Abstract: A shale gas well type classification method includes: S1, acquiring raw data of evaluation parameters for stable production wells and trial production wells; S2, performing standardization on the raw data; S3, calculating distance coefficients; S4, classifying the stable production wells; S5, after determining a class number of the stable production wells, calculating comprehensive evaluation parameters S of the stable production wells based on standardized data; S6, for each class of the stable production wells, calculating average values of standardized data of the evaluation parameters for the stable production wells to construct a standard fuzzy set A, and constructing a to-be-identified fuzzy set B for each of the trial production wells, S7, calculating a closeness degree between each to-be-identified fuzzy set and each standard fuzzy set, and classifying a trial production well corresponding to the to-be-identified fuzzy set into a class of the stable production wells with a highest closeness degree.

Abstract: A method for quantitative evaluation of self-sealing property of organic-rich shale includes: S1, selecting geological parameters for evaluating the self-sealing property of the organic-rich shale; S2, taking organic-rich shale samples, and measuring the geological parameters of each sample; S3, calculating a weight coefficient w; of each geological parameter; S4, calculating a self-sealing evaluation coefficient S, and correcting the S to obtain a corrected self-sealing evaluation coefficient S?; S5, establishing a self-sealing evaluation standard of the organic-rich shale according to the S?, wherein if S??0.6, the self-sealing grade is excellent, if 0.45?S?<0.6, the self-sealing grade is good, if 0.3?S?<0.45, the self-sealing grade is medium, and if S?<0.3, the self-sealing grade is poor. According to the method, the self-sealing property of the shale is quantitatively evaluated, and the preservation condition of the shale gas in the shale formation can be more accurately predicted.

Abstract: A quantitative identification method for provenance of clastic rocks is provided, which includes: determining a study area, a target horizon of the study area and potential provenances of the target horizon, and performing sampling on known wells of the target horizon and the potential provenances to obtain rock samples; measuring contents of light minerals, heavy minerals, and trace elements to obtain measurement results of the known wells and measurement results of the potential provenances, establishing standard fuzzy sets based on the measurement results of the potential provenances, and establishing to-be-identified fuzzy sets based on the measurement results of the known wells; assigning weight coefficients to three indicators; calculating weighted closenesses between the to-be-identified fuzzy sets and the standard fuzzy sets according to the weight coefficients; and determining a provenance of the target horizon according to the weighted closenesses and a principle of proximity selection.

Abstract: A calculation method for fractal dimensions of shale pores includes steps as follows. S1: multiple shale samples from a target stratum of a study area are obtained, parameter values of geological parameters of each of the multiple shale samples are obtained, followed by dividing the multiple shale samples into target shale samples and experimental shale samples. S2: PCA is performed on the parameter values to obtain principal components representing a variation of the geological parameters. fractal dimensions are calculated based on an existing fractal dimension calculation method. S3: the principal components are used as independent variables and the fractal dimensions are used as dependent variables, followed by performing regression analysis to obtain a quantitative calculation model. S4: the fractal dimensions of the target shale sample are calculated according to the parameter values and the quantitative calculation model for the fractal dimension based on the parameter values.

Abstract: A quantitative prediction method for gas content of deep marine shale includes: obtaining raw data of known wells; establishing relationship formulas between pore specific surface areas and adsorbed gas contents of a known well in an area as an adsorbed gas content quantitative prediction model; establishing relationship formulas between pore volumes and free gas contents of the known well as a free gas content quantitative prediction model; summing the adsorbed gas contents and corresponding free gas contents to obtain total gas contents; calculating adsorbed gas contents, free gas contents and total gas contents of the known wells; drawing a predicted adsorbed gas content contour map, a predicted free gas content contour map and a predicted total gas content contour map; and reading an adsorbed gas content, a free gas content and a total gas content of an unknown well in the area from the above contour maps.

Abstract: A quantitative prediction method for gas content of deep marine shale includes: obtaining raw data of known wells; establishing relationship formulas between pore specific surface areas and adsorbed gas contents of a known well in an area as an adsorbed gas content quantitative prediction model; establishing relationship formulas between pore volumes and free gas contents of the known well as a free gas content quantitative prediction model; summing the adsorbed gas contents and corresponding free gas contents to obtain total gas contents; calculating adsorbed gas contents, free gas contents and total gas contents of the known wells; drawing a predicted adsorbed gas content contour map, a predicted free gas content contour map and a predicted total gas content contour map; and reading an adsorbed gas content, a free gas content and a total gas content of an unknown well in the area from the above contour maps.

Abstract: A method for calculating a surface relaxation rate of a shale includes: a relaxation time T distribution curve and a pore throat radius r distribution curve are obtained through experiments; abscissas of the two distribution curves are standardized, and the abscissa of the relaxation time T distribution curve is expanded or shrunk to ensure an abscissa value corresponding to a maximum ordinate value in the transformed relaxation time T distribution curve is same as an abscissa value corresponding to a maximum ordinate value in the pore throat radius r distribution curve; straight lines with a number of N parallel to a y-axis of a combined curve graph including the two distribution curves are drawn and a ? value corresponding to each straight line is calculated; and ? value with the number of N are processed to obtain a final surface relaxation rate ??.

