Konstantin Tertyshnikov

Safe and reliable carbon capture and storage (CCS) requires long-term monitoring to verify secure CO₂ containment. Distributed acoustic sensing vertical seismic profiling (DAS-VSP) provides a practical approach for subsurface monitoring due to its permanent installation, dense spatial sampling, and reduced operational costs. However, monitoring programs spanning decades may require hardware upgrades, including interrogator replacements and changes to fibre-optic cable configurations, affecting time-lapse repeatability and interpretability. This field case study evaluates whether the CO₂-induced 4D DAS-VSP response remains interpretable across a combined change in interrogator, fibre type and optical path, whose individual contributions cannot be isolated. The assessment uses data from the CO2CRC Otway Project Stage 4, where approximately 10 kilotons of CO₂-rich reservoir gas were injected into a saline aquifer at approximately 1500 m depth. Baseline and monitor surveys using different DAS configurations are compared at two monitoring wells, with a same-configuration case as a repeatability benchmark. An established 4D DAS-VSP processing workflow, supplemented by system-specific noise attenuation, is applied to mitigate configuration-dependent differences. The results show that, despite higher non-repeatability, the principal CO₂-related time-lapse anomaly remains spatially localised and distinguishable, indicating that the DAS-VSP response can remain interpretable across a combined configuration change under favourable acquisition and signal-quality conditions.

Passive distributed acoustic sensing (DAS) is an attractive tool for monitoring geological CO₂ storage, but its dense, continuous recordings create a data-volume problem: a multi-month, multi-well archive yields enormous numbers of detector triggers, of which confirmed seismic events form a vanishingly small fraction, and conventional supervised classification is ill-posed when labels remain scarce and the negative class undefined because the non-event population is open-ended and spans noise families that vary over time and between wells. We present Argus, a sparse-label machine-learning workflow that converts continuous DAS recordings into a reproducible, auditable catalogue of event candidates. A deterministic front end reduces the archive to comparable trigger objects, each described by a 67-feature interpretable representation of its two-dimensional time–channel character (e.g., duration and channel span, detector-mask morphology, apparent moveout, inter-channel waveform coherence, and spectral shape); a retrieval-first machine-learning layer then ranks these triggers by their similarity, in this interpretable feature space, to a small seed catalogue of independently confirmed events, within an iterative human-in-the-loop process that introduces local supervised noise-rejection gates only for recurrent artefact families once they have been labelled. Applied to the CO2CRC Otway Stage 4 dataset—120 days of recordings on two wells, comprising roughly 23 TB and 14.14 million raw triggers—the workflow expanded a 39-event seed catalogue into 631 analyst-reviewed events, demonstrating complementarity with an independent template-matching analysis: two additional induced-event candidates were recovered, one within the CRC4 template-matching coverage and one on CRC7 during a CRC4 data gap. The induced-event class itself grew only from four to six candidates, and its counts are reported as a reviewed lower bound rather than a complete census. The result is a provenance-preserving, conservatively interpreted event inventory rather than an opaque classifier output, an outcome aligned with the reproducibility and audit requirements of CO₂ storage assurance.

To investigate gas migration along a near-surface fault and assess the performance of various seismic monitoring techniques, a controlled shallow CO2 release experiment was carried out at the CO2CRC Otway International Test Centre (Victoria, Australia). Sixteen tons of CO2 were injected at approximately 70 m depth into the Brumbys Fault. The monitoring program combined reverse 4D vertical seismic profiling using a downhole sparker source with a dense surface geophone array, cross-well seismic tomography with distributed acoustic sensors (DAS), passive borehole DAS data analysis of Rayleigh-wave amplitudes, and data from permanently deployed surface orbital vibrators. All methods detected the evolving plume consistently and provided complementary information on its geometry and migration. The results demonstrated that the integrated monitoring framework is capable of resolving small-scale CO2 movement and can be scaled up for continuous, cost-effective surveillance in larger storage projects.

