Distributed Acoustic Sensing Vertical Seismic Profile Research Articles

3D DAS VSP for Coal Seam Exploration: A Case Study from Queensland, Australia.

Seismic methods are extensively used in coal mining for expanding resource discoveries and definition as well as for mine monitoring. However, the use of borehole seismic methods is relatively uncommon due to the high cost of borehole seismic acquisition using conventional downhole tools. The introduction of distributed acoustic sensing (DAS), which uses optical fibres to record seismic data, has dramatically increased the cost-effectiveness of borehole seismic methods. Fibre-optic cables are inexpensive and, once deployed in a borehole, can be abandoned or used later for further monitoring of the subsurface. The case study presented here concerns the use of DAS to record a 3D VSP (vertical seismic profiling) for coal seam exploration in Queensland, Australia. This study trialled effective strategies for deploying cables into boreholes and demonstrated how this technology could be incorporated into the standard coal exploration process. The final processing results produced a high-resolution 3D seismic cube where the coal seams below the basalt cover are clearly identifiable around the boreholes. Permanent installation of the fibre-optic cables into a set of boreholes provides immediate benefits of 3D seismic imaging and can create additional value in utilising these sensors for further discrete or continuous subsurface measurements, including stability monitoring of underground workings and detection of methane accumulations.

Quality Control of DAS VSP Data in Desert Environment Using Simulations and Matching Filters.

The unconsolidated near surface and large, daily temperature variations in the desert environment degrade the vertical seismic profiling (VSP) data, posing the need for rigorous quality control. Distributed acoustic sensing (DAS) VSP data are often benchmarked using geophone surveys as a gold standard. This study showcases a new simulation-based way to assess the quality of DAS VSP acquired in the desert without geophone data. The depth uncertainty of the DAS channels in the wellbore is assessed by calibrating against formation depth based on the concept of conservation of the energy flux. Using the 1D velocity model derived from checkshot data, we simulate both DAS and geophone VSP data via an elastic pseudo-spectral finite difference method, and estimate the source and receiver signatures using matching filters. These field geophone data show high amplitude variations between channels that cannot be replicated in the simulation. In contrast, the DAS simulation shows a high visual similarity with the field DAS first arrival waveforms. The simulated source and receiver signatures are visually indistinguishable from the field DAS data in this study. Since under perfect conditions, the receiver signatures should be invariant with depth, we propose a new DAS data quality control metric based on local variations of the receiver signatures which does not require geophone measurements.

Time-lapse attenuation variations using distributed acoustic sensing vertical seismic profile data during CO2 injection at CaMI Field Research Station, Alberta, Canada

For seismic monitoring of geologic [Formula: see text] storage, it is useful to measure time-lapse (TL) variations of seismic attenuation. Seismic attenuation directly connects to different petrophysical parameters within the [Formula: see text] storage complex. We have developed an approach to derive smooth time-variant amplitude spectra from seismic signals by using the sparse strongest signal peaks, and then measuring two different attributes (conventional Q factor and its “geometric” counterpart) characterizing the path effects of seismic attenuation from the smooth spectra. This approach is straightforward and does not require sophisticated algorithms or parameterization schemes. We apply this approach to TL distributed acoustic sensing (DAS) vertical seismic profile (VSP) data from the Field Research Station (FRS) [Formula: see text] injection project in southern Alberta, Canada. High-quality stacked reflection records are obtained from the baseline and monitor DAS VSP surveys at the FRS and TL attenuation-attribute differences are derived from these reflection records. TL variations of attenuation are observed within the injection zone at the FRS, which are interpreted as being related to the injected [Formula: see text]. Although there is always a significant trade-off between the accuracy and temporal resolution of the measured attenuation parameters, reliable attenuation measurements around the injection zone are still achieved with an in-use reflection signal of a sufficient length and bandwidth. Attenuation attributes measured from this approach can be an advantageous tool for monitoring the distribution and migration of [Formula: see text] plumes.

