Southwest Petroleum University

China University of Geosciences (wuhan)

China University of Petroleum

University of Electronic Science and Technology of China

Oil & Gas Survey Center, China Geological Survey

Sinopec Exploration Company

Shale Gas Research Institute, Petrochina Southwest Oil and Gas Field Company

Sichuan Hengyi Petroleum Technology Services Co., Ltd

Petrochina Zhejiang Oilfield Company

Sinopec Southwest Petroleum Bureau Co., Ltd

Jiangxi Provincial Shale Gas. Investment Company, Ltd

Fengli Han

Kun Zhang

Xueying Wang

Lei Chen

Xinyang He

Yipeng Liu

Xuefei Yang

Hengfeng Gou

Jingru Ruan

Lintao Li

Ping Liu

Weishi Tang

Hulin Niu

Shu Jiang

Jiayi Liu

Chengzao Jia

Bin Li

Yan Song

Jun Peng

Xiong Ding

Yi Zhang

Yi Shu

Lu Lu

Yi Xiao

Yiting Qiao

Meijia Wu

Jinhua Liu

Xuejiao Yuan

Pei Liu

Zeyun Wang

Binsong Zheng

Laiting Ye

Jia He

Jing Li

Lin Jiang

Zhenxue Jiang

Xuri Huang

Tian Tang

Cheng Yang

Xiaoming Zhao

Pu Huang

Xingmeng Wang

Shoucheng Xu

Yongjun Li

Zhujiang Liu

Tianyou Zhi

Songyang Wan

Yunbo Zhang

Sihong Cheng

Daiyu Wu

Yanhua Lin

Daojun Wang

Jingyu Hao

Bo Li

Feiran Chen

Ruobing Liu

Liang Xu

Fubin Wei

Hanbing Zhang

Hong Liu

Xiangyu Fan

Yijia Wu

Yubing Ji

Yiming Yang

Zhi Gao

Jiajie Wu

Yongyang Liu

Weiwei Liu

Xiangfeng Wei

Xiaoxia Dong

Jun Dong

Hu Zhao

Jiasui Ouyang

Ruisong Li

Tong Wang

Nanxi Zhang

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.

Method for quantitative evaluation of self-sealing property of organic-rich shale

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.

Calculation method for fractal dimension of shale pores

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.

Quantitative prediction method for gas content of deep marine shale

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 ρ value corresponding to each straight line is calculated; and ρ value with the number of N are processed to obtain a final surface relaxation rate ρ'.

Method for calculating surface relaxation rate of shale

A method for combined characterization of pore structure includes steps as follows. Firstly, CO, Nand high-pressure mercury intrusion porosimetry characterization curves are plotted based on actual measurement data, then, average values of the overlapping range of the COand Ncharacterization curves are calculated, and a function y'=ƒ(x) is fitted. Each pore volume y′ corresponding to each pore diameter xis calculated, and a curve is plotted with xas a horizontal coordinate and y′ as a vertical coordinate, thereby obtaining a characterization curve of the overlapping range between COand Nadsorptions. The same data processing is used to process the overlapping range data of the Nand high-pressure mercury intrusion porosimetry characterization curves, to obtain the characterization curve between them. The characterization curves are spliced with the original CO, N, and high-pressure mercury intrusion porosimetry characterization curves to obtain a combined characterization curve.

Method for combined characterization of pore structure

Provided is an analysis method for determining gas-bearing situation of an unknown shale reservoir, includes: S, 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; S, calculating average values of the parameters of each of the reservoirs A and B respectively; S, calculating average differences of the parameters of each of the reservoirs; S, calculating covariance values of the parameters of each of the reservoirs; S, establishing, according to the covariance values, an equation group and resolving discriminant coefficients; S, establishing a discriminant equation according to the discriminant coefficients and solving a discriminant index; and S, 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.

Analysis method for determining gas-bearing situation of unknown shale reservoir

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 COadsorption experiment, the test data with pore sizes in 1.5 nm to 250 nm in Nadsorption experiment and the test data with pore sizes in 10 nm to 1000 &#x3bc;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.

Joint characterization method of pore structures based on least square method and data processing method

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Chengdu, China

Fengli Han