Development of oil formation volume factor model using adaptive neuro-fuzzy inference systems ANFIS

Fahd Saeed Alakbari, Mysara Eissa Mohyaldinn, Mohammed Abdalla Ayoub, Ali Samer Muhsan, Ibnelwaleed Ali Hussein

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

5 Citations (Scopus)

Abstract

The oil formation volume factor is one of the main reservoir fluid properties that plays a crucial role in designing successful field development planning and oil and gas production optimization. The oil formation volume factor can be acquired from pressure-volume-temperature (PVT) laboratory experiments; nonetheless, these experiments' results are time-consuming and costly. Therefore, many studies used alternative methods, namely empirical correlations (using regression techniques) and machine learning to determine the formation volume factor. Unfortunately, the previous correlations and machine learning methods have some limitations, such as the lack of accuracy. Furthermore, most earlier models have not studied the relationships between the inputs and outputs to show the proper physical behaviors. Consequently, this study comes to develop a model to predict the oil formation volume factor at the bubble point (Bo) using an adaptive neuro-fuzzy inference system (ANFIS). The ANFIS model was built based on 924 data sets collected from published sources. The ANFIS model and previous 28 models were validated and compared using the trend analysis and statistical error analysis, namely average absolute percent relative error (AAPRE) and correlation coefficient (R). The trend analysis study has shown that the ANFIS model and some previous models follow the correct trend analysis. The ANFIS model is the first rank model and has the lowest AAPRE of 0.71 and the highest (R) of 0.9973. The ANFIS model also has the lowest average percent relative error (APRE), root mean square error (RMSE), and standard deviation (SD) of -0.09, 1.01, 0.0075, respectively.

Original languageEnglish
Title of host publicationSociety of Petroleum Engineers - SPE/IATMI Asia Pacific Oil and Gas Conference and Exhibition 2021, APOG 2021
PublisherSociety of Petroleum Engineers
ISBN (Electronic)9781613997833
DOIs
Publication statusPublished - 2021
Externally publishedYes
EventSPE/IATMI Asia Pacific Oil and Gas Conference and Exhibition 2021, APOG 2021 - Virtual, Online
Duration: Oct 12 2021Oct 14 2021

Publication series

NameSociety of Petroleum Engineers - SPE/IATMI Asia Pacific Oil and Gas Conference and Exhibition 2021, APOG 2021

Conference

ConferenceSPE/IATMI Asia Pacific Oil and Gas Conference and Exhibition 2021, APOG 2021
CityVirtual, Online
Period10/12/2110/14/21

Keywords

  • Adaptive neuro-fuzzy inference system
  • ANFIS
  • Oil formation volume factor
  • PVT
  • Reservoir fluid properties

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Fuel Technology

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