|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|1754640||1522804||2016||10 صفحه PDF||سفارش دهید||دانلود رایگان|
• We present a new approach for estimating both aspects of operation and economics including RF and NPV.
• A LSSVM model optimized with CSA optimization tool is proposed for this purpose.
• There is excellent agreement between the results of CSA–LSSVM and the measured data of RF and NPV.
• To check whether the CSA–LSSVM model is correct and valid, Leverage approach is utilized.
• It is found that almost all of the experimental data seem to be reliable.
The combination of surfactant and polymer in injecting water will improve the oil recovery during a water flood. The surfactant–polymer (SP) flooding would be more effective if economic policies are considered in addition to technical issues. The present communication introduces two reliable models for the performance evaluation of surfactant–polymer flooding in terms of both technical and economical approaches. To this end, a promising methodology called least square support vector machine (LSSVM) is applied for the accurate determining both recovery factor (RF) and net present value (NPV) related to SP flooding. The results obtained in this study reveal that there is an acceptable agreement between the data estimated by LSSVM approach and the actual data of RF and NPV. Moreover, to perform a comprehensive modelling to predict RF and NPV properly, an analysis is conducted on the different assignments of data for training, validation and testing phases. The results display that the value/percentage of data assigned for training set must be balanced and reasonable to avoid over-fitting problem, and also achieve an accurate and tested prediction. Finally, in order to show the importance degree of each input parameter on RF and NPV, a sensitivity analysis is conducted in this study. The results demonstrate the positive and negative impacts of those variables on both RF and NPV.
Journal: Journal of Petroleum Science and Engineering - Volume 137, January 2016, Pages 87–96