Distributed Temperature Sensing Research Articles

Testing big data analysis technology with the use of deconvolution algorithm to increase oil production

Objective. To develop and test new approaches to the analysis of big data sets in the process of hydrocarbon field development to reveal the potential of production increase. Materials and methods. The article discusses the improvement of a technology for analyzing the results of the fixed-depth and the fiber-optic-based distributed temperature monitoring during well production by deconvolution algorithm. Based on the methodology for interpreting a single bottomhole temperature sensor, an algorithm for calculating the production dynamics was proposed and tested when temperature is measured near the interval of the reservoir. Results. The data obtained using the deconvolution algorithm were compared with the results of analytical calculations and the technique was tested on the real well data obtained with a fiber-optic sensor cable, confirming the effectiveness of the technique. By utilizing information on bottomhole pressure dynamics and well production history reconstructed from thermal field data, it is possible to make quick decisions on well intervention to improve production. Conclusions. The use of the proposed algorithm enabled to expand the limits of applicability of big data obtained using a fiber-optic distributed temperature sensor, which significantly increases the efficiency of monitoring the hydrocarbon field development.

Characterization of artesian flow and heat transition in an ATES research wellbore using DTS monitoring and numerical modelling

Abstract. In the practice of Aquifer Thermal Energy Storage (ATES), the hydraulic connection to the wellbore of any other aquifers besides the planned thermal storage should be identified and prohibited in order to operate the ATES system in a sustainable way. The present study was aimed at locating an artesian aquifer other than the planned thermal storage in an ATES research wellbore (Berlin, Germany). Therefore, we analysed the wellbore temperature as monitored with a single fiber optic cable using the Distributed Temperature Sensing (DTS) technique in a series of artesian flow tests. The change in the wellbore temperature, the depth-dependent thermal gradients and the isotherms as derived from the DTS-monitored data helped positioning the artesian aquifer in the depth interval from 220 to 230 m. In addition, the transition from cooling to heating in the wellbore sections above the depth of 40 m was applied calculating the velocity of the artesian flow. A numerical model accounting for such artesian flow was built via matching the simulated volumetric flow rate to the wellhead measurements. The consistency of the simulated wellbore temperature with the DTS measurements validated this numerical model as well as the positioning of the artesian aquifer. These simulates extensively visualized the effect of the artesian flow on the near-wellbore temperature field.

Geophysical Monitoring Technologies for the Entire Life Cycle of CO2 Geological Sequestration

Geophysical monitoring of CO2 geological sequestration represents a critical technology for ensuring the long-term safe storage of CO2 while verifying its characteristics and dynamic changes. Currently, the primary geophysical monitoring methods employed in CO2 geological sequestration include seismic, fiber optic, and logging technologies. Among these methods, seismic monitoring techniques encompass high-resolution P-Cable three-dimensional seismic systems, delayed vertical seismic profiling technology, and four-dimensional distributed acoustic sensing (DAS). These methods are utilized to monitor interlayer strain induced by CO2 injection, thereby indirectly determining the injection volume, distribution range, and potential diffusion pathways of the CO2 plume. In contrast, fiber optic monitoring primarily involves distributed fiber optic sensing (DFOS), which can be further classified into distributed acoustic sensing (DAS) and distributed temperature sensing (DTS). This technology serves to complement seismic monitoring in observing interlayer strain resulting from CO2 injection. The logging techniques utilized for monitoring CO2 geological sequestration include neutron logging methods, such as thermal neutron imaging and pulsed neutron gamma-ray spectroscopy, which are primarily employed to assess the sequestration volume and state of CO2 plumes within a reservoir. Seismic monitoring technology provides a broader monitoring scale (ranging from dozens of meters to kilometers), while logging techniques operate at centimeter to meter scales; however, their results can be significantly affected by the heterogeneity of a reservoir.

