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A Petrophysical Modeling-Guided Method For Predicting Parameters Of Low-Permeability Reservoirs, Wang Rui, Li Fang, Liu Shiyou, Sun Wanyuan, Li Songling, Huang Sheng
A Petrophysical Modeling-Guided Method For Predicting Parameters Of Low-Permeability Reservoirs, Wang Rui, Li Fang, Liu Shiyou, Sun Wanyuan, Li Songling, Huang Sheng
Coal Geology & Exploration
Backgroud Accurately predicting reservoir parameters is significant for characterizing subsurface reservoirs, establishing gas accumulation patterns, releasing production capacity, and understanding fluid migration. The traditional approaches based on core measurement or mathematical-petrophysical modeling are limited by the strong multiplicity of solutions and low accuracy of elastic parameters inversion results, making it difficult to meet the demands of modern exploration.Objective and Methods To more effectively predict reservoir parameters, this study proposed a petrophysical modeling-guided method for predicting parameters of low-permeability reservoirs. With the convolutional neural network (CNN) as a deep learning framework, the proposed method can predict water saturation, clay content, …
A Non-Uniform Interpolation Method For Seismic Data Based On A Diffusion Probabilistic Model, Chen Yao, Yu Siwei, Lin Rongzhi
A Non-Uniform Interpolation Method For Seismic Data Based On A Diffusion Probabilistic Model, Chen Yao, Yu Siwei, Lin Rongzhi
Coal Geology & Exploration
Objective The non-uniform interpolation of seismic data is identified as a prolonged challenge in energy exploration. Since geophones cannot be precisely placed at positions corresponding to theoretical grid points, current uniform interpolation techniques frequently suffer deviations and detail distortion. Methods This study proposed a novel non-uniform interpolation method based on a diffusion probabilistic model, which is an emerging generative model in deep learning that involves the diffusion and generation processes. In the diffusion process, noise is added to the complete seismic data iteratively to train the denoising capability of the neural network. In the generation process, the neural network is …
Seismic Data Denoising Based On The Convolutional Neural Network With An Attention Mechanism In The Curvelet Domain, Bao Qianzong, Zhou Mei, Qiu Yi
Seismic Data Denoising Based On The Convolutional Neural Network With An Attention Mechanism In The Curvelet Domain, Bao Qianzong, Zhou Mei, Qiu Yi
Coal Geology & Exploration
[Objective] Noise in seismic data significantly affects the accurate interpretation of subsurface stratigraphic information. Given that effective signals with pronounced lateral correlations in seismic data are distributed in specific coefficients but random noise typically spreads uniformly over all coefficients in the curvelet domain, more effective separation of signals can be achieved. [Methods] The convolutional neural network based on the attention mechanism can adaptively extract key information by focusing on important features of images. Hence, this study proposed a noise attenuation method for seismic data using a convolutional neural network based on the curvelet transform and attention mechanism (Curvelet-AU-Net). First, the …
Prediction Of Rockburst Intensity Grade Based On Convolutional Neural Network, Li Kangnan, Wu Yaqin, Du Feng, Zhang Xiang, Wang Yiqiao
Prediction Of Rockburst Intensity Grade Based On Convolutional Neural Network, Li Kangnan, Wu Yaqin, Du Feng, Zhang Xiang, Wang Yiqiao
Coal Geology & Exploration
Rockburst is one of the urgent problems to be addressed in the process of deep resource extraction. In order to predict the rockburst disasters safely and efficiently, a rockburst intensity grade prediction model (MICE-CNN) based on the Multiple Imputation by Chained Equations (MICE) and Convolutional Neural Network (CNN) was proposed. Specifically, a predictive indicator system was established based on the main influencing factors and the acquisition conditions of rockburst. A total of 120 sets of raw data from rockburst cases were collected, with the outliers processed by pauta criterion. Then, the missing data were interpolated with the four interpolation models …
An Intelligent Prediction Method And Interpretability For Drag And Torque Of Drill String, Liu Muchen, Song Xianzhi, Li Dayu, Zhu Shuo, Fu Li, Zhu Zhaopeng, Zhang Chengkai, Pan Tao
An Intelligent Prediction Method And Interpretability For Drag And Torque Of Drill String, Liu Muchen, Song Xianzhi, Li Dayu, Zhu Shuo, Fu Li, Zhu Zhaopeng, Zhang Chengkai, Pan Tao
Coal Geology & Exploration
