TY - GEN
T1 - Modeling PVT properties of crude oil systems based on type-2 fuzzy logic approach and sensitivity based linear learning method
AU - Selamat, Ali
AU - Olatunji, S. O.
AU - Abdul Raheem, Abdul Azeez
PY - 2012
Y1 - 2012
N2 - In this paper, we studies on a prediction model of Pressure-Volume- Temperature (PVT) properties of crude oil systems using a hybrid type-2 fuzzy logic system (type-2 FLS) and sensitivity based linear learning method (SBLLM). The PVT properties are very important in the reservoir engineering computations whereby an accurate determination of PVT properties is important in the subsequent development of an oil field. In the formulation used, for the type-2 FLS the value of a membership function corresponding to a particular PVT properties value is no longer a crisp value; rather, it is associated with a range of values that can be characterized by a function that reflects the level of uncertainty, while in the case of SBBLM, the sensitivity analysis coupled with a linear training algorithm by human subject selections for each of the two layers is employed which ensures that the learning curve stabilizes soon and behave homogenously throughout the entire process operation based on the collective intelligence algorithms. Results indicated that type-2 FLS had better performance for the case of dataset with large data points (782-dataset) while SBLLM performed better for the small dataset (160-dataset).
AB - In this paper, we studies on a prediction model of Pressure-Volume- Temperature (PVT) properties of crude oil systems using a hybrid type-2 fuzzy logic system (type-2 FLS) and sensitivity based linear learning method (SBLLM). The PVT properties are very important in the reservoir engineering computations whereby an accurate determination of PVT properties is important in the subsequent development of an oil field. In the formulation used, for the type-2 FLS the value of a membership function corresponding to a particular PVT properties value is no longer a crisp value; rather, it is associated with a range of values that can be characterized by a function that reflects the level of uncertainty, while in the case of SBBLM, the sensitivity analysis coupled with a linear training algorithm by human subject selections for each of the two layers is employed which ensures that the learning curve stabilizes soon and behave homogenously throughout the entire process operation based on the collective intelligence algorithms. Results indicated that type-2 FLS had better performance for the case of dataset with large data points (782-dataset) while SBLLM performed better for the small dataset (160-dataset).
KW - Bubblepoint pressure
KW - Formation volume factor
KW - PVT properties
KW - Sensitivity based linear learning method (SBLLM)
KW - Type-2 fuzzy logic system
UR - https://www.scopus.com/pages/publications/84870913143
U2 - 10.1007/978-3-642-34630-9_15
DO - 10.1007/978-3-642-34630-9_15
M3 - Conference contribution
AN - SCOPUS:84870913143
SN - 9783642346293
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 145
EP - 155
BT - Computational Collective Intelligence
T2 - 4th International Conference on Computational Collective Intelligence, ICCCI 2012
Y2 - 28 November 2012 through 30 November 2012
ER -