Application of Distributed Acoustic Sensing in Geophysics Exploration: Comparative Review of Single-Mode and Multi-Mode Fiber Optic Cables
Abstract
:1. Introduction
1.1. Aim and Scope
1.2. Brief Explanation of DAS
2. Single-Mode Fiber (SMF)
2.1. Theory and Sensing Principle
2.2. Limitations of SMF
3. Multi-Mode Fiber (MMF)
3.1. Theory and Sensing Principle
3.2. Limitations of MMF
4. Application of Fiber Optic Cables in Geophysics Exploration
4.1. VSP of SMF
- Reservoir Monitoring of SMF
4.2. Microseismic Monitoring of SMF
4.3. ML Application in SMF Processing
4.4. VSP of MMF
- Reservoir Monitoring of MMF
4.5. Microseismic of MMF
- ML Application in MMF Processing
4.6. Subsurface Imaging of Combined SMF and MMF
5. Conclusions
- Successfully employed SMF cables in the domain of surface and subsurface geophysics exploration was inspected in this review. The various SMFs were used to record acoustic properties of a signal along the cable and have become the most used type of fiber optic cable for DAS. However, in this review, when SMF and MMF were combined, MMF was found to be preferable for DTS measurement in most research studies in terms of temperature variations and high bandwidth.
- The cable deployment technique is a crucial initial factor, leading to considerable efforts with respect to its DAS data processing and application in the field of geophysics exploration. This can be improved by the enhancement of preprocessing steps not limited to the control of DAS acquisition parameter optimization, improving SNR values, in addition to a comparison with other seismic measurement tools such as conventional geophones.
- DAS VSP is the most popular technique used to image the subsurface along the borehole. Furthermore, DAS for microseismic measurement was able to detect small microseismic events with high resolution. Although this method works well, it has some major drawbacks that may lead to reduced imaging resolution due to various noise for both SMF and MMF. To achieve a satisfactory resolution of the DAS data, preprocessing steps are imperative to improve data denoising. Subsequently, the ML algorithm approach offers automated DAS data denoising with high accuracy.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Fiber Optic Cable Type | Advantages | Limitations |
---|---|---|
SMF | Most widely used in DAS monitoring and imaging | Sensitive into acquisition optical noise |
Higher spatial resolution | ||
Lower signal loss that minimizes signal attenuation | Interference fading | |
MMF | Capable of DTS measurement | Unpredictable signal that can produce noisy data |
Lower manufacturing cost | Intermodal coupling due to multiple paths of laser travel | |
Suitable for sort-distance DAS measurement |
Authors | Scope of Work | SMF Deployment | Outcome |
---|---|---|---|
Harris et al. [52] | DAS VSP | Installed inside steel tubing, strapped to the casing, and cemented in the well | Comparison of seismic images obtained by a baseline DAS and traditional geophone VSP at a CO2 storage site |
Yurikov et al. [53] | 3D DAS VSP | Cemented along the four wells | 3D DAS VSP imaging of key stratigraphy horizon and CO2 injection target |
Olofsson and Martinez [54] | DAS VSP | Permanently cemented behind casing | Conversion of DAS VSP data to equivalent geophone data proved to help in the up- and down-going separation step without using deep filters |
Mad et al. [55] | DAS VSP | Four deployments strategies: cemented behind casing, cable behind an inflatable liner, strapped to production tubing, and wireline deployment | Cementing the fiber cable behind casing was considered the most effective method for coupling with the subsurface formation |
Ellmauthaler et al. [56] | Real-time DAS VSP | Permanently deployed behind casing in vertical and lateral sections of a well | Utilization of SMF cable in the DAS system facilitated efficient data management and enabled real-time generation of seismic and navigation data sampled at specific intervals |
Wilson et al. [61] | Subsea reservoir monitoring | Attached to subsea infrastructure and deployed in a borehole | The repercussions of increased noise on the subsea DAS image quality |
Correa et al. [62] | Autonomous permanent reservoir monitoring | Cemented behind casing inside a borehole | Evident in time-lapse operations carried out using the DAS SMF system with favorable data quality attributes and encompassing SNR |
Authors | Scope of Work | SMF Deployment | Outcome |
---|---|---|---|
Karrenbach et al. [66] | Microseismic event detection | Installed in a horizontal well behind casing in a deviated drilled well | Automatic detection of microseismic events with high accuracy in an unconventional reservoir |
Bublin [72] | Machine learning algorithm | Laid out on a surface in a suburban area | Machine learning and deep learning applied to DAS data could detect various intrusion events along the pipeline, including manual digging and tapping |
