Characterization of a Fractured Reservoir Using DAS VSP—A Model-Based Reflection Amplitude Study

A walkaround vertical seismic profile (VSP) survey offers full-azimuth seismic measurements for fracture or stress-field characterization near a well. Since downhole geophones may be placed close to a target zone, high quality P-wave reflection data can be obtained to study seismic amplitude anisotropy or provide key parameters for possible calibration of surface seismic amplitude versus offset and azimuth (AVOAz). This paper explores the feasibility of using a zero-offset and walkaround VSP to determine azimuthal anisotropy in P-wave reflection amplitude. The case study in the paper shows evidence of azimuthal anisotropy in the reflection amplitude observed from a fractured reservoir. A workflow is developed to extract and analyse the AVOAz data from the walkaround VSP. The results demonstrate that the walkaround VSP can provide a quantitative measure for the orientation and magnitude of fracture-induced azimuthal amplitude anisotropy. It can also provide a key data set to calibrate surface seismic amplitude, including P-wave reflection coefficients, offset-to-angle conversion, and amplitude gradients.

Distributed Acoustic Sensing (DAS) is a Fiber Optic (FO) cable based technology which is gaining importance for VSP surveys, especially for time-lapse monitoring of reservoirs. DAS offers distinct advantages over geophones but it also poses unique challenges, including receiver depth uncertainty and low signal-to-noise ratio. Here, we present an analysis of a DAS VSP acquired for monitoring purposes in a treatment well while a multi-stage hydraulic fracture treatment was ongoing in the same well. We describe the pre-processing needed before further time-lapse analysis of the VSP. We focus on two key steps - depth calibration and noise removal. Large spike noise, most likely from the DAS acquisition system, appears to be a major source of noise in the raw DAS data, but can be removed with careful processing. Other sources of noise include temperature fluctuations in the well and optical noise. We show that excellent quality VSP data can be obtained in a hydrofrac treatment well.

Summary We discuss the processing and imaging solutions designed to assess 4D signal in a monitor Vertical Seismic Profiling (VSP) survey acquired with Distributed Acoustic Sensing (DAS) over the Culzean field, Central North Sea. With a baseline DAS VSP survey acquired during a pilot programme in 2019, a monitor survey acquired in 2021 aimed to provide the means to assess reservoir and overburden time shifts in the Culzean field ahead of full field 4D seismic acquisition. We show how a 4D compliant processing workflow effectively tackled non-repeatability aspects of the monitor survey including significantly increased background noise levels compared to the baseline and variation in the source signatures and the recorded data. An improved de-multiple workflow utilizing Wave Equation Deconvolution Imaging and final imaging with Reverse Time Migration, achieved a high quality image in the overburden and at the key reservoir section for analysis of 4D time shift signal.

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 CO2 flooding 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 (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.

The industry treats the distributed acoustic sensing (DAS) system, which uses an optical fiber cable in vertical seismic profiling (VSP) data acquisition, as a new and fast-growing technology. The high-quality data set acquired from the DAS acquisition system can produce high-precision VSP images and obtain more detailed checkshots. However, in field data, the acquired VSP data set suffers from strong coherent DAS coupling noise. Many factors may cause coherent DAS coupling noise, such as the cable slapping and ringing due to the physical placement, the regular swing of the wireline in the well, and the uncoupling between the fiber cable and the casing. This DAS coupling noise reduces the signal-to-noise ratio and affects the subsequent processing and interpretation. Removing the DAS coupling noise can help to improve the quality of the VSP data set acquired with the DAS system. We have developed a sparse-optimization-based DAS coupling noise removing method. In the DAS-based VSP data set, the effective signal and the coupling noise have distinct morphological characteristics. The effective VSP signal has a wide bandwidth, whereas the DAS coupling noise appears in some narrow frequency bands in the frequency domain. The continuous wavelet transform and the discrete cosine transform can sparsely represent the effective VSP signal and DAS coupling noise, respectively. Therefore, we choose these two transforms as two sparse dictionaries and combine them to form an overcomplete dictionary. The morphological component analysis (MCA) can use the morphological difference between different components and the overcomplete dictionary to sparsely represent all components in the complicated signal. Based on the MCA theory, we use the block coordinate relaxation algorithm to separate the effective VSP signal and DAS coupling noise. Applications on a synthetic data set and two field data sets have validated the effectiveness of our method.

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.

