## 1 Introduction  
Cementing is an important and indispensable part of drilling and completion operations. The purpose of cementing is to protect and support the casing in oil and gas wells and to seal off oil, gas, and water formations. The quality of cementing has an important impact on the production of oil and gas wells. If the quality of cementing is low, it may lead to a decline in oil and gas well production, shorten the service life, and even cause accidents. Therefore, improving the quality of cementing is a very important task in drilling engineering. By predicting the quality of cementing, it is possible to optimize measures to put new wells into production, improve the quality of cementing, and prolong the service life of oil, gas, and water wells. Therefore, many domestic and foreign experts are attempting to improve the quality of cementing [1, 2]. There are many factors affecting the quality of cementing, such as the geological environment, construction conditions, and material properties. The mutual constraints and uncertainties among these factors increase the difficulty of implementing traditional cementing quality analysis and even affect the analysis results. Therefore, it is very difficult to establish a cementing quality prediction model under multifactorial conditions. Scholars at home and abroad are using different methods to analyze the correlation between the main influencing factors and initially establish some cementing quality evaluation methods [3, 4]. However, there are some shortcomings in the accuracy and stability of these methods; thus, further research is necessary. [PERSON] et al. proposed a convolutional neural network (CNN) approach to evaluate the performance of neural networks in automatic interpretation and combined it with fuzzy systems [5]. [PERSON] et al. proposed an assisted cement log interpretation tool based on supervised ML, and the implemented tool, which can be used for cementing quality evaluation, allows the interpretation of logging results to be automated [6]. [PERSON] et al. proposed a multi-scale perceptual convolutional neural network with kernels of different sizes, which is suitable for recognizing logging variable density images and evaluating cementing quality [7]. [PERSON] et al. combined an experimental setup simulating several well conditions with machine learning as a diagnostic tool to demonstrate that support vectors are more suitable than other vectors for cementing quality evaluation [8]. [PERSON] et al. investigated a method of evaluating the quality of cementing with an acoustic quantitative model [9; 10]. [PERSON] et al. used gray relational theory for multifactorial statistics on cementing quality [11].  
An analysis shows that current cementing quality evaluation methods mainly rely on amplitude logging, whereas cementing quality prediction mainly relies on fuzzy evaluation theory and neural network theory to establish relevant models [12]. For the current situation, the existing cementing quality prediction methods do not satisfy the requirements of cementing design. In recent years, support vector regression (SVR) has proven to be a reliable machine learning algorithm that can overcome the many shortcomings of artificial neural networks; thus, it has been widely applied in many scientific and engineering fields [13; 14]. In this paper, a method for cementing quality prediction based on a genetic support vector regression machine is proposed. Based on the cementing historical data of the Shunbei block in the Northwest Oilfield, a cementing quality prediction model is established and optimized. This method has high prediction accuracy and can be used to predict the cementing quality in the Shunbei block.