Distributed Temperature Sensor With Fine Gauge Length Based on Adaptive Morphological Processing

Distributed Temperature Sensor With Fine Gauge Length Based on Adaptive Morphological Processing

Flow Profiling Analysis of a Refractured Tight Oil Well Using Distributed Temperature Sensing

Flow Profiling Analysis of a Refractured Tight Oil Well Using Distributed Temperature Sensing

Long-Term Distributed Temperature Sensing Monitoring for Near-Wellbore Gas Migration and Gas Hydrate Formation

Summary Well integrity monitoring has always been a critical component of subsurface oil and gas operations. Distributed fiber-optic sensing is an emerging technology that shows great promise for monitoring processes, both in boreholes and in other settings. In this study, we present a case study of using distributed temperature sensing (DTS) technology to monitor a cemented and plugged well in the Alaska North Slope (ANS). The well was drilled as part of a long-term gas hydrate study, and the downhole DTS data were recorded over a period of approximately 2 years. By applying a temporal gradient and removing instrument instability noise, we reveal subtle (<0.001°C/h) thermal anomalies, which are characterized by brief warming periods followed by longer cooling periods at discrete depths along the borehole. The observed coherent events show an upward trajectory from deeper formations into the overlying permafrost interval, with the thermal anomalies concentrated in relatively coarse-grained sandstone layers. We also observe that the upward migration rate of the DTS anomalies varies with formation lithology and that there is a spatial and temporal correlation between the subsurface events and measured wellhead annular pressures. We interpret that the observed warming events represent the exothermic process of gas hydrate formation that is occurring in association with the upward migration of gas outside the well casing, and this interpretation is confirmed by numerical simulations. These observations demonstrate the ability of suitably processed DTS data to detect subtle processes and highlight the value of DTS technologies for wellbore integrity monitoring.

Determination of the Temperature Development in a Borehole Heat Exchanger Field Using Distributed Temperature Sensing

The use of geothermal borehole heat exchangers (BHEs) in combination with ground-source heat pumps represents an important part of shallow geothermal energy production, which is already used worldwide and becoming more and more important. Different measurement techniques are available to examine a BHE field while it is in operation. In this study, a field with 54 BHEs up to a depth of 120 m below ground level was analyzed using fiber optic cables. A distributed temperature sensing (DTS) concept was developed by equipping several BHEs with dual-ended hybrid cables. The individual fiber optics were collected in a distributor shaft, and multiple measurements were carried out during active and inactive operation of the field. The field trial was carried out on a converted, partly retrofitted, residential complex, “Lagarde Campus”, in Bamberg, Upper Franconia, Germany. Groundwater and lithological changes are visible in the depth-resolved temperature profiles throughout the whole BHE field.

Spatial resolution improvement method of RDTS assisted by small-scale thermal region length recognition model

The Total Variation Deconvolution (TVD) algorithm plays an important role in signal reconstruction, however, when it is used to improve the spatial resolution of Raman Distributed Temperature Sensor (RDTS), there are certain challenges in parameter settings. This paper proposes to use Fully-Connected Neural Network to identify the length of small-scale thermal regions(SSTR), and based on the recognition results to set the TVD parameters automatically. We constructed training sets based on the periodic changes of SSTR signals in RDTS (which we call Thermal Region Response Modes, TRRM), to verify performance, we conducted comparative experiments between models obtained from a training set containing 100 types of TRRMs and 25 types of TRRMs, the Macro-F1 value of the former one is 0.2749 higher, reaching 0.9087, performed well in SSTR length recognition tasks. the traditional TVD assisted by this model can increase the spatial resolution of RDTS from 1.6 m to 0.4 m without manual intervention, which complements the lack of automation in applications of TVD and has practical value.

Gas leakage physical simulation and its distributed optical fiber sensing experiments in the casing annulus of injection-production pipe column for underground gas storage

Aiming at the problems of high-pressure gas leakage monitoring of injection-production pipe columns in deep underground gas storage under the high-temperature and high-pressure environment, and the problems that distributed optical fiber sensing system is not convenient to carry out detection accuracy and sensitivity testing and verification in actual wellbores, an experimental device for the pipe column gas leakage simulation test of gas storage wellbore is designed and developed to simulate the actual operation conditions of injection-production wellbores. A distributed optical fiber sensing testing environment is built, and the gas leakage signal analysis and location in the casing annulus of injection-production pipe column for the wellbore of underground gas storage are realized by using a distributed temperature sensing (DTS) system and a distributed acoustic sensing (DAS) system. The high-pressure gas leakage signals of the small crack in the damaged area of the wellbore connection can be obtained through the armored optical cable arranged along the casing annulus of pipe columns in the physical device. The optical fiber and its systems can monitor and detect the change of temperature difference and acoustic vibration field in the perimeter caused by high-pressure gas leakage. The spatial-temporal evolution of the casing annulus leakage law and the purpose of pipe column gas leakage can be realized for the wellbore safety monitoring of underground gas storage. The temperature measurement accuracy and positioning sensitivity of the DTS and DAS systems were simulated and verified by designing the pressure difference between inside and outside annular tubes and the leakage source with an interval of 1.0 m. The effectiveness of the pipe column leakage physical simulation experimental device and the reliability of DTS/DAS testing positioning accuracy are verified. This experimental device based on physical model test and its DTS/DAS testing system effectively verify the feasibility and reliability of distributed optical fiber wellbore leakage monitoring for underground gas storage, and its application provides a novel way for the evaluation and analysis of the wellbore tightness and safety of underground energy storage.

