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Strictness Petroleum Prediction System Based on Fuzzy Model
Abstract
Petroleum exploration and production is an industry that provides researchers with multi-variant challenging “real world” properties. Recently, some petroleum soft computing techniques have gained a greater interest in prediction within the oil industry. This paper is interested in the analysis, classifying, mining and predictions, based on fuzzy as an intelligent system and an intelligent system called the Strictness Petroleum Prediction System (SPPS), predicted results and statues of crude oil wells and they are compared with other measurement petroleum values. The evaluation study applies test cases, regression models and time series forecasting of vague petroleum datasets to achieve more accurate results. A regression model was made to show the effect of re-testing the prediction processes of petroleum factors. Prediction in time series using a non-parametric functional technique is considered, based on data which was collected from different sources (Daqing oilfield in China and distinct oilfields in Yemen).
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