Implementing Fiber Optics Distributed Sensing as a Key Surveillance Tool

Designs and preliminary results from a multi-year project for simultaneous Distributed Acoustic Sensing (DAS) and Distributed Temperature Sensing (DTS) executed across multiple wells on a Steam-Assisted Gravity Drainage (SAGD) field in Canada is presented. Primary objectives of the project were to demonstrate prototypical execution of DAS and DTS data collection on a standard SAGD pad setup and to characterize inflow across the production zone. Challenges and learnings from this technically demanding project are explored alongside a discussion on the improving viability of continuous acoustic monitoring to augment temperature sensing commonly utilized in SAGD instrumentation design. The DTS technique employs a combination of an optoelectronics topside instrument and a downhole sensing fiber to produce a distributed temperature profile of the fiber string. The topside instrument comprises a laser light source, a fast optical detector and a combination of electrical and optical circuits to control light pulsing, and the detection and measurements of Raman backscattered light containing encoded temperature information about the temperature profile. The sensing fiber type is selected based on suitability with the topside instrument and the expected maximum operating temperature of its intended sensing environment. While DTS is compatible with both single-mode (SM) and multi-mode (MM) fiber type, MM graded-index fibers are often selected for SAGD environment due to its larger optical core size. Using a MM fiber allows for increased capture of the backscatter Raman light, a much weaker light signal than the laser light launched into the optical fiber. Temperature dependent Raman signals are extracted from the returned light and by applying the Optical Time Domain Reflectometry (OTDR) technique, which produces a temperature trace consisting of temperature magnitude matched to each unit distance along the sensing fiber. The temperature trace can vary in its spatial resolution subject to the topside instrument configuration with typical resolving resolution between 0.5 – 1.5 meters. Other general DTS sensing techniques are described in the literature (for example, Hartog (2017)) but are not described here. Some of the earliest uses of DTS in thermal enhanced oil recovery (EOR) was to monitor steam breakthrough and pump vapor locking in California's steam flood as reported by Carnahan (1999). Since then, advancement in specialty fiber optics suitable for thermal environment upwards to 300°C has enabled mainstream adoption within SAGD. Uses of DTS data has expanded to include, but not limited to, monitoring of well and steam chamber conformance, tubing and casing integrity, gas ingression and water coning. DAS is an optical interferometry technique employing an optoelectronics topside instrument and a downhole sensing fiber to detect minute changes in fiber length at each location along the fiber string. The instrument is sensitive at the pico-strain (ρε) level with a spatial resolution equal to a configurable distance referred to as the gauge length. The term "acoustic" in Distributed Acoustic Sensing broadly designates any mechanical perturbation able to alter the local state of strain anywhere along the optical fiber, be it from dynamic pressure changes, or dynamic strain (e.g. vibration). While DAS is compatible with both SM and MM fiber type, deployment alongside SM fiber types have been traditionally preferred as the laser light coherence is better preserved throughout its round-trip propagation compared to MM fiber. However, DAS with MM fiber can still be used for typical fiber lengths in SAGD wells without significant penalty in signal quality as shown in literatures (MacPhail (2016), Burke (2022). This cross-compatibility with MM fiber allows collection of DAS on existing DTS fibers deployments without further intervention. Prior to DAS adoption in oil and gas, it was predominantly deployed in defense and security application to perform parameter monitoring and ground disturbance detection. As DAS technology evolved, it was first trialed in Shell's unconventional tight gas wells in February 2009 to monitor plug setting, perforation shots and active clusters during fracking operation (Molenaar et al. 2012). In EOR application like SAGD, DAS provides an augmenting dataset correlating to production inflow phases (MacPhail et al. 2016) which would not be readily detectable in the temperature domain alone. Efforts like the one presented in this paper are underway to progress horizontal inflow profiling and inferred relative volumes which aids in understanding well productivity and efficacy of added inflow control devices (ICD). Unlike DTS, acoustic data is largely unaffected by temperature masking due to well completions. For events where insufficient temperature deviation are produced, DAS may be used to surveille for common industry challenges such as steam coning, gas ingression, loss of tubing/casing integrity and more. The emerging market segment for Carbon Capture and Underground Storage (CCUS) is poised to utilize DAS as one of the many leak detection toolkit for the injection monitoring wells.

