Autocorrelogram Migration for Field Data Generated by A Horizontal Drill-bit Source Jianhua Yu, Lew Katz Fred Followill and Gerard T. Schuster.

Slides:

Advertisements

Similar presentations

Time-Lapse Monitoring of CO2 Injection with Vertical Seismic Profiles (VSP) at the Frio Project T.M. Daley, L.R. Myer*, G.M. Hoversten and E.L. Majer.

Advertisements

Designer Seismic VSP Ernie Majer (LBNL) J. Queen ( Hi –Q Geophysics) T. Dalely (LBNL) Roy Long ( DOE)

Geological Model Depth(km) X(km) 2001 Year.

Adaptive Grid Reverse-Time Migration Yue Wang. Outline Motivation and ObjectiveMotivation and Objective Reverse Time MethodologyReverse Time Methodology.

Locating Trapped Miners Using Time Reversal Mirrors Sherif M. Hanafy Weiping CaoKim McCarter Gerard T. Schuster November 12, 2008.

First Arrival Traveltime and Waveform Inversion of Refraction Data Jianming Sheng and Gerard T. Schuster University of Utah October, 2002.

Closure Phase Statics Correction Closure Phase Statics Correction University of Utah Jianhua Yu.

Interferometric Interpolation of 3D OBS Data Weiping Cao, University of Utah Oct

Prestack Migration Deconvolution Jianxing Hu and Gerard T. Schuster University of Utah.

Imaging Multiple Reflections with Reverse- Time Migration Yue Wang University of Utah.

Depth (m) Time (s) Raw Seismograms Four-Layer Sand Channel Model Midpoint (m)

Reduced-Time Migration of Converted Waves David Sheley and Gerard T. Schuster University of Utah.

Primary-Only Imaging Condition Yue Wang. Outline Objective Objective POIC Methodology POIC Methodology Synthetic Data Tests Synthetic Data Tests 5-layer.

Solving Illumination Problems Solving Illumination Problems in Imaging:Efficient RTM & in Imaging:Efficient RTM & Migration Deconvolution Migration Deconvolution.

CROSSWELL IMAGING BY 2-D PRESTACK WAVEPATH MIGRATION

3-D Migration Deconvolution Jianxing Hu, GXT Bob Estill, Unocal Jianhua Yu, University of Utah Gerard T. Schuster, University of Utah.

Improve Migration Image Quality by 3-D Migration Deconvolution Jianhua Yu, Gerard T. Schuster University of Utah.

Joint Migration of Primary and Multiple Reflections in RVSP Data Jianhua Yu, Gerard T. Schuster University of Utah.

Depth (m) Time (s) Raw Seismograms Four-Layer Sand Channel Model Midpoint (m)

Bedrock Delineation by a Seismic Reflection/Refraction Survey at TEAD Utah David Sheley and Jianhua Yu.

Overview of Utah Tomography and Modeling/Migration (UTAM) Chaiwoot B., T. Crosby, G. Jiang, R. He, G. Schuster, Chaiwoot B., T. Crosby, G. Jiang, R. He,

Migration and Attenuation of Surface-Related and Interbed Multiple Reflections Zhiyong Jiang University of Utah April 21, 2006.

Kirchhoff vs Crosscorrelation

Acquisiton of Passive Seismic Data at Tooele Army Depot, Utah David Sheley, Travis Crosby, Jianming Sheng.

Autocorrelogram Migration of Drill-Bit Data Jianhua Yu, Lew Katz, Fred Followill, and Gerard T. Schuster.

Depth (m) Time (s) Raw Seismograms Four-Layer Sand Channel Model Midpoint (m)

Depth (m) Time (s) Raw Seismograms Four-Layer Sand Channel Model Midpoint (m)

Local Migration with Extrapolated VSP Green’s Functions Xiang Xiao and Gerard Schuster Univ. of Utah.

Midyear Overview of Year 2001 UTAM Results T. Crosby, Y. Liu, G. Schuster, D. Sheley, J. Sheng, H. Sun, J. Yu and M. Zhou J. Yu and M. Zhou.

