Distributed Acoustic Sensing Data Research Articles

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.

Comparison of Raman, Brillouin, and Rayleigh Distributed Temperature Measurements in High-Rate Wells

With the maturity of and demand for fiber-optic sensing technology growing steadily over the last few years across multiple basins, operators are seeking fiber-optic sensing solutions that address the technology challenges associated with the life-of-field monitoring of subsea developments. Single-ended distributed temperature sensing (DTS) measurements have been acquired for decades now, typically using Raman optical time-domain reflectometry (OTDR) on multimode fiber. However, for topside interrogation of subsea completions, Raman DTS performs poorly. This is due to the available optical power budget and the potential wavelength dependency of optical attenuation across multiple connectors and splices comprising the optical subsea infrastructure. Any wavelength-dependent attenuation as the signals pass through connectors, splices, and optical feedthrough systems will generate step changes in the measured Raman DTS temperature profile. Brillouin OTDR can provide a DTS alternative that overcomes these challenges and operates on single-mode fiber. Brillouin OTDR operates with a large dynamic range to measure a wavelength (frequency) shift of the Stokes/anti-Stokes components that is proportional to both strain and temperature. Since downhole cables are manufactured with optical fibers suspended in a gel and with appropriate extra fiber length (EFL), any fiber strain relaxes, and the Brillouin wavelength shift is an absolute temperature measurement. An additional alternative is also explored here. We typically associate coherent Rayleigh OTDR with distributed acoustic sensing (DAS) on single-mode fibers, but low frequencies also contain a relative temperature dependence. The low-pass filtering of DAS data can then be used as a form of Rayleigh DTS with appropriate data processing. In this paper, we report on a comparison of Raman, Brillouin, and Rayleigh DTS simultaneously acquired in the same high-rate producer and injector wells. We validate that, with appropriate cable design, Brillouin DTS can be simultaneously operated on the same single-mode fiber with DAS and can deliver absolute temperature measurements suitable for production analysis. We also use a laboratory experiment to show that Rayleigh DTS provides an accurate measure of temperature changes comparable in precision to a traditional thermocouple. We conclude with a discussion about the implementation of this DAS-DTS solution for sensing subsea completions.

Seismic Noise Interferometry and Distributed Acoustic Sensing (DAS): Inverting for the Firn Layer S‐Velocity Structure on Rutford Ice Stream, Antarctica

AbstractFirn densification profiles are an important parameter for ice‐sheet mass balance and palaeoclimate studies. One conventional method of investigating firn profiles is using seismic refraction surveys, but these are difficult to upscale to large‐area measurements. Distributed acoustic sensing (DAS) presents an opportunity for large‐scale seismic measurements of firn with dense spatial sampling and easy deployment, especially when seismic noise is used. We study the feasibility of seismic noise interferometry (SI) on DAS data for characterizing the firn layer at the Rutford Ice Stream, West Antarctica. Dominant seismic energy appears to come from anthropogenic noise and shear‐margin crevasses. The DAS cross‐correlation interferometry yields noisy Rayleigh wave signals. To overcome this, we present two strategies for cross‐correlations: (a) hybrid instruments—correlating a geophone with DAS, and (b) stacking of selected cross‐correlation panels picked in the tau‐p domain. These approaches are validated with results derived from an active survey. Using the retrieved Rayleigh wave dispersion curve, we inverted for a high‐resolution 1D S‐wave velocity profile down to a depth of 100 m. The profile shows a “kink” (velocity gradient inflection) at ∼12 m depth, resulting from a change of compaction mechanism. A triangular DAS array is used to investigate directional variation in velocity, which shows no evident variations thus suggesting a lack of azimuthal anisotropy in the firn. Our results demonstrate the potential of using DAS and SI to image the near‐surface and present a new approach to derive S‐velocity profiles from surface wave inversion in firn studies.