Fiber optic measurements including Distributed Acoustic Sensing (DAS) and Distributed Temperature Sensing (DTS) have been established as common tools to monitor the downhole condition during hydraulic fracturing treatment as well as during production. Several authors presented a methodology of quantitative interpretation for DAS and DTS data to estimate injected/produced fluid volume distribution among perforation clusters. The quantitative interpretation allows us to not only diagnose the stimulation efficiency but also optimize the completion design for subsequent fracturing jobs. This study investigates how completion parameters are correlated with fracturing stimulation performance based on the field application of DAS and DTS interpretation methodology. The field data from MIP-3H provided by the Marcellus Shale Energy and Environmental Laboratory is analyzed in this work. As previously presented, a DAS/DTS integrated interpretation method estimates fracture half-length distribution for each stage. In this approach, fractures are assumed to be created in a swarm pattern from each perforation cluster. We do not assign fractures only at the perforation cluster locations but assume that multiple fractures are initiated along a stage interval with much smaller spacing than cluster spacing. This fracture swarm model is supported by several field observations. Once the DAS interpretation estimates the injected fluid volume for each perforation cluster, by considering the fluid volume distribution as an input parameter for the DTS interpretation, the temperature inversion provides the half-length of each fracture. This interpretation approach allows us to estimate uniformity of the fracture half-length distribution. To statistically describe the fracture uniformity, a uniformity index is defined. The estimated uniformity index is compared with inflow rate for each stage to investigate how the uniformity of fracture distribution contributes to well performance. The inflow profile is estimated by interpreting the DTS data during production. The comparison shows that the stages with uniform fracture half-length distribution are more productive. This result implies that the stages more uniformly stimulated become more productive. By observing the relationship between the uniformity index and several completion parameters such as injection rate, total volume of injected fluid, proppant concentration and so on, we can investigate what parameter is more influential to the fracture stimulation efficiency. The statistical analysis illustrates that the uniformity of the fracture half-length distribution and high productivity are correlated with high injection rate. Based on this study, the injection rate would be one of the primary design parameters to maximize the fracturing stimulation performance in this field case. As demonstrated in this study, the evaluation of fracturing stimulation design based on the interpretation of DAS and DTS data would be an essential approach to optimize it.

Injector well completions are typically carried out using two methods: simple and selective. The performance of injector wells has traditionally been evaluated using spinner-based injection logging tools (ILTs) or on-demand fiber optic distributed temperature and acoustic (DTS or DAS) sensing while logging the well performance every 3 months or once a year depending on the application. However, with such methods utilizing a snap shot approach, critical well performance information or possible anomalies are often missed due to measurements taken only during certain periods. Therefore, operators have explored the use of advanced fiber-optics methods such as permanent distributed temperature sensing (DTS) and permanent distributed acoustic sensing (DAS), that provide continuous, real-time measurements to enable understanding of dynamic well behavior at all times and mitigate any deferred or behavioral problems. Of relevance to this work is a vertical, injector well in a competent sandstone formation of the Heavy Oil Chichimene field in Llanos Basin, Colombia. As a first step of the fiber-optics monitoring strategy, a careful evaluation of DTS and DAS based fiber-optics methods was conducted. Based on the data analysis and operational history, DAS-based fiber optics monitoring was chosen as the most effective monitoring solution for this well. Subsequently, a proprietary DAS algorithm was developed to analyze the data and estimate the flow allocation for all the four zones. The results include waterfall acoustic energy maps, temporal flow allocation profiles and most importantly, the zonal flow allocation values. Predicted (DAS) zonal flow allocation data was compared with traditional injection logs (ILT) under different operational conditions (varied injection flow rates and valve choke settings). Based on comparable agreement between DAS and ILT data, the operator decided to replace ILT runs with DAS-based fiber optic monitoring, resulting in lower operational costs while enabling near real-time monitoring, and providing the continuous distributed data essential for the dynamic monitoring of the well. The successful application of fiber-optics monitoring to provide an injection profile in conjunction with a surface-controlled electric valve system demonstrates a significant potential to optimize the injection process in complex injector wells. Further, remotely controlling, monitoring and optimizing injection rates into the multi-segmented zones improves the service life of the injection operations, eliminates future intervention costs, and increases ultimate recovery.

