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IEEE Final Year Project Topic for CSE

Base Paper Title

Research and application of element logging intelligent identification model based on data mining

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IEEE Project Abstract

Underground strata are reflected in various information sources in petroleum exploration including well 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 GWO-SVM (Grey Wolf Optimization-Support Vector Machine) 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. Firstly, the data is normalized, and then the best 𝜁 and σ value 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 GWO-SVM, the model accuracy of SVM, PSO-SVM (Particle Swarm Optimization-Support Vector Machine) algorithm is lower 53% and 23%, respectively. 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 GWO-SVM in stratum dynamic identification is 93.5%. This model is implemented to support the logging system to improve application strength.

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