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A real-time intelligent lithology identification method based on a dynamic felling strategy weighted random forest algorithm

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Petroleum Science 2024年第2期21卷 1135-1148页

作者： Tie Yan Rui Xu Shi-Hui Sun Zhao-Kai Hou Jin-Yu Feng School of Petroleum Engineering Northeast Petroleum UniversityDaqing163318HeilongjiangChina Sanya Offshore Oil&Gas Research Institute Northeast Petroleum UniversitySanya572025HainanChina

Real-time intelligent lithology identification while drilling is vital to realizing downhole closed-loop drilling. The complex and changeable geological environment in the drilling makes lithology identification face many challenges. This paper studies the problems of difficult feature information extraction,low precision of thin-layer identification and limited applicability of the model in intelligent lithologic identification. The author tries to improve the comprehensive performance of the lithology identification model from three aspects: data feature extraction, class balance, and model design. A new real-time intelligent lithology identification model of dynamic felling strategy weighted random forest algorithm(DFW-RF) is proposed. According to the feature selection results, gamma ray and 2 MHz phase resistivity are the logging while drilling(LWD) parameters that significantly influence lithology identification. The comprehensive performance of the DFW-RF lithology identification model has been verified in the application of 3 wells in different areas. By comparing the prediction results of five typical lithology identification algorithms, the DFW-RF model has a higher lithology identification accuracy rate and F1 score. This model improves the identification accuracy of thin-layer lithology and is effective and feasible in different geological environments. The DFW-RF model plays a truly efficient role in the realtime intelligent identification of lithologic information in closed-loop drilling and has greater applicability, which is worthy of being widely used in logging interpretation.

关键词： Intelligent drilling Closed-loop drilling lithology identification Random forest algorithm Feature extraction

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Deep learning of rock microscopic images for intelligent lithology identification: Neural network comparison and selection

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Journal of Rock Mechanics and Geotechnical Engineering 2022年第4期14卷 1140-1152页

作者： Zhenhao Xu Wen Ma Peng Lin Yilei Hua Geotechnical and Structural Engineering Research Center Shandong UniversityJinan250061China School of Qilu Transportation Shandong UniversityJinan250061China Institute of Robotics and Intelligent Systems Wuhan University of Science and TechnologyWuhan430000China

An intelligent lithology identification method is proposed based on deep learning of the rock microscopic *** on the characteristics of rock images in the dataset,we used Xception,MobileNet_v2,Inception_ResNet_v2,Inception_v3,Densenet121,ResNet101_v2,and ResNet-101 to develop microscopic image classification models,and then the network structures of seven different convolutional neural networks(CNNs)were *** shows that the multi-layer representation of rock features can be represented through convolution structures,thus better feature robustness can be *** the loss function,cross-entropy is used to back propagate the weight parameters layer by layer,and the accuracy of the network is improved by frequent iterative *** expanded a self-built dataset by using transfer learning and data ***,accuracy(acc)and frames per second(fps)were used as the evaluation indexes to assess the accuracy and speed of model *** results show that the Xception-based model has the optimum performance,with an accuracy of 97.66%in the training dataset and 98.65%in the testing ***,the fps of the model is 50.76,and the model is feasible to deploy under different hardware conditions and meets the requirements of rapid lithology *** proposed method is proved to be robust and versatile in generalization performance,and it is suitable for both geologists and engineers to identify lithology quickly.

关键词： Deep learning Rock microscopic images Automatic classification lithology identification

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Studying the lithology identification Method from Well logs Based on DE-SVM

Studying the Lithology Identification Method from Well logs ...

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2009中国控制与决策会议

作者： Jiang An-nan~1,Jin Lu~2 1.Traffic and Logistics College,Dalian Maritime University,Dalian 116026,China 2.Resource and Civil Engineering College of Northeastern,Shenyang,110004,China

＜正＞Identify the rock lithology has important meaning for estimating the reserve of petroleum,adopting proper drilling technology and improving *** lithology identification from well log based on DE-SVM was proposed and *** digitization and collection the data of the well logs and cores observation results,the mapping model between well logs and strata lithology is established by Support vector machine（SVM）,so the strata lithology of wells without rock cores can be automatically gotten by well *** the penal factor c and kernel parameterσaffect the identification accuracy evidently,the global optimization arithmetic- difference evolutionary（DE） is coupled with SVM to optimize above parameters in order to improve the performance of SVM *** model theory and algorithm are discussed as well as the true example is calculated,it is stated that the proposed method is feasible and can get satisfied results.