The usefulness of distributed acoustic sensing (DAS) VSP data to map reservoir structures and extract rock properties is limited only by our understanding of the fidelity and characteristics of the measurement. In order to calibrate both the kinematics and dynamics of DAS VSP products, we document the correspondence of the DAS and geophones measurements for extracted slowness values, corridor stacks, amplitudes, linearity with source effort, and response to incident angle. The DAS slowness values match very well with the corresponding geophone slowness and up-scaled sonic log. The arrival times and amplitudes of the corridor stacks show a very good agreement. The average earth attenuation extracted from the first break amplitudes are nearly identical for the DAS (Q=86) and geophone (Q=88) data sets. Tests varying the source effort confirm that the DAS measurement performs linearly with seismic amplitude. We also confirm the cosine squared response of the fiber to the incident angle of the seismic wave field. These tests show that properly acquired DAS VSP data sets can be trusted for both travel times and amplitude information.

Seismic data are a significant facilitator for monitoring in carbon capture and sequestration projects, providing high-resolution images of fluid migration, using, for example, full-waveform inversion (FWI). Distributed acoustic sensing (DAS), a relatively novel technology for wavefield sampling, is well suited for this type of monitoring. Using noninvasive optical fibers, DAS allows for dense spatial sampling along the entire length of the wellbore, without disrupting operations. Permanently installed in the wellbore, typically behind casing, DAS offers highly repeatable and dense sampling of the transmitted wave modes crucial to seismic monitoring of injected carbon dioxide ([Formula: see text]). However, the DAS data consist of measurements of strain along the tangent of the fiber and therefore do not transfer directly to conventional FWI algorithms. Incorporation of DAS data in their native strain (or strain-rate) form in standard FWI algorithms, requires changing the definition of the receiver sampling operator to use geometric information about the fiber to supply tangential strain measurements to the FWI residual. The theoretical developments are applied to invert field vertical seismic profile data acquired with DAS fiber and accelerometers at a [Formula: see text] sequestration site in Newell Country, Alberta. Our method incorporates DAS data and accelerometer data in one objective function and allows us to tune the relative importance we wish to place on each data set. This method also transfers to noncollocated sensors, for example, surface-deployed geophones and borehole fiber. The inverted models contain features expected from the geology of the field site, and data modeled in the inverted models compare favorably with the field data for these sensor types. The models are derived from data acquired prior to [Formula: see text] injection, representing baseline models for future time-lapse studies planned at the field research station.

Seismic surveys are widely used to characterise the properties of glaciers, their basal material and conditions, and ice dynamics. The emerging technology of Distributed Acoustic Sensing (DAS) uses fibre optic cables as seismic sensors, allowing observations to be made at higher spatial resolution than possible using traditional geophone deployments. Passive DAS surveys generate large data volumes from which the rate of occurrence and failure mechanism of ice quakes can be constrained, but such large datasets are computationally expensive and time consuming to analyse. Machine learning tools can provide an effective means of automatically identifying seismic events within the data set, avoiding a bottleneck in the data analysis process.Here, we present a novel approach to machine learning for a borehole-deployed DAS system on Store Glacier, West Greenland. Data were acquired in July 2019, as part of the RESPONDER project, using a Silixa iDAS interrogator and a BRUsens fibre optic cable installed in a 1043 m-deep borehole. The data set includes controlled-source vertical seismic profiles (VSPs) and a 3-day passive record of cryoseismicity.  To identify seismic events in this record, we used a convolutional neural network (CNN). A CNN is a deep learning algorithm and a powerful classification tool, widely applied to the analysis of images and time series data, i.e. to recognise seismic phases for long-range earthquake detection.For the Store Glacier data set, a CNN was trained on hand-labelled, uniformly-sized time-windows of data, focusing initially on the high-signal-to-noise-ratio seismic arrivals in the VSPs. The trained CNN achieved an accuracy of 90% in recognising seismic energy in new windows. However, the computational time taken for training proved impractical. Training a CNN instead to identify events in the frequency-wavenumber (f-k) domain both reduced the size of each data sample by a factor of 340, yet still provided accurate classification. This decrease in input data volume yields a dramatic decrease in the time required for detection. The CNN required only 1.2 s, with an additional 5.6 s to implement the f-k transform, to process 30 s of data, compared with 129 s to process the same data in the time domain. This suggests that f-k approaches have potential for real-time DAS applications.Continuing analysis will assess the temporal distribution of passively recorded seismicity over the 3 days of data. Beyond this current phase of work, estimated source locations and focal mechanisms of detected events could be used to provide information on basal conditions, internal deformation and crevasse formation. These new seismic observations will help further constrain the ice dynamics and hydrological properties of Store Glacier that have been observed in previous studies of the area.The efficiency of training a CNN for event identification in the f-k domain allows detailed insight to be made into the origins and style of glacier seismicity, facilitating further development to passive DAS instrumentation and its applications.

