| With the development of exploration on CBM, researchers have deepened their research on CBM reservoir and strengthened their knowledge on geological factors which restrict the CBM production. Multiple disciplines and analytical methods are continuously introduced into the research field of CBM. CBM well logging technology, coming from conventional oil and gas, is developed with CBM industry. Nowadays, CBM logging technology as an important technology in CBM exploration is used in qualitative identification of coal reservoir, quantitative interpretation of coal quality, gas content, porosity, and permeability etc.. In addition to the conventional technologies of CBM well logging such as AC, DEN, GR, CNL, DLR, SP etc., the unconventional well logging technologies have been used including array acoustic logging, imaging logging, spectral logging etc..Coal facies research is very important to geological research of coal reservoir. Through factor analysis on affecting coal-forming marsh environment including water depth of marsh, distinction of oxidation-deoxidizing circumstance, nutrition supply, etc., it can be used to research the difference of coal forming in different marsh and influence on enrichment and development of CBM. Laboratory tests is the most common mean to research coal facies, and there is no record of coal facies research by well logging.Through researching coal industrial component, macrolithotype of coal, coal facies, gas content, porosity, permeability of 3# coal of Permian Shanxi Group in southern Qinshui basin based on conventional well logging, the dissertation was in order to research divided technology of coal facies by conventional well logging, and analyze the control mechanism between coal facies and reservoir physical properties.This dissertation makes the following primary conclusions:Through the statistical analysis of 3# coal industrial components in Shizhuang block of southern Qinshui basin, including FC, AAD, Vm and Mad, the content of FC varies from 44.52% to 89.15%, with an average of 75.28%. The content of AAD varies from 3.76% to 46.82%, with an average of 14.88%. The content of Vm varies from 2.58% and 31.72%, with an average of 9.04%. The content of Mad varies from 0.1% to 2.41%, with an average of 0.80%. Statistical data shows that 3# coal of research area has the typical characteristics of high-rank coal, like the high FC and low Vm, and has good coal quality, low AAD and Mad. In this block, the range of apparent density of coal is between 1.39 g/cm3 and 1.87g/cm3, with an average of 1.53g/cm3. The range of the gas content is between 6.81m3/t and 20.55m3/t, with an average of 12.94m3/t. The range of porosity is between 1.39% and 13.9%, with an average of 5.19%. The range of permeability is between 0.01 mD and 0.04 mD. Through the statistical analysis of logging data of 3# coal, the responding range of RD is between 258.61?·m and 25488.68?·m, with an average of 5373.76?·m; the responding range of RS is between 44.01?·m and 17153.26?·m, with an average of 372.46?·m; the responding range of AC is between 392.33?s/m and 520.17?s/m, with an average of 421.64?s/m; the responding range of DEN is between 1.2g/cm3 and 1.55g/cm3, with an average of 1.38g/cm3; the responding range of CNL is between 36.69% and 53.75%, with an average of 46.09%; the responding range of GR is between 29.34 API and 77.99 API, with an average of 53.9API; the responding range of SP is between-206.58 mV and 258.51 mV, with an average of 95.26mV; the responding range of CAL is between 22.49 cm and 48.54 cm, with an average of 29.49 cm.In aspect of macrolithotype well logging interpretation, by analyzing the correlativities of content of vitrain-clarain with FC, AAD, Vm, Mad, and DEN, the correlativities are figured out: content of vitrain-clarain and FC have good positive correlation; content of vitrain-clarain and AAD have good negative correlation; content of vitrain-clarain and DEN have certain negative correlation; the correlations of content of vitrain-clarain with Vm and Mad are not obvious. Therefore, the macrolithotype logging interpretation model is established with the parameters of FC, AAD, DEN by multivariable regression analysis. Due to the good correlations among DEN, FC, AAD, each parameters has collinearity problem. Collinearity problem can lead to information overlap so that information of parameters cannot be reflected truly and accurately. Principal component analysis is suitable method to solve it. The logging interpretation model content of vitrain-clarain can be established by principal component analysis. Finally, macrolithotype is interpretated by corresponding relation between content of vitrain-clarain and macrolithotype.In aspect of coal facies logging distinguishing, the correlativities of coal facies with content of vitrain-clarain, FC, AAD, Vm, Mad are analyzed systematically by crossplot technique on the basis of distinguishing result of coal facies and testing result of content of vitrain-clarain and coal industrial components of samples in research area. It is found that when the type of coal facies varies from dry forest marsh facies to overlying water forest marsh facies, the content of FC and vitrain-clarain is increased continually