Quantitative multistage fracturing efficiency evaluation using cross-well strain measurements

Cross-well strain measurements acquired through low-frequency distributed acoustic sensing (LF-DAS) is a maturing technique used to monitor and diagnose the efficiency of hydraulic fracturing treatments. Although LF-DAS has demonstrated potential in characterizing far-field fracture communication and geometry, the prevailing analysis in this field has historically relied on qualitative interpretations, focusing on the timing and location of frac hits. In response to the evolving landscape of quantitative studies in this area, we present an advanced quantitative technique using our novel Green-function based inversion algorithm to calculate time-dependent far-field fracture width. The adopted algorithm uses the 3D displacement discontinuity method to relate fracture aperture to strains measured by LF-DAS along a monitor well during stimulation treatments. This approach is demonstrated on a subset of four treatment stages where distributed acoustic sensing (DAS) and distributed temperature sensing (DTS) were acquired within the injection well, alongside LF-DAS from a nearby monitor well. LF-DAS inversion results indicate the loss of zonal isolation in three of the monitored stages, leading to significantly smaller fracture widths at the monitor well for targeted treatment stages, and reactivated fractures in the adjacent previous stages. These interpretations are cross validated through the integration of in-well DAS and DTS analysis, where severe interstage fluid communication is observed. The inverted fracture widths quantify the impact on far-field fracture geometry associated with poor stage isolation and decreased fracturing efficiency. This new approach demonstrates the potential of LF-DAS for quantitative analysis and interpretation, facilitating improved understanding and optimization of hydraulic stimulation, going beyond its conventional qualitative role in fracturing diagnostics.

Detection of hydrogen gas leak using distributed temperature sensor in green hydrogen system

This study addresses the challenges of hydrogen gas detection in pipelines, focusing on the highly flammable nature and low ignition energy of hydrogen. It highlights the limitations of traditional point-based detection methods and emphasizes the need for advanced safety technologies. An integrated approach combining onsite monitoring with Internet of Things (IoT) technology is proposed to enhance systematic safety management and quick leak detection in hydrogen infrastructures. The study reviews various hydrogen detection methods, emphasizing distributed temperature sensing (DTS) techniques to identify leaks through temperature variations. Over nine months of monitoring, the results indicate that DTS temperature variations are more influenced by sensor location than chamber configuration, suggesting that external DTS chamber installation is more effective for leak detection. Additionally, the study integrates Gaussian Process Regression with Machine Learning to predict temperature distribution in pipelines, providing valuable insights for future hydrogen leak detection research.

Evaluation of Fluid Distribution and Perforation Erosion in Multistage Fracture Treatment

