Distributed Acoustic Sensing in Geophysics

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Группа авторов. Distributed Acoustic Sensing in Geophysics

Table of Contents

List of Tables

List of Illustrations

Guide

Pages

Geophysical Monograph Series

Geophysical Monograph 268

Distributed Acoustic Sensing in Geophysics. Methods and Applications

LIST OF CONTRIBUTORS

LIST OF REVIEWERS

PREFACE

1 High Definition Seismic and Microseismic Data Acquisition Using Distributed and Engineered Fiber Optic Acoustic Sensors

ABSTRACT

1.1. DISTRIBUTED ACOUSTIC SENSOR (DAS) PRINCIPLES AND MEASUREMENTS

1.1.1. DAS Concept

1.1.2. DAS Interferometric Optical Response

1.1.3. DAS Optical Phase Recovery

1.1.4. DAS Dynamic Range Algorithms

1.1.5. DAS Signal Processing and Denoising

1.1.6. Time Integration of DAS Signal

1.2. DAS SYSTEM PARAMETERS AND COMPARISON WITH GEOPHONES

1.2.1. DAS Optimization for Seismic Applications

1.2.2. DAS Directionality in Seismic Measurements

1.2.3. DAS Field Data Examples

1.3. DAS WITH PRECISION ENGINEERED FIBER

1.3.1. Precision engineered fiber concept

1.3.2. Sensitivity and Dynamic Range

1.3.3. Field Trial Results

ACKNOWLEDGMENTS

REFERENCES

TABLE OF VARIABLES

2 Important Aspects of Acquiring Distributed Acoustic Sensing (DAS) Data for Geoscientists

ABSTRACT

2.1. INTRODUCTION

2.2. FIBER‐OPTIC SENSOR. 2.2.1. Sensing from Backscattered Light

2.2.2. Single vs. Multi‐mode Fiber

2.2.3. Deploying Fiber

2.2.4. Handling Fiber‐Optic Cables

2.3. INTERROGATOR UNIT. 2.3.1. Types of Interrogators

2.3.2. Synchronizing Source Information and Time Stamps

2.4. ACQUISITION PARAMETER SELECTION. 2.4.1. Gauge Length

2.4.2. Sampling Rate

2.4.3 Pulse Width

2.5. PREPROCESSING ISSUES. 2.5.1. Fading

2.5.2. Common‐Mode Noise

2.5.3. Spatial Calibration of Channels

2.6. PROCESSING ISSUES. 2.6.1. Angle of Incidence

2.6.2. Single Component vs. Three Components

2.7. DATA QUALITY: DAS VS. GEOPHONE COMPARISONS. 2.7.1. Lower Intrinsic SNR and Higher Channel Density

2.7.2. Strain, Strain Rate, and Particle Velocity

2.8. SUMMARY

REFERENCES

Chapter 3 Distributed Microstructured Optical Fiber (DMOF) Based Ultrahigh Sensitive Distributed Acoustic Sensing (DAS) for Borehole Seismic Surveys