Acoustic Signals of a Meteoroid Recorded on a Large-NSeismic Network and Fiber-Optic Cables

AbstractA common challenge in acoustic meteoroid signal analyses is to discriminate whether the observed wavefield can be better described by line-source or point-source models. This challenge typically arises from a sparse availability of observations. In this work, we present an outstanding record of ground-coupled waves from local large-N seismic and distributed acoustic sensing (DAS) observations of a meteoroid in Iceland. Our complete data set includes additional regional stations located within 300 km of the meteoroid’s trajectory. The dense large-N and DAS data allow identification of acoustic phases that are almost impossible to discriminate on sparser networks, including a weak late arrival resolved mostly only by DAS. Using this data set with a new Bayesian inversion model, we estimate the trajectory parameters of one fragment from the meteoroid. With these results we investigate its orbit in the solar system and propose a classification of the Icelandic event as a slow meteoroid of asteroidal origin with an energy on the order of 4–40 GJ, a probable size on the order of centimeters, and an orbit range consistent with the main asteroid belt.

Sensing whales, storms, ships and earthquakes using an Arctic fibre optic cable

Our oceans are critical to the health of our planet and its inhabitants. Increasing pressures on our marine environment are triggering an urgent need for continuous and comprehensive monitoring of the oceans and stressors, including anthropogenic activity. Current ocean observational systems are expensive and have limited temporal and spatial coverage. However, there exists a dense network of fibre-optic (FO) telecommunication cables, covering both deep ocean and coastal areas around the globe. FO cables have an untapped potential for advanced acoustic sensing that, with recent technological break-throughs, can now fill many gaps in quantitative ocean monitoring. Here we show for the first time that an advanced distributed acoustic sensing (DAS) interrogator can be used to capture a broad range of acoustic phenomena with unprecedented signal-to-noise ratios and distances. We have detected, tracked, and identified whales, storms, ships, and earthquakes. We live-streamed 250 TB of DAS data from Svalbard to mid-Norway via Uninett’s research network over 44 days; a first step towards real-time processing and distribution. Our findings demonstrate the potential for a global Earth-Ocean-Atmosphere-Space DAS monitoring network with multiple applications, e.g. marine mammal forecasting combined with ship tracking, to avoid ship strikes. By including automated processing and fusion with other remote-sensing data (automated identification systems, satellites, etc.), a low-cost ubiquitous real-time monitoring network with vastly improved coverage and resolution is within reach. We anticipate that this is a game-changer in establishing a global observatory for Ocean-Earth sciences that will mitigate current spatial sampling gaps. Our pilot test confirms the viability of this ‘cloud-observatory’ concept.

Denoising of distributed acoustic sensing data using supervised deep learning

Distributed acoustic sensing (DAS) is an emerging technology for acquiring seismic data due to its high-density and low-cost advantages. Because of the harsh acquisition environment and other unexpected reasons, the seismic signals acquired in DAS are masked by various types of complex noise, which seriously decreases the signal-to-noise ratio of seismic data. We propose a fully convolutional neural network with dense and residual connections to attenuate complex noise in DAS data. The network is designed to learn features of useful reflection signals recorded from a large number of earthquake and microseismic events, aiming at obtaining an unprecedented generalization ability. First, we generate labels using an integrated framework that attenuates specific types of noise in real DAS data, where the integrated framework includes carefully designed band-pass, structure-oriented median, and dip filters. Then, we use the patching technique to segment the training samples into many small-scale patches to reduce computational cost and improve the extraction of essential features from large-scale passive seismic data. Finally, we use the well-trained network to estimate the heavily polluted hidden signals. Compared with two advanced deep-learning methods and a traditional denoising framework, our proposed method can more effectively attenuate strong and complex noise and recover weak hidden signals in synthetic and real DAS data tests.

