scholarly journals IMPACTS OF SEISMIC VELOCITY MODEL CALIBRATION FOR TIME-DEPTH CONVERSION:A CASE STUDY

2018 ◽  
Vol 36 (4) ◽  
pp. 1
Author(s):  
Frank Cenci Bulhões ◽  
Gleidson Diniz Ferreira ◽  
José Fernando Caparica Jr.
Keyword(s):  
Model Building ◽  
Seismic Velocity ◽  
Velocity Model ◽  
Seismic Interpretation ◽  
Time Data ◽  
Velocity Modeling ◽  
Seismic Resolution ◽  
Velocity Model Building ◽  
The Impact

ABSTRACT. In this work we discuss the impact of the uncertainties in the seismic interpretation on the velocity model building and time-depth conversion. The case study presented is located in the Campos Basin, Brazil. The main objective of this work is to show how the input data and the parameters affect substantially the velocity modeling. The methodology uses velocity model building methods and calibration parameters to integrate seismic interpretation and wells. It presents scenarios with calibration by time-depth tables and horizons-geological markers. The data converted to depth are compared to the time data and the geological markers. The data converted by the calibrated model with horizon-marker presented smaller differences compared to the markers and lower correlations in the pseudo-impedance. In the time-depth table calibration scenarios, the differences of the horizons compared to the markers were higher, but in the range of the seismic resolution and higher correlations.Keywords: seismic migration; wells; geological markers; exploration; interpretation.RESUMO. Neste trabalho é apresentado como as incertezas na interpretação sísmica impactam na cons-trução do modelo de velocidades e na conversão tempo-profundidade resultante. A área de estudo de estudo está localizada na Bacia de Campos, Brasil. O principal objetivo deste trabalho é mostrar como os dados de entrada e parâmetros afetam na modelagem de velocidade e conversão tempo x profundidade. A metodologia é comparar três diferentes cenários para calibração da velocidade de processamento e imageamento com as interpretações sísmicas e de poços: o cenário 1 utiliza ajuste por horizonte com marcador geológico e raio de influência 5 km; no cenário 2 é utilizada as tabelas tempo-profundidade, raio de influência 5 km por krigagem com derivada externa; e o cenário 3 utilizou-se tabelas tempo-profundidade, raio de influência 2 km por krigagem com deriva externa. O controle de qualidade dos três modelos de velocidade são avaliados pela conversão dos horizontes, seções sísmicas e perfis de pseudo-impedância. No cenário 1, os horizontes convertidos apresentam menores diferenças de profundidade em relação aos marcadores comparados aos demais cenários. Por outro lado, os cenários 2 e 3 apresentam maiores correlações entre o sismograma sintético e a seção sísmica convertida para o cenário 1.Palavras-chave: migração sísmica; poços; marcadores geológicos; exploração; interpretação.

Seismic velocity model building by NIP-wave tomography — a case study in Iran

2012 ◽  
Author(s):  
Hashem shahsavani ◽  
Juergen Mann ◽  
Mehrdad Soleimani ◽  
Reza Sokooti ◽  
Mostafa Vahid Hashemi
Keyword(s):  
Model Building ◽  
Seismic Velocity ◽  
Velocity Model ◽  
Velocity Model Building ◽  
Wave Tomography

Geologically constrained seismic imaging — Workflow

Geophysics ◽  
2008 ◽  
Vol 73 (5) ◽  
pp. VE313-VE319 ◽  
Author(s):  
Stig-Kyrre Foss ◽  
Mark Rhodes ◽  
Bjørn Dalstrøm ◽  
Christian Gram ◽  
Alastair Welbon
Keyword(s):  
Image Quality ◽  
Model Building ◽  
Velocity Model ◽  
Seismic Interpretation ◽  
Gravity Modeling ◽  
Final Model ◽  
Depth Migration ◽  
Velocity Models ◽  
Velocity Model Building

We present the geologically constrained workflow for velocity-model building as a case study from offshore Brazil. The workflow involves basin reconstruction, gravity modeling, and seismic interpretation in addition to standard prestack depth migration (PSDM) model-building tools. Building a salt model based on seismic evidence can be highly nonunique. In a geologically constrained seismic-processing workflow, the main aim is to use geologic understanding with geophysical models and datasets to improve an input velocity realization for the PSDM loop, thereby improving image quality. All of these methods are inherently uncertain, and a final model is based on a range of subjective choices. Thus a final result that agrees with all sciences still can be completely wrong. However, an understanding of these choices enables a unique way of testing and constraining the number of antimodels: velocity models that fit the observations but are different from the final result. This can reduce time spent and uncertainty in geologic evaluation.

scholarly journals Seismic velocity estimation: A deep recurrent neural-network approach

Geophysics ◽  
2019 ◽  
Vol 85 (1) ◽  
pp. U21-U29
Author(s):  
Gabriel Fabien-Ouellet ◽  
Rahul Sarkar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Model Building ◽  
Seismic Velocity ◽  
Velocity Model ◽  
Velocity Estimation ◽  
Interval Velocity ◽  
Velocity Model Building ◽  
3D Velocity Model

Applying deep learning to 3D velocity model building remains a challenge due to the sheer volume of data required to train large-scale artificial neural networks. Moreover, little is known about what types of network architectures are appropriate for such a complex task. To ease the development of a deep-learning approach for seismic velocity estimation, we have evaluated a simplified surrogate problem — the estimation of the root-mean-square (rms) and interval velocity in time from common-midpoint gathers — for 1D layered velocity models. We have developed a deep neural network, whose design was inspired by the information flow found in semblance analysis. The network replaces semblance estimation by a representation built with a deep convolutional neural network, and then it performs velocity estimation automatically with recurrent neural networks. The network is trained with synthetic data to identify primary reflection events, rms velocity, and interval velocity. For a synthetic test set containing 1D layered models, we find that rms and interval velocity are accurately estimated, with an error of less than [Formula: see text] for the rms velocity. We apply the neural network to a real 2D marine survey and obtain accurate rms velocity predictions leading to a coherent stacked section, in addition to an estimation of the interval velocity that reproduces the main structures in the stacked section. Our results provide strong evidence that neural networks can estimate velocity from seismic data and that good performance can be achieved on real data even if the training is based on synthetics. The findings for the 1D problem suggest that deep convolutional encoders and recurrent neural networks are promising components of more complex networks that can perform 2D and 3D velocity model building.

Velocity model building for variable-depth streamer data: Brunei case study

2013 ◽  
Author(s):  
Fatiha Gamar-Sadat ◽  
Olivier Michot ◽  
Robert Soubaras ◽  
Geoffroy Pignot ◽  
Amir Kabbej
Keyword(s):  
Model Building ◽  
Velocity Model ◽  
Variable Depth ◽  
Velocity Model Building ◽  
Streamer Data
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