ABSTRACT

3.1. INTRODUCTION

3.2. PRINCIPLES AND METHODS OF DMOF‐DAS. 3.2.1. Principles of DAS Using Optical Fiber

3.2.2. Concept and Characteristics of DMOF

3.2.3. Fabrication and Performance Test of DMOF

3.2.4. System Configuration and Working Principle of the DMOF‐DAS

3.2.5. Performance of the DMOF‐DAS

3.3. BOREHOLE SEISMIC SURVEY TESTS AND RESULTS. 3.3.1. Zero‐Offset VSP Survey in Fushan Oil Field

3.3.2. Walkaway VSP Survey in Suning Oil Field

3.4. DISCUSSIONS

3.5. CONCLUSIONS

ACKNOWLEDGMENTS

REFERENCES

4 Distributed Acoustic Sensing System Based on Phase‐Generated Carrier Demodulation Algorithm

ABSTRACT

4.1. INTRODUCTION

4.2. PRINCIPLE

4.3. EXPERIMENTS AND RESULTS

4.4. FIELD TRIAL OF NEAR‐SURFACE SEISMIC EXPERIMENT WITH PGC‐DAS SYSTEM

4.5. CONCLUSIONS

ACKNOWLEDGMENTS

REFERENCES

5 Field Trial of Distributed Acoustic Sensing in an Active Room‐and‐Pillar Mine

ABSTRACT

5.1. INTRODUCTION

5.2. EXPERIMENTAL METHODS. 5.2.1. Cable Layout and Source Locations

5.2.2. Data Acquisition

5.3. CABLE COUPLING COMPARISONS

5.4. DAS SENSITIVITY

5.5. LOCATING A SEISMIC SOURCE

5.6. SURFACE WAVE TRAVEL‐TIME TOMOGRAPHY

5.7. P‐WAVE DIFFERENTIAL TRAVEL‐TIME TOMOGRAPHY

5.8. DISCUSSION AND CONCLUSIONS

ACKNOWLEDGMENTS

REFERENCES

6 On the Surmountable Limitations of Distributed Acoustic Sensing (DAS) Vertical Seismic Profiling (VSP) – Depth Calibration, Directionality, and Noise: Learnings From Field Trials

ABSTRACT

6.1. INTRODUCTION

6.2. DEPTH CALIBRATION

6.3. DIRECTIONALITY

6.4. NOISE

6.5. OVERCOMING THE FULL SUITE OF CHALLENGES – EXAMPLE FROM DEEP WATER

6.6. CONCLUSIONS

ACKNOWLEDGMENTS

REFERENCES

Note

7 Denoising Analysis and Processing Methods of Distributed Acoustic Sensing (DAS) Vertical Seismic Profiling (VSP) Data

ABSTRACT

7.1. INTRODUCTION

7.2. FIBER DEPLOYMENT TYPES AND NOISE SOURCES

7.3. CABLE RESONANCE REMOVAL

7.4. RANDOM NOISE SUPPRESSION

7.5. SNR ENHANCEMENT

7.6. CONCLUSION

REFERENCES

8 High‐Resolution Shallow Structure at Brady Hot Springs Using Ambient Noise Tomography (ANT) on a Trenched Distributed Acoustic Sensing (DAS) Array

