Distributed and Multiplexed Fiber Optic Sensors
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Cited by 3 publications
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Joint Inversion of DTS and DAS Data Through Deep Learning and Intelligent Multi-Objective Optimization Algorithm: A Case Study of Horizontal Injection Well in Middle East Carbonate Reservoir
Distributed optical fiber sensing for real-time downhole monitoring is an essential technology in the efficient development of Middle Eastern carbonate reservoirs, in which distributed temperature sensing (DTS) and distributed acoustic sensing (DAS) are two frequently utilized monitoring techniques. Efficiently and accurately inversing DTS and DAS data is important in identifying key water injection channels, capitalizing on residual oil reserves, and accurately forecasting production metrics. Meanwhile, there are two aspects of challenges in inversing DTS and DAS data, the first one is the inversion algorithms developed so far lack robustness and efficiency when facing an extensive set of parameters and computationally expensive forward models. The other one is that existing inversion techniques for distributed fiber optic monitoring data rely solely on either DTS or DAS data, with no research conducted on the combined inversion of DTS and DAS data. With those in mind, a joint inversion method coupling deep learning (DL) and multi-objective optimization (MOO) algorithm called DL-MOO is proposed for simultaneous inversion DTS and DAS so as to obtain the comprehensive inversing results with reservoir parameters including reservoir permeability, water saturation, and grid well indices. The proposed DL-MOO method integrates DL and MOO to address the joint inverse problem of DTS and DAS data with an extensive set of parameters and the computationally expensive forward model. In detail, the Long Short-Term Memory auto-encoder (LSTMAE) technique effectively condenses interpretation parameter sets into compact latent vector representations to achieve the goal of reducing the dimensionality of the parameter space. Subsequently, the inversion process is conducted within the neural network's latent variable space rather than the conventional parameter space of the forward model, leading to notable enhancements in efficiency and robustness. After that, the hybrid multi-objective particle swarm optimization algorithm (HMPSO) is adopted to search and update latent variables into the forward model to obtain the Pareto front (PF) for maximum R2 of temperature profile with DTS data and the R2of frequency band extracted with DAS data. Furthermore, a case study is conducted on a horizontal injection well in the Middle East carbonate reservoir to demonstrate the superior performance of the DL-MOO method. The results indicate that the PF of the DL-MOO method matched well with the PF of the commercial software-based MOO method, which validates its effectiveness and reliability. Additionally, a series of comparison analyses among the DL-MOO method against, the DL-MOPSO (Multi-objective Particle Swarm Optimization) method and the DL-NSGA-II (non-dominated sorting genetic algorithm-II) are executed to demonstrate the remarkable enhancements in the quality of inversion results achieved by the DL-MOO method. Under the same iteration steps, the convergence and diversity of the PF the DL-MOPSO and the DL- NSGA-II method are dominated by the PF of DL-MOO method. To the best of our knowledge, this is the first time that the joint inversion of DTS and DAS data for interpreting reservoir parameters. Through the integrated inversion of DTS and DAS data, the DL-MOO method realizes the purpose of robustness and efficient interpretation of parameter sets along the wellbore direction, encompassing reservoir permeability, water saturation, and grid well indices. Moreover, the precise interpretation results attained through the DL-MOO method could substantially enhance the effectiveness and accuracy of evaluating and monitoring horizontal well performance, which holds significant importance for optimizing the development of water-flooding carbonate reservoirs with horizontal wells.