关键词： lithology identification Well Logging Support Vector Machine Difference Evolutionary

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Adaptive identification of Logging lithology Based on VPSO-ENN Hybrid Algorithm

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Journal of Southwest Jiaotong University(English Edition) 2008年第4期16卷 329-334页

作者：郭健王元汉李银平 School of Civil Engineering and Mechanics Huazhong University of Science and Technology Institute of Rock and Soil Mechanics Chinese Academy of Sciences

Particle swarm optimization （PSO） was modified by variation method of particle velocity, and a variation PSO （VPSO） algorithm was proposed to overcome the shortcomings of PSO, such as premature convergence and local optimization. The VPSO algorithm is combined with Elman neural network （ENN） to form a VPSO-ENN hybrid algorithm. Compared with the hybrid algorithm of genetic algorithm （GA） and BP neural network （GA-BP）, VPSO-ENN has less adjustable parameters, faster convergence speed and higher identification precision in the numerical experiment. A system for identifying logging parameters was established based on VPSO-ENN. The results of an engineering case indicate that the intelligent identification system is effective in the lithology identification.

关键词： Variation PSO Logging parameter lithology identification Elman neural network

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Multi-scale data joint inversion of minerals and porosity in altered igneous reservoirs—A case study in the South China Sea

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Petroleum Science 2024年第1期21卷 206-220页

作者： Xin-Ru Wang Bao-Zhi Pan Yu-Hang Guo Qing-Hui Wang Yao Guan College of Geo-exploration Science and Technology Jilin UniversityChangchun130046JilinChina Research Institute CNOOC Ltd.-ShenzhenShenzhen518000GuangdongChina

There are abundant igneous gas reservoirs in the South China Sea with significant value of research,and lithology classification,mineral analysis and porosity inversion are important links in reservoir ***,affected by the diverse lithology,complicated mineral and widespread alteration,conventional logging lithology classification and mineral inversion become considerably *** the same time,owing to the limitation of the wireline log response equation,the quantity and accuracy of minerals can hardly meet the exploration requirements of igneous *** overcome those issues,this study takes the South China Sea as an example,and combines multi-scale data such as micro rock slices,petrophysical experiments,wireline log and element cutting log to establish a set of joint inversion methods for minerals and porosity of altered igneous ***,we define the lithology and mineral characteristics through core slices and mineral data,and establish an igneous multi-mineral volumetric *** we determine element cutting log correction method based on core element data,and combine wireline log and corrected element cutting log to perform the lithology classification and joint inversion of minerals and ***,it is always difficult to determine the elemental eigenvalues of different minerals in *** paper uses multiple linear regression methods to solve this ***,an integrated inversion technique for altered igneous formations was *** results show that the corrected element cutting log are in good agreement with the core element data,and the mineral and porosity results obtained from the joint inversion based on the wireline log and corrected element cutting log are also in good agreement with the core data from X-ray *** results demonstrate that the inversion technique is applicable and this study provides a new direction for the mineral inversion research of altered igneous formation

关键词： Joint inversion Altered igneous rock Element correction method lithology identification Multi mineral volume model

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Laser-induced breakdown spectroscopy applied to the characterization of rock by support vector machine combined with principal component analysis

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Chinese Physics B 2016年第6期25卷 290-295页

作者：杨洪星付洪波王华东贾军伟 Markus W Sigrist 董凤忠 Anhui Provincial Key Laboratory of Photonic Devices and Materials Anhui Institute of Optics and Fine Mechanics Chinese Academy of Sciences Hefei 230031 China University of Science and Technology of China Hefei 230026 China ETH Zurich Institute for Quantum Electronics Otto-Stern- Weg 1 CH-8093 Zurich Switzerland

Laser-induced breakdown spectroscopy（LIBS） is a versatile tool for both qualitative and quantitative *** this paper,LIBS combined with principal component analysis（PCA） and support vector machine（SVM） is applied to rock *** emission lines including Fe,Mg,Ca,Al,Si,and Ti are selected as analysis lines.A good accuracy（91.38% for the real rock） is achieved by using SVM to analyze the spectroscopic peak area data which are processed by *** can not only reduce the noise and dimensionality which contributes to improving the efficiency of the program,but also solve the problem of linear inseparability by combining PCA and *** this method,the ability of LIBS to classify rock is validated.

关键词： laser-induced breakdown spectroscopy(LIBS) principal component analysis(PCA) support vector machine(SVM) lithology identification

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Study of S-wave ray elastic impedance for identifying lithology and fluid

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Applied Geophysics 2013年第2期10卷 145-156,235页

作者：龚雪萍张峰李向阳陈双全中国石油大学(北京)"油气资源与探测"国家重点实验室北京102249 中国石油大学(北京)CNPC物探重点实验室北京102249

In this paper, we derive an approximation of the SS-wave reflection coefficient and the expression of S-wave ray elastic impedance （SREI） in terms of the ray parameter. The SREI can be expressed by the S-wave incidence angle or P-wave reflection angle, referred to as SREIS and SREIP, respectively. Our study using elastic models derived from real log measurements shows that SREIP has better capability for lithology and fluid discrimination than SREIS and conventional S-wave elastic impedance （SEI）. We evaluate the SREIP feasibility using 25 groups of samples from Castagna and Smith （1994）. Each sample group is constructed by using shale, brine-sand, and gas-sand. Theoretical evaluation also indicates that SRE1P at large incident angles is more sensitive to fluid than conventional fluid indicators. Real seismic data application also shows that SRE1P at large angles calculated using P-wave and S-wave impedance can efficiently characterize tight gas-sand.

