Research and Application of Element Logging Intelligent Identification Model Based on Data Mining

Abstract

Underground strata are reflected in various information sources in petroleum exploration, including good logging and drilling data. Real-time measurement parameters obtained from mud logging can provide data support for the early discovery of oil and gas resources and the prevention of safety accidents. It plays a forward-looking role in the drilling process. In this paper, we aim at the defection of fuzzy and random characteristics of the big data of drilling element parameters in the current drilling process. A new method named grey wolf optimization-support vector machine (GWO-SVM) is proposed by analyzing the relationship between logging data and formation to solve the serious problem of formation misjudgment. Using element content and Gamma-ray value, data mining is performed by a large number of real-time data obtained from the drilling site. The obtained information is used for comprehensive estimation and prediction of strata. First, the data is normalized, and then, the best ζ and σ values are found through the optimization of gray wolf algorithm, next the SVM training is carried out, finally, the formation prediction model is established, and the error analysis of the results was conducted. In the paper, the algorithm model is subsequently applied to three actual wells. The GWO-SVM model based on drilling data is used to predict the formation, and the error analysis showed that the error range of the GWO-SVM algorithm is within 10%. Compared with the GWO-SVM, the model accuracy of SVM, Particle Swarm Optimization-Support Vector Machine (PSO-SVM) algorithm is lower 53% and 23%, respectively. The GWO-SVM has higher robustness, reliability, and achieves faster convergence speed, stronger generalization effect, and improves the identification accuracy of elements for the formation. The average accuracy of the GWO-SVM in stratum dynamic identification is 93.5%. This model is implemented to support the logging system to improve application strength.INDEX TERMS Data mining, element logging, error analysis, gray wolf algorithm, support vector machine. I. INTRODUCTIONData mining includes 8 processes, such as data cleaning, data transformation, data mining process, pattern evaluation, and knowledge representation [1]. Data mining can acquire useful analytical information by the selection of appropriate analytical tools, using of statistical methods, rule reasoning, fuzzy sets, genetic algorithms and other methods to handle information. The four main data mining tasks include modeling prediction, association analysis, clustering analysis, anomaly detection. Among the classification methods used to predict discrete target variables, the nature-inspired optimizationThe associate editor coordinating the review of this manuscript and approving it for publication was Patrick Hung.

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