The article presents the methodology of petrographic thin section analysis, combining the algorithms of image processing and statistical learning. The methodology includes the structural description of thin sections and rock classification based on images obtained from polarized optical microscope. To evaluate the properties of structural objects in thin section (grain, cement, voids, cleavage), first they are segmented by watershed method with advanced noise reduction, preserving the boundaries of grains.
Analysis of segmentation for test thin sections showed a fairly accurate contouring of mineral grains which makes possible automatically carry out the calculation of their key features (size, perimeter, contour features, elongation, orientation, etc.). The paper presents an example of particle size analysis – definition of grains size class. The roundness and rugosity coefficients of grains are estimated also. Statistical analysis of templates for manual determination of roundness and rugosity coefficients revealed drawback of examined templates in terms statistical accuracy (high dispersion of coefficient for all grain within one template, outliers presence).
In the frame of classification problem the feature importance analysis and clustering of non-correctly segmented grains are handled. The classifier for rock type definition (sandstone, limestone, dolomite) is trained with decision tree method, while the classifier of mineral composition of sandstones (greywackes, arkose) is learnt with "random forest" method. Both classifiers are learnt in the feature space generated from segmented grains and their evaluated properties.
As a result, we proved the possibility to conduct automatic quantitative and qualitative analysis of thin sections applying image processing and statistical learning methods.
The paper represents the study of hydrocarbon reservoir characteristics of Bazhenov formation and evaluation of perspective recovery technologies conducted as a part of the complex investigation by the consortium including Moscow Institute of Physics and Technology (MIPT), Gubkin Russian State University of Oil and Gas, Faculty of Geology in Moscow State University and Skolkovo Institute of Science and Technology (Skoltech).
The research examines the ability of thermal treatment recovery technology to involve the low permeability collectors and increase oil recovery ratio through implementation of oil generation potential of source rocks. The paper describes the results of numerical modelling in reservoir simulator with compositional and thermochemical modules what allows to take into account the process of kerogen pyrolysis – the key feature of reservoirs of Bazhenov formation. In addition, the approach based on special Matlab module is proposed, which allows simulating the permeability changes, occurs due to thermal treatment. That module allows calculating the permeability value depending on porosity, porous pressure and temperature in each cell of the model on each time step.
Within the framework of the present approach, parametric studies were conducted to evaluate the influence of the uncertainty of main input parameters on the results of calculations such as cumulative oil production and amount of generated synthetic oil. The range of input parameters values was chosen based on the results of research conducted by a consortium on the rock samples and reservoir fluids of Bazhenov formation. Moreover, the paper represents the evaluation of thermal treatment recovery technology effectiveness depending on technology parameters such as duration of production and injection cycles.
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