Abstract: A method for combined characterization of pore structure includes steps as follows. Firstly, CO2, N2 and high-pressure mercury intrusion porosimetry characterization curves are plotted based on actual measurement data, then, average values of the overlapping range of the CO2 and N2 characterization curves are calculated, and a function yi?=ƒ(x) is fitted. Each pore volume yi? corresponding to each pore diameter xi is calculated, and a curve is plotted with xi as a horizontal coordinate and yi? as a vertical coordinate, thereby obtaining a characterization curve of the overlapping range between CO2 and N2 adsorptions. The same data processing is used to process the overlapping range data of the N2 and high-pressure mercury intrusion porosimetry characterization curves, to obtain the characterization curve between them. The characterization curves are spliced with the original CO2, N2, and high-pressure mercury intrusion porosimetry characterization curves to obtain a combined characterization curve.

Abstract: Provided is an analysis method for determining gas-bearing situation of an unknown shale reservoir, includes: S1, selecting a shale reservoir of a target interval of a place; and collecting data of parameters of cores of each of a known gas-bearing shale reservoir A and a known water-bearing shale reservoir B; S2, calculating average values of the parameters of each of the reservoirs A and B respectively; S3, calculating average differences of the parameters of each of the reservoirs; S4, calculating covariance values of the parameters of each of the reservoirs; S5, establishing, according to the covariance values, an equation group and resolving discriminant coefficients; S6, establishing a discriminant equation according to the discriminant coefficients and solving a discriminant index; and S7, obtaining values of parameters of cores of a sample of the unknown shale reservoir, calculating a discriminant value, and determining gas-bearing situation of the unknown shale reservoir.

Abstract: Provided is an analysis method for determining gas-bearing situation of an unknown shale reservoir, includes: S1, selecting a shale reservoir of a target interval of a place; and collecting data of parameters of cores of each of a known gas-bearing shale reservoir A and a known water-bearing shale reservoir B; S2, calculating average values of the parameters of each of the reservoirs A and B respectively; S3, calculating average differences of the parameters of each of the reservoirs; S4, calculating covariance values of the parameters of each of the reservoirs; S5, establishing, according to the covariance values, an equation group and resolving discriminant coefficients; S6, establishing a discriminant equation according to the discriminant coefficients and solving a discriminant index; and S7, obtaining values of parameters of cores of a sample of the unknown shale reservoir, calculating a discriminant value, and determining gas-bearing situation of the unknown shale reservoir.

Abstract: A data processing method includes: collecting test data of a target rock sample in different gas adsorption experiments; the test data including pore sizes and pore volumes corresponding to the pore sizes and including at least two selected from the group consisting of the test data with pore sizes less than 3 nm in CO2 adsorption experiment, the test data with pore sizes in 1.5 nm to 250 nm in N2 adsorption experiment and the test data with pore sizes in 10 nm to 1000 ?m in high-pressure mercury adsorption experiment; and fitting the test data in overlapping ranges of the pore sizes using a least square method, and obtaining target pore volumes corresponding to the pore sizes respectively. The accuracy of joint characterization of shale pore structures can be improved by using mathematical methods to process the data in overlapping ranges of pore sizes among different characterization methods.

Abstract: A data processing method includes: collecting test data of a target rock sample in different gas adsorption experiments; the test data including pore sizes and pore volumes corresponding to the pore sizes and including at least two selected from the group consisting of the test data with pore sizes less than 3 nm in CO2 adsorption experiment, the test data with pore sizes in 1.5 nm to 250 nm in N2 adsorption experiment and the test data with pore sizes in 10 nm to 1000 ?m in high-pressure mercury adsorption experiment; and fitting the test data in overlapping ranges of the pore sizes using a least square method, and obtaining target pore volumes corresponding to the pore sizes respectively. The accuracy of joint characterization of shale pore structures can be improved by using mathematical methods to process the data in overlapping ranges of pore sizes among different characterization methods.

Abstract: The present invention provides an anti-HPV E7 protein monoclonal antibody and the use thereof. The antibody can detect the HPV16 E7 protein with high specificity and recognize the HPV18 E7 protein, thereby it can distinguish between the cancerous cervical epithelial cells and the cervical abnormal or non-cancerous cervical epithelial cells.

Abstract: The present invention provides an anti-HPV E7 protein monoclonal antibody and the use thereof. The antibody can detect the HPV16 E7 protein with high specificity and recognize the HPV18 E7 protein, thereby it can distinguish between the cancerous cervical epithelial cells and the cervical abnormal or non-cancerous cervical epithelial cells.