2017_EJRNL_PP_SALAHELDIN_ELKATATNY_1.pdf
Terbatas  Suharsiyah
» Gedung UPT Perpustakaan
Terbatas  Suharsiyah
» Gedung UPT Perpustakaan
Oil formation volume factor (OFVF) is considered one of the main parameters required to characterize the
crude oil. OFVF is needed in reservoir simulation and prediction of the oil reservoir performance. Existing
correlations apply for specific oils and cannot be extended to other oil types. In addition, big errors were
obtained when we applied existing correlations to predict the OFVF. There is a massive need to have a
global OFVF correlation that can be used for different oils with less error.
The objective of this paper is to develop a new empirical correlation for oil formation volume factor
(OFVF) prediction using artificial intelligent techniques (AI) such as; artificial neural network (ANN),
adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM). For the first time we
changed the ANN model to a white box by extracting the weights and the biases from AI models and
form a new empirical equation for OFVF prediction. In this paper we present a new empirical correlation
extracted from ANN based on 760 experimental data points for different oils with different compositions.
The results obtained showed that the ANN model yielded the highest correlation coefficient (0.997)
and lowest average absolute error (less than 1%) for OFVF prediction as a function of the specific gravity
of gas, the dissolved gas to oil ratio, the oil specific gravity, and the temperature of the reservoir
compared with ANFIS and SVM. The developed empirical equation from the ANN model outperformed
the previous empirical correlations and AI models for OFVF prediction. It can be used to predict the OFVF
with a high accuracy