Published February 25, 2024 | Version v1
Publication Open

Gas- Oil Ratio Prediction Using Machine Learning Procedures for Niger Delta Region

Description

The laboratory measurement of Gas-Oil Ratio (GOR) is highly expensive and time consuming, hence the use of predictive
models like empirical correlations, equation of state and artificial intelligent tools. The solution gas–oil ratio (GOR) is the
quantity of gas dissolved at reservoir pressures in reservoir fluids. This study adopted two machine learning procedures of
Artificial Neural Network (ANN) and Support Vector Machine (SVM) to predict GOR. A total number of 852 data set was
obtained from PVT report from Niger-Delta, out of which, 70% (596) were used to train the models, 15% (127) for testing and
15% (127) for validation. Quantitative and qualitativeanalysis were carried out to compare the performance and reliability of the
new developed machining learning models with some selected empirical correlations. The result revealed that the Artificial
Neural Network performed better than the Support Vector Machine (SVM) as well as some common selected GOR
correlations.ANN performed better than other evaluated tool with the best rank of 0.139, highest correlation coefficient of 0.98,
Mean Absolute Error (Ea
) of 0.41, with a better performance plot, followed by Support Vector Machine model with correlation
coefficient of 0.95, Mean Absolute Error (Ea) of 0.163 and the rank of 0.1616. Obomanu and Okpobiri (1947) performed better
than other evaluated empirical correlations with the Rank of 0.1751 and correlation coefficient 0.95.This study recommends
Obomanu and Okpobiri (1947) correlation to be used to predict GOR for Niger Delta region in absence of this new intelligent
tool developed in this research.The new developed Artificial Neural Network model can potentially replace the empirical
models for gas-oil ratio predictions for Niger Delta region for quick predictions and higher accuracy.

Files

IJSRED-V7I1P82.pdf

Files (523.4 kB)

Name Size Download all
md5:a799765372caf0801ff0cf89027bef11
523.4 kB Preview Download