (4) ◽  
pp. SAE29-SAE58 ◽  
Author(s):  
Tao Zhao ◽  
Vikram Jayaram ◽  
Atish Roy ◽  
Kurt J. Marfurt
Keyword(s):  
Seismic Data ◽  
Gaussian Mixture ◽  
Seismic Facies ◽  
Support Vector ◽  
3D Seismic ◽  
Generative Topographic Mapping ◽  
Learning Methods ◽  
Facies Classification ◽  
Data Volume ◽  
3D Seismic Data

During the past decade, the size of 3D seismic data volumes and the number of seismic attributes have increased to the extent that it is difficult, if not impossible, for interpreters to examine every seismic line and time slice. To address this problem, several seismic facies classification algorithms including [Formula: see text]-means, self-organizing maps, generative topographic mapping, support vector machines, Gaussian mixture models, and artificial neural networks have been successfully used to extract features of geologic interest from multiple volumes. Although well documented in the literature, the terminology and complexity of these algorithms may bewilder the average seismic interpreter, and few papers have applied these competing methods to the same data volume. We have reviewed six commonly used algorithms and applied them to a single 3D seismic data volume acquired over the Canterbury Basin, offshore New Zealand, where one of the main objectives was to differentiate the architectural elements of a turbidite system. Not surprisingly, the most important parameter in this analysis was the choice of the correct input attributes, which in turn depended on careful pattern recognition by the interpreter. We found that supervised learning methods provided accurate estimates of the desired seismic facies, whereas unsupervised learning methods also highlighted features that might otherwise be overlooked.

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.

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