This work presents an experimental study conducted using a modular flow-loop apparatus to evaluate flow diagnostics with Distributed Acoustic Sensing (DAS) and Distributed Temperature Sensing (DTS) across simulated well scenarios. Dedicated simulators, including horizontal and vertical injection and production wells, enable repeatable emulation of subsurface dynamics such as steady-state and transient flow regimes, leak inflow/outflow, time-varying injectivity and productivity, and intervention responses. Each simulator incorporates programmable flow conditions with synchronized acquisition of DAS and DTS data, co-registered with reference pressure and flow instrumentation. In addition to passive sensing, we investigate active modalities that strengthen flow-signal detectability, reduce interpretation ambiguity, and increase confidence in physics-based flow profiling. In active DAS, low-frequency flow stimulation generates broadband harmonics that enhance spectral content and signal-to-noise ratio. For active DTS, a novel distributed anemometry approach uses the temperature response to a controlled, steady heat input to estimate the flow velocity profile. The results provide experimental validation and example self-learning flow allocation workflows for distributed sensing as a quantitative diagnostic tool for transient well dynamics. With high-resolution sensing, operators gain immediate insight into downhole flow phenomena, supporting on-the-fly data-driven adjustments to interventions and robust assessments of zonal performance in complex subsurface environments.

Summary Distributed acoustic sensing(DAS) and Distributed Temperature Sensing(DTS) are being actively used in various exploration and monitoring applications. DTS is used for measurements and monitoring of the temperature and DAS is used for recording seismic wavefield. At the same time, DAS measurements are sensitive to temperature changes. The response of DAS to temperature is proportional to a time derivative of temperature as DAS naturally measures strain. Understanding of how DAS measurements can be affected by changing temperature is paramount to avoid or/and eliminate noise related to temperature variations. This can be particularly critical for the DAS time-lapse seismic and passive monitoring. We utilised the Curtin/NGL research facility and Rock-Physics Laboratory to estimate temperature’s effect on three various DAS cables. Two different fibres were tested in the laboratory and one cable (installed in the Curtin/NGL well), examined at the site. We demonstrated that DAS is sensitive to temperature changes and its response is proportional to a time derivative of temperature. Our study shows that by using DAS and temperature data together, it is possible to estimate strain – temperature change dependency (microstrain/degree) for a particular cable. Coefficients estimated in three tests indicate that cable type/design can affect DAS response to temperature changes.

Interstage communication during well stimulation reducing the effectivity of the well completion is known to be a concern in the oil and gas industry. The leading cause of this is fracture communication due to the presence of natural fractures where the formation is being hydraulically fractured. In this study, a technique was developed to map natural fractures, on a larger scale, underground to be able to avoid the high fracture intensity zones when hydraulically fracturing. This study developed a technique to optimize well completion designs by introducing the ability of locating natural fractures zones in the formation (Marcellus Shale) which leads to better cluster efficiency, the minimization of clusters merging, and the absence of fracture communication. The MSEEL well pads, MIP and Boggess, were equipped with fiber optic cables that provides data such as DAS (Distributed Acoustic Sensing), DTS (Distributed Temperature Sensing), and Slow Strain data. Additional data were generated such as the formation geomechanical properties (brittleness and fracability) as well as the completion reports (injection pressure, fluids concentrations). Basically, the approach used in this study was to integrate the completion designs of the boggess pad wells with the generated resistivity image logs, to identify the fracture intensity. Furthermore, the fiber optic DAS data was used to determine the cluster efficiency for each stage, while the fiber optic slow strain was mapped to identify the locations and times of the fracture hits from the treatment well (Boggess 5H) onto the monitoring well (Boggess 1H). At first, the fracture intensity was plotted with respect to depth in order to locate the high fracture intensity zones in the formation. Secondly, the fiber optic slow strain was used to determine the time and depth of the frac hits. Following that was the generation of the cluster efficiency log to identify, on each stage, the clusters with the highest and lowest energy. Knowing that boggess 5H and 1H are spaced by 1500ft, it was found that the depth of the frac hit on the treatment well 5H was different from where the monitoring well detected the same frac hit. In stage 13 for instance, well 5H shows a frac hit at a depth of 16,762ft while the well 1H shows the hit at a depth of 16,820ft, showing that this was not a 1:1 correlation. It was also observed that the clusters with the lowest energy received were located in high fracture intensity zones