The accurate characterization and dynamic analysis of drilling string mechanics are essential to ensure the safe and efficient drilling. In the classical soft/rigid string model for drag & torque of drilling string, the friction coefficient of the drilling string is determined by empirical estimation or post-drilling inversion, of which the accuracy and timeliness needs to be improved. Based on the effectiveness of artificial intelligence technology applied in complex nonlinear mapping, a drag and torque prediction method of drill string with mechanism-data fusion was proposed by predicting the friction coefficient. Firstly, the friction coefficient was inversed using the drilled and logged …
Drilling Core Identification Based On Natural Image, Gao Hui, Wu Zhenkun, Ke Yu, Tan Songcheng, He Siqi, Duan Longchen
Drilling Core Identification Based On Natural Image, Gao Hui, Wu Zhenkun, Ke Yu, Tan Songcheng, He Siqi, Duan Longchen
Coal Geology & Exploration
The traditional on-site core identification and recording mainly rely on the experience of technicians, and there are many uncertain factors. Limited by the site conditions, using mobile phones or cameras to capture the natural images is the most convenient way to collect the core information. Therefore, it is necessary to study the feature information extraction technology of core image and apply it to the identification and prediction of core type and other information. Specifically, a large number of core samples were collected, the thin-section identification method was employed to determine the core types and names, and then the core images …
Intelligent Lithology Prediction Method Based On Vibration Signal While Drilling And Deep Learning, Wang Sheng, Lai Kun, Zhang Zheng, Bai Jun, Luo Zhongbin, Li Bingle, Zhang Jie
Intelligent Lithology Prediction Method Based On Vibration Signal While Drilling And Deep Learning, Wang Sheng, Lai Kun, Zhang Zheng, Bai Jun, Luo Zhongbin, Li Bingle, Zhang Jie
Coal Geology & Exploration
Intelligent lithology prediction is of great importance in geological drilling, capable of improving exploration and mining efficiency, as well as the quality of results. In this study, a method of intelligent lithology prediction while drilling was proposed based on the vibration signals produced by the drill bit breaking rocks during drilling. Specifically, seven types of rocks with the same size and different lithologies were selected, and a micro-drilling experiment was designed to apply different drilling rates and rotary speeds to the rocks, in order to collect the triaxial vibration signals while drilling under multiple drilling conditions. The signals were preprocessed …
Mine Water Inrush Prediction Method Based On Vmd-Dbn Model, Liu Hui, Liu Guiqin, Ning Dianyan, Fan Juan, Chen Weiming
Mine Water Inrush Prediction Method Based On Vmd-Dbn Model, Liu Hui, Liu Guiqin, Ning Dianyan, Fan Juan, Chen Weiming
Coal Geology & Exploration
In the process of coal mining, the loss of people and property caused by mine water inrush is extremely serious. To prevent the occurrence of water inrush accidents and grasp the law of change of water inrush, the water inrush prediction and forecasting, especially the accurate estimation of mine water inrush, is very important, which is also an important task in the prevention and control of mine water damage. To increase the prediction accuracy of mine water inrush, an efficient time series prediction model combining Variational Mode Decomposition (VMD) and Deep Belief Network (DBN) was proposed for the series of …
Recognition Of Land Use On Open-Pit Coal Mining Area Based On Deeplabv3+ And Gf-2 High-Resolution Images, Zhang Chengye, Li Feiyue, Li Jun, Xing Jianghe, Yang Jinzhong, Guo Junting, Du Shouhang
Recognition Of Land Use On Open-Pit Coal Mining Area Based On Deeplabv3+ And Gf-2 High-Resolution Images, Zhang Chengye, Li Feiyue, Li Jun, Xing Jianghe, Yang Jinzhong, Guo Junting, Du Shouhang
Coal Geology & Exploration
A highly efficient means is provided by remote sensing and deep learning to keep tracking of land use in open-pit coal mining area. Based on the high–resolution images from the domestic GF-2 satellite, a DeepLabv3+ model was utilized to achieve recognition of land use on open-pit coal mining area. In addition, a comparison was made among Deeplabv3+, U-Net, FCN, Random Forest, Support Vector Machine, and Maximum Likelihood Method. Firstly, samples data from high-resolution images were produced and sensitivity tests were conducted to determine the optimal cutting size and mode of the sample. Then, the deep neural network model (DeepLabv3+) was …