Development of Oil Formation Volume Factor Model using Adaptive Neuro-Fuzzy Inference Systems ANFIS

2021 ◽  
Author(s):  
Fahd Saeed Alakbari ◽  
Mysara Eissa Mohyaldinn ◽  
Mohammed Abdalla Ayoub ◽  
Ali Samer Muhsan ◽  
Ibnelwaleed Ali Hussein
Keyword(s):  
Machine Learning ◽  
Trend Analysis ◽  
Relative Error ◽  
Fuzzy Inference ◽  
Volume Factor ◽  
Anfis Model ◽  
Neuro Fuzzy ◽  
Oil Formation Volume Factor ◽  
Oil Formation ◽  
Percent Relative Error

Abstract The oil formation volume factor is one of the main reservoir fluid properties that plays a crucial role in designing successful field development planning and oil and gas production optimization. The oil formation volume factor can be acquired from pressure-volume-temperature (PVT) laboratory experiments; nonetheless, these experiments' results are time-consuming and costly. Therefore, many studies used alternative methods, namely empirical correlations (using regression techniques) and machine learning to determine the formation volume factor. Unfortunately, the previous correlations and machine learning methods have some limitations, such as the lack of accuracy. Furthermore, most earlier models have not studied the relationships between the inputs and outputs to show the proper physical behaviors. Consequently, this study comes to develop a model to predict the oil formation volume factor at the bubble point (Bo) using an adaptive neuro-fuzzy inference system (ANFIS). The ANFIS model was built based on 924 data sets collected from published sources. The ANFIS model and previous 28 models were validated and compared using the trend analysis and statistical error analysis, namely average absolute percent relative error (AAPRE) and correlation coefficient (R). The trend analysis study has shown that the ANFIS model and some previous models follow the correct trend analysis. The ANFIS model is the first rank model and has the lowest AAPRE of 0.71 and the highest (R) of 0.9973. The ANFIS model also has the lowest average percent relative error (APRE), root mean square error (RMSE), and standard deviation (SD) of -0.09, 1.01, 0.0075, respectively.

scholarly journals Classification of Gait Patterns in Patients with Neurodegenerative Disease Using Adaptive Neuro-Fuzzy Inference System

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Qiang Ye ◽  
Yi Xia ◽  
Zhiming Yao
Keyword(s):  
Motor Neurons ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Pso Algorithm ◽  
Anfis Model ◽  
Inference System ◽  
Gait Dynamics ◽  
Double Support ◽  
Adaptive Capabilities ◽  
Neuro Fuzzy

A common feature that is typical of the patients with neurodegenerative (ND) disease is the impairment of motor function, which can interrupt the pathway from cerebrum to the muscle and thus cause movement disorders. For patients with amyotrophic lateral sclerosis disease (ALS), the impairment is caused by the loss of motor neurons. While for patients with Parkinson’s disease (PD) and Huntington’s disease (HD), it is related to the basal ganglia dysfunction. Previously studies have demonstrated the usage of gait analysis in characterizing the ND patients for the purpose of disease management. However, most studies focus on extracting characteristic features that can differentiate ND gait from normal gait. Few studies have demonstrated the feasibility of modelling the nonlinear gait dynamics in characterizing the ND gait. Therefore, in this study, a novel approach based on an adaptive neuro-fuzzy inference system (ANFIS) is presented for identification of the gait of patients with ND disease. The proposed ANFIS model combines neural network adaptive capabilities and the fuzzy logic qualitative approach. Gait dynamics such as stride intervals, stance intervals, and double support intervals were used as the input variables to the model. The particle swarm optimization (PSO) algorithm was utilized to learn the parameters of the ANFIS model. The performance of the system was evaluated in terms of sensitivity, specificity, and accuracy using the leave-one-out cross-validation method. The competitive classification results on a dataset of 13 ALS patients, 15 PD patients, 20 HD patients, and 16 healthy control subjects indicated the effectiveness of our approach in representing the gait characteristics of ND patients.

scholarly journals Energy Consumption, Economic Growth, and CO2 Emissions in G20 Countries: Application of Adaptive Neuro-Fuzzy Inference System

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2771 ◽  
Author(s):  
Abbas Mardani ◽  
Dalia Streimikiene ◽  
Mehrbakhsh Nilashi ◽  
Daniel Arias Aranda ◽  
Nanthakumar Loganathan ◽  
...  
Keyword(s):  
Economic Growth ◽  
Energy Consumption ◽  
Co2 Emissions ◽  
Fuzzy Inference System ◽  
Global Economy ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy

Understanding the relationships among CO2 emissions, energy consumption, and economic growth helps nations to develop energy sources and formulate energy policies in order to enhance sustainable development. The present research is aimed at developing a novel efficient model for analyzing the relationships amongst the three aforementioned indicators in G20 countries using an adaptive neuro-fuzzy inference system (ANFIS) model in the period from 1962 to 2016. In this regard, the ANFIS model has been used with prediction models using real data to predict CO2 emissions based on two important input indicators, energy consumption and economic growth. This study made use of the fuzzy rules through ANFIS to generalize the relationships of the input and output indicators in order to make a prediction of CO2 emissions. The experimental findings on a real-world dataset of World Development Indicators (WDI) revealed that the proposed model efficiently predicted the CO2 emissions based on energy consumption and economic growth. The direction of the interrelationship is highly important from the economic and energy policy-making perspectives for this international forum, as G20 countries are primarily focused on the governance of the global economy.

scholarly journals Development of Machine Learning Models to Evaluate the Toughness of OPH Alloys

Materials ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6713
Author(s):  
Omid Khalaj ◽  
Moslem Ghobadi ◽  
Ehsan Saebnoori ◽  
Alireza Zarezadeh ◽  
Mohammadreza Shishesaz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Mechanical Alloying ◽  
Fuzzy Inference ◽  
Oxide Dispersion Strengthened ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Anfis Model ◽  
Inference Systems ◽  
The Impact

Oxide Precipitation-Hardened (OPH) alloys are a new generation of Oxide Dispersion-Strengthened (ODS) alloys recently developed by the authors. The mechanical properties of this group of alloys are significantly influenced by the chemical composition and appropriate heat treatment (HT). The main steps in producing OPH alloys consist of mechanical alloying (MA) and consolidation, followed by hot rolling. Toughness was obtained from standard tensile test results for different variants of OPH alloy to understand their mechanical properties. Three machine learning techniques were developed using experimental data to simulate different outcomes. The effectivity of the impact of each parameter on the toughness of OPH alloys is discussed. By using the experimental results performed by the authors, the composition of OPH alloys (Al, Mo, Fe, Cr, Ta, Y, and O), HT conditions, and mechanical alloying (MA) were used to train the models as inputs and toughness was set as the output. The results demonstrated that all three models are suitable for predicting the toughness of OPH alloys, and the models fulfilled all the desired requirements. However, several criteria validated the fact that the adaptive neuro-fuzzy inference systems (ANFIS) model results in better conditions and has a better ability to simulate. The mean square error (MSE) for artificial neural networks (ANN), ANFIS, and support vector regression (SVR) models was 459.22, 0.0418, and 651.68 respectively. After performing the sensitivity analysis (SA) an optimized ANFIS model was achieved with a MSE value of 0.003 and demonstrated that HT temperature is the most significant of these parameters, and this acts as a critical rule in training the data sets.

Development of an adaptive neuro-fuzzy inference system (ANFIS) model to predict sea surface temperature (SST)

2020 ◽  
Vol 49 (4) ◽  
pp. 354-373
Author(s):  
Semih Kale
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Mean Square Error ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Sea Surface ◽  
Mean Square ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy

Abstract An accurate estimation of the sea surface temperature (SST) is of great importance. Therefore, the objective of this work was to develop an adaptive neuro-fuzzy inference system (ANFIS) model to predict SST in the Çanakkale Strait. The observed monthly air temperature, evaporation and precipitation data from the Çanakkale meteorological observation station were used as input data. The Takagi–Sugeno fuzzy inference system was applied. The grid partition method (ANFIS-GP) and the subtractive clustering partitioning method (ANFIS-SC) were used with Gaussian membership functions to generate the fuzzy inference system. Six performance evaluation criteria were used to evaluate the developed SST prediction models, including mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), Nash-Sutcliffe efficiency (NSE) and correlation of determination (R2). The dataset was randomly divided into training and testing datasets for the machine learning process. Training data accounted for 75% of the dataset, while 25% of the dataset was allocated for testing in ANFIS. The hybrid algorithm was selected as a training algorithm for the ANFIS. Simulation results revealed that the ANFIS-SC4 model provided a higher correlation coefficient of 0.96 between the observed and predicted SST values. The results of this study suggest that the developed ANFIS model can be applied for predicting sea surface temperature around the world.

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