000			04517namaa22004211i 4500
003			OSt
005			20240622101504.0
008			240601s2023 |||a|||f |m|| 000 0 eng d
040			_aEG-GICUC _beng _cEG-GICUC _dEG-GICUC _erda
041	0		_aeng _beng _dara
049			_aDeposit
082	0	4	_a622.18
092			_a622.18 _221
097			_aM.Sc
099			_aCai01 13 12 M.Sc 2023 Mo.D
100	0		_aMohamed Hamdy Mohamed Ibrahim El-Sersy, _epreparation.
245	1	0	_aDevelopment of six machine learning models for pump intake pressure calculations in esp wells / _cBy Mohamed Hamdy Mohamed Ibrahim El-Sersy; Under the Supervision of Prof. Dr. Mohamed Helmy Sayyouh, Prof. Dr. Ahmed Hamdy El-Banbi, Prof. Dr. Mahmoud Abu El Ela
246	1	5	_aتطوير ست نماذج للتعلم الآلي للتنبؤ بضغط سحب المضخة في الابار التي تستخدم طرق الرفع بالمضخات الغاطسة /
264		0	_c2023.
300			_a117 pages : _billustrations ; _c30 cm. + _eCD.
336			_atext _2rda content
337			_aUnmediated _2rdamedia
338			_avolume _2rdacarrier
502			_aThesis (M.Sc.)-Cairo University, 2023.
504			_aBibliography: pages 66-69.
520			_aThe pump intake pressure (PIP) is a crucial parameter for optimizing the performance improvement of pumped oil wells. Recently, Electrical submersible pump (ESP) systems have usually adopted downhole gauges to evaluate PIP. In this study, six new models (Linear Regression (LR) model, Polynomial Regression (PR) model, Decision Tree (DT) model, Random Forest (RF) model, Support Vector Machine (SVM) model, and finally, Artificial Neural Network (ANN) model) were developed based on machine learning algorithms and Artificial Intelligence (AI) techniques to predict pump intake pressure (PIP) from readily available data, and field measurements in ESP pumped wells. A database of 2352 field data points was collected from 105 ESP wells to develop the six new models. The data was split into 60% (1411 data points) for training and 40% (941 data points) for testing. The developed models rely on the following measurements as input parameters: wellhead pressure, total production rate, water cut, oil gravity, pump setting depth, the net liquid above the pump, tubing size, casing size, and casing pressure. The models' accuracies were compared against each other. Then, the developed models were compared against the accuracy of previous correlations and actual readings obtained from ESP downhole pressure sensors. The results indicate that the accuracy of the ANN model is significantly higher than that of the other developed machine learning models and the previously available correlations. Using the ANN model, the Average Mean Absolute Error (AMAE) comparing the calculated and the measured PIP is 11.93% and 12.33% for the training and testing data, respectively. These results demonstrate the strength of the developed model to predict the PIP with better accuracy and without the need for a downhole pressure sensor.
520			_aتم استنباط ستة نماذج جديدة وهي نموذج الانحدار الخطي ونموذج الانحدار متعدد الحدود ونموذج شجرة القرار ونموذج الغابة العشوائية ونموذج آلة المتجهات الداعم وأخيراً نموذج الشبكة العصبية الاصطناعية. تم تطوير هذه النماذج استنادًا إلى خوارزميات التعلم الآلي وتقنيات الذكاء الاصطناعي، للتنبؤ بضغط سحب المضخة من القياسات الحقلية المتاحة بسهولة وبيانات الآبار التي تستعمل المضخات الغاطسة الكهربائية
530			_aIssued also as CD
546			_aText in English and abstract in Arabic & English.
650		7	_aPetroleum Engineering _2qrmak
653		0	_aArtificial Intelligence _a ESP wells _aArtificial Neural Networks
700	0		_aMohamed Helmy Sayyouh _ethesis advisor.
700	0		_aAhmed Hamdy El Banbi _ethesis advisor.
700	0		_aMahmoud Abu El-Ela _ethesis advisor.
900			_b01-01-2023 _cMahmoud Abu El-Ela _cAhmed Hamdy El Banbi _cMohamed Helmy Sayyouh _dKhaled Ahmed Abdel Fattah _dKhaled Mohamed Mwafy _UCairo University _FFaculty of Engineering _DDepartment of Petroleum Engineering
905			_aNourhan _eHuda
942			_2ddc _cTH _e21 _n0
999			_c167248