Intelligence Test Case Based-Approach for Crude Oil Prediction System

Ghallab, Senan A.; Badr, Nagwa L.; Tolba, Mohamed F.

doi:10.1007/978-3-319-11310-4_78

Cited by 2 publications

(1 citation statement)

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“…The regression model shows that the temperature and pressure memberships as petroleum parameters achieve the best predictable test cases of a petroleum prediction system, whereas, the other three memberships are neglected because just two memberships effect the test case results and the applied regression model produced higher values. Regression test importance represents results, re-testing and system efficiency (Ghallab et al 2015). In petroleum domains, re-run the predicted results and retesting the test case statues will ensure precision of the prediction systems.…”

Section: Regression Modelmentioning

confidence: 99%

Strictness Petroleum Prediction System Based on Fuzzy Model

Ghallab

Badr

Salem

et al. 2014

International Journal of Service Science, Management, Engineering, and Technology

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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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Section: Regression Modelmentioning

confidence: 99%