Lapins et al. [74] | Machine learning-assisted | Deployed on the surface in an ice stream | The supervised ML method was able to denoise DAS data automatically with a higher SNR and faster processing speed |
Author | Scope of Work | MMF Deployment | Outcome |
---|---|---|---|
Yu et al. [75] | DAS VSP | Deployed within hybrid optical–electric wireline cable | Excellent vertical and lateral imaging resolution and detailed subsurface structure |
Ellmauthaler et al. [56] | Real-time DAS VSP | Permanently deployed behind casing in vertical and lateral sections of a well | Reduction in SNR for DAS VSP and MMF had sufficient capacity for the DAS interrogator |
Kiyashchenko et al. [76] | 4D reservoir monitoring | Deployed in active deep-water wells | DAS 4D images exhibited qualitative similarity to OBN 4D outcomes and subsurface images, facilitating the assessment of water flood injection to the reservoir |
Wilson et al. [61] | Subsea reservoir monitoring | Attached to subsea infrastructure and deployed in a borehole | The repercussions of increased noise for subsea DAS image quality |
Correa et al. [62] | Autonomous permanent reservoir monitoring | Cemented behind casing inside a borehole | Evident in time-lapse operations carried out using the DAS SMF system, with favorable data quality attributes and encompassing SNR |
Authors | Scope of Work | MMF Deployment | Outcome |
---|---|---|---|
Karrenbach et al. [66] | Microseismic events detection | Installed in a horizontal well behind a casing in a deviated drilled well | Automatically detected microseismic events with high accuracy in an unconventional reservoir |
Ma et al. [84] | Machine learning algorithm | Deployed on multiwell DAS datasets acquired during hydraulic fracturing well completions | Machine learning algorithm and CNN automatically detected microseismic events (hypocenter location) recorded in low-SNR DAS data |
Authors | Scope of Work | Cable Deployment | Outcome |
---|---|---|---|
Daley et al. [28] | Simultaneous DAS VSP | Cemented outside of the well casing | No significant difference in the comparison of SMF and MMF recording results, and SNRs were improved by weighted stacking pf DAS VSP data |
Booth et al. [85] | DAS VSP on Store Glacier | Installed in a borehole enclosed in a gel-filled, stainless-steel capillary tube | DAS VSP was able to detect the sediment layer and interpret temperate ice and seismic properties in transitions of ice-crystal fabric and temperature regime |
Reinsch et al. [86] | Seismic exploration in geothermal areas | Surface layout (x and y directions) and deployed behind casing inside the well (z direction) | DAS with data from the seismic network deployed at the surface increased the resolution of the seismic data and proved to be suitable for geothermal environments |
White et al. [87] | DAS VSP configuration test | Cemented behind well casing and buried in a shallow trench (surface) | Surface DAS fiber configurations increased the sensitivity to steep-angle P-waves of seismic reflections |
Harris et al. [88] | Aquistore reservoir imaging | Inside stainless-steel tubing clamped to the outside of the well casing and cemented in place | DAS VSP produced time-lapse monitoring and accurately imaged NRMS values and CO2-based anomalies at specific reservoir depths |
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Rafi, M.; Mohd Noh, K.A.; Abdul Latiff, A.H.; Otchere, D.A.; Tackie-Otoo, B.N.; Putra, A.D.; Riyadi, Z.A.; Asfha, D.T. Application of Distributed Acoustic Sensing in Geophysics Exploration: Comparative Review of Single-Mode and Multi-Mode Fiber Optic Cables. Appl. Sci. 2024, 14, 5560. https://doi.org/10.3390/app14135560
Rafi M, Mohd Noh KA, Abdul Latiff AH, Otchere DA, Tackie-Otoo BN, Putra AD, Riyadi ZA, Asfha DT. Application of Distributed Acoustic Sensing in Geophysics Exploration: Comparative Review of Single-Mode and Multi-Mode Fiber Optic Cables. Applied Sciences. 2024; 14(13):5560. https://doi.org/10.3390/app14135560
Chicago/Turabian StyleRafi, Muhammad, Khairul Arifin Mohd Noh, Abdul Halim Abdul Latiff, Daniel Asante Otchere, Bennet Nii Tackie-Otoo, Ahmad Dedi Putra, Zaky Ahmad Riyadi, and Dejen Teklu Asfha. 2024. "Application of Distributed Acoustic Sensing in Geophysics Exploration: Comparative Review of Single-Mode and Multi-Mode Fiber Optic Cables" Applied Sciences 14, no. 13: 5560. https://doi.org/10.3390/app14135560
APA StyleRafi, M., Mohd Noh, K. A., Abdul Latiff, A. H., Otchere, D. A., Tackie-Otoo, B. N., Putra, A. D., Riyadi, Z. A., & Asfha, D. T. (2024). Application of Distributed Acoustic Sensing in Geophysics Exploration: Comparative Review of Single-Mode and Multi-Mode Fiber Optic Cables. Applied Sciences, 14(13), 5560. https://doi.org/10.3390/app14135560