Ocean-Bottom Cable for Multicomponent-Seismic Data

2000 ◽  
Vol 52 (01) ◽  
pp. 20-21
Author(s):  
Dennis Denney
2019 ◽  
Vol 37 (4) ◽  
pp. 397
Author(s):  
Nelson Ricardo Coelho Flores Zuniga ◽  
Eder Cassola Molina ◽  
Renato Luiz Prado

AbstractThe processing of multicomponent seismic data is already a challenge concerning the velocity analysis. When it is performed for offshore survey, the difficulty increases a lot more with the use of OBN (Ocean Bottom Nodes) technology. The ray tracing asymmetry generated by the wave conversion and the difference of datum between source and receptor are not the only factors which contribute for a strongly nonhyperbolic travel-time event. The layered subsurface models and the large offsets employed in the offshore surveys make the nonhyperbolicity even stronger. Aiming to solve this problem, eight approximations to perform the velocity analysis were tested for two models. The complexity analysis of each nonhyperbolic multiparametric approximation was also studied to understand their behaviors during the optimization process. The relative error between the observed curve and the calculated curve with each approximation was computed for PP and PS reflection events of two models. With these informations, it was possible to determine which approximation is the most reliable one for this kind of models.Keywords: multicomponent, OBN, nonhyperbolic, multiparametric. ResumoO processamento de dados sísmicos multicomponentes já é um desafio com relação à análise de velocidades. Quando realizado para levantamentos marítimos, a dificuldade aumenta muito mais com o uso da tecnologia OBN (Ocean Bottom Nodes). A assimetria no traçado de raios gerada pela conversão de onda e pela diferença de profundidade entre fonte e receptor não são os únicos fatores que contribuem para um evento de tempos de trânsito fortemente não-hiperbólico. Os modelos estratificados de subsuperfície e os grandes afastamentos aplicados nos levantamentos marítimos tornam a não-hiperbolicidade ainda mais forte. Visando resolver este problema, oito aproximações para realizar a análise de velocidades foram testadas para dois modelos. A análise de complexidade de cada aproximação não-hiperbólica multiparamétrica também foi estudada para entender seus comportamentos durante o processo de otimização. Os erros relativos entre as curvas observadas e calculadas com cada aproximação foram calculados para os eventos de reflexão PP e PS dos dois modelos. Com estas informações, foi possível determinar qual aproximação é a mais confiável para estes tipos de modelos.Palavras-chave: multicomponente, OBN; não-hiperbólico, multiparamétrico.


Geophysics ◽  
2018 ◽  
Vol 83 (4) ◽  
pp. S333-S343 ◽  
Author(s):  
Pengfei Yu ◽  
Jianhua Geng ◽  
Jiqiang Ma

The acoustic-elastic coupled equation (AECE) has several advantages when compared with conventional scalar-wave-based elastic reverse time migration (ERTM) methods used to image ocean-bottom multicomponent seismic data. In particular, vector-wave-based ERTM requires vectorial P- and S-waves on the source and receiver sides, but these cannot be directly obtained from wavefield extrapolation using AECE. Therefore, we have developed a P- and S-wave vector decomposition (VD) approach within AECE; this approach enables the deduction of a novel VD-based AECE, from which vectorial P- and S-waves can be obtained directly via wavefield extrapolation. We are also able to derive a new formulation suitable for vector-wave-based ERTM of ocean-bottom multicomponent seismic data that can generate a phase-preserved PS-image. Three synthetic examples illustrate the validity and effectiveness of our new method.


2007 ◽  
Author(s):  
Zhongping Qian ◽  
Xiang‐Yang Li ◽  
Mark Chapman ◽  
Yonggang Zhang ◽  
Yanguang Wang

2021 ◽  
Vol 14 (13) ◽  
Author(s):  
Jianguang Han ◽  
Zhiwei Liu ◽  
Yun Wang ◽  
Jiayong Yan ◽  
Bingluo Gu

2010 ◽  
Vol 7 (2) ◽  
pp. 149-157 ◽  
Author(s):  
Xiang-Chun Wang ◽  
Chang-Liang Xia ◽  
Xue-Wei Liu

2021 ◽  
Author(s):  
Rick Schrynemeeckers

Abstract Current offshore hydrocarbon detection methods employ vessels to collect cores along transects over structures defined by seismic imaging which are then analyzed by standard geochemical methods. Due to the cost of core collection, the sample density over these structures is often insufficient to map hydrocarbon accumulation boundaries. Traditional offshore geochemical methods cannot define reservoir sweet spots (i.e. areas of enhanced porosity, pressure, or net pay thickness) or measure light oil or gas condensate in the C7 – C15 carbon range. Thus, conventional geochemical methods are limited in their ability to help optimize offshore field development production. The capability to attach ultrasensitive geochemical modules to Ocean Bottom Seismic (OBS) nodes provides a new capability to the industry which allows these modules to be deployed in very dense grid patterns that provide extensive coverage both on structure and off structure. Thus, both high resolution seismic data and high-resolution hydrocarbon data can be captured simultaneously. Field trials were performed in offshore Ghana. The trial was not intended to duplicate normal field operations, but rather provide a pilot study to assess the viability of passive hydrocarbon modules to function properly in real world conditions in deep waters at elevated pressures. Water depth for the pilot survey ranged from 1500 – 1700 meters. Positive thermogenic signatures were detected in the Gabon samples. A baseline (i.e. non-thermogenic) signature was also detected. The results indicated the positive signatures were thermogenic and could easily be differentiated from baseline or non-thermogenic signatures. The ability to deploy geochemical modules with OBS nodes for reoccurring surveys in repetitive locations provides the ability to map the movement of hydrocarbons over time as well as discern depletion affects (i.e. time lapse geochemistry). The combined technologies will also be able to: Identify compartmentalization, maximize production and profitability by mapping reservoir sweet spots (i.e. areas of higher porosity, pressure, & hydrocarbon richness), rank prospects, reduce risk by identifying poor prospectivity areas, accurately map hydrocarbon charge in pre-salt sequences, augment seismic data in highly thrusted and faulted areas.


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