Evaluating Stimulation Effectiveness with Permanent Optical Fiber and Sealed Wellbore Pressure Monitoring: Bakken Shale Case Study

Summary In 2019, the operator embarked on a data acquisition project in the Bakken, an unconventional shale play in the USA, with the goal of mapping far-field drainage and characterizing completion performance. The project consisted of a six-well pad (10,000-ft laterals) with a dedicated observation lateral located in the Three Forks formation instrumented with cemented pressure gauges and external optical fiber along the 10,000-ft lateral. The observation lateral was offset by Middle Bakken (MB) wells ~450 ft on either side (~900-ft MB-MB well spacing). One of the offset MB wells was instrumented with external optical fiber for cluster-level completion measurements and fracture hit detection. Characterizing hydraulic fracture geometry is critical to improving completion designs and optimizing well spacing. Until recently, microseismic has been the primary diagnostic for estimating “bulk” or stage-level fracture geometry (Warpinski 2009) and parent-child interactions for modern multicluster plug-and-perf completions (Cipolla et al. 2018). However, microseismic cannot provide details on individual fractures or cluster-level measurements. With the continued advances in fiber-optic technologies, we can measure cluster-level fracture behavior at the wellbore and in the far field (Ugueto et al. 2016, 2019). Characterizing the relationship between wellbore and far-field fracture geometry, referred to as fracture morphology, is important when simultaneously optimizing completion design and well spacing. Microseismic and fiber optics are very robust, but expensive, technologies, and this limits the frequency of their application. The recent introduction of sealed wellbore pressure monitoring (SWPM) enables lower-cost, higher-volume data acquisition but may not provide the same level of detail compared with microseismic and fiber-optic measurements. This paper presents a case history that details the application of permanent fiber optics and SWPM to characterize fracture geometry and morphology using distributed acoustic sensing (DAS) for near-wellbore (NWB) measurements along with SWPM and crosswell strain for far-field investigation. This is the first example of both NWB and far-field optical fiber measurements being used to evaluate cluster efficiency in conjunction with SWPM. The SWPM results compared favorably with the fiber measurements; stages with more uniform distribution based on the NWB DAS data also had higher volume to first responses (VFRs) measured using crosswell strain and SWPM.

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.

A deep learning approach for signal identification in the fluid injection process during hydraulic fracturing using distributed acoustic sensing data

Full-cycle and real-time monitoring of the wellbore flow during hydraulic fracturing is challenging in unconventional oil and gas development. In the past few years, distributed acoustic sensing (DAS) provides opportunities to measure the acoustic energy distribution along the entire horizontal well. It is a promising tool for real-time monitoring and understanding of the fluid injection process. However, the signal identification of effective flow in the wellbore from DAS data is cumbersome and prone to error. We propose a deep learning approach to solve this problem. The neural network is a combination of Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory Networks (BiLSTM) to extract the spatial and temporal features from the DAS data. The trained model is applied to the field data collected in the horizontal well. The results demonstrate its capability for intelligent monitoring and real-time evaluation for hydraulic fracturing.

Locating the Precise Sources of High‐Frequency Microseisms Using Distributed Acoustic Sensing

AbstractAlthough microseisms have been observed for more than 100 years, the precise locations of their excitation sources in the oceans are still elusive. Underwater Distributed Acoustic Sensing (DAS) brings new opportunities to study microseism generation mechanisms. Using DAS data off the coast of Valencia, Spain, and applying a cross‐correlation approach, we show that the sources of high‐frequency microseisms (0.5–2 Hz) are confined between 7 and 27 km from the shore, where the water depth varies from 25 to 100 m. Over time, we observe that these sources move quickly along narrow areas, sometimes within a few kilometers. Our methodology applied to DAS data allows us to characterize microseisms with a high spatiotemporal resolution, providing a new way of understanding these global and complex seismic phenomena happening in the oceans.

