Harnessing the Power of Type-2 Fuzzy Logic System in the Prediction of Reservoir Properties
Sunday Olusanya Olatunji, Ali Selamat, Abdur Raheem Abdul Azeez · 2015
Abstract The unique power of type-2 fuzzy logic system is harnessed in this work by way of using it to improve the prediction accuracy of permeability and PVT properties in a hybrid intelligent model setting. In this proposed setup, hybridization of type-2 FLS (T2) and sensitivity based linear learning method (SBLLM) is presented and have been shown to considerably achieved improved performance over the constituent models, particularly the SBLLM. SBLLM, as a learning tool, have gained popularity due to its unique characteristics and performance. However, the generalization capability of SBLLM and other neural network based solutions often depend, to a large extent on the characteristics of the dataset, particularly on whether uncertainty is present in the dataset or not. The celebrated unique ability of type-2 fuzzy logic system in modeling uncertainties cannot be overemphasized. In this work, a hybrid system through the combination of type-2 fuzzy logic systems (type-2 FLS) and SBLLM is established, and then use to predict both permeability and PVT properties, which are very germane to the field of reservoir engineering and management; type-2 FLS has been chosen to be a precursor to SBLLM in order to harness the power of type-2 FLS thereby facilitating a means to better handle uncertainties existing in datasets. The dataset first pass through the type-2 FLS for possible uncertainty handling and prediction and then the output from the type-2 FLS is then passed to the SBLLM for its training and testing and then final prediction is done using the unseen testing dataset. Simulations have been carried out, using the built hybrid model, on different industrial datasets, for permeability and PVT properties separately. Results from empirical studies show that the proposed enhanced hybrid system performed better than each of the constituent parts, though the improvement made over that of SBLLM performance is higher compared to that of type-2 FLS, possibly because type-2 FLS is originally adept at modeling uncertainties. Thus it has been established here the huge benefit that could accrue from properly harnessing the power of type-2 fuzzy logic system.