and the content of AAD is reduced. They appears obvious characteristics of tendency and partition. The correlation of coal facies with Vm and Mad is not obvious. According to the correlations of coal facies with FC, AAD, content of vitrain-clarain, distinguishing model of coal facies is established by multivariable regression analysis. The parameter of coal facies is qualitative variable. It indicates the classification of coal facies but has not numerical variation. Linear regression analysis is not suit to qualitative variable. Logistic regression analysis is appropriate. Because of the correlation of content of vitrain-clarain with FC and AAD, principal component analysis is considerable. Finally, distinguishing model of coal facies being suitable for research area is established by principal component Logistic regression analysis.The main application of conventional logging is to analyze coal quality, calculate gas content, porosity and permeability, but there are still a lot of controversies and questions on logging interpretation accuracy. In the aspect of analysis of coal quality, through the analysis of volumetric model, it is figured out that the accuracy of frame parameters largely affects the interpretation accuracy. Based on the above analysis, regression analysis is suitable to establish the logging interpretation model of coal industrial components in research area. Through crossploting analysis of coal industrial components including FC, AAD, Vm and Mad with conventional well logging parameters, it is showed that FC has a good linear correlation with DEN but has weak correlation with other logging parameters and AAD has a good linear correlation with FC and organic content. Therefore, DEN is chosen to establish the logging interpretation models of coal industrial components which is suitable for 3# coal in research area by using unitary linear regression analysis. In calculation of gas content aspect, through introducing Kim equation, Langmuir coal rank equation, volume equation, regression analysis and analyzing characteristics of research area, multivariate regression analysis is used to establishing interpretation model of gas content. Through corssploting analysis of gas content measured with conventional logging parameters, it is showed that measured gas content varies in a large range with discrete data and has not obvious correlation with single logging parameter. There is an obvious trend correlation with depth and RD. Therefore, the gas content logging interpretation model which is suitable for research area is established by multivariate regression analysis. Six parameters which is used to matching interpretation model including depth, DEN, CNL, AC, GR, RD are considered. By comparing interpretative gas content to measured gas content, the model established is reliability. In aspect of logging interpretation of porosity and permeability, the volumetric method of calculating total porosity, calculating method of fracture porosity by dual lateral resistivity and finite element method to calculating fracture permeability by fracture porosity are introduced. Considering the correlativities of porosity, permeability with logging parameters and the form of RD, RS in theoretical model, six parameters including AC, DEN, GR, CNL, depth, difference of reciprocal of RD and RS are chosen, then the interpretation models of porosity and permeability which are suitable for research area are established by multivariate regression analysis.Based on the established equations, macrolithotype and coal facies of 3# coal in Shizhuang block are interpretated. In vertical direction, there are a relatively stable dull coal about 1 meter above the floor of 3# coal. Taking it as a boundary, 3# coal can be divided into two sets of macrolithotype. The characteristic of each set is that the top and bottom develop dull coal or dirt band and the brightness is increased from top and bottom to center. A set of dry coal facies is developed in lower part of 3# coal. Taking it as a boundary, 3# coal develops two sets of cycles of coal facies. The characteristics of cycles are that in the lower cycle, duration of sediment is short and changing of coal facies is fast; in the higher cycle, duration of sediment is long and marsh has stable overlying water. In this cycle, overlying water forest marsh and deep overlying water forest marsh are more developed. On the plane, overlying depth of coal-forming marsh in south of Shizhuang block is totally deeper than in north. In the north, the mainly type of coal facies are wetland forest marsh facies and dry forest marsh facies. In the south, the mainly type of coal facies are overlying water forest marsh facies and deep overlying water forest marsh facies. According comparing coal facies distribution map to distribution map of gas content, porosity and permeability, the control functions of coal facies to gas content and porosity are figured out. With the coal facies varying from deep overlying water marsh facies to dry forest marsh facies, gas content and porosity is decreased gradually. The permeability is mainly affected by later tectonic movement, but affect by coal facies is less. |
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