Evaluation of Different Artificial Intelligent Models to Predict Reservoir Formation Water Density
| Autor: | Alireza Bahadori, Saeid Naseri, Tomoaki Kashiwao, Afshin Tatar, Moonyong Lee, Meysam Bahadori, Jake Rozyn |
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| Rok vydání: | 2015 |
| Předmět: |
geography
geography.geographical_feature_category Petroleum engineering General Chemical Engineering Natural water Energy Engineering and Power Technology Aquifer General Chemistry Water flooding Geotechnical Engineering and Engineering Geology Salinity Fuel Technology Brining Formation water Reservoir pressure Environmental science Geothermal gradient |
| Zdroj: | Petroleum Science and Technology. 33:1749-1756 |
| ISSN: | 1532-2459 1091-6466 |
| Popis: | Nearly all hydrocarbon reservoirs are bounded by water-saturated rocks, namely aquifers. In addition to natural water drive, there is an artificial water drive mechanism in which water is injected into formation to intensify the reservoir pressure. This method, employed to induce the hydrocarbon production, is called water flooding. Several laboratory researches have shown that oil recovery can be heightened by making some alterations to injected brine salinity through water flooding. Accordingly, acquiring exact information about the PVT characteristics of brine is necessary. Density is a property of great importance as it is employed in various physical, chemical, geothermal, and geochemical aspects. The authors aimed to develop a dependable intelligent method to accurately predict the brine density at elevated temperatures and pressures. MLP and GA-RBF models were utilized in this study. The results showed that the proposed model is capable of accurately predicting the brine density at elevated pressur... |
| Databáze: | OpenAIRE |
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