An efficient deep learning method for VSP wavefield separation: A DAS-VSP case

The quality of wavefield separation on vertical seismic profiling (VSP) data directly affects subsequent imaging and inversion processes. However, the traditional methods have various defects in separating upgoing and downgoing waves. The application of deep learning brings another train of thought to solve related problems. The traditional methods, such as f-k filtering and Radon transform, produce results with spatial aliasing and inaccurate amplitudes. The median filtering method relies on accurate first-break picking and waveform consistency. Moreover, the results of traditional methods are subject to manual intervention. To overcome these problems, a deep-learning method is developed for the automatic wavefield separation of VSP data. First, the traditional Radon transform method is used to produce training data sets. Then, to ensure amplitude preservation and suppress spatial aliasing, new input data is formed by recombining the upgoing and downgoing waves extracted by Radon transform. The developed deep-learning network is highly efficient, and its accuracy exceeds that of the traditional methods only after one epoch training. A practical workflow of wavefield separation based on deep learning is established, and it is applied to synthetic data and field distributed acoustic sensing VSP data. The results indicate that our method is superior in terms of amplitude preservation and spatial aliasing suppression. The time consumption of our method is very acceptable and can be further minimized by training the network using downsampling data.

Interpretable denoising of distributed acoustic sensing vertical seismic profile data using adaptive consistent prior net

Distributed Acoustic Sensing (DAS) is vital in seismic exploration, especially Vertical Seismic Profile (VSP) studies, due to its high sensitivity, fine velocity modeling, and resistance to harsh conditions. However, DAS VSP data often suffer from low quality due to complex noise sources. Previous studies show that convolutional neural network (CNN) denoising methods can achieve good results, but they often lack interpretable mechanisms for denoising, posing a challenge in understanding and interpreting the results. To overcome these limitations, we propose an adaptive consistency prior network (ACPNet) algorithm, based on a model-guided design. First, we introduce an adaptive consistency prior algorithm to form a model-based denoising method that guides the network design, leading to an end-to-end trainable and interpretable denoising network. We constructed a comprehensive training dataset, and the network is trained using the L1 loss function to achieve effective signal-to-noise separation. The combined synthetic data and field data denoising results demonstrate that the proposed method can effectively suppress various types of noise in DAS-VSP data and has a strong signal recovery capability. Furthermore, the denoising performance of the proposed method surpasses that of traditional methods and several deep learning techniques.

Effectiveness of Fiber Optic Distributed Acoustic Sensing (DAS) in Vertical Seismic Profiling (VSP) Field Survey

The evolution of fiber optic technology in the past few decades has led to significant advancements in various fields, including high-speed and long-distance communication, big data transport, optical imaging, and sensing. However, relatively few studies have examined the use of fiber optic sensors (FOSs) as point and distributed sensors in geophysics. Distributed Acoustic Sensing (DAS) is a widely used method for subsurface imaging and monitoring in wells, specifically in Vertical Seismic Profiling (VSP) surveys. This method allows for detailed analysis of subsurface structures and properties of reservoirs. Four different strategies for deploying FOS cables in DAS VSP are evaluated and compared: cementing behind casing, cable behind inflatable liner, strapping to production tubing, and wireline deployment. Cementing the fiber behind casing is considered the most effective method for coupling with the formation. However, the other methods also have their own advantages and limitations. The fiber cable behind inflatable liner, for example, allows for accessibility to the fiber without affecting the acoustic signal, while strapping the fiber to production tubing can still record DAS signals; tubing noise and signal attenuation from the annular fluid, however, can make it difficult to differentiate from the seismic signal. Nonetheless, this method has the benefit of being simpler to deploy and replace in case of failure. Wireline deployment can pick up some acoustic signals in regions where the cable touches the well wall, but in vertical sections where the cable is not in contact with the wall, the signal is attenuated. Results from pilot tests in a field in Canada are discussed and evaluated, and suggestions for improving the VSP signal are provided.