3-D PRESTACK WAVEPATH MIGRATION H. Sun Geology and Geophysics Department University of Utah.

Applications of Time-Domain Multiscale Waveform Tomography to Marine and Land Data C. Boonyasiriwat 1, J. Sheng 3, P. Valasek 2, P. Routh 2, B. Macy 2,

Migration Deconvolution vs Least Squares Migration Jianhua Yu, Gerard T. Schuster University of Utah.

MD + AVO Inversion Jianhua Yu, University of Utah Jianxing Hu GXT.

3-D Migration Deconvolution: Real Examples Jianhua Yu University of Utah Bob Estill Unocal.

Interferometric Multiple Migration of UPRC Data

4C Mahogony Data Processing and Imaging by LSMF Method Jianhua Yu and Yue Wang.

Multisource Least-squares Reverse Time Migration Wei Dai.

Seeing the Invisible with Seismic Interferometry: Datuming and Migration Gerard T. Schuster, Jianhua Yu, Xiao Xiang and Jianming Sheng University of Utah.

Last week’s problems a) Mass excess = 1/2πG × Area under curve 1/2πG = × in kgs 2 m -3 Area under curve = -1.8 ×10-6 x 100 m 2 s -2 So Mass.

Coherence-weighted Wavepath Migration for Teleseismic Data Coherence-weighted Wavepath Migration for Teleseismic Data J. Sheng, G. T. Schuster, K. L. Pankow,

Arnim B. Haase and Robert R. Stewart

The main instrument used is called the sonde. A basic sonde consists of a source and two receivers one-foot apart. The sonde is lowered down the borehole.

Impact of MD on AVO Inversion

EXPLORATION GEOPHYSICS. EARTH MODEL NORMAL-INCIDENCE REFLECTION AND TRANSMISSION COEFFICIENTS WHERE:  1 = DENSITY OF LAYER 1 V 1 = VELOCITY OF LAYER.

Review of Coherent Noise Suppression Methods Gerard T. Schuster University of Utah.

Moveout Correction and Migration of Surface-related Resonant Multiples Bowen Guo*,1, Yunsong Huang 2 and Gerard Schuster 1 1 King Abdullah University of.

Multisource Least-squares Migration of Marine Data Xin Wang & Gerard Schuster Nov 7, 2012.

Data QC and filtering Bryce HutchinsonSumit Verma Objective: Consider the frequency range of different seismic features Look for low frequency and high.

LEAST SQUARES DATUMING AND SURFACE WAVES PREDICTION WITH INTERFEROMETRY Yanwei Xue Department of Geology & Geophysics University of Utah 1.

Super-virtual Interferometric Diffractions as Guide Stars Wei Dai 1, Tong Fei 2, Yi Luo 2 and Gerard T. Schuster 1 1 KAUST 2 Saudi Aramco Feb 9, 2012.

Interferometric Traveltime Tomography M. Zhou & G.T. Schuster Geology and Geophysics Department University of Utah.

Wave-Equation Waveform Inversion for Crosswell Data M. Zhou and Yue Wang Geology and Geophysics Department University of Utah.

Seismic Methods Geoph 465/565 Vertical Seismic Profiling– Nov 2, 2015

The Earth’s Near Surface as a Vibroseis Signal Generator Zhiyong Jiang University of Utah.

Jianhua Yu University of Utah Robust Imaging for RVSP Data with Static Errors.

Enhancing Migration Image Quality by 3-D Prestack Migration Deconvolution Gerard Schuster Jianhua Yu, Jianxing Hu University of Utah andGXT

MD+AVO Inversion: Real Examples University of Utah Jianhua Yu.

GDF Suez Holland E 16-4 VSP Part 1:

R. G. Pratt1, L. Sirgue2, B. Hornby2, J. Wolfe3

Primary-Only Imaging Condition And Interferometric Migration

Sisgbee2b / Data Release

SEISMIC DATA GATHERING.

