Development of a Fuzzy System Model for Candidate-well Selection for Hydraulic Fracturing in a Carbonate Reservoir

2012 ◽  
Author(s):  
Mansoor Zoveidavianpoor ◽  
Ariffin Samsuri ◽  
S.R. Shadizadeh
Keyword(s):  
Hydraulic Fracturing ◽  
Fuzzy System ◽  
Carbonate Reservoir ◽  
System Model ◽  
Selection For

A New Optimization Approach to Clustering Fuzzy Data for Type-2 Fuzzy System Modeling

2012 ◽  
pp. 499-508
Author(s):  
Mohammad Hossein Fazel Zarandi ◽  
Milad Avazbeigi
Keyword(s):  
Fuzzy Clustering ◽  
Fuzzy System ◽  
Distance Measure ◽  
System Model ◽  
Rule Base ◽  
Optimization Approach ◽  
Fuzzy Data ◽  
System Models ◽  
Fuzzy System Models

This chapter presents a new optimization method for clustering fuzzy data to generate Type-2 fuzzy system models. For this purpose, first, a new distance measure for calculating the (dis)similarity between fuzzy data is proposed. Then, based on the proposed distance measure, Fuzzy c-Mean (FCM) clustering algorithm is modified. Next, Xie-Beni cluster validity index is modified to be able to valuate Type-2 fuzzy clustering approach. In this index, all operations are fuzzy and the minimization method is fuzzy ranking with Hamming distance. The proposed Type-2 fuzzy clustering method is used for development of indirect approach to Type-2 fuzzy modeling, where the rules are extracted from clustering fuzzy numbers (Zadeh, 1965). Then, the Type-2 fuzzy system is tuned by an inference algorithm for optimization of the main parameters of Type-2 parametric system. In this case, the parameters are: Schweizer and Sklar t-Norm and s-Norm, a-cut of rule-bases, combination of FATI and FITA inference approaches, and Yager parametric defuzzification. Finally, the proposed Type-2 fuzzy system model is applied in prediction of the steel additives in steelmaking process. It is shown that, the proposed Type-2 fuzzy system model is superior in comparison with multiple regressions and Type-1 fuzzy system model, in terms of the minimization the effect of uncertainty in the rule-base fuzzy system models an error reduction.

Fuzzy System Model to Assist with Real Estate Appraisals

2007 ◽  
pp. 260-269 ◽  
Author(s):  
Dariusz Król ◽  
Tadeusz Lasota ◽  
Wojciech Nalepa ◽  
Bogdan Trawiński
Keyword(s):  
Real Estate ◽  
Fuzzy System ◽  
System Model

Mechanical Layering: Implications for Hydraulic Fracturing in an Unconventional Tight Carbonate Reservoir in Abu Dhabi, UAE

2014 ◽  
Author(s):  
Manhal Sirat ◽  
Xing Zhang ◽  
Janelle Simon ◽  
Aurifullah Vantala ◽  
Magdalena Povstyanova
Keyword(s):  
Hydraulic Fracturing ◽  
Carbonate Reservoir ◽  
Abu Dhabi ◽  
Tight Carbonate

Review of the Proppant Selection for Hydraulic Fracturing

2019 ◽  
Vol 8 (9) ◽  
pp. 418-422
Author(s):  
Thiha Ngwe ◽  
Dr. Myo Min Swe ◽  
Myint Than
Keyword(s):  
Hydraulic Fracturing ◽  
Selection For

Improved GA-NFS-Based Decision Rule Acquisition in Route Selection for Public Transport Line

2013 ◽  
Vol 278-280 ◽  
pp. 1255-1259
Author(s):  
Min An Tang ◽  
Xiao Ming Wang ◽  
Shuang Yuan
Keyword(s):  
Public Transport ◽  
Fuzzy System ◽  
Analytical Investigation ◽  
System Model ◽  
Route Selection ◽  
Improved Genetic Algorithm ◽  
Rule Acquisition ◽  
Transport Line ◽  
Neural Fuzzy ◽  
Selection Of

Aimed at the difficulty of the route selection of urban public transport line, a new method was proposed for the improved genetic algorithm BP neural fuzzy system model and algorithm. For the problem of route selection, it is required to acquire evolution rule of system status from the changes of multiple environment variable factors. The BP neural fuzzy system model based on Mamdani inference was given, in order to overcome the existing limitations of BP neural network, it puts forward the thought of algorithm improved by GA. The algorithm as the foundation is applied in route selection of urban road public transport line, and the method’s usability is proven through simulation, calculation and analytical investigation of practical problems on the main lines of bus rapid transit the route selection.

scholarly journals Developing a Genetic Fuzzy System Model for Cost-Benefit Analysis.

2014 ◽  
Vol 13 (4) ◽  
pp. 4416-4421 ◽  
Author(s):  
Saurabh Singh ◽  
Praveen Kumar Shukla ◽  
Rashmi Ranjan ◽  
Anurag Kumar
Keyword(s):  
Soft Computing ◽  
Fuzzy System ◽  
Systematic Approach ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
System Model ◽  
Genetic Optimization ◽  
Benefit Analysis ◽  
Genetic Fuzzy System ◽  
The Cost

Cost benefit analysis is a systematic approach for calculation and analyzing the cost of a project. Soft computing approaches are also applicable to deal with cost benefit analysis. In this paper Mamdani fuzzy system has been developed for cost benefit analysis. The genetic optimization of the model is carried out. The interpretability and accuracy features are also analyzed.

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