Recently, oil and gas companies started to invest in fiber optic technology to remotely monitor subsurface response to stimulation. Distributed Acoustic Sensing (DAS) and Distributed Temperature Sensing (DTS) record vibration and temperature around the fiber, respectively. In this research, we introduce new seismic attributes calculated from the DAS data that could suggest cross-stage fluid communication during hydraulic fracturing. The DAS data covers the entire 28 stimulated stages of the lateral MIP-3H well close to Morgantown, WV. We calculated the energy attribute for the DAS data of the studied stages. Subsequently, a Hilbert transform is applied to the DAS data to evaluate the instantaneous frequency of each trace in the DAS. In addition, we applied a fast Fourier transform to each trace for all the SEGY files to calculate the dominant frequency with a 30 second temporal window. The dominant frequency is compared to the DTS data and energy attribute for the stages in the horizontal MIP-3H well. The DTS analysis shows that stimulation of the stages 10 causes a temperature rise in the previous stage 9; in contrast, stage 18 stimulation does not affect stage 17 temperature. We suggest that the common low frequency zone identified in instantaneous frequency and dominant frequency attributes between stages 10 and 9 is related to presence of fluid and gas that transferred cross-stage during hydraulic fracturing. The fluid and results in the frequency damping of the vibrations around the fiber. We show that the frequency attribute reveals increases detail about the stimulation than conventional signal energy attribute of the DAS data.

A series of fiber optic surveys were performed in an Onshore well in Abu Dhabi to prove the concept of using both Distributed Acoustic Sensing (DAS) and Distributed Temperature Sensing (DTS) for water injection monitoring. This technology aims to provide new and novel techniques where these surveys could be carried out without wellbore intervention, thereby reducing cost, removing HSE risk and lowering carbon footprint. In addition to these significant benefits fiber optic data provides a dense array of measurements from surface to total depth. The well selected for this proof-of-concept pilot was an onshore horizontal water injector located south of Abu Dhabi. Total depth of the well is approximately 13,000ft, of which approximately 5,000ft is an open hole injection interval. The fiber optic cable was clamped to a 3.5″ tubing string at the time of completion, all the way from surface to total depth. The reservoir section was completed open hole with slotted 3.5″ tubing below the production packer allowing injection through perforated pipe into five separate intervals, each isolated by four swells packers. DAS and DTS data were recorded on the first injection into this well. Although water injection had started in the field the project team obtained dispensation from the reservoir management team to keep the well shut in until fiber optic acquisition could be acquired. This allowed a stable geothermal baseline to be obtained, and after initial injection the well only had to be shut for a few hours to obtain a good warmback response.

In this paper, recent EOR field trials are discussed to demonstrate the value of in-well fiber optic (FO) applications. The trials are in a field in Oman that has been considered for an enhanced oil recovery (EOR) development. Polymer Flood and Steam Injection have been proposed as alternative EOR methods and both are being studied. A recent polymer injectivity test and steam trial were conducted in order to de-risk uncertainties in a full field development and this paper presents the application of FO in these EOR trials to help meet these objectives.The fiber optic approach is viewed as an alternative technology to production logging and it provides the option of real time acquisition. In one area of the field, a shut-in well was converted to a polymer injection well and utilized for a single well injection test (SWIT). The test was carried out for six months to quantify the inflow profiles and injectivity to the reservoir under matrix conditions. For the steam trial in another area of the field, three producers and one injector with FO were utilized to demonstrate the feasibility of a horizontal well steam development and to evaluate the requirement and effectiveness of injection conformance.FO analysis from both trials concluded that the Distributed Temperature Sensing (DTS) and Distributed Acoustic Sensing (DAS) combination can be successfully used for injection conformance profiling. DTS/DAS measurements were taken during the course of the polymer injection test. These indicated that conformance was incomplete from the onset and deteriorated during the course of the test, with a large proportion of fluids in-fluxing into the reservoir through a narrow section at the toe of the well. One of the main objectives from the steam trial was to measure and manage conformance along the horizontal steam injector. Use of FO helped demonstrate the importance of managing conformance in order to achieve satisfactory sweep efficiency.This paper explains how these new technologies play a key role in reducing the uncertainties in reservoir performance in EOR developments. The combination of FO (DTS and DAS) data sets allowed the interpretation of data gathered to complement each other and provide more comprehensive conclusions.

