Fiber–optic sensors have been widely deployed in various applications, and their use has gradually increased since the 1980 s. Distributed fiber–optic sensors, which enable continuous and real–time measurements along the entire length of an optical fiber cable, have undergone significant improvements in underlying industries. In the oil and gas industry, distributed fiber–optic sensors can provide significantly valuable information throughout the life cycle of a well and can monitor pipelines transporting hydrocarbons over great distances. Here, we review the deployment of fiber–optic Rayleigh–based distributed acoustic sensing (DAS), Raman–based distributed temperature sensing (DTS), and Brillouin–based distributed temperature and strain sensing (DTSS) in the oil and gas industry. In particular, we describe the operation principle and basic experimental setups of the DAS, DTS, and DTSS, highlighting their applications in the upstream, midstream, and downstream sectors of the oil and gas industry. We further developed a prototype of a fiber–optic hybrid DAS–DTS system that simultaneously measures vibration and temperature along a multimode fiber (MMF). The reported hybrid sensing system was tested in an operational oil well. This work also discusses the challenges that might hinder the growth of the distributed fiber–optic sensing market in the petroleum industry, and we further point out the future directions of related research.

Red palm weevil (RPW) is a detrimental pest, which has wiped out many palm tree farms worldwide. Early detection of RPW is challenging, especially in large-scale farms. Here, we introduce the combination of machine learning and fiber optic distributed acoustic sensing (DAS) techniques as a solution for the early detection of RPW in vast farms. Within the laboratory environment, we reconstructed the conditions of a farm that includes an infested tree with ∼12 day old weevil larvae and another healthy tree. Meanwhile, some noise sources are introduced, including wind and bird sounds around the trees. After training with the experimental time- and frequency-domain data provided by the fiber optic DAS system, a fully-connected artificial neural network (ANN) and a convolutional neural network (CNN) can efficiently recognize the healthy and infested trees with high classification accuracy values (99.9% by ANN with temporal data and 99.7% by CNN with spectral data, in reasonable noise conditions). This work paves the way for deploying the high efficiency and cost-effective fiber optic DAS to monitor RPW in open-air and large-scale farms containing thousands of trees.

Red palm weevil (RPW) is a harmful pest that destroys many date, coconut, and oil palm plantations worldwide. It is not difficult to apply curative methods to trees infested with RPW; however, the early detection of RPW remains a major challenge, especially on large farms. In a controlled environment and an outdoor farm, we report on the integration of optical fiber distributed acoustic sensing (DAS) and machine learning (ML) for the early detection of true weevil larvae less than three weeks old. Specifically, temporal and spectral data recorded with the DAS system and processed by applying a 100–800 Hz filter are used to train convolutional neural network (CNN) models, which distinguish between “infested” and “healthy” signals with a classification accuracy of ∼97%. In addition, a strict ML-based classification approach is introduced to improve the false alarm performance metric of the system by ∼20%. In a controlled environment experiment, we find that the highest infestation alarm count of infested and healthy trees to be 1131 and 22, respectively, highlighting our system’s ability to distinguish between the infested and healthy trees. On an outdoor farm, in contrast, the acoustic noise produced by wind is a major source of false alarm generation in our system. The best performance of our sensor is obtained when wind speeds are less than 9 mph. In a representative experiment, when wind speeds are less than 9 mph outdoor, the highest infestation alarm count of infested and healthy trees are recorded to be 1622 and 94, respectively.

Anisotropic diffusion (AD) is employed to enhance the signal-to-noise ratio (SNR) of Brillouin distributed optical fiber sensor. A Brillouin optical time-domain analyzer (BOTDA) with 99-km-long fiber under test is set up, where a section of 2.3 m and another 182 m section at the end of the fiber are heated for experimental verification. The SNR of experimental data sets with different sampling point numbers are enhanced to several improvement levels by AD and three other methods for comparison. Results show that the 2.3 m section and the temperature transition region of the 182 m section are better preserved by AD than other methods for the same SNR improvement. Objective criteria analysis shows that AD achieves the best measured spatial resolution and temperature accuracy among the four methods, the location of temperature transition can be detected more accurately for data with low SPNs after AD denoising. In addition, the processing time of AD is 1/3 that of non-local means (NLM) and 6% that of block-matching and 3D filtering (BM3D). The edge-preserving quality and fast processing speed allow the proposed algorithm to be a competitive denoising alternative for sensors based on Brillouin scattering.

A method of robust and fast temperature extraction for Brillouin optical time-domain analyzer (BOTDA) sensing systems using the denoising autoencoder (DAE) based deep neural networks (DNN) is demonstrated. After appropriate training, the DAE suppresses the noise on the measured Brillouin gain spectrum (BGS), and improves the signal-to-noise ratio (SNR) by 9.96dB in our experiment. To extract temperature, the DAE as a basic block is stacked to form the DNN model. Since the DNN model is based on DAE, both denoising and fast temperature extraction can be simultaneously finished using only one DNN model. Thus, it is more robust and faster than the conventional Lorentzian curve fitting (LCF) method, especially when the input data has low SNR level. In the case of 4.6dB SNR, the standard deviation (SD) of the measured temperature at the end of 40km fiber under test (FUT) is reduced from 2.4°C to 1.2°C by using DAE based DNN when compared with that using the LCF method, and the corresponding root-mean-square error (RMSE) is reduced from 2.4°C to 1.3°C. Moreover, since the temperature information can be extracted directly from the experimental BGS data without the need of time-consuming curve fitting and subsequent conversion from Brillouin frequency shift (BFS) to temperature, the speed of temperature extraction using the DAE based DNN is faster by 500 times than that using LCF. Combining the advantages of both denoising and fast processing speed, the DAE based DNN would be a practical way of temperature extraction for the BOTDA systems.

We propose the asynchronous control of anisotropic diffusion (AD) algorithm, and such asynchronous anisotropic diffusion (AAD) algorithm is demonstrated experimentally to reduce noise from the sensing signals obtained from Brillouin distributed optical fiber sensors. The performance of the proposed AAD algorithm is analyzed in detail for different experimental conditions and compared with that of block-matching and 3D filtering, two-dimensional wavelet denoising, AD, and non-local means algorithms. Some key factors of the proposed algorithm, such as the impact of convolution kernel size on the performance of AD algorithms, the influence of low sampling point number (SPN) on the quality of Brillouin frequency shift and the selection of diffusion thresholds are analyzed and discussed with experimental results. The experimental results validate that the AAD algorithm can provide better root-mean-square error (RMSE) and spatial resolution (SR) than the other four algorithms, especially for higher signal-to-noise ratio (SNR) improvement and higher SPNs. For lower SPNs, the performance of AAD is also not inferior to the RMSE performance of NLM and AD. The runtime of the AAD algorithm is also quite low. Moreover, the proposed algorithm offers the best SR performance as compared to other noise reduction algorithms investigated in this study. Thus, the proposed AAD algorithm can be an effective candidate to improve the measurement accuracy of Brillouin distributed optical fiber sensors.