Joint Inversion of DTS and DAS Data Through Deep Learning and Intelligent Multi-Objective Optimization Algorithm: A Case Study of Horizontal Injection Well in Middle East Carbonate Reservoir
Distributed optical fiber sensing for real-time downhole monitoring is an essential technology in the efficient development of Middle Eastern carbonate reservoirs, in which distributed temperature sensing (DTS) and distributed acoustic sensing (DAS) are two frequently utilized monitoring techniques. Efficiently and accurately inversing DTS and DAS data is important in identifying key water injection channels, capitalizing on residual oil reserves, and accurately forecasting production metrics. Meanwhile, there are two aspects of challenges in inversing DTS and DAS data, the first one is the inversion algorithms developed so far lack robustness and efficiency when facing an extensive set of parameters and computationally expensive forward models. The other one is that existing inversion techniques for distributed fiber optic monitoring data rely solely on either DTS or DAS data, with no research conducted on the combined inversion of DTS and DAS data. With those in mind, a joint inversion method coupling deep learning (DL) and multi-objective optimization (MOO) algorithm called DL-MOO is proposed for simultaneous inversion DTS and DAS so as to obtain the comprehensive inversing results with reservoir parameters including reservoir permeability, water saturation, and grid well indices. The proposed DL-MOO method integrates DL and MOO to address the joint inverse problem of DTS and DAS data with an extensive set of parameters and the computationally expensive forward model. In detail, the Long Short-Term Memory auto-encoder (LSTMAE) technique effectively condenses interpretation parameter sets into compact latent vector representations to achieve the goal of reducing the dimensionality of the parameter space. Subsequently, the inversion process is conducted within the neural network's latent variable space rather than the conventional parameter space of the forward model, leading to notable enhancements in efficiency and robustness. After that, the hybrid multi-objective particle swarm optimization algorithm (HMPSO) is adopted to search and update latent variables into the forward model to obtain the Pareto front (PF) for maximum R2 of temperature profile with DTS data and the R2of frequency band extracted with DAS data. Furthermore, a case study is conducted on a horizontal injection well in the Middle East carbonate reservoir to demonstrate the superior performance of the DL-MOO method. The results indicate that the PF of the DL-MOO method matched well with the PF of the commercial software-based MOO method, which validates its effectiveness and reliability. Additionally, a series of comparison analyses among the DL-MOO method against, the DL-MOPSO (Multi-objective Particle Swarm Optimization) method and the DL-NSGA-II (non-dominated sorting genetic algorithm-II) are executed to demonstrate the remarkable enhancements in the quality of inversion results achieved by the DL-MOO method. Under the same iteration steps, the convergence and diversity of the PF the DL-MOPSO and the DL- NSGA-II method are dominated by the PF of DL-MOO method. To the best of our knowledge, this is the first time that the joint inversion of DTS and DAS data for interpreting reservoir parameters. Through the integrated inversion of DTS and DAS data, the DL-MOO method realizes the purpose of robustness and efficient interpretation of parameter sets along the wellbore direction, encompassing reservoir permeability, water saturation, and grid well indices. Moreover, the precise interpretation results attained through the DL-MOO method could substantially enhance the effectiveness and accuracy of evaluating and monitoring horizontal well performance, which holds significant importance for optimizing the development of water-flooding carbonate reservoirs with horizontal wells.
Smart Concrete Using Optical Sensors Based on Bragg Gratings Embedded in a Cementitious Mixture: Cure Monitoring and Beam Test
Smart concrete is a structural element that can combine both sensing and structural capabilities. In addition, smart concrete can monitor the curing of concrete, positively impacting design and construction approaches. In concrete, if the curing process is not well developed, the structural element may develop cracks in this early stage due to shrinkage, decreasing structural mechanical strength. In this paper, a system of measurement using fiber Bragg grating (FBG) sensors for monitoring the curing of concrete was developed to evaluate autogenous shrinkage strain, temperature, and relative humidity (RH) in a single system. Furthermore, K-type thermocouples were used as reference temperature sensors. The results presented maximum autogenous shrinkage strains of 213.64 με, 125.44 με, and 173.33 με for FBG4, FBG5, and FBG6, respectively. Regarding humidity, the measured maximum relative humidity was 98.20 %RH, which was reached before 10 h. In this case, the recorded maximum temperature was 63.65 °C and 61.85 °C by FBG2 and the thermocouple, respectively. Subsequently, the concrete specimen with the FBG strain sensor embedded underwent a bend test simulating beam behavior. The measurement system can transform a simple structure like a beam into a smart concrete structure, in which the FBG sensors’ signal was maintained by the entire applied load cycles and compared with FBG strain sensors superficially positioned. In this test, the maximum strain measurements were 85.65 με, 123.71 με, and 56.38 με on FBG7, FBG8, and FBG3, respectively, with FBG3 also monitoring autogenous shrinkage strain. Therefore, the results confirm that the proposed system of measurement can monitor the cited parameters throughout the entire process of curing concrete.
Optical Fiber Sensors
Optical fiber sensors have become an indispensable technological advancement due to their exceptional sensitivity, resilience against electromagnetic interference, and durability under challenging conditions. Their uses cover a wide range of industries, including environmental sensing, structural health monitoring, and medical diagnostics. Their performance has been improved by developments in materials, computation of signals, and miniaturization. Future developments will tackle present issues like high prices and interference from the environment by concentrating on hybrid systems, versatile capabilities, and quantum sensing. Wider acceptance will be made easier by standardization and compatibility. This chapter highlights the revolutionary power of optical fiber sensors across sectors by examining their technological developments, prospective innovations, and future possibilities.
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