Volve Oil Field S-Wave Log Data Prediction Using GBR and MLPR

Authors

  • Amany G. Fadhil Department of Mathematics, College of Sciences, University of Basrah, Basrah, Iraq https://orcid.org/0009-0000-3309-7601
  • Hana M. Ali Department of Mathematics, College of Sciences, University of Basrah, Basrah, Iraq
  • Zainab A. Khalaf Department of Mathematics, College of Sciences, University of Basrah, Basrah, Iraq https://orcid.org/0000-0002-8964-0113
  • Musa Ahmed Department of Petroleum Engineering, College of Engineering, University of Houston, Texas, USA https://orcid.org/0000-0002-0657-4705
  • Semaa H. Ahmed Department of Petroleum Engineering, College of Engineering, University of Houston, Texas, USA https://orcid.org/0000-0003-2137-2084

DOI:

https://doi.org/10.24996/ijs.2024.65.4.40

Keywords:

Artificial Neural Networks, Decision Trees, Gradient Boosting, hierarchical clustering, shear wave sonic

Abstract

     The shear wave sonic (S-wave) log data is essential for identifying the reservoir's geomechanical properties, which is an important factor for the drilling, completion, and optimization processes, where obtaining S-wave requires capital investment and reduces the cost. In this paper, we used the multi-layer perceptron (MLPR) and the gradient boosting regression (GBR) to predict the missing S-Wave log data for the Volve Oil Field in the North Sea. Prescriptive and predictive analysis were carried out in series to achieve a high blind data accuracy rate of 0.943 and 0.982 for the multi-layer perceptron regression and the gradient boosting regression, respectively. It was observed that the gradient-boosting algorithm achieved higher accuracy than the MLPR algorithm for the limited dataset. It was also found that hierarchical clustering can reveal information regarding the feature's importance similar to the relevant AI tools, which makes hierarchical clustering a faster tool in eliminating the nonimportant inputs from the dataset while the use of artificial intelligence tools showed a significant effect in predicting the missing values of the sonic wave log and the neutron porosity log in an efficient way by selecting the relevant important features.

Downloads

Published

2024-04-30

Issue

Section

Computer Science

How to Cite

Volve Oil Field S-Wave Log Data Prediction Using GBR and MLPR. (2024). Iraqi Journal of Science, 65(4), 2264-2274. https://doi.org/10.24996/ijs.2024.65.4.40

Similar Articles

1-10 of 476

You may also start an advanced similarity search for this article.