A methane telemetry system for 1653 nm DFB laser based on TDLAS‐WMS technology is developed in this article. The focus tunable lens is used as the collimating system of the telemetry device to solve the problem that the telemetry device cannot be dynamically adjusted under different detection environments. Experimental results show that the root mean square error was 7.6205 and the theoretical limit of detection (LoD) was 1.473 parts per million (ppm) while the optimal integration time reaches 30 s. The near‐infrared CH4 telemetry system is suitable for different detection environments of natural gas leakage and has good detection performance and stable and reliable operation.

A distribution feedback laser sensor for high precision and high sensitivity detection of the hydrogen sulfide in associated gas from oil fields is developed in this paper. Tunable diode laser absorption spectroscopy and wavelength modulation spectroscopy and were utilized for the H 2 S concentration detection in the oilfield. Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm is used to remove the noise from the spectral signal. Particle swarm optimization-least squares support vector machine (PSO-LSSVM) model is applied for concentration prediction. The root mean square error of long-term stability is 0.323. Allan-Werle deviation analysis shows that the theoretical limit of detection is 71 parts per billion while the optimal integration time reaches 96 s. The sensor is of great significance for realtime detection of hydrogen sulfide concentration in associated gas in oil fields.

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Carbon dioxide (CO 2 ) gas is one of the main greenhouse gases. The detection of CO 2 gas content is of great significance to the study of the greenhouse effect. The CO 2 detection system based on the principle of tunable diode laser absorption spectroscopy (TDLAS) was demonstrated. A distributed feedback (DFB) laser with a central wavelength of 1580 nm was used as the laser source, multipass gas cell (MPGC) was used as gas cell, and indium gallium arsenic (IGA) photodetector was used to complete photoelectric conversion, and the original extracted second harmonic signal was smooth and denoised by wavelet transform. The signal-to-noise ratio (SNR) of the wavelet-filtered spectrum improved from 6.88 to 13.87 dB, an improvement of 2.02 times. Principal component analysis was used to reduce the complexity of the data by compressing the spectrum from 800 dimensions to 2 dimensions. For the concentration inversion, the back-propagation deep neural network (BP-DNN) model was used to perform standard gas concentration step experiments and compared with the back-propagation neural network (BPNN). The experimental results show that the BP-DNN inversion of CO 2 concentration has improved computational accuracy and the root mean square error (RMSE) is 3.55 times lower than that of the traditional BPNN, showing favorable application prospects.

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