关键词： S-wave impedance ray parameter lithology identification fluid indicator

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A Study on the Classification and Well-Logging identification of Eclogite in the Main Hole of Chinese Continental Scientific Drilling Project

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Journal of China University of Geosciences 2007年第4期18卷 357-365页

作者：景建恩魏文博金胜叶高峰邓明 Key Laboratory of Geo-detection (China University of Geosciences Beijing)Ministry of Education State Key Laboratory of Geological Processes and Mineral Resources China University of Geosciences School of Geophysics and Information Technology China University of Geosciences

Eclogite, one of the important lithologies in the main hole of the Chinese Continental Scientific Drilling （CCSD） Project, exists above the depth of 3 245 m and has distinctive responses of gamma-ray, compensating density and neutron well-logging, and so on. In this study, according to the diversities of minerals and chemical components and well-logging responses, edogites are classified from three aspects of origin, content of oxygen, and sub-mineral. We studied the logging identification method for eclogite sub-classes based on multi-element statistics and reconstructed 11 kinds of eclogite. As a result, eclogites can be divided into 6 types using well logs. In the light of this recognition, the eclogite in the main hole is divided into 20 sections, and the distribution characters of all sub-classes of eclogite are analyzed, which will provide important data for geological research of CCSD.

关键词： Chinese Continental Scientific Drilling (CCSD) ultrahigh pressure metamorphic rock eclogite well-logging lithology identification classification.

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Stratigraphic identification using real-time drilling data

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第9期16卷 3452-3464页

作者： Minglong You Zhikai Hong Fei Tan Hao Wen Zhanrong Zhang Jiahe Lv Faculty of Engineering China University of GeosciencesWuhan430074China China Railway Siyuan Survey and Design Group Co. Wuhan430063China

identification of stratigraphic interfaces and lithology is a key aspect in geological and geotechnical *** this study,a monitoring while-drilling system was developed,along with a corresponding data pre-processing *** method can handle invalid drilling data generated during manual *** correlation between various drilling parameters was analyzed,and a database of stratigraphic interfaces and key lithology identification based on the monitoring parameters was *** average drilling speed was found to be the most suitable parameter for stratigraphic and lithology identification,and when the average drilling speed varied over a wide range,it corresponded to a stratigraphic *** average drilling speeds in sandy mudstone and sandstone strata were in the ranges of 0.1e0.2 m/min and 0.2e0.29 m/min,*** results obtained using the present method were consistent with geotechnical survey *** proposed method can be used for realtime lithology identification and represents a novel approach for intelligent geotechnical surveying.

关键词： Monitoring while-drilling Drilling parameters Geotechnical stratigraphy lithology identification

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Applicability of deep neural networks for lithofacies classification from conventional well logs: An integrated approach

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Petroleum Research 2024年第3期9卷 393-408页

作者： Saud Qadir Khan Farzain Ud Din Kirmani Mari Petroleum Company Limited IslamabadPakistan Department of Petroleum and Gas Enginering NFC.Institute of Engineering&Technology MultanPakistan

Parametric understanding for specifying formation characteristics can be perceived through conven-tional ***,attributes of reservoir lithology are practiced for hydrocarbon *** logging is conventional approach which is applicable to predict lithology efficiently as compared to geophysical modeling and petrophysical analysis due to cost effectiveness and suitable interpretation ***,manual interpretation of lithology identification through well logging data requires domain expertise with an extended length of time for ***,in this study,Deep Neural Network(DNN)has been deployed to automate the lithology identification process from well logging data which would provide support by increasing time-effective for monitoring *** model has been developed for predicting formation lithology leading to the optimization of the model through the thorough evaluation of the best parameters and hyperparameters including the number of neurons,number of layers,optimizer,learning rate,dropout values,and activation *** of the model is examined by utilizing different evaluation metrics through the division of the dataset into the subdomains of training,validation and ***,an attempt is contributed to remove interception for formation lithology prediction while addressing the imbalanced nature of the associated dataset as well in the training process using class *** is assessed that accuracy is not a true and only reliable metric to evaluate the lithology classification *** model with class weight recognizes all the classes but has low accuracy as well as a low F1-score while LSTM based model has high accuracyas well as a high F1-score.

关键词： lithology identification Deep learning LSTM Imbalanced dataset Conventional well logs

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