The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
The accurate prediction of crude oil output plays an important role in the deployment of oilfield development and ensuring stable production. Crude oil output forecast is the premise and the core project management system of the whole oil production, while crude oil output is a dynamic system affected by multivariate variables. To accurately predict crude oil output, this paper presents a method to...
The accurate prediction of crude oil output plays an important role in the development of oilfield planning. This paper proposes a least squares support vector machine model based on the optimization of particle swarm algorithm (PSO-LSSVM) to predict the crude oil output. Each pair of penalty factor and kernel function parameter was taken as a particle, which follows the optimal particle in the current...
Crude oil output prediction is an important basis for oil field development plan. How to accurately predict crude oil output based on sample data is a problem to be tackled. In order to improve prediction accuracy of crude oil output, a crude of oil forecasting method based on combining gray model and neural network is proposed in this paper. The method firstly uses the gray model to predict the crude...
Collaborative filtering algorithms, such as matrix factorization techniques, are recently gaining momentum due to their promising performance on recommender systems. However, most collaborative filtering algorithms suffer from data sparsity. Active learning algorithms are effective in reducing the sparsity problem for recommender systems by requesting users to give ratings to some items when they...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.