Denoising and wavefield separation method for DAS VSP via deep learning

Distributed acoustic sensing (DAS) vertical seismic profiling (VSP) is, a new combined method of borehole seismic exploration, that has the advantages of low cost, high resistance to temperature and pressure, and high sampling rate. However, the collected data generally suffer from high noise, detracting from the potential high-precision imaging benefits and limiting the full application of the technique. The quality of wavefield separation is also crucial to VSP processing. To mitigate the various problems, a regularized residual channel attention network (RRCAN) is proposed for improved denoising and wavefield separation. Based on two prediction tasks, the networks are built (i.e., RRCAN_1 and RRCAN_2) by adding a regularization term to the loss function. To reduce the loss of effective signals during the denoising process, a wavefield separation method that includes secondary effective information extraction is adopted. The final separation results reflect the denoised and removed noise data, a total of 500 geological models are constructed using a random method and RRCAN_1 is trained with synthetic and field noise data. The conventional plane-wave destruction method is improved by using velocity information, and high-precision separation results for upgoing and downgoing waves are obtained for RRCAN_2 training. The test results obtained for the synthetic and field data show that the proposed network effectively suppresses DAS VSP noise while yielding excellent separation results for downgoing and upgoing waves. Therefore, denoising and wavefield separation methods based on RRCAN provide effective high-precision DAS VSP imaging.

Unpaired training: Optimize the seismic data denoising model without paired training data

With the development of seismic exploration technology, distributed acoustic sensing (DAS) has recently received attention in geophysics. However, owing to the complexity of the layout techniques in the DAS systems, and the unknown or harsh exploration environment, seismic data acquired by this technique usually contain the noise of diverse components, which increases the difficulty in subsequent data analysis and interpretation. This study has (1) trained a deep learning model that effectively suppressed noise with augmented noise data sets to obtain a high signal-to-noise ratio in the DAS vertical seismic profile (VSP) records, (2) introduced an attention module to enhance the extraction and recognition of signal features to recover effective signals under substantial noise interference, and (3) introduced adversarial loss and cycle-consistent loss to replace the commonly used L1 norm or L2 norm to train the network. The obtained hybrid training set containing unpaired synthetic and unpaired field data sets for model pretraining and fine tuning effectively improves the denoising performance of the seismic field data. In summary, this study develops an unpaired training-based DAS seismic data denoising method that transformed noisy DAS VSP data into noise-free data. By analyzing the noise suppression results of other methods, including qualitative and quantitative analyses, we demonstrate that our method successfully suppressed multiple types of noise in the DAS VSP data. The study has indicated clear and continuous signals in the denoising results and improved the denoising performance on the seismic field data.

Full-Waveform Inversion of Fiber-Optic VSP Data From Deviated Wells

Distributed acoustic sensing (DAS) technology permits measuring the strain or strain rate along a fiber deployed in a well due to seismic wave propagation. In vertical seismic profiling (VSP) acquisitions where seismic waves are excited at the surface and measured by subsurface receivers in the well, the DAS serves as a fast alternative to a conventional geophone array. Like conventional multicomponent VSP velocity measurements, DAS VSP data contain information about the subsurface properties that are encoded in seismic events and attributes such as compressional and shear direct arrivals, reflections, converted modes, multiples, and amplitude and phase variations. These seismic data can be used to obtain elastic properties of the formation and characterize the reservoir. Full-waveform inversion (FWI) extracts the elastic properties of the formation from the seismic wavefield by minimizing the misfit between real measurements and synthetically modeled wavefields. The synthetically generated wavefields are obtained by solving partial differential equations that accurately model the physical phenomena of the real waveforms. For conventional geophone sensors, FWI is formulated for the particle velocity field. In this paper, we present a formulation of the modeling and inversion specifically for DAS measurements as an averaged strain along the fiber. The algorithm does not require converting the data to velocities. The gradient of the misfit function, by adjoint formulation, is a cross correlation in time of the forward-propagated wavefield and backward-propagated residuals injected from the receivers. For conventional sensors, the residuals are injected as force terms, but for DAS data, the residuals are averaged in space first and then injected as moment tensor sources with radiation patterns determined by the well deviation. For multi-offset in-plane and out-of-plane VSP acquisitions, a two-dimensional (2D) algorithm has been developed that inverts for a 2D distribution of elastic medium properties and includes three-dimensional (3D) well deviation effects. This paper will present the results of applying the inversion to real multi-offset, highly out-of-plane VSP fiber data acquired in a deviated well. Simulations confirm that the well trajectory has a considerable impact on the data amplitude and must be included in the modeling and inversion to reproduce amplitude variations. The inverted compressional and shear velocities agree well with the reference model based on sonic data. Another real data example is a multi-offset walk-above acquisition where a targeted domain is located below a deviated portion of the well. The inversion matches reflections and produces an image in the target area.

