Research On Evaluation Method Of Potential Sensitivity Of Oil And Gas Layer Based On Machine Learning

During oilfield development,oil drilling operations may cause oil and gas layer damage such as increased oil and gas flow resistance and decreased oil layer permeability.The oil and gas layer damage is mainly caused by five sensitive reactions between the oil and gas layer and foreign fluids.Caused by clogging the pores of the oil and gas layer.In order to reduce the damage to the oil and gas layers,it is necessary to evaluate the potential sensitivity of the oil and gas layers reasonably.However,the traditional method for evaluating the potential sensitivity of oil and gas layers is mainly based on the oil and gas layer sensitivity flow experiment.It has been a long time,and in some cases,the evaluation of complex oil and gas layer conditions is not ideal.Therefore,it is particularly important to explore efficient and reliable oil and gas layer sensitivity evaluation methods.This article is based on this to carry out research.Aiming at the problems existing in the oil and gas layer sensitivity flow experiment,this paper mainly proposes a machine learning-based oil and gas layer potential sensitivity evaluation method to realize rapid and efficient evaluation of oil and gas layer potential sensitivity.The main work includes:First,by analyzing and studying the principles and characteristics of the machine learning process,decision tree algorithm,and BP neural network algorithm,combined with the characteristics of the decision tree algorithm and neural network algorithm,a potential sensitivity evaluation method for oil and gas layers based on machine learning is proposed.The method mainly includes two parts of optimization: one is a feature selection method based on improved information gain.This method mainly uses the influence formed by the feature information gain and category distribution as the evaluation criteria,selects the optimal feature subset,and is based on traditional information.The feature selection method of gain considers more situations than others;the second is the neural network structure and weight initialization method based on the decision tree.This method mainly uses the processing power of the decision tree algorithm to determine the initial structure and initial value of the BP neural network Weights.Compared with the traditional BP neural network algorithm,this algorithm improves the rationality of the network structure and reduces the randomness of the initial weight of the network,which is conducive to generating an optimal BP neural network model.Secondly,through the analysis of various parameters of the oil and gas layer,the five factors that affect the sensitivity are selected and combined with the sensitivity degree evaluated by the oil and gas layer sensitivity flow experiment as sample data.Using the evaluation method proposed in this paper and the evaluation method based on traditional BP neural network to evaluate the five sensitivities,the experimental results show that the evaluation method proposed in this paper is superior to the evaluation method based on traditional BP neural network in accuracy and time complexity.Has certain advantages and effectiveness.Finally,develop visual machine learning-based oil and gas layer sensitivity evaluation software to realize efficient and fast evaluation of oil and gas layer potential sensitivity,which is convenient to use the method proposed in the subsequent research and provide reference for oil field development and oil and gas layer protection in accordance with.