A seismic observation using Distributed Acoustic Sensing (DAS) using seafloor cable can provide spatially high-density data for a long distance in marine areas. A seafloor seismic and tsunami observation system using an optical fiber cable off Sanriku, northeastern Japan was deployed in 1996. Short-term DAS measurements were sporadically repeated since February 2019 using spare fibers of the Sanriku system (Shinohara et al., 2022). A total measurement length is approximately 100 km. It has been concluded that measurement with a sampling frequency of 100 Hz, a ping rate of 500 Hz, gauge length of 100 m, and a spatial interval of 10 m is adequate for earthquake and tsunami observation. From March 2025, we started a continuous DAS observation to observe seismic activity. When the continuous DAS observation was commenced, we developed quasi real time data transmission system through the internet. Because a DAS measurement generates a huge mount of data per unit time and capacity of internet is limited, decimation for spatial direction is adopted. In addition, data format is converted from HDF5 to conventional seismic data exchange format in Japan (win format). An interrogator generates a HDF5 file every 30 seconds. After the file generation, the telemetry system reads the HDF5 file, and decimates data for spatial domain. Then, the data format is changed to the win format and the data are sent to the internet. In other words, data transmission is delayed for a slightly greater than 30 seconds. Data with the win format can be applied to various seismic data processing which has been developed before. To locate a hypocenter using DAS data, seismic phases in DAS data must be identified. To evaluate performance of hypocenter location using DAS records, arrival times of P- and S-waves were picked up on the computer display for local earthquakes. Every 100 channel records on DAS data and data from surrounding ordinary seismic stations were used. Location program with absolute travel times and one-dimensional P-wave velocity structure was applied. Results of location of earthquakes were evaluated by mainly using location errors. Errors of the location with DAS data were smaller than those of the location without the DAS data. Increase of arrival data for DAS records seems to be efficient to improve a resolution. However, picking up signals for all channels (seismic station) manually are costly due to a large number of channels. To expand the location method, an improved automatic pick-up program using evaluation function from conventional seismic network data by seismometers for DAS data (Horiuchi et al., 2025) was applied to the DAS data obtained by the Sanriku system. As a result, arrivals time of P, S and converted PS waves can be precisely identified with high resolution. We have a plan to locate earthquakes using all DAS channels (seismic stations) and surrounding ordinary marine and land seismic stations.

In this study, we used data from optical fiber-based Distributed Acoustic Sensor (DAS) and Distributed Temperature Sensor (DTS) to estimate pressure along the fiber. A machine learning workflow was developed and demonstrated using experimental datasets from gas–water flow tests conducted in a 5163-ft deep well instrumented with DAS, DTS, and four downhole pressure gauges. The workflow is successfully demonstrated on two experimental datasets, corresponding to different gas injection volumes, backpressure, injection methods, and water circulation rates. The workflow utilizes the random forest algorithm and involves a two-step process for distributed pressure prediction. In the first step, single-depth predictive modeling is performed to explore the underlying relationship between the DAS (in seven different frequency bands), DTS, and the gauge pressures at the four downhole locations. The single-depth analysis showed that the low-frequency components (< 2 Hz) of the DAS data, when combined with DTS, consistently demonstrate a superior capability in predicting pressure as compared to the higher frequency bands for both the datasets achieving an average coefficient of determination (or R2) of 0.96. This can be explained by the unique characteristic of low-frequency DAS which is sensitive to both the strain and temperature perturbations. In the second step, the DTS and the low-frequency DAS data from two gauge locations were used to predict pressures at different depths. The distributed pressure modeling achieved an average R2 of 0.95 and an average root mean squared error (RMSE) of 24 psi for the two datasets across the depths analyzed, demonstrating the distributed pressure measurement capability using the proposed workflow. A majority of the current DAS applications rely on the higher frequency components. This study presents a novel application of the low-frequency DAS combined with DTS for distributed pressure measurement.

