Oil and gas production wells are often equipped with modern, permanent or temporary in-well monitoring systems, either electronic or fiber-optic, typically for measurement of downhole pressure and temperature. Consequently, novel methods of pressure and temperature transient analysis (PTTA) have emerged in the past two decades, able to interpret subtle thermodynamic effects. Such analysis demands high-quality data. High-level reduction in data noise is often needed in order to ensure sufficient reliability of the PTTA. This paper considers the case of a state-of-the-art intelligent well equipped with fiber-optic, high-precision, permanent downhole gauges. This is followed by screening, development, verification and application of data denoising methods that can overcome the limitation of the existing noise reduction methods. Firstly, the specific types of noise contained in the original data are analyzed by wavelet transform, and the corresponding denoising methods are selected on the basis of the wavelet analysis. Then, the wavelet threshold denoising method is used for the data with white noise and white Gaussian noise, while a data smoothing method is used for the data with impulse noise. The paper further proposes a comprehensive evaluation index as a useful denoising success metrics for optimal selection of the optimal combination of the noise reduction methods. This metrics comprises a weighted combination of the signal-to-noise ratio and smoothness value where the principal component analysis was used to determine the weights. Thus the workflow proposed here can be comprehensively defined solely by the data via its processing and analysis. Finally, the effectiveness of the optimal selection methods is confirmed by the robustness of the PTTA results derived from the de-noised measurements from the above-mentioned oil wells.

石油和天然气生产井通常配备有现代的，永久的或临时的井内监测系统，无论是电子的还是光纤的，通常用于测量井下压力和温度。因此，在过去的二十年中出现了新颖的压力和温度瞬态分析（PTTA）方法，能够解释微妙的热力学效应。这种分析需要高质量的数据。为了确保PTTA具有足够的可靠性，通常需要在数据上进行高水平的降低。本文考虑了配备有光纤，高精度，永久性井下测量仪的先进智能井的情况。接下来是筛选，开发，验证和应用数据降噪方法，可以克服现有降噪方法的局限性。首先，通过小波变换分析原始数据中包含的特定类型的噪声，并在小波分析的基础上选择相应的降噪方法。然后，将小波阈值去噪方法用于具有白噪声和白高斯噪声的数据，而将数据平滑方法用于具有脉冲噪声的数据。本文还提出了一种综合评估指标，作为降噪方法的最佳组合的最佳选择的有用降噪成功指标。该度量包括信噪比和平滑度值的加权组合，其中使用主成分分析来确定权重。因此，此处提出的工作流程可以仅通过数据的处理和分析来全面定义。最后，