Image-domain DAS 3D VSP elastic transmission tomography

SUMMARY Full-wavefield elastic imaging of active-source seismic data acquired by downhole receivers commonly offers higher-resolution subsurface images in the vicinity of a borehole compared to conventional surface seismic data sets, which can lack higher-frequency wavefield components due to longer travel paths and increased attenuation. An increasingly used approach for downhole acquisition is vertical seismic profiling (VSP), which has become more attractive when coupled with distributed acoustic sensing (DAS) using optical fibres installed in wells. The main difficulty for generating high-quality images with full-wavefield imaging tools for DAS VSP data, though, is the need for an accurate velocity model. To build plausible velocity models using active-source DAS VSP data, we adopt a 3-D image-domain elastic transmission tomography technique, originally developed for surface-recorded passive (microseismic) data, by exchanging the source and receiver positions (i.e. reciprocity) to mimic a passive-seismic surface monitoring scenario. The inversion approach exploits various images for each source constructed through time-reverse imaging (TRI) of downgoing P- and S-wave first-arrival waveforms. The TRI process uses the kinetic term of the (extended) PS energy imaging condition that exhibits sufficient sensitivity to velocity model errors. The method automatically updates the P- and S-wave velocity models to optimize image focusing via adjoint-state inversion. We illustrate the efficacy of the adopted elastic inversion technique using an active-source DAS 3-D VSP field data set acquired in the North Slope of Alaska. The numerical experiments demonstrate that the inverted elastic velocity models can be further used in full-wavefield acoustic/elastic imaging algorithms to obtain accurate subsurface images.

Case study on amplitude and phase response comparison between DAS and 3C geophone VSP surveys at a test site in Japan

Distributed acoustic sensing (DAS) is an effective tool for seismic surveys and is widely used as the preferred sensor for acquiring dense Vertical Seismic Profile (VSP). However, DAS has some disadvantages compared to conventional geophone acquisition, such as single-component data, sensor directivity pattern, low S/N, etc. To apply DAS data to VSP processing, it is important to understand the characteristics of DAS measurements. This study first examined DAS and geophone responses for a VSP survey in a 1D earth model. The relationship of signal amplitude as a function of incident angle and phase shift between wavelets was confirmed. Next, we validated this relationship in a walk-away VSP survey, which was conventionally applied to monitor geological CO2 storage. The survey was carried out at a test site in Japan. We obtained both three-component (3C) geophone and DAS data using a single-mode fiber installed behind a casing. The observed amplitudes of the first P-waves by DAS and geophone showed very good agreement with the theoretical expectations. Finally, we performed imaging using acquired DAS data following the workflow for conventional VSP processing. These basic behaviors of DAS are useful for designing further analyses, such as VSP in deviated wells and full waveform inversion.