4C Mahogony Data Processing and Imaging by LSMF Method

Interferometric Least Squares Migration

Interpreting a 3C-3D seismic survey, Ross Lake, Saskatchewan

The Relationship between Uncertainty and Quality in Seismic Data

Review of Coherent Noise Suppression Methods

—Based on 2018 Field School Seismic Data

Processing and Binning Overview

EXPLORATION GEOPHYSICS

Presentation transcript:

Autocorrelogram Migration for Field Data Generated by A Horizontal Drill-bit Source Jianhua Yu, Lew Katz Fred Followill and Gerard T. Schuster

Outline Motivation and Objective Motivation and Objective Autocorrelogram Migration Autocorrelogram Migration Examples Examples Summary Summary

Outline Motivation and Objective Motivation and Objective Autocorrelogram Migration Autocorrelogram Migration Examples Examples Summary Summary

IVSPWD Objective Provide Look-ahead Image Below Drill Bit Provide Look-ahead Image Below Drill Bit Reduce Uncertainty in Drilling Reduce Uncertainty in Drilling ?

Problems No Source Wavelet No Source Wavelet No Source Initiation Time No Source Initiation Time Not Easy to Get Pilot Signal in Not Easy to Get Pilot Signal in Deviated Well Deviated Well

Autocorrelogram Migration No need to know source wavelet No limits to a deviated well No need for initiation time Solution

Outline Outline Motivation and Objective Motivation and Objective Autocorrelogram Migration Autocorrelogram Migration Examples Examples Summary Summary

Well Drill bit Receiver Primary wave Primary, Ghost and Direct Wave Direct Wave Ghost

x g s Primary Autocorrelogram Imaging Condition:

x g s

g Ghost Autocorrelogram Imaging Condition Ghost Autocorrelogram Imaging Condition: x s g’

x g s Ghost Autocorrelogram Imaging Condition Ghost Autocorrelogram Imaging Condition:

Autocorrelogram Migration Migrated Image Autocorrelation Function

Outline Outline Motivation and Objective Motivation and Objective Autocorrelogram Migration Autocorrelogram Migration Examples Examples Summary Summary

Acquisition Survey East (kft) North (kft) Well Rig 3C Receivers Drill bit 10 Depth (kft) 0

Main Acquisition Parameters Drill-bit Depth: 9188 ft Offset Range: ft Recording Length: 20 s Sample Interval: 2 ms Station Number: 10

Main Processing Steps Orienting receivers, trace editing, and static shift Orienting receivers, trace editing, and static shift Frequency panel analysis and noise elimination Frequency panel analysis and noise elimination Velocity analysis and beam steering Velocity analysis and beam steering Amplitude balance and energy normalization Calculating autocorrelograms, vertical stacking Calculating autocorrelograms, vertical stacking Autocorrelogram migration

Raw Data Frequency Panel Analysis Time (s) Time (s) < 5 Hz 5-15 Hz

Time (s) Time (s) Hz Hz Raw Data Frequency Panel Analysis

Processed CSG 96 Part of CRG Time (s) Time (s)

Autocoreelograms Associated With CRG Time (s)

Primary Image Time (s) 501 Ghost Image Autocorrlogram Migration Images Traces Traces No reflections

Acquisition Survey Map Well Rig 3C Receivers Drill bit East (ft) North (ft) C Line AC4

Time (s) SP Drilling hole Primary Image with CDP Section

Time (s) SP Ghost Image with CDP Section (Reverse Polarity) Drilling hole

Outline Outline Motivation and Objective Motivation and Objective Autocorrelogram Migration Autocorrelogram Migration Examples Examples Summary Summary

SUMMARY Autocorrelogram Migration works for deviated well Primary autocorrelogram image correlates well with the surface- CDP section Ghost autocorrelogram migration result is comparable to surface-CDP section but suffers from more noise

SUMMARY The upgoing and downgoing wave separation can cause some artifacts in migration image

Acknowledgements We greatly appreciate DOE for Financial supportWe greatly appreciate DOE for Financial support We are grateful to Union Pacific Resources for donating this dataWe are grateful to Union Pacific Resources for donating this data I am grateful to the 1999 sponsors of the UTAM consortium for financial supportI am grateful to the 1999 sponsors of the UTAM consortium for financial support