654 TM
Ir. Zuher Syihab, M.Sc., Ph.D.,
Prodi Teknik Perminyakan ITB
2014
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| No | Nomor Induk | Lokasi | Koleksi |
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| 1 | a2014202082 | Perpustakaan Teknik Perminyakan (Gd. Teknik Perminyakan Lt.2) |
| No Antrian | Tanggal Reservasi | User |
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| Materi Koleksi | : Tugas Akhir Thesis-ITB |
| Bahasa | : Indonesia |
| Subjek | : Enhanced Oil Reocvery |
| Kata Kunci | : ASP flooding, predictive model, neural networks |
| Keterangan | : Alkaline Surfacrant Polymer flooding is one of the most complicated yet promising EOR methods for the future. The ability to increase both microscopic and macroscopic displacement has made ASP flooding as one of the sendible solution to produce the residul oil after waterflood treatment. The current available screening is based on the field projects, eith still very few ASP flooding implementations in the field, the accuracy of the screening criteria become questionable. A predictive model is proposed to tackle this problem. The predictive model will be able to give rough estimation in a quick, simple and accurate way. Neural network as the artificial intelligence that is able to mimic the pattern and learn the ralation between input variables to determine the appropriate predictive output is chosen as the backbone of the predictive model. A frrdforward-backpropagation algorithm is chosen to build the neural networks predictive model. The datase that cover possible combinations of screening criteria for ASP flooding is used as the input parameter for the neural network model. The proposed predictive model has high accuracy compared with the established reservoir simulator. The error of the model is acceptable, 1.3% to give the oil recovery predictive and 6% to predict the incremental oil recovery compared to waterflood process. |
| URL | : Inggris |