Southwest Petroleum University

China University of Geosciences (wuhan)

University of Electronic Science and Technology of China

China University of Petroleum

Jiangxi Provincial Shale Gas. Investment Company, Ltd

Yi Zhang

Lei Chen

Kun Zhang

Xueying Wang

Xinyang He

Fengli Han

Yipeng Liu

Hengfeng Gou

Jingru Ruan

Xuefei Yang

Hulin Niu

Ping Liu

Lintao Li

Weishi Tang

Shu Jiang

Jiayi Liu

Chengzao Jia

Yi Shu

Xiong Ding

Lu Lu

Yi Xiao

Zeyun Wang

Yiting Qiao

Laiting Ye

Meijia Wu

Yadong Jiang

Gao Li

Yijian Chen

Dong Liu

Bin Li

Yan Song

Haibo Liang

Jian Lv

Longcheng Que

Jun Peng

Yun Zhou

Binsong Zheng

Jinhua Liu

Kai Yang

Lin Jiang

Zhenxue Jiang

Xin Yu

Daogang Min

Ze Chen

Yongjun Li

Tianyou Zhi

Weiwei Liu

Songyang Wan

Sihong Cheng

Hai Yang

Daiyu Wu

Zhongbing Li

Jialing Zou

Pengbo Ni

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 method for detecting a borehole caving based on cuttings and elements logging data includes: integrating real-time elements logging data and real-time cuttings return data of a target well, and historical elements logging data and stratum evaluation data of an adjacent well; calculating a root mean square deviation (RMSD) Δ of a relative content of each element of the target well and the adjacent well and a real-time cuttings return ratio; setting a threshold λ of the RMSD Δ of the relative content of each element and a threshold range (a,b) of the real-time cuttings return ratio; and establishing an intelligent stratum identification model based on a support vector machine (SVM) for real-time determination of a horizon from which current cuttings are returned. The method can achieve effective borehole caving detection, such that on-site personnel can deal with borehole caving in time and prevent it from developing into a complicated drilling accident.

Method for detecting borehole caving based on cuttings and elements logging data

A strain and acoustic wave testing device includes an acoustic wave transmitting terminal, an upper pressure-bearing shaft, corundum ejector pins, an upper displacement slide, a lower displacement slide, a heat insulation shell, a carbon fiber sleeve, a rock sample, a lower pressure-bearing shaft, an acoustic wave receiving terminal, a lower copper electrode, pearl powder, a temperature sensor, a transformer, a temperature-acoustic wave control box, an oscilloscope, an upper copper electrode, and a data collection and processing system.

Strain and acoustic wave testing device and method for high-temperature rock sample

The present disclosure relates to an exponential model based uncooled infrared focal plane array readout circuit, including: a first microbolometer unit and a second microbolometer unit, for obtaining a detection signal and a reference signal; a conversion unit, connected with the first and second microbolometer units, and configured for converting the detection signal and reference signal to obtain a linear detection signal and linear reference signal; a subtraction unit, connected with the conversion unit, and configured for calculating a difference between the detection signal and the reference ratio signal to obtain a difference signal; and an integration unit, connected to the subtraction unit, and configured for integrating the difference signal to obtain an electrical signal for characterizing the infrared light signal of the to-be-detected object.

Exponential model based uncooled infrared focal plane array readout circuit

Growing community of inventors

Chengdu, China

Yi Zhang