Seismic monitoring of CO2 geosequestration using multi-well 4D DAS VSP: Stage 3 of the CO2CRC Otway project

An important part of any CO2 geosequestration project is to ensure CO2 containment and conformance in the subsurface. This is generally done by implementing a comprehensive, risk-based Measurement, Monitoring and Verification plan, a key element of which is active time-lapse seismic monitoring. However, high cost and environmental impact of the standard surface seismic monitoring dictate the need for a cost-effective and environmentally friendly alternative. An opportunity to develop such method emerges with advances in distributed acoustic sensing (DAS) technology, which turns an optical fibre into a seismic sensor with dense spatial sampling. DAS can be permanently deployed in multiple wells across the geosequestration site providing a robust and non-intrusive network of seismic receivers. This approach was developed and tested in the CO2CRC Otway project, where injection of 15 kt of CO2 at 1.5 km depth was monitored with a 4D vertical seismic profiling (VSP) using five borehole DAS arrays and mobile vibroseis sources. The 4D DAS VSP in each of the five wells provides broadly consistent images of the CO2 plume with some differences due to different illumination of the target horizon, lateral variation of velocities, and seismic anisotropy. When the newly injected CO2 reaches a CO2 plume created as a result of an earlier injection into the same formation ∼600 m updip, 4D DAS VSP shows a change in reflectivity in that area and beyond. This shows a potential of 4D DAS VSP for monitoring gas injection into gas-saturated reservoirs.

Quantification of DAS VSP Quality: SNR vs. Log-Based Metrics.

The initial quantification of data quality is an important step in seismic data acquisition design, including the choice of sensing strategy. The signal-to-noise ratio (SNR) often drives the choice of distributed acoustic sensing (DAS) parameters in vertical seismic profiling (VSP). We compare this established approach for data quality assessment with metrics comparing DAS data products to available well logs. First, we create kinematic and dynamic data products derived from original seismic data, such as the interval velocity and amplitude of P-wave arrivals. Next, we quantify the quality of derived data products using well log data by calculating various statistical metrics. Using a large dataset of 220 different VSP experiments with a fixed source location and various DAS acquisition parameters, such as gauge length (GL), conveyance type, and lead-in length, we analyzed the statistical distribution of various metrics. The results indicate the decoupling between seismic-based and log-based metrics as well as between the quality of dynamic and kinematic data-products for the same record. Therefore, we propose using fit-for-purpose metrics to optimize the acquisition cost. In particular, for ray-based tomographic processing, it is sufficient to use traveltime-based metrics. On the other hand, for advanced dynamic analysis, amplitude-based metrics define the quality of final processing products. Hence, it is crucial to use fit-for-purpose metrics to optimize DAS VSP acquisition.

Background Noise Suppression for DAS-VSP Records Using GC-AB-Unet

Distributed acoustic sensing (DAS) is one of the most popular sensors for seismic acquisition. Compared with traditional seismic acquisition technology, DAS has the advantages of full-well coverage, high density, high efficiency, high sensitivity, low cost, strong resistance to high temperature and high pressure, and anti-electromagnetic field interference. However, the DAS vertical seismic profile (VSP) data are contaminated by strong noise interference, which brings challenges for practical applications and difficulties to seismic inversion and interpretation. A deep learning method named U-net with Global Context Block and Attention Block (GC-AB-Unet) is proposed to suppress the background noise and increase the data quality for DAS-VSP records without knowing any prior information. In GC-AB-Unet, several dropout layers are added to the U-net to avoid overfitting. Meanwhile, to speed up the network training, the residual units are set as the output of the network. Furthermore, GC-Block is introduced for better capturing shallow and deep features by extracting global context information. In addition, Attention Block is used to emphasize seismic event features and restrain irrelevant details in seismic data. We also construct a training dataset by utilizing the synthetic data and real noise of DAS-VSP data. The denoising results for both synthetic data and field DAS-VSP data show that compared to original U-net and Damped Rank Reduction (DRR) method, the GC-AB-Unet network is able to preserve the effective signals with almost no energy leakage while suppressing a large amount of background noise.