Summary Fracture monitoring and diagnosis by fiber-optic sensing technology provides invaluable information about stimulation efficiency. This technology becomes more critical for multistage fracturing over long horizontal wells. The combined analysis of distributed temperature sensing (DTS) and distributed acoustic sensing (DAS) measurements has been used to map the fluid distribution during hydraulic fracturing, and fracture volume at the cluster level and at the stage level can be estimated from the analysis. In this paper, based on the interpretation of models for DTS and DAS, we extend the application to fluid containment and stage isolation evaluation. In plug and perforation hydraulic fracturing, when a stage is finished pumping, the stage is isolated by a plug, and the next stage is perforated and then fractured. If the plug is not functioning correctly, fracture fluid enters the previous stage, causing an uneven fluid distribution, and possibly more severe perforation erosion. Both DAS and DTS monitoring can record this phenomenon when it happens. Interpretation models are built to quantify the fluid distribution with the effect of plug leakage included. From DTS measurements, cooling below the stage being completed is counted as the fluid from the current stage. Given this information, the reservoir thermal model can estimate the volume of leakage. From the DAS measurement, we interpret the volume distribution based on frequency band energy to compare with the DTS interpretation. Perforation erosion measurements from a downhole camera are used to confirm the estimation. With this approach, we assess the fluid distribution, plug performance, and the importance of stage isolation on perforation erosion during a multistage fracture treatment. The information can then be used to analyze and optimize completion and fracture treatment design. Field examples are presented in the paper to support the discussion, findings, and conclusions. Based on the results of the fluid distribution estimated, we observed that higher injection rate and larger fluid volumes may create more uniformly distributed fluid between clusters in a stage. When considering communication between stages, the DTS and downhole camera show that there is a correlation between fluid distribution and perforation eroded area. An inconsistency of eroded area and fluid received is observed at toe-side clusters from DTS, DAS, and downhole images. This could be caused by a short erosion time at the toe-side clusters. In the completion design, only a few parameters show a strong impact on fluid distribution and perforation erosion.

High-resolution monitoring of soil infiltration using distributed fiber optic

Monitoring the infiltration process in soil is of great importance in various fields. However, the majority of existing infiltration monitoring methods have certain limitations that hinder their ability to meet the technical requirements for continuous spatial monitoring of the infiltration process. To address this issue, this study proposes a new method for achieving spatially continuous monitoring of the infiltration process. This method employs the improved Active Heating Fiber Optics (AHFO) technique as the underlying mechanism and utilizes Optical Frequency Domain Reflectometry (OFDR) monitoring technology characterized by exceptionally high spatial resolution and monitoring sensitivity. The proposed method, referred to as Active Heating Optical Frequency Domain Reflectometry (AH-OFDR). The feasibility of AH-OFDR was validated through a set of experimental tests, involving the installation of optical fibers within cylindrical soil samples for infiltration monitoring. A comparison was conducted between the infiltration patterns obtained through AH-OFDR and those acquired using the well-established Distributed Temperature Sensing (DTS) method. Additionally, the temperature distribution within the sample during the infiltration process was monitored by employing infrared thermography technology. The experimental results demonstrated an excellent agreement between the infiltration patterns obtained through AH-OFDR and DTS, reaffirming the viability and accuracy of AH-OFDR for soil infiltration monitoring. However, when compared to DTS, AH-OFDR excels in achieving dynamic monitoring of water content variations at a spatial resolution as fine as the centimeter level, empowering high-resolution and continuous monitoring. The changes in the soil temperature field recorded by the infrared thermography system further confirm the accuracy of the AH-OFDR monitoring results.

Distributed temperature sensing on silicon-on-insulator chip by optical frequency domain reflectometry.

This study introduces a novel distributed temperature sensing (DTS) technique on silicon-on-insulator (SOI) chips by the optical frequency domain reflectometry (OFDR) technology. In contrast to traditional on-chip silicon photonics temperature sensors which rely on transmission spectrum detection, this method is based on Rayleigh backscatter induced by the sidewall roughness of as-fabricated waveguides, eliminating the need for a specially designed structure. On-chip DTS results with a remarkable sensing spatial resolution of 200 μm and a high temperature sensitivity of 88 pm/K are demonstrated within the temperature range from 22.8 ∘C to 200 ∘C. Furthermore, the technology is employed to measure non-uniform temperature distributions along an SOI waveguide generated by integrated heaters. Importantly, this approach offers a straightforward sensing structure, opening new possibilities for investigating temperature profiles and thermal crosstalk across the chip.

