Dissertation/Thesis Abstract

Development and Testing of a Combined Neural-Genetic Algorithm to Identify CO2 Sequestration Candidacy Wells
by Zhang, Xiaohui, M.S., University of Louisiana at Lafayette, 2015, 140; 1594272
Abstract (Summary)

This study was motivated by how to use statistical tool to identify the candidacy wells for CO2 Capture and Sequestration based on the idea of using Artificial Neural Networks to predict the leakage index of a well.

A Combined Neural-Genetic Algorithm was introduced to avoid BP neural network getting a local minimum because Genetic Algorithm simulates the survival of the fittest among individuals over consecutive generation. Based on the algorithm, 1356 lines of C code were composed using Microsoft Visual Studio 2010. The Combined Neural-Genetic Algorithm developed in this thesis is able to handle large size of data sample with at least 10 factors.

Several parameters were considered as factors that may have an effect on the performance of Combined Neural-Genetic Algorithm, including the population size, max epoch, error goal, probability of crossover, probability of mutation, number of neurons in hidden layer, number of factors and size of data sample. The accuracy of the BP neural network and the CPU time are the two major parameters to evaluate the performance of the Combined Neural-Genetic Algorithm.

A sensitivity analysis was performed to identify the effect these factor have on the performance. Based on the result of the sensitivity analysis, some recommendations are provided about initializing these factors.

Indexing (document details)
Advisor: Guo, Boyun
Commitee: Boukadi, Fathi, Feng, Yin
School: University of Louisiana at Lafayette
Department: Petroleum Engineering
School Location: United States -- Louisiana
Source: MAI 54/06M(E), Masters Abstracts International
Source Type: DISSERTATION
Subjects: Engineering, Petroleum engineering
Keywords: Artificial neural network, Co2 sequestration, Data analysis, Genetic algorithm
Publication Number: 1594272
ISBN: 9781321912432
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