China University of Petroleum

Yongfei Yang

Jun Yao

Kai Zhang

Hai Sun

Lei Zhang

Jie Liu

Xin Wang

Lei Liu

Ke Wang

Xia Yan

Shuaishi Fu

Ning Wu

Wenjie Zhang

Huaisen Song

Yuanbo Wang

Wenhui Song

Zhihui Liu

Liu Tao

Fugui Liu

Bozhao Xu

Dongyan Fan

Xinze Li

Liang Zhou

Haiyuan Yang

Pengfei Liu

Qian Sang

Fei Luo

Yuda Kan

Yingwen Li

Qianfei Lv

Provided is a method for shale gas production prediction, including: acquiring real shale gas production data, and determining a target production decline curve model according to the real shale gas production data; setting a time step for production prediction, and obtaining a linear production decline curve by performing production prediction by using the target production decline curve model based on the time step; determining target shale gas production residuals according to the linear production decline curve and the real shale gas production data, inputting the target shale gas production residuals to a long short-term memory, and obtaining a residual prediction result by performing production prediction according to the long short-term memory and the time step; and determining a target production prediction result of shale gas well to be subjected to production prediction based on the linear production decline curve and the residual prediction result.

Method for shale gas production prediction, device, and storage medium

Disclosed a method for predicting permeability of a multi-mineral phase digital core based on deep learning. In the present disclosure, a three-dimensional digital core is constructed and a pore structure is randomly generated; after a plurality of multi-mineral digital core images is acquired by performing image segmentation on the three-dimensional digital core, permeability corresponding to each of the multi-mineral digital core images is acquired by simulation using multi-physics field simulation software and a multi-mineral digital core data set is constructed based on the plurality of multi-mineral digital core images and the permeability corresponding to each of the multi-mineral digital core images; an SE-ResNet18 convolutional neural network is constructed and trained with the multi-mineral digital core data set; and an image of a multi-mineral core to be predicted is input into the trained SE-ResNet18 convolutional neural network to determine the permeability of the multi-mineral core.

Method for predicting permeability of multi-mineral phase digital core based on deep learning

The present disclosure provides a method and system for constructing a digital rock, and relates to the technical field of digital rocks. According to the method, a three-dimensional (3D) digital rock image that can reflect real rock information is obtained using an image scanning technology, and the image is preprocessed to obtain a digital rock training image for training a generative adversarial network (GAN). The trained GAN is stored to obtain a digital rock construction model. The stored digital rock construction model can be directly used to quickly construct a target digital rock image. This not only greatly reduces computational costs, but also reduces costs and time consumption for obtaining high-resolution sample images. In addition, the constructed target digital rock image can also reflect real rock information.

Method and system for constructing digital rock

A method and system for determining contact angles of porous media, including: in a displacement simulation experiment, injecting a displacement fluid into a actual core sample, and performing computed tomography scanning on the core to determine three-dimensional images at different oil displacing time points; segmenting different phases of the three-dimensional images to obtain segmented three-dimensional images; marking a contact line between different phases according to the segmented three-dimensional images, where the phases are underground fluids or displacement fluids and a rock matrix in the displacement simulation experiment; and determining a contact angle of the porous media at different 3D positions at different time points according to the contact line. The method and system provided can obtain the dynamic changing process of the contact angle at different 3D positions in the core at different displacement time points, reflect the wettability of the real reservoir environment more accurately.

Method and system for determining contact angle of porous media

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Qingdao, China

Yongfei Yang