High-resolution crosshole seismic surveying is a powerful technique for monitoring potential CO ₂ leakage in the shallow subsurface and tracking the injection process. A set-up employing a high-frequency seismic source in one well and a fibre-optic receiver array in another enables rapid and efficient seismic data acquisition, with repeat surveys allowing for time-lapse analysis. Such data were collected during the controlled shallow CO ₂ release experiment at the CO2CRC's Otway International Test Centre in April 2024. Straight-ray tomography images from the initial surveys revealed a velocity anomaly near the injection interval, which migrated upward following the end of injection. To further investigate the CO ₂ plume evolution, three additional surveys were conducted 10, 12 and 14 months after injection ceased. In this study, all crosshole datasets were analysed to provide a comprehensive overview of both short-term and long-term monitoring results. A new data processing workflow was developed, incorporating advanced tomography that accounts for seismic wave refraction caused by velocity variations in the medium. The updated results are consistent with previous findings but exhibit reduced uncertainty. The recent monitoring data indicate that the velocity anomaly near the injection zone persists but has decreased in size, while its further evolution continues at a slower rate compared with the initial post-injection period.

This article presents an advanced processing workflow for a Seismic While Drilling (SWD) dataset acquired using Distributed Acoustic Sensing (DAS) in a cross-well setting at the Otway International Test Centre (OITC) in Victoria, Australia, where no pilot signals were recorded. Recording the drill bit signature enables and simplifies the decoding of passive seismic signals emitted by the drill bit while drilling. In conventional SWD, a measured drill bit signature is used to correlate passive seismic recordings and to determine source trigger times, analogous to vibroseis processing. Without this reference, both source timing and signature must be inferred from the recorded wavefield. This can typically be achieved by backpropagating the recorded seismic data over short time windows, estimating the source location and trigger time based on the peak RMS energy in space and time. However, to simplify the processing of SWD data, a data processing workflow is presented, guided by travel time and seismic modelling, which transforms passive SWD data into active equivalents. The transformed data can then be used to characterize the subsurface by implementing travel time tomography and cross-well imaging. The results demonstrate reliable velocity and structural information can be recovered from DAS-based SWD data without pilot measurements, enabling simplified and scalable deployment of passive seismic while-drilling workflows.

Distributed acoustic sensors (DAS) deployed in the boreholes enable high-resolution monitoring of subsurface changes. Typically seismic monitoring uses DAS amplitudes of reflected and transmitted seismic waves recorded above and below the geological objects that undergo changes, targets. We develop a monitoring framework relying on the DAS amplitudes inside the targets. The underlying model considers the anomalies as near-field scattering of seismic waves. We applied the proposed method to a field data set, in which five deep boreholes instrumented with DAS monitored the movement of a small injection of supercritical CO2. We accurately matched the DAS anomalies for diverse types of seismic signals: controlled high-frequency seismic sources, regional earthquakes, and oceanic microseisms from remote storms and local winds. For low-frequency signals, the microseisms and remote earthquakes, the strain-rate measurements may track the evolution of the reservoir changes providing a single-hole source-free monitoring tool. Our study lays the groundwork for a joint inversion of the DAS data, in situ, and reflected and transmitted signals. Plain Language Summary: Boreholes provide the only means for direct observations of the subsurface changes associated with injections and extractions of fluids, such as geological carbon sequestration, energy storage, and petroleum production. Our study shows that boreholes instrumented with fiber-optic seismic sensors can track very subtle reservoir changes associated with these operations. Because the fiber-optic installation is cheap and permanent, we can continuously monitor the reservoir conditions to optimize the fluid injection/extraction and ensure safety. Unlike the previous seismic techniques, we use the amplitudes of seismic signals inside the reservoir to quantitatively estimate reservoir parameters. We show that our framework is both accurate and practical using the data recorded for a small injection of supercritical gas. We showed that the noise produced by remote storms and local winds, regional earthquakes, and man-controlled seismic sources obey the proposed methodology. Therefore, our technique provides a cheap, single-hole, source-free monitoring tool.