Attribute-Guided Target Data Separation Network for DAS VSP Data

Distributed acoustic sensing (DAS) technology is a rapidly evolving fiber-optic sensing technology that has been gradually applied to vertical seismic profile (VSP) data. The DAS VSP data are often contaminated by multiple interference waves, such as random noise, coupled noise, horizontal noise, and the existing methods can only map it from noisy data to effective reflections. However, interference waves are also a relative concept, and many interference waves still have certain application values. Therefore, this article proposes an innovative algorithm called attribute-guided target data separation network (Att-TDSN), which can not only complete the conventional signal-noise separation task but also achieve noise-noise separation task. Specifically, we first propose a flexible and efficient training set, namely, multidimensional weak label training set (Mul-WLTS), which introduces attribute features as the weak labels and specifies the dimension of weak labels according to the number of target data types (effective reflections and several common interference waves). Then, we use the weak labels to guide our network to map training data to specified data types, assisting the network to focus its attention on each kind of target data. Finally, the network parameters are trained by a training mode called “one-way matching and two-way constraint.” “One-way matching” makes data separation result unique, and “two-way constraint” can improve the algorithm’s amplitude preserving ability. Experiments on synthetic and field DAS VSP data show that our network can accurately separate target data. Moreover, for the traditional denoising task, Att-TDSN also has a better denoising performance than existing methods.

Denoising Deep Learning Network Based on Singular Spectrum Analysis—DAS Seismic Data Denoising With Multichannel SVDDCNN

Distributed acoustic sensing (DAS) is a new tool with low cost, sensitive signal capture, and complete coverage for vertical seismic profile (VSP) acquisition. Although DAS has obvious advantages over geophones, some weaknesses may limit its application. The main challenge is that DAS is polluted by various types of noise, including optical abnormal noise, random background noise, fading noise, and so on. To suppress these novel noises, we developed a new denoising neural network based on singular spectrum analysis—multichannel singular value decomposition denoising convolutional neural network (SVDDCNN). The network can simultaneously extract data features from singular spectrum instead of the time domain, which can represent geophysical features more accurately and help separate signals from noises. Second, a multichannel input layer is designed, and the input is decomposed into three subspaces by singular spectrum analysis, which provides records of different signal-to-noise ratios (SNRs) for training and improves generalization ability of the network. Third, to enhance the quality of the data set, we added the noise subspace records removed by SVD into the training set to provide various forms of noise with different singular spectra. Both synthetic and field examples show that our network has achieved impressive denoising of DAS VSP and demonstrated competitive performance compared with other methods. Furthermore, the structure similarity (SSIM) map is introduced to evaluate the signal leakage by calculating the similarity between the denoised record and the removed noise record. The lowest SSIM index of the proposed network indicated superior signal preservation ability.

Evaluating 3D and 4D DAS VSP image quality of subsea carbon storage

In all carbon capture, utilization, and storage (CCUS) projects, monitoring, verification, and accounting is required to span short to long periods to ensure plume extents and geologic integrity meet operational and regulatory objectives. Fiber-optic cables permanently deployed in the completions of injector wells enable time-lapse (4D) vertical seismic profile (VSP) imaging using distributed acoustic sensing (DAS). Most CCUS projects are onshore, and the majority of those utilize supercritical CO2flooding for enhanced oil recovery (EOR). In these cases, fiber-optic installations, acquisition, and analysis are relatively straightforward. Offshore transport and storage projects, whether for EOR or sequestration in saline aquifers, may utilize subsea wells wherein fiber-optic deployment is not as straightforward. Unlike dry-tree wells, DAS of subsea wells requires advanced optical engineering solutions to compensate for the reduced acoustic bandwidth, optical losses, and back reflections accumulated through umbilicals, multiple wet- and dry-mate optical connectors, splices, optical feedthrough systems, and downhole fibers. In this paper, we simulate VSP shot records of a carbon storage site for a variety of proposed subsea DAS concepts and evaluate the 3D and 4D seismic image quality of each using Kirchhoff migration of upgoing seismic wavefields with their expected signal-to-noise ratios. We normalize interrogator performance, so we are evaluating the sensing topology only. We show that a subsea fiber topology consisting of dual transmission fibers, a remote circulator with selective amplification of the backscattered light, and enhanced backscatter sensing fiber results in high-quality seismic images suitable for both 3D and 4D VSPs of carbon storage.

