A New Model for Predicting Minimum Miscibility Pressure (MMP) in Reservoir-Oil/Injection Gas Mixtures Using Adaptive Neuro Fuzzy Inference System

M. A. Ayoub, Mysara Eissa Mohyaldinn, Alexy Manalo, Anas M. Hassan, Quosay A. Ahmed

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

One of the critical concerns in determining the effectiveness of Enhanced Oil Recovery (EOR) is the understanding and evaluation of the Minimum Miscibility Pressure (MMP). Minimum miscibility pressure is the lowest possible pressure required to attain the mixing of injected fluid and the hydrocarbons in the reservoir into one phase. It is believed that optimum recovery and better sweep efficiency could only be achieved by reaching this minimum pressure. MMP determination, however usually depends on reservoir condition, reservoir fluid composition, and injected gas properties. The reservoir fluid composition could be represented by the Molecular weight C7+. However, Reservoir condition is represented by a reservoir temperature that affects MMP response. Selection of hydrocarbon gasses as the injection fluids is represented by the injected gas composition (Mole fraction C2–C6, and Mole fraction C1). Determination of the minimum pressure could be either through experimental or empirical approaches. The objective of this study is to provide an empirical correlation to estimate the MMP by using Adaptive Neuro-Fuzzy Inference System (ANFIS). To develop the model, a code is generated under MATLAB environment. A total of 177 data points have been used in training the proposed model while 98 data sets have been used for testing the model performance. The proposed ANFIS correlation is then being compared with other previously published correlations. The best model currently used by industry has scored an average absolute percent error (AAPE) equivalent to 15% while the proposed ANFIS model managed to score 4.12%. By using the new ANFIS model, the study was able to produce a reliable and accurate correlation for estimating Minimum Miscibility Pressure as compared to other previously tested correlations.

Original languageEnglish
Title of host publicationAdvances in Material Sciences and Engineering, ICMMPE 2018
EditorsMokhtar Awang, Seyed Sattar Emamian, Farazila Yusof
PublisherSpringer Science and Business Media Deutschland GmbH
Pages527-545
Number of pages19
ISBN (Print)9789811382963
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event4th International Conference on Mechanical, Manufacturing and Plant Engineering, ICMMPE 2018 - Melaka, Malaysia
Duration: Nov 14 2018Nov 15 2018

Publication series

NameLecture Notes in Mechanical Engineering
ISSN (Print)2195-4356
ISSN (Electronic)2195-4364

Conference

Conference4th International Conference on Mechanical, Manufacturing and Plant Engineering, ICMMPE 2018
Country/TerritoryMalaysia
CityMelaka
Period11/14/1811/15/18

Keywords

  • Adaptive Neuro-Fuzzy inference system
  • Artificial intelligence
  • Minimum miscibility pressure
  • Trend analysis

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Mechanical Engineering
  • Fluid Flow and Transfer Processes

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