TY  - CONF
N1  - cited By 6; Conference of 2022 International Conference on Data Analytics for Business and Industry, ICDABI 2022 ; Conference Date: 25 October 2022 Through 26 October 2022; Conference Code:186761
N2  - Calculations for the production of petroleum, modeling, and reservoir characterization primarily rely on reservoir fluid parameters including the 'bubble point pressure' (Pb), 'formation volume factor' (βo), 'solution gas oil ratio' (Rs), and viscosity. This paper aims to predict the Yemeni crude oil reservoir fluid parameters using different fuzzy approaches. The fuzzy model was optimized using eight different types of input membership functions, ten cluster radius values, linear and constant output membership function in order to obtain the best fuzzy logic (FL) parameters. Field data was used to build the proposed model, such as temperature and the specific gravity of gas and oil. The data was gathered from a variety of wells in well-known Yemeni reservoirs. Various evolution criteria were employed using statistical error analysis, including an 'average absolute percent relative error' (AAPRE), 'standard deviation' (SD), and the 'correlation coefficient' (R2), to assess the effectiveness and correctness of the suggested FL models. The statistical analysis showed that the gaussmf function was the best input membership function, while the linear function was the best output function. The ideal cluster radius for the radius was 0.04. Correlation coefficients of 0.993, 0.995, and 0.990 were obtained by the best fuzzy logic models for 'bubble point pressure' (Pb), 'formation volume factor' (βo), and 'solution gas oil ratio' (Rs), respectively. © 2022 IEEE.
SP  - 761
TI  - Modeling Yemeni Crude Oil Reservoir Fluid Properties Using Different Fuzzy Methods
ID  - scholars17224
KW  - Computer circuits; Crude oil; Fuzzy logic; Petroleum reservoir engineering; Petroleum reservoirs
KW  -  Bubble point pressure; Crude oil reservoirs; Fluid parameters; Fluid property; Formation volume factors; Memberships function; Modeling; Reservoir fluid; Reservoir fluid property; Yemeni crude oil
KW  -  Membership functions
AV  - none
A1  - Baarimah, S.O.
A1  - Al-Gathe, A.A.
A1  - Baarimah, A.O.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149309787&doi=10.1109%2fICDABI56818.2022.10041519&partnerID=40&md5=fa78950bd3b89b350039bc1210b09d4c
EP  - 765
Y1  - 2022///
PB  - Institute of Electrical and Electronics Engineers Inc.
SN  - 9781665490580
ER  -