The advent of fiber optic technology in geophysics exploration has grown in its use in the exploration, production, and monitoring of subsurface environments, revolutionizing the way data are gathered and interpreted critically to speed up decision-making and reduce expense and time. Distributed Acoustic Sensing (DAS) has been increasingly utilized to build relationships in complex geophysics environments by utilizing continuous measurement along fiber optic cables with high spatial resolution and a frequency response of up to 10 KHz. DAS, as fiber optic technology examining backscattered light from a laser emitted inside the fiber and measuring strain changes, enables the performance of subsurface imaging in terms of real-time monitoring for Vertical Seismic Profiling (VSP), reservoir monitoring, and microseismic event detection. This review examines the most widely used fiber optic cables employed for DAS acquisition, namely Single-Mode Fiber (SMF) and Multi-Mode Fiber (MMF), with the different deployments and scopes of data used in geophysics exploration. Over the years, SMF has emerged as a preferred type of fiber optic cable utilized for DAS acquisition and, in most applications examined in this review, outperformed MMF. On the other side, MMF has proven to be preferable when used to measure distributed temperature. Finally, the fiber optic cable deployment technique and acquisition parameters constitute a pivotal preliminary step in DAS data preprocessing, offering a pathway to improve imaging resolution based on DAS measurement as a future scope of work.

As part of the Midwest Regional Carbon Sequestration Partnership (MRCSP) Phase III project, a monitoring study was conducted to assess the effectiveness of DAS (Distributed Acoustic Sensing) -based VSP (Vertical Seismic Profiling) technology for delineating CO2 injected into the Silurian-age pinnacle reefs in northern Michigan, the host rocks for the MRCSP Phase III demonstration project. The DAS VSP study was conducted in the Chester 16 reef, one of several reefs in Otsego County Michigan that is operated by Core Energy, LLC, of Traverse City, Michigan.

A complete comparison is made between a rig-source vertical seismic profile (VSP) recorded in a deviated well performed with conventional geophones data and with distributed acoustic sensing (DAS) fiber optics. The DAS acquisition was performed using a hybrid wireline cable during the logging descent of an ultrasonic tool for casing evaluation. This rig-source VSP project offers two rich data sets for which a one-to-one comparison of the most common quality indicators in borehole seismic can be made. The objective is to highlight commonalities, advantages, and potential shortcomings of both acquisitions when performed in a real oilfield operating environment. The comparison herein presented can be of interest to the industry, specifically looking at the overall cost reduction for gathering the well seismic information, versus the value of the information that can be achieved with both technologies.

Understanding hydraulic fracturing is crucial to improving the stimulation of unconventional reservoirs and increasing fluid production. This study develops a novel seismic monitoring technology using distributed acoustic sensing (DAS) and surface orbital vibrators (SOV) to capture fracture seismic response and mechanical properties at high temporal intervals. We analyze continuous time-lapse vertical seismic profiling (VSP) data acquired every hour during the first nine days of treatment of an unconventional reservoir in the Austin Chalk/Eagle Field Laboratory. The VSP data contain clear seismic signals scattered from the activated fractures. The spatiotemporal changes of the fracture reflectivity revealed by the SOV/DAS data correlate well with the observations of fracture locations inferred from low-frequency DAS data. These results capture the fracture opening and closure processes, as well as highlight potential prestage activations of the fractures due to hydraulic connectivity with preexisting fracture systems. Therefore, analysis of the presented data set provides a unique opportunity to understand fracture initiation and subsequent evolution, not only in the context of unconventional resources but also in enhanced geothermal systems.

Distributed Acoustic Sensing (DAS) technology repurposes fiber optic cables (FOCs) into seismic arrays, offering unprecedented dense strain/strain-rate measurements. The metre-scale virtual sensor spacing is typically unattainable with standard seismological equipment. Consequently, DAS provides an extraordinary amount of suitable data for seismic monitoring applications. However, intrinsic characteristics of this technology, such as signal axial polarisation, coupling inhomogeneities, or sensitivity to site conditions, can affect seismic phase amplitudes and their coherence, potentially reducing the number of useful measurement points. To gain a deeper understanding on the relative importance of these phenomena, this study analyses 'real data' from various seismic events recorded by shallow-horizontal DAS deployments. Thus, we take advantage of the pool of different array dimensions and geometries to avoid biased observations. We focus on the spatial variability of P-wave amplitudes, signal-to-noise ratios and waveform correlation, ideally mimicking the usage of absolute and differential arrival times for seismological monitoring purposes. We observed significant amplitude variations, which cannot be fully explained by signal polarisation along the FOC. Additionally, waveform correlation often exhibits a complex and faster decay with interchannel distance. These findings suggest the importance of avoiding 'blind' usage of shallow-horizontal DAS arrays and emphasise the need for case-dependent data selection/weighting procedures.