RESUMO
The low-distortion processing of well-testing geological parameters is a key way to provide decision-making support for oil and gas field development. However, the classical processing methods face many problems, such as the stochastic nature of the data, the randomness of initial parameters, poor denoising ability, and the lack of data compression and prediction mechanisms. These problems result in poor real-time predictability of oil operation status and difficulty in offline interpreting the played back data. Given these, we propose a wavelet-based Kalman smoothing method for processing uncertain oil well-testing data. First, we use correlation and reconstruction errors as analysis indicators and determine the optimal combination of decomposition scale and vanishing moments suitable for wavelet analysis of oil data. Second, we build a ground pressure measuring platform and use the pressure gauge equipped with the optimal combination parameters to complete the downhole online wavelet decomposition, filtering, Kalman prediction, and data storage. After the storage data are played back, the optimal Kalman parameters obtained by particle swarm optimization are used to complete the data smoothing for each sample. The experiments compare the signal-to-noise ratio and the root mean square error before and after using different classical processing models. In addition, robustness analysis is added. The proposed method, on the one hand, has the features of decorrelation and compressing data, which provide technical support for real-time uploading of downhole data; on the other hand, it can perform minimal variance unbiased estimates of the data, filter out the interference and noise, reduce the reconstruction error, and make the data have a high resolution and strong robustness.
RESUMO
Azole fungicides have been reported to be accumulated in fish tissue. In this study, a sensitive and robust method using high-performance liquid chromatography-tandem mass spectrometry combined with ultrasonic extraction, solid-liquid clean-up, liquid-liquid extraction and solid-phase extraction (SPE) for enrichment and purification have been proposed for determination of azole fungicides in fish muscle samples. According to the results of non-statistical analysis and statistical analysis, ethyl acetate, primary secondary amine (PSA) and mixed-mode cation exchange cartridge (MCX) were confirmed as the best extraction solvent, clean-up sorbent and SPE cartridge, respectively. The satisfied recoveries (81.7-104%) and matrix effects (-6.34-7.16%), both corrected by internal standards, were performed in various species of fish muscle matrices. Method quantification limits of all azoles were in the range of 0.07-2.83ng/g. This optimized method was successfully applied for determination of the target analytes in muscle samples of field fish from Beijiang River and its tributaries. Three azole fungicides including climbazole, clotrimazole and carbendazim were detected at ppb levels in fish muscle tissues. Therefore, this analytical method is practical and suitable for further clarifying the contamination profiles of azole fungicides in wild fish species.