Extracting high-resolution, multi-mode surface wave dispersion data from distributed acoustic sensing measurements using the multichannel analysis of surface waves

Distributed acoustic sensing (DAS) is a rapidly expanding tool to sense wave propagation and system deformations in many engineering applications. In terms of site characterization, DAS presents the ability to make dynamic strain measurements at scales (e.g., kilometers) and spatial resolutions (e.g., meters) that were previously unattainable with traditional measurement technologies. In this study, we use both vibroseis truck and sledgehammer sources to demonstrate that DAS can be used to make multichannel analysis of surface waves (MASW) dispersion measurements that are equivalent in quality to geophones when: (a) a tight-buffered or strain-sensing fiber-optic cable is used, (b) the cable is buried in a shallow trench to enhance coupling, and (c) short gauge lengths and small channel separations are used. We analytically demonstrate the impact gauge length has on DAS measurements in terms of phase and amplitude for off-end MASW surveys. We further show that the choice of waveform units (i.e., phase, strain, strain-rate, displacement, velocity) do not impact the dispersion results when frequency-dependent normalization is applied to the dispersion images. Finally, we show that shear wave velocity profiles recovered from the DAS data using an uncertainty-consistent, multi-mode inversion agree favorably with cone penetration tests performed at the site. This study demonstrates that DAS, when appropriate considerations are made, can be used instead of traditional sensors (i.e., geophones) for making high-resolution, multi-mode measurements of surface wave dispersion data using the MASW technique.

Detection and Characterization of Microseismic Events from Fiber-Optic DAS Data Using Deep Learning

Abstract Microseismic analysis is a valuable tool for fracture characterization in the Earth’s subsurface. Distributed acoustic sensing (DAS) fibers are deployed at depth inside wells, so they hold vast potential for high-resolution microseismic analysis. However, the accurate detection of microseismic signals in continuous DAS data is challenging and time consuming. We designed, trained, and deployed a deep learning model to detect microseismic events in DAS data automatically. We created a curated dataset of nearly 7000 manually selected events and an equal number of background noise examples. We optimized the deep learning model’s network architecture together with its training hyperparameters by Bayesian optimization. The trained model achieved an accuracy of 98.6% on our benchmark dataset and even detected low-amplitude events missed during manual labeling. Our methodology detected more than 100,000 events, allowing for a far more accurate and efficient reconstruction of spatiotemporal fracture development than would have been feasible by traditional methods.

Can DAS be used to monitor mining induced seismicity?

Distributed acoustic sensing (DAS) is being applied in an increasing number of geoscientific fields. We present herein preliminary results of applying DAS to monitor microseismicity in underground mines. We installed 275 m of fiber optic cable approximately 1500 m below the surface in an operational mine and recorded mine seismicity for two weeks during November of 2018. The first approximately 50 m was tied to the sidewall safety mesh and a further 225 m was grouted into a down-dipping borehole alongside four three-component geophone sensors with 14 Hz high-sensitivity elements. The geophone sensors are located 132 m, 154 m, 176 m and 198 m down the borehole. We show that, for the larger seismic events (MW > −0.1), there is relatively good agreement in the data collected with DAS compared to the geophone data. There is some amplitude variation, especially at higher frequencies, but the phase of the recordings match well. We were able to include the DAS data in processing select events using PhaseNet, a machine-learning based algorithm, to pick phase arrivals and then locate these events using a recently developed interferometric method. In our comparison of the background noise of the DAS and that of the geophones, we found that the DAS as installed in our experiment has a noise floor orders of magnitude higher than the seismic background of the mine. Furthermore we compared the recordings in the different fiber sections (tied to the safety mesh and grouted, respectively) to analyze the feasibility of using existing mining communication infrastructure as sufficiently accurate seismic sensors. Fiber optic communication cables are typically tied to the sidewall mesh in the absence of dedicated conduits, and thus our mesh-coupled section was a suitable proxy for this scenario. We found extremely limited utility in the recordings of the mesh coupled fiber.

