Electrical and Computer Engineering Department-College of Engineering-Sultan Qaboos University
Neural Network-Based Estimation of Oil Well Flowing Bottom-hole
Pressure in Oman Fields
Ahmed Khalifa Khamis Al-Hinai
Abstract
The flowing bottom-hole pressure (FBHP) of an oil well is important information for reservoir engineers
and production technologies. It is an essential parameter to enable well production forecasting, production
monitoring and well artificial lift system optimization. Therefore, the installation of down-hole gauges in
oil wells has become a common practice in the oil petroleum industry; especially in wells lifted with
electrical submersible pumps. However, these down-hole gauges require continuous maintenance and
calibration. Also, due to the harsh down-hole environment there is a high risk they will fail and will be
expensive to repair or replace. In addition, intervening a well from time to time to measure the FBHP is an
expensive task and associated with production risk and interruption. For these reasons the motivation of
the estimation of the FBHP has come out.
Estimating the FBHP of wells with multiphase flow is a challenging and very complex problem.
Experience has shown that empirical correlations and mechanistic models failed to provide a satisfactory
and reliable tool for estimating pressure drop in multiphase flowing wells. Large errors are usually
associated with these models and correlations. The recent development and success of applying artificial
neural networks (ANN) to solve various difficult and complex engineering problems has drawn the
attention to its potential applications in the petroleum industry. With regard to this field, few researchers
applied ANN techniques to resolve some problems associated with multiphase problems including
pressure drop estimations.
The objective of this study is to develop an ANN model to estimate the FBHP in vertical oil wells by using
measured data from oil fields of Oman. This is achieved by designing and developing a multilayer
perceptrons feedforward neural network (FFNN) with back-propagation algorithm. The selection of the
best number of hidden layers is done through trial and performance assessment. Similarly, the best number
of neurons in each hidden layer is selected. Also, a radial basis function neural network model is
developed and compared to the single and two hidden layers models performance. The best neural
network model performance is also compared against an empirical model developed to calculate the FBHP
using available input data. The software package used is the MATLAB Neural Networks Toolbox 2009
edition. Field data is used to train and test the model. Field data has been gathered from pumped oil wells
from Oman oil fields. Model inputs data were selected from the list of available surface well
measurements. Data is preprocessed prior to training and testing processes. The model performance
evaluation conducted by means of testing the model against actual well down-hole measurements data
from the same oil fields. These testing data have not been used in the model training. Standard statistical
analyses preformed on the results to evaluate the models estimation accuracy such as; root mean square
error, standard deviation of error, minimum/maximum/average absolute relative error and correlation
coefficient. These statistical analyses have shown that the FFNN has a very strong ability in estimating the
FBHP compared to the empirical formula.