Distributed Acoustic Sensing Vertical Seismic Profile Data Denoiser Based on Convolutional Neural Network

Distributed acoustic sensing (DAS) is a novel technology, which has the advantages of full well coverage, high sampling density, and strong tolerance to harsh environments. However, compared with conventional geophones, the signal-to-noise ratio (SNR) of vertical seismic profile (VSP) data obtained using DAS is low, and there are many types of noise (such as random noise, coupled noise, fading noise, background abnormal interference, horizontal noise, and checkerboard noise). These noises bring great difficulties to the interpretation of seismic data. Existing DAS VSP data denoising methods generally can only suppress one type of noise. Faced with DAS VSP data with many types of noise, the denoising process is extremely complicated. To solve the above problems, we propose a DAS VSP data denoiser based on the convolutional neural network (CNN), which can suppress a variety of common noise at one time, and the denoising process is more convenient and efficient. In addition, since there is currently no publicly available training set for DAS VSP data, we also use field data and synthetic data to construct a training set for the denoiser. The denoising results show that the proposed method can effectively suppress a variety of common noise in DAS VSP data and the effective signal has almost no energy attenuation. Both the shallow layer signal affected by strong noise and the deep layer signal with weak energy are well recovered.

Real-Time DAS VSP Acquisition and Processing on Single- and Multi-Mode Fibers

Distributed acoustic sensing (DAS) is a rapidly evolving fiber-optic sensing technology that converts any standard fiber-optic cable into a distributed sensor capable of measuring acoustic events every meter over large (100 km+) distances. We describe a conveyance-agnostic DAS system for vertical seismic profiling (VSP) that has the capability to sense either single- or multi-mode sensing fibers, and encode all auxiliary seismic source signals directly onto the optical data stream using a series of piezo-electric fiber stretchers in-line with the sensing fiber. The DAS system can operate with latency-free synchronization via wireless telemetry between the seismic source controller and the DAS unit in either master or slave modes. Similar to geophone-based VSP data acquisition systems, the time-break signal is used to trigger data acquisition to ensure precise time synchronization at sub-millisecond accuracy between the DAS system and the seismic source. The system enables effective data management and the real-time generation of 1 millisecond sampled seismic and navigation data that conforms to the industry-standard seismic data formats, thus eliminating any latency between acquisition and field data deliverables.

3D vertical seismic profile acquired with distributed acoustic sensing on tubing installation: A case study from the CO2CRC Otway Project

Distributed acoustic sensing (DAS) can revolutionize the seismic industry by using fiber-optic cables installed permanently to acquire on-demand vertical seismic profile (VSP) data at fine spatial sampling. With this, DAS can solve some of the issues associated with conventional seismic sensors. Studies have successfully demonstrated the use of DAS on cemented fibers for monitoring applications; however, such applications on tubing-deployed fibers are relatively uncommon. Application of tubing-deployed fibers is especially useful for preexisting wells, where there is no opportunity to install a fiber behind the casing. In the CO2CRC Otway Project, we acquired a 3D DAS VSP using a standard fiber-optic cable installed on the production tubing of the injector well. We aim to analyze the quality of the 3D DAS VSP on tubing, as well as discuss lessons learned from the current DAS deployment. We find the limitations associated with the DAS on tubing, as well as ways to improve the quality of the data sets for future surveys at Otway. Due to the reduced coupling and the long fiber length (approximately 20 km), the raw DAS records indicate a high level of noise relative to the signal. Despite the limitations, the migrated 3D DAS VSP data recorded by cable installed on tubing are able to image interfaces beyond the injection depth. Furthermore, we determine that the signal-to-noise ratio might be improved by reducing the fiber length.