De-noising distributed acoustic sensing data using an adaptive frequency–wavenumber filter

Summary Data recorded by distributed acoustic sensing (DAS) along an optical fibre sample the spatial and temporal properties of seismic wavefields at high spatial density. Often leading to massive amount of data when collected for seismic monitoring along many kilometre long cables. The spatially coherent signals from weak seismic arrivals within the data are often obscured by incoherent noise. We present a flexible and computationally efficient filtering technique, which makes use of the dense spatial and temporal sampling of the data and that can handle the large amount of data. The presented adaptive frequency–wavenumber filter suppresses the incoherent seismic noise while amplifying the coherent wavefield. We analyse the response of the filter in time and spectral domain, and we demonstrate its performance on a noisy data set that was recorded in a vertical borehole observatory showing active and passive seismic phase arrivals. Lastly, we present a performant open-source software implementation enabling real-time filtering of large DAS data sets.

Quantifying microseismic noise generation from coastal reflection of gravity waves recorded by seafloor DAS

SUMMARY Secondary microseisms are the most energetic noise in continuous seismometer recordings. They are generated by interactions between ocean waves, including between gravity waves incident on and reflected from the coast. Coastal reflections of ocean waves leading to coastal microseismic sources are hard to estimate in various global numerical wave models, and independent quantification of these coastal sources through direct measurements can greatly improve these models. Here, we exploit a 41-km-long submarine optical fibre (OF) cable located offshore Toulon, France, using distributed acoustic sensing (DAS). We record both the amplitude and frequency of seafloor strains induced by ocean surface gravity waves, as well as secondary microseisms caused by the interaction of gravity waves incident and reflected from the coast. By leveraging the spatially distributed nature of DAS measurements, additional fundamental information is recovered such as the velocity and azimuth of the waves. We find that on average 30 per cent of the gravity waves are reflected at the coast generating local sources of secondary microseisms that manifest as Scholte waves. These local sources represent the most energetic contribution to the seismic noise recorded along the OF and by an onshore broad-band station located near the DAS interrogator. Furthermore, we estimate a coastal reflection coefficient of ocean surface gravity waves R2 of about 0.07, which provides improved constraints for seismic noise generation models. In addition, we show that new local sources of microseisms can be generated when gravity waves characteristics (azimuth and frequency content) change and lead to some delays between the OF cable and buoy recordings. These analyses pave the way for a wide use of DAS data to monitor ocean–solid earth interactions as they provide a wealth of information on the reflection of gravity waves, coastal microseismic sources, and new constraints for numerical models of microseismic noise.

Transform learning in the synchrosqueezing frequency domain—A novel denoising strategy for optical fiber seismic records

Distributed acoustic sensing (DAS), a new geophone for effective acquisition of vertical seismic profile, has several advantages, including low-cost, high-precision, and high temperature-resistance. However, it is susceptible to various noises that can contaminate the desired weak signals, thus denoising is an essential and independent procedure in DAS processing. Therefore, we propose the transform learning method to train a denoising model in the temporal-frequency domain considering that DAS noise is multiple and performs complexly in the time domain. Initially, synchrosqueezing transform is used to generate DAS data effective sparse representation in the frequency domain where the data remains more centered and uniform. Further, a redesigned convolutional neural network based on residual learning is built to extract different features between the noise and signal components, thus separating them. Finally, the denoised results are obtained by inversely transforming the denoised components into the time domain. Three factors improve the denoising performance of our method: 1. It can directly provide high-dimensional features for training, reducing data-dependency to an extent. 2. The designed network calculates a powerful nonlinear mapping between the additive noise and input noisy component, significantly reducing the training difficulties. 3. A new objective function in the synchrosqueezing frequency domain is designed for nonlinear mapping optimization. Both synthetic and real examples can demonstrate the method's effectiveness in denoising and improving the signal-to-noise ratio of DAS. Further, the denoised results can contribute to a more accurate velocity analysis.

