(1) Recently, metabolic profiling of the tissue in the native state or extracts of its metabolites has become increasingly important in the field of metabolomics. An important factor, in this case, is the presence of blood in a tissue sample, which can potentially lead to a change in the concentration of tissue metabolites and, as a result, distortion of experimental data and their interpretation. (2) In this paper, the metabolomic profiling based on NMR spectroscopy was performed to determine the effect of blood contained in the studied samples of brain tissue on their metabolomic profile. We used 13 male laboratory CD-1® IGS mice for this study. The animals were divided into two groups. The first group of animals (n = 7) was subjected to the perfusion procedure, and the second group of animals (n = 6) was not perfused. The brain tissues of the animals were homogenized, and the metabolite fraction was extracted with a water/methanol/chloroform solution. Samples were studied by high-frequency 1H-NMR spectroscopy with subsequent statistical data analysis. The group comparison was performed with the use of the Student’s test. We identified 36 metabolites in the brain tissue with the use of NMR spectroscopy. (3) For the major set of studied metabolites, no significant differences were found in the brain tissue metabolite concentrations in the native state and after the blood removal procedure. (4) Thus, it was shown that the presence of blood does not have a significant effect on the metabolomic profile of the brain in animals without pathologies.
Among a large number of geophysical well logging methods, the spontaneous potential (SP) logging is a most demanded one for studying geological sections, and is widely used in all drilled wells. This paper presents a brief review of the main studies on the enhancement of a theoretical model for the SP phenomenon, and on the creation of algorithms for numerical modeling of SP logging data. First of all, we discuss the studies that were conducted shortly after the discovery of the phenomenon and became fundamental in the field of SP method theory. Most of the first works were aimed at identifying the key factors that influence the shape and amplitude of SP signals. The research vector of these works contributed to the creation of interpretation charts, which are widely used even today. This review also analyses the main results of the more recent theoretical works aimed at developing a quantitative SP logging model that takes into account the petrophysical properties of the geological environment, and works related to numerical approaches for the modeling of well logging data. In addition, to demonstrate the effectiveness of modern computational methods, the paper presents an original algorithm based on the finite element method and utilizing a correct physical and mathematical model of the SP phenomenon. The proposed approach makes it possible to calculate SP signals in the intervals of clayed reservoirs, with consideration to their porosity, water saturation, as well as the type and content of clay minerals. Comparison of the SP modeling results and field logging data obtained from wells in Western Siberian fields shows a high quality of our theoretical model. The presented review of key works devoted to the theoretical description of the SP method, as well as modern numerical approaches for analysing SP logging curves in complex geological conditions, demonstrates the potential of the SP method for new areas of practical application.
In this paper, we propose a new method for the numerical simulation of self-potential (SP) signals based on the finite element method. A detailed physical and mathematical model is introduced, reflecting the influence of the geometric and petrophysical parameters of the investigated area on the observed SP signals. The choice of an adequate parameterization of the reservoir made it possible to clearly demonstrate the dependence of the SP signals on the shale content of the reservoir and the type of shale minerals. The proposed approach to modeling SP signals allows obtaining new petrophysical characteristics and expanding the field of application of SP logging.
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