Optimized Application of CGA-SVM in Tight Reservoir Horizontal Well Production Prediction

Horizontal well production prediction is crucial for the efficient development of tight reservoirs. However, owing to the complexity of the parameters affecting horizontal well production, improving the prediction accuracy has always been the unremitting goal pursued by the oil and gas industry. Limited by the number of parameters, the traditional linear fitting method has low computational efficiency and a large error, which brings difficulties to horizontal well production prediction. In this paper, chaotic genetic algorithm is used to optimize the traditional support vector machine, and the problems of slow convergence and local convergence are solved by chaotic genetic algorithm, and an improved support vector machine horizontal well production prediction method is established. At the same time, on the basis of the previous data processing, the fuzzy set classification method is used to build the model, and the learning model is more close to different types of well production, which enhances the applicability of the model in the field practice. Compared with traditional support vector machine, BP neural network, KNN and naive Bayes, the improved support vector machine has a higher prediction accuracy, and the average error is only 2.7%. The results show that the improved support vector machine method has high accuracy in the prediction of small sample data, and the method can be widely used in the production prediction of horizontal Wells in tight reservoirs, providing a reference for the efficient development of tight reservoirs.