ABSTRACT

8.1. INTRODUCTION

8.2. DATA AND METHODS

8.3. NCF RESULTS

8.4. DISPERSION MEASUREMENT RESULTS

8.5. SHEAR WAVE VELOCITY MODEL

8.6. CONCLUSIONS

ACKNOWLEDGMENTS

REFERENCES

9 Introduction to Interferometry of Fiber‐Optic Strain Measurements

ABSTRACT

9.1. INTRODUCTION

9.1.1. DAS Measurement Process

9.2. SENSITIVITY OF DAS TO FAR‐FIELD SOURCES

9.3 Sensitivity of DAS Cross‐Correlations to Plane Wave Sources

9.4. THOUGHT EXPERIMENT DEMONSTRATING AMBIENT NOISE INTERFEROMETRY TRENDS

9.4.1. Simple Case: Radial‐Radial Cross‐Correlations

9.4.2. Transverse‐Transverse Cross‐Correlations

9.5. SIMULATED AMBIENT NOISE INTERFEROMETRY ALONG CABLES

9.5.1. Signals Extracted Between two Parallel Fiber Cables

9.5.2. Virtual Source Perpendicular to Receiver Cable

9.6. CONCLUSIONS

ACKNOWLEDGMENTS

REFERENCES

10 Using Telecommunication Fiber Infrastructure for Earthquake Monitoring and Near‐Surface Characterization

ABSTRACT

10.1. INTRODUCTION

10.2. THE SFSO

10.3. CONTINUOUS MONITORING AND ANALYSIS OF LOCAL AND REGIONAL EARTHQUAKES

10.3.1. Weak Event Detection by Template Matching

10.3.2. Estimates of Event Amplitudes

10.4. CONTINUOUS MONITORING OF NEAR‐SURFACE CONDITIONS BY INTEFEROMETRY

10.4.1. Time‐Lapse Inteferometry Using Raleigh Waves Synthesized by Ambient Noise

10.4.2. Time‐Lapse Interferometry Using Surface Waves Generated by Quarry Blasts

10.5. IS THE COUPLING BETWEEN CABLES AND THE GROUND THE LIMITING FACTOR?

10.6. PROCESSING CHALLENGES FOR LARGE DAS ARRAYS IN URBAN ENVIRONMENTS

10.6.1. Analysis in Real Time of a Huge Stream of Data with Nonstationary and UnpredicTable Noise Sources

10.6.2. Automatic Identification and Muting of Bad Channels

10.6.3. Semiautomatic Determination of Virtual Receivers’ Location in DAS Arrays

10.7. CONCLUSIONS

ACKNOWLEDGMENTS

REFERENCES

11 Production Distributed Temperature Sensing versus Stimulation Distributed Acoustic Sensing for the Marcellus Shale

ABSTRACT

11.1. INTRODUCTION. 11.1.1. Marcellus Shale Energy and Environment Laboratory

11.1.2. Fiber‐Optic Technology and its Applications

11.2. METHODOLOGY

11.3. RESULTS AND DISCUSSIONS

11.4. CONCLUSIONS

ACKNOWLEDGMENTS

REFERENCES

12 Coalescence Microseismic Mapping for Distributed Acoustic Sensing (DAS) and Geophone Hybrid Array: A Model‐Based Feasibility Study

ABSTRACT

12.1. INTRODUCTION

12.2. DAS SYNTHETIC DATA FOR MICROSEISMIC EVENTS. 12.2.1. Expected Signature of DAS Microseismic Data

12.2.2. Simulation of DAS and Geophone Data for a Single Monitoring Array

12.3. THE LOCATION ALGORITHM FOR DAS-GEOPHONE HYBRID ARRAY

12.4. TESTS

12.4.1. Test Case 1: Monitoring Only at the Horizontal Section of the Array

12.4.2. Test Case 2: Monitoring by large aperture array

12.5. DISCUSSION AND CONCLUSION

REFERENCES

13 Continuous Downhole Seismic Monitoring Using Surface Orbital Vibrators and Distributed Acoustic Sensing at the CO2CRC Otway Project: Field Trial for Optimum Configuration