Summary Distributed acoustic-sensing technology is a diagnostic method that has been implemented in the oil and gas industry for flow monitoring during injection or production. One of the applications is to estimate fluid distribution during hydraulic fracturing treatment. This is an invaluable diagnostic tool for multistage fracture treatments because of the large number of parameters and high uncertainty involved in these fracture treatments. Distributed acoustic sensor (DAS) measurements are based on data extracted from a fiber-optic cable installed in a wellbore. Fiber-optic cable strain is sensitive to temperature and acoustic variations induced by fluid flow with different fluid properties and fracture geometries. These parameters are evaluated from the backscattered laser pulse through the fiber using coherent Rayleigh backscattering for DAS and Raman backscattering for distributed temperature sensing (DTS). The DAS interrogation system acquires strain response to acoustic variations along the measured depth of the wellbore for all periods of time. This study presents a method of interpretation of flow-rate distribution from acoustic signals from DAS measurements. Raw acoustic measurements are transformed into energy-attribute distribution for all necessary depth locations and time frame. It allows calculating energy for each perforation cluster location. This calculation demands proper depth-interval consideration for each perforation cluster. A depth-windowing procedure allowing location of intervals where DAS channel measurements must be integrated for each timestep is applied to all stages. Based on previous experimental and computational fluid-dynamic investigations, the correlation between acoustic signal and flow rate is applied to interpret the measured DAS data to flow distribution. In this paper, the system of equations that connects acoustic energy response with clusters flow-rate distribution during the injection period using these correlations is introduced. The solution of this system allows calculating cumulative volume for each perforation cluster on each timestep and at the end of fracture treatment. As additional verification of flow-rate distribution, the results of DAS interpretation were compared with the interpretation results of DTS. A field example is used to illustrate the interpretation procedure. From this work, it is concluded that, besides qualitative analysis, the DAS interpretation method provides a quantitative estimation of flow distribution. Based on current assumptions, the interpretation results from DAS and DTS are comparable with a satisfactory agreement. The combined DAS and DTS interpretations help in understanding cluster efficiency in multistage fracture treatments.

In order to gain insights into the hydraulic and thermal long-term behavior of deep geothermal boreholes during operation, a fiber-optic monitoring (FOM) system was implemented at the Stadtwerke Munich's (SWM) geothermal plant 'Sch&amp;#228;ftlarnstrasse' in the national project Geothermal-Alliance Bavaria (GAB). The permanently installed fiber optic cables enable continuous measuring of the temperature in boreholes with high spatial and temporal resolution via Distributed Temperature Sensing (DTS) and of acoustics/vibration via Distributed Acoustic Sensing (DAS). In 2019, one production and one injection well were equipped with FOM in different setups and have been collecting data since then. In the injector, the cable was cemented behind the anchor pipe in the upper section of the borehole, whereas the producer was equipped with a fiber-optic cable until the total depth of the reservoir. Additionally, a fiber-optic gauge provides pressure and temperature at the top of the reservoir in this well. Among other things, precise inflow profiling in the reservoir section of the producer could be carried out using the temperature data, which helps deepen the understanding of the hydraulic behavior of the reservoir. Changes over time in the temperature of the produced thermal water can be traced back to changes in the hydraulically active zones or inflow temperatures in the reservoir. Other factors that influence the wellhead temperature, such as borehole heat losses to the surrounding rock, can be quantified using the DTS data. At the beginning of 2024, a third fiber-optic cable will be installed in an injection well at the Sch&amp;#228;ftlarnstrasse geothermal site. A similar setup as in the production well will allow for continuous measuring in the entire borehole until total depth (TD) and pressure and temperature from p/T gauges at the top of the reservoir and at TD. Therefore, both production and injection conditions in the reservoir and wellbore will be continuously monitored at all depths using DTS and DAS for the first time. We present the first interpretations of the data collected from the newly installed FOM in the injection well as well as insights into the long-term monitoring of the hydraulic and thermal dynamics in the production well to underline the importance of permanent downhole monitoring to ensure efficient and sustainable use of the geothermal reservoir.

