Research On Intelligent Lithology Classification Method

The accurate classification of lithology is not only the premise of calculating geological parameters,but also the basis of reservoir characteristics research,reserve estimation and geological modeling.With the development of logging technology,the traditional lithology classification method has become more and more difficult to meet the needs of high efficiency and precision.With the rise of artificial intelligence and big data technology,machine learning technology has been gradually applied in the process of oil and gas field exploration and development.Aiming at the problems of automatic well log data storage and processing and intelligent formation lithology classification,this thesis uses machine learning algorithms to carry out systematic process of well log data preprocessing,and builds NLDIW-PSO-XGBoost model to realize intelligent formation lithology classification based on well log data.Finally,the formation intelligent lithology classification module is developed based on petroleum cloud platform.The main research contents and results are as follows:(1)A database for storing geological data is established,and a whole series of processes for well log data processing are proposed,including well location cluster analysis,well log data mining,visualization and outlier detection.The automation and intellectualization of well log data processing are preliminarily realized.(2)The gated recurrent neural network(GRU)is used to carry out the well log completion and generation.This method provides higher quality data sets for subsequent modeling by generating missing well logs and serves as a reference for real formation interpretation.(3)An improved particle swarm optimization algorithm using nonlinear decreasing inertia weight is proposed,and then the NLDIW-PSO-XGBoost model is established to identify the lithology.The prediction results of the model are compared with decision tree,support vector machine,multi-layer perceptron,random forest and gradient boosting tree model.The experimental results show that the optimized XGBoost model not only achieves the best effect in blind well test,but also shows strong generalization ability.(4)The cloud application module of intelligent lithology classification is developed based on the petroleum cloud platform.The module can independently define training parameters and visually display the prediction results of different models from the graphical user interface.Finally,a set of efficient process for intelligent lithology classification is formed.Based on the above results,this thesis can provide technical reserves for intelligent drilling,and has guiding significance for the design of automatic well log data processing and intelligent lithology classification method,which promotes the development of digitalization and intelligence in the field of formation interpretation.