The Potential of Using Fiber Optic Distributed Acoustic Sensing (DAS) in Earthquake Early Warning Applications

ABSTRACT As the seismological community embraces fiber optic distributed acoustic sensing (DAS), DAS arrays are becoming a logical, scalable option to obtain strain and ground-motion data for which the installation of seismometers is not easy or cheap, such as in dense offshore arrays. The potential of strain data in earthquake early warning (EEW) applications has been recently demonstrated using records from borehole strainmeters (BSMs). However, current BSM networks are sparse, installing more BSMs is expensive and often impractical, and BSMs have the same limitations in offshore environments as other traditional seismic instruments. Here, we aim to provide a road map about how DAS data could be used in existing EEW applications, using the ShakeAlert EEW System for the West Coast of the United States as an example. We review the data requirements for EEW systems, examine ways in which strain-derived ground-motion data can be incorporated into such systems without significant modifications, and determine what is still needed for full utilization of DAS data in these applications. Importantly, EEW algorithms require ground-motion amplitude information for rapid earthquake source characterization; thus, accurate strain amplitude observations, not only phase information, are necessary for deriving these ground-motion metrics from DAS data. To obtain high-quality ground-motion observations, EEW-compatible DAS arrays need to be multicomponent, well coupled, and low noise. We suggest ways to achieve such data requirements using existing DAS technology and discuss areas in which further research is needed to optimize DAS array performance for EEW.

Experimental study of temperature change effect on distributed acoustic sensing continuous measurements

Distributed fiber-optic sensing is useful in geophysical exploration and monitoring applications. Distributed temperature sensing (DTS) is used for measuring and monitoring temperature and distributed acoustic sensing (DAS) for recording the seismic wavefield. However, DAS measurements also are sensitive to temperature changes. To understand and quantify the DAS signature of temperature changes during water injections at CO2CRC Otway site, a series of experiments have been conducted at the Curtin University/National Geosequestration Laboratory (NGL) well research facility and Curtin rock-physics laboratory. Overall, three DAS cables are examined. Two fibers are tested in the laboratory and one cable, which is installed behind the casing in the Curtin/NGL well, is examined in the well. Laboratory measurements and observations made during analysis of passive DAS and DTS field data recorded in four Otway wells demonstrate that DAS is sensitive to long-period temperature changes, and its response is proportional to the time derivative of temperature. Induced fiber strain is linearly related to slow temperature change, and this dependency can be estimated for a particular cable. Obtained proportionality constants between strain and temperature change indicate some dependency on the cable type/design and acquisition setup, but they are all of the same order of magnitude. DAS measurements also can be affected by low-frequency noise possibly associated with the effect of temperature on the DAS acquisition unit itself. The results can help compensate for the effect of temperature on low-frequency DAS signals and show that DAS can be used as a distributed temperature sensor if direct temperature measurements are not available.

Distributed acoustic sensing as a tool for subsurface mapping and seismic event monitoring: a proof of concept

