Prediction of penetration rate by coupled simulated annealing-least square support vector machine (CSA_LSSVM) learning in a hydrocarbon formation based on drilling parameters

Abstract Field information analysis is the main element of reducing costs and improving drilling operations. Therefore, the development of field data analysis tools is one of the ways to improve drilling operations. This paper uses mathematical programming and optimization-based methods to present and review learning models for data classification. A comprehensive multi-objective optimization model is proposed by extracting commonalities and the same philosophy of some of the most popular mathematical optimization models in the last few years. The geometric representation of the model will be to make it easier to understand the characteristics of the proposed model. Then it is shown that a large number of models studied in the past and present are subsets, and exceptional cases of this proposed comprehensive model and how to convert the proposed comprehensive model to these methods will be examined. This seeks to bridge the gap between new multi-objective programming models and the powerful and improved CSA-LSSVM methods presented for classification in data mining and to generalize studies to improve each of these methods.