Heated fibre optics to monitor soil moisture under successive saturation–drying cycles: An experimental approach

AbstractIn recent decades, distributed temperature sensing (DTS) has emerged as a robust technology for environmental applications, enabling high‐resolution temperature measurements along fibre optic cables (FOCs). The actively heated fibre optic (AHFO) method is employed to monitor soil moisture (, m3 m−3), wherein the soil temperature subsequent to the application of a heat pulse is measured by a DTS (AHFO‐DTS approach). Despite significant improvements in the application of AHFO‐DTS under controlled and natural conditions, the thermal behaviour of soil during multiple saturation–natural drying cycles has been insufficiently evaluated. This study aimed to address this gap by constructing an experimental horizontal soil profile in the laboratory for the application of the AHFO‐DTS method during two successive saturation–drainage–evaporation (SDE) cycles. Three heating strategies were applied to a metallic alloy in contact with a FOC, and calibration models were used to correlate with the thermal conductivity (), cumulative temperature increase (), and maximum temperature increase (). The results indicated that during the second SDE cycle, the highest errors in estimates were observed with the low power‐short heat pulse, whereas the application of the low power‐long duration and high power‐short duration pulses improved the accuracy of calculations. Additionally, errors in estimates escalated under wetter conditions, attributed to a shift in soil heat transfer capacity from the first to the second SDE cycle for > 0.10 m3 m−3. This behaviour was ascribed to thermal hysteresis, arising from the contact resistance of the FOC and the alloy with the surrounding soil. Furthermore, the method exhibited the least sensitivity to this effect and yielded reliable estimates, thus its adoption is recommended. Moreover, the use of the low power‐long duration heating strategy is suggested as it promotes a trade‐off between energy saving and accurate estimates. We concluded that assessing soil thermal response under multiple SDE cycles enhances the comprehension of the AHFO‐DTS method. Overall, our findings provide insights into enhancing the applicability of this approach under field conditions, particularly following irrigation schedules and natural rainfall events.

Cryogenic temperature 3D mapping via a distributed temperature sensor with centimeter resolution.

We conduct 3D mapping of cryogenic temperatures via a Raman-based distributed temperature sensor, employing standard telecom single-mode fibers and polarization-independent superconducting nanowire single photon detectors (SNSPDs). By coiling a test fiber around various stages of a liquid helium cooled cryostat, our device demonstrates a lower temperature sensing limit of (48 ± 2) K, below the nitrogen boiling point. This achievement is made possible by the low dark count rates of SNSPDs, as validated by theoretical simulations. Furthermore, we utilize our device to map cryogenic temperatures on the 350 cm2 surface of a specially designed hollow cylindrical aluminum sample, accommodating approximately 2 m of standard single-mode optical fiber. During nitrogen cooling, we monitor the temporal evolution of the spatially dependent temperature gradient on the metallic sample with a temporal sampling down to one minute. Fiber-based distributed temperature sensing with centimetric spatial resolution can be effectively applied for 3D mapping at cryogenic temperatures of superconducting, quantum computing and aerospace instrumentation.

Thermal response to background leakages around an actively heated fiber optic sensor for leak detection in water distribution mains: modeling the effect of heating power and time

Fiber optic distributed temperature sensing (DTS) has been recently proposed as a promising technology to detect leaks in water infrastructure. However, it requires a suitable temperature difference between leaked water and the subsurface. Coupling active heating with fiber optic DTS could overcome this limitation, but very limited information is available on its application to leak detection in water pipelines. In this regard, this study addresses for the first time the use of active distributed temperature sensing (ADTS) to detect background leakages, ubiquitous and persistent leaks responsible for large losses and practically undetectable with conventional technologies. The 3D transient analysis of the thermal response to background leakages under different heating powers and times shows that there is potential to detect and locate incredibly small leaks (∼ L/d) with a detection threshold of 3 °C when actively heating a fiber optic sensor placed at distance from the pipe, in a location suitable for new and existing pipelines as well as for passive sensors. A potential to quantify background leakages also exists, since nonlinear relationships linking temperature alterations to the leak rate were predicted. The analysis further shows that it is crucial to determine for how long the detection threshold is overcome, and that increasing the heating power is not necessarily the answer. Finally, forced convection is the main heat transfer mechanism around the active sensor once reached by leaked water, and it appears that the chance of altering temperature and quality of water flowing within the nearby pipe and surrounding soil/groundwater would be very limited with ADTS.