Passive seismic surveys have attracted interest for use in many geological and geotechnical applications in the past few decades, mainly in reconstructing models of near-surface properties. They are also of interest in the mineral exploration of shallow deposits where targets lay on or within the bedrock and are covered by loose sediments above. The goal of this article was to test the effectiveness of cheap methods to understand the cover thickness and its lateral variations, which is essential to map the top of the bedrock. We investigated the use of passive seismic surveys to retrieve Rayleigh surface waves and invert them by analyzing their dispersion to reconstruct near-surface shear-wave velocity profiles. Using readily available passive seismic sources is advantageous compared to using costly active sources. Passive seismic data acquired by geophones and DAS showed the potential and challenges of using different sensing technologies. We demonstrated an approach combining passive seismic interferometry and multichannel analysis of surface waves (MASW). Computed dispersion images from both geophone and DAS data provided an improved understanding of their usability for subsurface model building and factors affecting their quality. Some of these factors are related to the surrounding environment, present noise sources, acquisition setup, and the methods used in reconstructing the dispersion images and inverting them. Successful demonstration of MASW was achieved with a relatively short period of continuous recording using a 2D array of geophones at a mineral exploration site in the Pilbara region of Western Australia.

Over the last decade, distributed acoustic sensing (DAS) has received growing attention in the field of seismic acquisition and monitoring due to its potential high spatial sampling rate, low maintenance cost and high resistance to temperature and pressure. Despite its undeniable advantages, DAS faces some challenges, including a low signal-to-noise ratio, which partly results from the instrument-specific noise generated by DAS interrogators. We present a comparison between two deep learning approaches to address DAS hardware noise and enhance the quality of DAS data. These approaches have the advantage of including real instrumental noise in the neural network training dataset. For the supervised learning (SL) approach, real DAS instrumental noise measured on an acoustically isolated coil is added to synthetic data to generate training pairs of clean/noisy data. For the second method, the Noise2Noise (N2N) approach, the training is performed on noisy/noisy data pairs recorded simultaneously on the downgoing and upgoing parts of a downhole fiber-optic cable. Both approaches allow for the removal of unwanted noise that lies within the same frequency band of the useful signal, a result that cannot be achieved by conventional denoising techniques employing frequency filtering.

Exploring shallow mineral resources requires acquiring denser seismic data, for which Distributed Acoustic Sensing is an effective enabler and relevant to mining operations monitoring. Passive seismic can be of interest in characterizing the subsurface; however, dealing with large amounts of data pushes against the limits of existing computational systems and algorithms, especially for continuous monitoring. Hence, more than ever, novel data analysis methods are needed. In this article, we investigate using synthetic seismic data, paired with real noise recordings, as part of a supervised deep-learning neural network methodology to detect and locate induced seismic sources and explore their potential use to reconstruct subsurface properties. Challenges of this methodology were identified and addressed in the context of induced seismicity applications. Data acquisition and modelling were discussed, preparation workflows were implemented, and the method was demonstrated on synthetic data and tested on relevant seismic monitoring field dataset from the Otway CO₂ injection site. Conducted tests confirmed the effects of time shifts, signal-to-noise ratios, and geometry mismatches on the performance of trained models. Those promising results showed the method’s applicability and paved the way for potential application to more field data, such as seismic while drilling.

There are many challenges associated with monitoring industrial subsurface CO2 storage. Utilizing a reliable strategy for monitoring and assurance is paramount. Numerous aspects and trade-offs need to be considered when designing a cost-effective monitoring solution that must satisfy regulatory and social licensing requirements and minimize operational complexity. Among various geophysical techniques that are included in the suite of carbon capture and storage (CCS) monitoring programs, seismic surveys provide reliable observations of the deep subsurface. However, active surface seismic techniques are expensive, resulting in significant time separation between surveys (several months or years). Downhole seismic approaches combined with fiber-optic sensing provide flexible options in designing on-demand monitoring strategies. Distributed acoustic sensing offers the opportunity to utilize injection wells for downhole time-lapse seismic imaging and microseismic detection, minimizing the impact on production operations. This paper presents the results of a multioffset vertical seismic profiling survey acquired in CO2 injection wells using bare fibers. The fibers were deployed inside the production tubing through FiberLine Intervention technology developed by Well-SENSE. The test utilized two injection wells for active seismic acquisition. The study took place on Barrow Island (Western Australia) as part of the Gorgon CCS project. The study demonstrates the technological advantages of fast deployment of the sensing fiber and the high quality of acquired seismic data. The paper highlights characteristic noise patterns in the recorded data and suggests approaches to attenuate their effects on seismic image quality. Finally, the impact of survey geometry and directional sensitivity of fiber on the recorded wavefield and the quality of seismic imaging is discussed.