Southwest Petroleum University

China University of Geosciences (wuhan)

University of Electronic Science and Technology of China

China University of Petroleum

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

Yipeng Liu

Lei Chen

Kun Zhang

Xueying Wang

Xinyang He

Fengli Han

Jingru Ruan

Hengfeng Gou

Xuefei Yang

Lintao Li

Hulin Niu

Ping Liu

Weishi Tang

Jiayi Liu

Chengzao Jia

Shu Jiang

Xiong Ding

Yan Song

Bin Li

Yi Zhang

Yi Shu

Jun Peng

Lu Lu

Yi Xiao

Yiting Qiao

Meijia Wu

Jinhua Liu

Zeyun Wang

Laiting Ye

Binsong Zheng

Jia He

Kai Liu

Ce Zhu

Jing Li

Lin Jiang

Zhenxue Jiang

Pei Liu

Xuri Huang

Tian Tang

Cheng Yang

Xuejiao Yuan

Nanxi Zhang

Hanbing Zhang

Pu Huang

Shoucheng Xu

Yubing Ji

Yongjun Li

Tianyou Zhi

Zhujiang Liu

Songyang Wan

Xiangfeng Wei

Sihong Cheng

Yunbo Zhang

Daiyu Wu

Yanhua Lin

Daojun Wang

Jingyu Hao

Feiran Chen

Bo Li

Ruobing Liu

Fubin Wei

Liang Xu

Xingmeng Wang

Hong Liu

Yijia Wu

Yiming Yang

Xiangyu Fan

Zhi Gao

Weiwei Liu

Yonghua Wang

Jiajie Wu

Yongyang Liu

Xiaoxia Dong

Jun Dong

Jiasui Ouyang

Han Qin

Ruisong Li

Hu Zhao

Tong Wang

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 temporal domain rate distortion optimization based on video content characteristic and QP-&#x3bb; correction provides the temporal domain rate distortion optimization based on the video content characteristic and the QP-&#x3bb; correction for a new generation encoder AV1, wherein according to a previous temporal domain dependency relationship under an HEVC-RA coding structure, a feature of the new generation encoder AV1 and a video sequence feature, an aggregation distortion of a current coding unit and an affected future coding unit is estimated and ta propagation factor of the current coding unit in a temporal domain distortion propagation model is calculated by constructing a temporal domain distortion propagation chain, wherein a Lagrange multiplier is adjusted through a more accurate propagation factor to realize a temporal domain dependency rate distortion optimization, and a relationship of QP-&#x3bb; is re-corrected and an I frame is adjusted to achieve a better coding effect.

Temporal domain rate distortion optimization based on video content characteristic and QP-&#x3bb;correction

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Chengdu, China

Yipeng Liu