A hybrid model through the fusion of type-2 fuzzy logic systems and sensitivity-based linear learning method for modeling PVT properties of crude oil systems

Abstract

Sensitivity based linear learning method (SBLLM) has recently been used as predictive tool due to its unique characteristics and performance, particularly its high stability and consistency during predictions. However, the generalization capability of SBLLM is sometimes limited depending on the nature of the dataset, particularly on whether uncertainty is present in the dataset or not. In order to reduce the effects of uncertainties in SBLLM prediction and improve its generalization ability, this paper proposes a hybrid system through the unique combination of type-2 fuzzy logic systems (type-2 FLS) and SBLLM; thereafter the hybrid system was used to model PVT properties of crude oil systems. In the proposed hybrid, the type-2 FLS is used to handle uncertainties in reservoir data so that the final output from the type-2 FLS is then passed to the SBLLM for training and then final prediction using testing dataset follows. Comparative studies have been carried out to compare the performance of the proposed T2-SBLLM hybrid system with each of the constituent type-2 FLS and SBLLM. Empirical results from simulation show that the proposed T2-SBLLM hybrid model greatly improved upon the performance of SBLLM.