Abstract. We use PoroTomo experimental data to compare the performance of distributed acoustic sensing (DAS) and geophone observations in retrieving data to execute standard subsurface mapping and seismic monitoring activities. The PoroTomo experiment consists of two “seismic systems”: (a) a 8.6 km long optical fibre cable deployed across the Brady geothermal field and covering an area of 1.5 × 0.5 km with 100 m long segments and (b) a co-located array of 238 geophones with an average spacing of 60 m. The PoroTomo experiment recorded continuous seismic data between 10 and 25 March 2016. During this period, a Ml 4.3 regional event occurred in the southeast, about 150 km away from the geothermal field, together with several microseismic local events related to the geothermal activity. The seismic waves generated from such seismic events have been used as input data in this study to tackle similarities and differences between DAS and geophone recordings of such wavefronts. To assess the quality of data for subsurface mapping tasks, we measure the propagation of the P wave generated by the regional event across the geothermal field in both seismic systems in term of relative time delays, for a number of configurations and segments. Additionally, we analyse and compare the amplitude and the signal-to-noise ratio (SNR) of the P wave in the two systems at high resolution. For testing the potential of DAS data in seismic event locations, we first perform an analysis of the geophone data to retrieve a reference location of a microseismic event, based on expert opinion. Then, we a adopt different workflow for the automatic location of the same microseismic event using DAS data. To assess the quality of the data for tasks related to monitoring distant events, we retrieve both the propagation direction and apparent velocity of the wave field generated by the Ml 4.3 regional event, using a standard plane-wave-fitting approach applied to DAS data. Our results indicate that (1) at a local scale, the seismic P-wave propagation (i.e. time delays) and their characteristics (i.e. SNR and amplitude) along a single cable segment are robustly consistent with recordings from co-located geophones (delay times δt∼0.3 over 400 m for both seismic systems); (2) the DAS and nodal arrays are in mutual agreement when it comes to site amplifications, but it is not immediately clear which geological features are responsible for these amplifications. DAS could therefore hold potential for detailed mapping of shallow subsurface heterogeneities, but with the currently available information of the Brady Hot Springs subsurface geology, this potential cannot be quantitatively verified; (3) the interpretation of seismic wave propagation across multiple separated segments is less clear due to the heavy contamination of scattering sources and local velocity heterogeneities; nonetheless, results from the plane-wave-fitting approach still indicate the possibility for a consistent detection and location of the distant event; (4) automatic monitoring of microseismicity can be performed with DAS recordings with results comparable to manual analysis of geophone recordings in the case of events within or close to the DAS system (i.e. maximum horizontal error on event location around 70 m for both geophone and DAS data); and (5) DAS data preconditioning (e.g. temporal subsampling and channel stacking) and dedicated processing techniques are strictly necessary for making seismic monitoring procedures feasible and trustable.

Distributed Fiber-Optic Sensing Enhances Flow Diagnostics in Gas Condensate Well

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 205435, “Flow Diagnostics in High-Rate Gas Condensate Well Using Distributed Fiber-Optic Sensing and Its Validation With Conventional Production Log,” by Fuad Aziz Atakishiyev, SPE, BP, and Alessandro Delfino and Cagri Cerrahoglu, Lytt, et al. The paper has not been peer reviewed. The authors describe a machine-learning (ML) approach for processing distributed-fiber-optic-sensing (DFOS) data that enables dynamic flow-profile monitoring using a fiber-optic electric-line cable deployed in a gas condensate well and compare the method with a conventional approach. DFOS technology has the potential to provide more-efficient and dynamic flow profiles compared with traditional methods, particularly in high-rate gas wells where production logs (PL) are recorded at reduced rates to avoid tool lifting. Technology and Field Introduction DFOS surface acquisition systems launch short pulses of light into an optical fiber, then receive its backscatter and convert it into physical measurements: temperature in the case of distributed temperature sensing (DTS) and acoustic intensity in the case of distributed acoustic sensing (DAS). These systems essentially turn a fiber-optic cable into a long array of thermometers (DTS) or multifrequency microphones (DAS). The DFOS systems record the backscatter signal continuously and measure at multiple points along the fiber. The major advantage of DFOS acquisition is the ability to record data throughout time at a very high sampling frequency. Unlike traditional PL tools, insights provided by DFOS offer a real-time view of changes in production without intervention. A successful trial was performed in a high-rate gas-producing well to test the quantitative inflow profiling algorithm on DFOS data. As part of the work, conventional PL data were acquired in high-rate gas production Well A01. An electric-line cable with built-in multimode and single-mode fiber-optic line was used to acquire conventional PL data. DAS and DTS data were acquired simultaneously for approximately 30 minutes while the well produced at a gas rate of approximately 70 MMscf/D. No sand-control completions were planned for Well A01 or for other wells in the field; therefore, in addition to the identification of a quantitative inflow profile, another main objective of the study was to identify intervals with sand production.