TY  - BOOK
AU  - Mohamed Hamdy Mohamed Ibrahim El-Sersy,
AU  - Mohamed Helmy Sayyouh
AU  - Ahmed Hamdy El Banbi
AU  - Mahmoud Abu El-Ela
TI  - Development of six machine learning models for pump intake pressure calculations in esp wells
U1  - 622.18 
PY  - 2023///
KW  - Petroleum Engineering
KW  - qrmak
KW  - Artificial Intelligence
KW  -  ESP wells
KW  - Artificial Neural Networks
N1  - Thesis (M.Sc.)-Cairo University, 2023.; Bibliography: pages 66-69.; Issued also as CD
N2  - The 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; تم 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
ER  -