Abstract
Sensitivity-based linear learning method (SBLLM) has recently been used as a predictive tool due to its unique characteristics and performance, particularly its high stability and consistency during predictions. However, the generalisation capability of SBLLM is sometimes limited depending on the nature of the dataset, particularly on whether uncertainty is present in the dataset or not. Since it made use of sensitivity analysis in relation to the data sets used, it is surely very prone to being affected by the nature of the dataset. In order to reduce the effects of uncertainties in SBLLM prediction and improve its generalisation ability, this paper proposes a hybrid system through the unique combination of type-2 fuzzy logic systems (type-2 FLSs) and SBLLM; thereafter the hybrid system was used to model PVT properties of crude oil systems. Type-2 FLS has been choosen in order to better handle uncertainties existing in datasets beyond the capability of type-1 fuzzy logic systems. In the proposed hybrid, the type-2 FLS is used to handle uncertainties in reservoir data so that the cleaned data from 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 newly 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 system has greatly improved upon the performance of SBLLM, while also maintaining a better performance above that of the type-2 FLS.
Highlights
Hybrid computational intelligence is any effective combination of intelligent techniques that performs superior or in a competitive way to simple standard intelligent techniques
The performance results from hybridized type2-sensitivity based linear learning method (SBLLM) model outperformed each of the constituting individual models, which is in line with the general establish fact, to date, that a hybrid scheme often performs better than the individual constituent parts
The hybrid has proven to be a better way to boost the performance of SBLLM as the results indicated that it has greatly improved upon the performance of SBLLM, up to 96.9% improvement in terms of standard deviation (SD), 8.6% improvement in terms of correlation coefficient (R2), and up to 95% improvement in terms of average absolute percent relative error (Ea)
Summary
Hybrid computational intelligence is any effective combination of intelligent techniques that performs superior or in a competitive way to simple standard intelligent techniques. In this paper, we propose a hybrid approach that will combine the unique attributes of type-FLS with those of sensitivity based linear learning method (SBLLM) by way of improving SBLLM performance in order to achieve better generalisation ability in all situations including uncertainty oriented environment. This paper investigate the feasibility of using type-2 FLS as a pre-cursor to improve the generalisation ability of SBLLM in the face of uncertainty during prediction, in a hybrid framework setting; we develop a new hybrid model based on type-2 FLS and SBLLM and use it for predicting PVT properties that include bubble point pressure (Pb) and oil formation volume factor (Bob) using different standard databases of four input parameters, namely, solution gas-oil ratio, reservoir temperature, oil gravity, and gas relative density.
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