Determination of the Gas-Oil Ratio below the Bubble Point Pressure Using the Adaptive Neuro-Fuzzy Inference System (ANFIS)

Mohammed Abdalla Ayoub Mohammed, Fahd Saeed Alakbari, Clarence Prebla Nathan, Mysara Eissa Mohyaldinn

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

Determining the solution gas-oil ratio (Rs) below the bubble point is a vital requirement that aids in multiple production engineering and reservoir analysis issues. Currently, there are some models available for the determination of the solution gas-oil ratio under the bubble point. However, they still may prove unreliable due to the applied assumptions and their specification to operate only under a particular range of data. In this study, the neuro-fuzzy, i.e., the adaptive neuro-fuzzy inference system (ANFIS) approach, is utilized to develop an accurate and dependable model for determining the Rs below the bubble point pressure. A total of 376 pressure-volume-temperature datasets from Sudanese oil fields were used to establish the proposed ANFIS model. The trend analysis was applied to affirm the proper relationships between the inputs and outputs. Furthermore, using different statistical error analyses, the developed model was benchmarked against widely used empirical methods to evaluate the proposed method's performance in predicting the Rs at pressures below the bubble point. The proposed ANFIS model performs with an average absolute percent relative error of 10.60% and a correlation coefficient of 99.04%, surpassing the previously studied correlations.

Original languageEnglish
Pages (from-to)19735-19742
Number of pages8
JournalACS Omega
Volume7
Issue number23
DOIs
Publication statusPublished - Jun 14 2022
Externally publishedYes

ASJC Scopus subject areas

  • General Chemistry
  • General Chemical Engineering

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