Robust CO2 plume imaging by joint tomographic inversion using distributed pressure and temperature measurements

The scientific community has become increasingly interested in geological CO2 sequestration and CO2 enhanced oil recovery (EOR). The tracking of the CO2 propagation in both space and time during geologic sequestration is necessary to ensure the secure and effective handling of a site for CO2 injection. Our objective is to develop efficient and novel models and monitoring techniques for visualizing CO2 plumes using field measurements. As a first step, the streamline-based data integration approach is extended to include data from distributed temperature sensors (DTS). The DTS and pressure data are then jointly history matched using a hierarchical workflow combining evolutionary and streamline methods. As a final step, we will create maps that visualize CO2 propagation during the sequestration process based on saturation and streamline maps. We validate the extended streamline-based inversion method using a synthetic model. An application of the hierarchical workflow is then made to the CO2 geologic storage test site in Michigan, USA. Monitoring data includes bottom-hole pressure of the injection well, DTS data at the monitoring well, and distributed pressure measurements from several downhole sensors along the monitoring well. Based on the history matching results, the CO2 movement is largely limited to the zones intended for injection, which is in agreement with an independent warmback analysis of the temperature data. The novelty of this work is the extension of the streamline-based inversion algorithm for the DTS data, its field application to the Department of Energy regional carbon sequestration project, and potential extensions to other CO2-EOR and/or associated geological storage projects.

Seepage Anomaly Detection via Fiber Optic Distributed Temperature Sensing: Insights from Physical and Numerical Modeling

Seepage Anomaly Detection via Fiber Optic Distributed Temperature Sensing: Insights from Physical and Numerical Modeling

An efficient deep learning-based workflow for real-time CO2 plume visualization in saline aquifer using distributed pressure and temperature measurements

Underground carbon dioxide (CO2) sequestration is widely accepted as a proven and established technology to respond to global warming from greenhouse gas emissions. It is crucial to monitor the CO2 plume effectively throughout the life cycle of a geologic CO2 sequestration project to ensure safety and storage efficacy. We propose a fast and efficient deep learning workflow for near real-time data assimilation, forecasting and visualization of CO2 plume evolution in saline aquifer and demonstrate its application at a field site.Our proposed workflow integrates field measurements, including distributed pressure and temperature at wells to visualize spatial and temporal migration of the CO2 plume in the subsurface. The deep learning framework has two key concepts that enhance the efficiency of the training and robustness of the CO2 plume prediction: first, instead of using multiple saturation images as output of the deep-learning model, a single Time of Flight (TOF) image are used to represent CO2 plume propagation; second, we use variational autoencoder-decoder (VAE) for high dimensional image data compression considering uncertainties in the predicted images. Time-of-Flight (TOF) is the travel time of a neural particle from the injection point, which is calculated along streamline based on the velocity field by considering reservoir heterogeneity and driving forces. The latent variables of VAE are estimated through a deep neural network model with inputs of distributed pressure and temperature measurements. Subsequently, the decoder part of the VAE expands the estimated latent variables to the original dimension of the TOF images. We demonstrate the efficacy of the proposed method by comparison with the more traditional pareto-based multi-objective Genetic Algorithm (MOGA) for the assimilation of the field measurements and forecasting of the CO2 plume migration.The power and utility of our proposed workflow are demonstrated by application to the Illinois Basin-Decatur Project (IBDP). The IBDP is a large scale 3-year CO2 storage test and is part of the Midwest Regional Carbon Sequestration Partnership (MRCSP). The field measurements include bottom-hole pressure and distributed temperature sensing (DTS) data at the injection well, and the distributed pressure measurements at several locations along the monitoring well. The dynamic model is a compositional model with 1.7 million cells. First, we identify the influential parameters using sensitivity analysis based on the pressure and temperature responses. Next, the sensitive parameters are sampled from a pre-specified range and a comprehensive training dataset is generated, including the pressure and temperature measurements and TOF images based on flow simulation results from a variety of geological model realizations. The trained neural network model can identify geological model realizations that capture the spatial and temporal migration of pressure and CO2 saturation based on Euclidean distance within the VAE latent space. For comparison with traditional history matching workflow, the selected sensitive parameters are also tuned using MOGA with the pressure and temperature measurements. A reasonable agreement is obtained for the predicted CO2 plume migration between two workflows. The proposed deep learning workflow shows significant speed-up compared to the traditional multi-objective optimization-based history matching workflow (5 h for training and seconds for prediction; compared to several days/weeks for the MOGA).The novelty of this work is the application of an efficient deep learning-based workflow to a large-scale CO2 storage test site for assimilating field measurements and predicting CO2 plume propagation in a near real-time and comparison with traditional history matching approach.