ABSTRACT

13.1. INTRODUCTION

13.2. PERMANENT MONITORING AT THE CO2CRC OTWAY PROJECT

13.3. FIELD EXPERIMENTS WITH DAS AND SOV SOURCES AT THE CO2CRC OTWAY PROJECT

13.4. OFFSET VSP PROCESSING

13.5. MAY 2017 FIELD TRIAL: CONVENTIONAL SINGLE‐MODE FIBER VS. CONSTELLATION FIBER

13.6. NOVEMBER 2017 FIELD TRIAL: PERFORMANCE OF SMALL AND LARGE MOTORS

13.7. SUMMARY AND CONCLUSIONS

ACKNOWLEDGMENTS

REFERENCES

14 Introduction to Distributed Acoustic Sensing (DAS) Applications for Characterization of Near‐Surface Processes

ABSTRACT

14.1. INTRODUCTION

14.2. CONSIDERATIONS FOR DEPLOYMENTS

14.2.1. A Continuous Sensor Array, Signal‐to‐noise Ratio, and Bandwidth

14.2.2. Signal‐to‐fiber Coupling

14.2.3. Economics for Near‐surface Characterization

14.3. SPECIFIC TOPICS IN THIS CHAPTER. 14.3.1. Ambient Noise Tomography

14.3.2. 4‐D Time‐lapse Imaging

14.3.3. Multichannel Analyses of Surface Waves

14.3.4. Utilization of Dark Fiber

14.3.5. Opportunities and Challenges

14.4. CONCLUSIONS

REFERENCES

15 Surface Wave Imaging Using Distributed Acoustic Sensing Deployed on Dark Fiber: Moving Beyond High‐Frequency Noise

ABSTRACT

15.1. INTRODUCTION

15.2. DARK FIBER NETWORKS: THE ESNET DARK FIBER TESTBED

15.3. STUDY SITE AND DATA ACQUISITION

15.4. DATA CHARACTERISTICS AND ANALYSIS OF NOISE SOURCES

15.5. PROCESSING STRATEGY

15.5.1. Data Selection

15.5.2. Ambient Noise Interferometry and Dispersion Analysis

15.5.3. Multimodal Inversion Using the Haskell‐Thomson Determinant Method

15.6. RESULTS. 15.6.1. Site Comparison Data

15.6.2. Inverted Vs Structure and Comparison with Ground Truth Data

15.7. DISCUSSION

15.8. CONCLUSIONS

ACKNOWLEDGMENTS

REFERENCES

16 Using Distributed Acoustic Sensing (DAS) for Multichannel Analysis of Surface Waves (MASW)

ABSTRACT

16.1. INTRODUCTION

16.2. DAS MEASUREMENT PRINCIPLES

16.3. STUDY AREA AND EQUIPMENT LAYOUT

16.4. LARGE SHAKER SEISMIC SOURCE

16.5. DAS AND GEOPHONE SENSORS

16.6. MULTICHANNEL ANALYSIS OF SURFACE WAVES (MASW)

16.7. SURFACE-WAVE DISPERSION ANALYSIS RESULTS

16.8. DISCUSSION

16.9. SUMMARY AND CONCLUSIONS

ACKNOWLEDGMENTS

REFERENCES

17 A Literature Review: Distributed Acoustic Sensing (DAS) Geophysical Applications Over the Past 20 Years

ABSTRACT

17.1. INTRODUCTION

17.2. FBG‐BASED QDAS GEOPHYSICAL APPLICATIONS

17.3. VARIOUS DAS GEOPHYSICAL APPLICATIONS. 17.3.1. DAS Principle, Instrument, Installation, Tests, and Advances

17.3.2. DAS Applications in VSP (Borehole Seismic)

17.3.3. DAS in Downhole Surveillance and Flow Monitoring

17.3.4. DAS in Monitoring Hydraulic Fracturing and Microseismicity

17.3.5. DAS in Carbon CCS and CO2 Injection Monitoring

17.3.6. DAS in Surface Seismic Exploration

17.3.7. DAS for Geothermal System, Mining, and Mineral Exploration

17.3.8. DAS Monitoring for Safety and Security

17.3.9. DAS for Near‐Surface and Earthquake Seismology, Fault Characterization, and Deformation

17.3.10. DAS Data Management, Processing, and Machine Learning

17.4. SOME THOUGHTS ON RECENT ADVANCES AND FUTURE APPLICATIONS

17.5. CONCLUSION

ACKNOWLEDGMENTS

REFERENCES

INDEX

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Отрывок из книги

212 The Early Earth: Accretion and Differentiation James Badro and Michael Walter (Eds.)

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Directionality of the DAS response depends on the fiber optic cable configuration and the cable design, as the device itself is sensitive only to fiber elongation. We will start our consideration where the fiber is placed linearly inside a cable, with no slippage between fiber and cable, nor between the cable and the ground. In this case, fiber displacement will follow ground displacement, and sensitivity will depend on the relative position of fiber and seismic source. A similar mechanical principle was used for the electromagnetic linear strain seismograph to measure variations in the distance between two points of the ground (Benioff, 1935). DAS directional response with respect to incident angle Γ can be found by transformation of the strain tensor components with rotation using geometrical consideration. For a longitudinal (P) apparent wave, it will be cos2Γ, and for transversal (S) wave sin Γ cos Γ, similar to Benioff (1935) (see Figure 1.14). Detailed analysis and diagrams for Rayleigh and Love waves can be found in Martin et al. (2018).

In vertical seismic profiling (VSP), in the vertical part of the well, both cable and seismic waves are in the same direction for near‐offsets, so the DAS is more sensitive to P‐waves, in which the acoustic displacement vector coincides with the fiber direction. In other applications, such as fracking, the microseismic source is usually on a side of the cable, so shear waves can be effectively detected.

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