Rational development of oil and gas reservoirs is possible only with efficient monitoring by various well logging techniques. This paper presents algorithms for processing data acquired by spectral noise logging (SNL) in memory mode. The SNL technology is designed to identify flowing reservoir intervals, cross-flows behind casing and tubing and casing leaks by spectral analysis of recorded noise signals. While moving through a reservoir, fluids and gases create turbulence and rock vibrations that in turn generate noise. This acoustic noise is recorded with a noise logging memory tool consisting of a high-sensitivity piezoelectric hydrophone sensor and an amplifier and data collection module. The tool records acoustic signals in the frequency range of 15 Hz to 60 kHz. The existing SNL technology excludes intense broadband noise created by the movement of the tool in the well. Useful information is extracted from background noise using a technique based on wavelet thresholding. Spectral noise density in the depth-frequency plane undergoes a wavelet transform. At each measurement depth, several tens of noise signals are recorded to determine mean wavelet coefficients and their typical variance. Then, they are analysed to remove statistically insignificant details from the signal spectrum and to suppress noise components that are present throughout large depth intervals. The processing of data acquired in tens of wells from various fields has show that the noise features identified by wavelet filtering correlate with open-hole data and are confirmed by conventional well logging techniques.

In recent years, methods based on word embedding models have been widely used for solving problems of semantic change estimation. The models are trained on text corpora of various years. Semantic change is detected by analyzing changes in distance between words using vector space alignment or by analyzing changes in a set of words that are most similar in meaning to a target word. Testing for statistical significance of the detected effects has not been detailly discussed in previous studies. This paper focuses on the problem of testing statistical significance of semantic change. Besides, we consider the problem of finding a confidence interval of estimates of semantic distance between words. We allow for the influence of two random factors. The first one is associated with the use of random initial conditions and stochastic optimization when training the model, the second one results from a random selection of texts for a training corpus. The proposed approach is based on the use of resampling of a training set of texts. The proposed method is tested on the COHA corpus.

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Solid particles (sand) production in oil and gas wells can significantly affect well productivity. The negative effect can be caused by sanding up of the perforations, tubing and downhole equipment, and abrasive wear of the well completion and surface equipment. The near-wellbore zone can also be damaged. Considering this, it is critically important to be able to identify sand production intervals for the purposes of ensuring sand control and preventing the negative impact of sand production. The paper presents a new method of locating sand production intervals using spectral acoustic logging tools, which consists of detecting signals generated by solid particles being transported by fluid flow and hitting the acoustic logging tool housing. The next generation spectral acoustic logging technology was used to identify reservoir intervals producing solid particles. The developed method was tested and utilised in a deviated well of one of the large gas fields in South East Asia, where sand production was a serious problem. Well logging operations, including spectral acoustic logging, were performed in two different production regimes. This approach not only demonstrated a good data repeatability, but also helped determine the optimum production regime of the well with sand production being under control. The acoustic logging data was processed by an adaptive recognition algorithm to identify acoustic signals generated by solid particles striking against the tool housing. The method had previously been tested in the lab. The proposed method of identifying sand production intervals in producing wells makes it possible to locate sand production zones and define a production profile in one logging run. The obtained information was used in planning a remedial job with the objective of isolating sand production intervals in the surveyed well.

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