Thanks to its ability to provide dense strain rate measurements along Optical Fiber (OF) cable, the Distributed Acoustic Sensing (DAS) technique spreads over different seismic and geophysical domains. They range from exploration geophysics (Mestayer et al. 2011, Daley et al. 2013), to underground structure imaging (e.g. Ajo-Franklin et al. 2019, Cheng et al. 2021) or seismic activity and background noise monitoring (Jousset et al 2018, Nayak et al. 2021). Beyond the advantage of its dense spatial sampling and given a better understanding of its instrument response (e.g. Lindsey et al. 2020), the detection performance of these new DAS measurements also depends on its ability to precisely characterize the amplitude and phase of the seismic background noise in different environments. According to recent offshore seismic noise studies (Ugalde et al. 2021, Lior et al. 2021, Guerin et al 2022), we propose a study based on DAS recordings of the seismic background noise in an on-land quiet environment.In 2020, a temporary seismic experiment PREMISE (PREliminary MIga Seismic Experiment) was carried out on the site of the underground low noise Laboratory (LSBB, Laboratoire Souterrain Bas Bruit) at Rustrel, France, in order to study the 3D seismic wave field properties in a pretty well-known underground geological structure. During this experiment, we deployed several kilometers of different OF in the LSBB galleries in order to create a multidirectional DAS array with a total fiber length of 10.5km and several ground-coupling conditions. We reprocessed two hours of &amp;#8220;raw&amp;#8221; DAS data, recorded with a FEBUS A1-R instrument, with different acquisition parameters to find the best configuration for enhancing the DAS measurement Signal to Noise Ratio. The power spectral density (PSD) of these reprocessed strain time-series reveals a peak in the background noise frequency range [0.08-0.25Hz] for gauge lengths of 90m and 150m. Independently, an estimation of the local strain field has been derived by a geodetic analysis (Spudich et al 1995) of the records from the LSBB broadband seismometers antenna. The comparison of the DAS and seismometers array-derived strain PSD shows a very good agreement with the secondary microseism peak in terms of frequency band, amplitude, and the wave field polarization, especially for DAS strain records processed with gauge-length of several tens of meters.

Accurate prediction of flow regime and flow profile in wellbore is among the main interests of production engineers in the quest of optimizing wellbore production and increasing reliability of downhole completion tools especially in SAGD projects. This study introduces a methodology for wellbore monitoring by detecting flow phase and flow regime. In order to develop this method, an advanced multi-phase flow injection experiment was designed and commissioned. A flow injection setup was developed to test distributed fiber optic sensor installation under different operating conditions, including multi-phase flow (oil, brine and gas), and flow fraction scenarios. Different signal processing methods were applied to extract meaningful features and filter the noise from the raw signals. A statistical analysis was performed to assess the trend of the driven data. Then, typical SAGD models were simulated to assess the results of experimental setup for scale-up purpose and determination of local breakthrough of steam along the well. Results showed that the Distributed Acoustic Sensing (DAS) data contains different levels of signals for each phase and flow regime. We also found that some level of uncertainties is involved in relating the flow regime and DAS information which could be resolved by improving the sensor installation procedure. In addition, the application of data-driven machine learning methods was found necessary to interpret the signal patterns. Initial results have shown that steam breakthrough along the well can be detected using real time DAS high energy/frequency signals. It can be concluded that including the DAS along with Distributed Temperature Sensing (DTS) is necessary to provide a better picture of steam conformance and SAGD wellbore monitoring. The limitations of the current experimental setup restricted further conclusions regarding the hybrid DAS and DTS application. This paper is a part of an ongoing project to address the application of the combined DAS and DTS in SAGD projects. The ultimate goal is a downhole monitoring system to oversee the flow phase, flow regime and sand ingress in thermal application. The next phase will address the required improvements for developing a flow loop to handle high temperatures, include sand production and mimic thermal operation conditions.