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<Article>
<Journal>
				<PublisherName>Research institute of Petroleum Industry (RIPI)</PublisherName>
				<JournalTitle>Journal of Petroleum Research</JournalTitle>
				<Issn>2345-2900</Issn>
				<Volume>26</Volume>
				<Issue>95</Issue>
				<PubDate PubStatus="epublish">
					<Year>2016</Year>
					<Month>04</Month>
					<Day>20</Day>
				</PubDate>
			</Journal>
<ArticleTitle>Optimal Well Placement Using Streamline Based Model</ArticleTitle>
<VernacularTitle>Optimal Well Placement Using Streamline Based Model</VernacularTitle>
			<FirstPage>45</FirstPage>
			<LastPage>58</LastPage>
			<ELocationID EIdType="pii">612</ELocationID>
			
<ELocationID EIdType="doi">10.22078/pr.2016.612</ELocationID>
			
			<Language>FA</Language>
<AuthorList>
<Author>
					<FirstName>Gholamreza</FirstName>
					<LastName>Khademi</LastName>
<Affiliation>Electrical Engineering Department, Shiraz University, Shiraz, Iran</Affiliation>

</Author>
<Author>
					<FirstName>Paknoosh</FirstName>
					<LastName>Karimaghaee</LastName>
<Affiliation>Electrical Engineering Department, Shiraz University, Shiraz, Iran</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2014</Year>
					<Month>01</Month>
					<Day>28</Day>
				</PubDate>
			</History>
		<Abstract>One of the key issues must be considered in management of reservoirs is optimal well placement. Optimal well placement is defined as a mathematical problem which maximizes an objective function such as net present value (NPV) by adjusting the well locations in an optimum place while the physical and economic constraints are considered at the same time . Modeling and simulation of reservoir is an important step in the well placement problem. The more the reservoir model is closer to the real one, the more precise the optimum well locations can be found. In most of prior suggested methods, the reservoir is modeled in Cartesian coordinate that makes the model complicated. In the present paper, a simpler reservoir model based on streamline technique is presented. Then, with the aid of the natural movement of flow in the streamline based reservoir model, we are trying to reach an effective and facile approach to solve optimal well placement problem. First, the unique and valuable information from streamlined based model is introduced. Then, this information is added to the well-known genetic optimization algorithm. With the aid of this combination, the stochastic search strategy of genetic algorithm is changed to a semi-stochastic search or based on knowledge. It is shown that the number of reservoir simulation runs is decreased and the convergence of the proposed hybrid genetic is faster than the simple genetic.</Abstract>
			<OtherAbstract Language="FA">One of the key issues must be considered in management of reservoirs is optimal well placement. Optimal well placement is defined as a mathematical problem which maximizes an objective function such as net present value (NPV) by adjusting the well locations in an optimum place while the physical and economic constraints are considered at the same time . Modeling and simulation of reservoir is an important step in the well placement problem. The more the reservoir model is closer to the real one, the more precise the optimum well locations can be found. In most of prior suggested methods, the reservoir is modeled in Cartesian coordinate that makes the model complicated. In the present paper, a simpler reservoir model based on streamline technique is presented. Then, with the aid of the natural movement of flow in the streamline based reservoir model, we are trying to reach an effective and facile approach to solve optimal well placement problem. First, the unique and valuable information from streamlined based model is introduced. Then, this information is added to the well-known genetic optimization algorithm. With the aid of this combination, the stochastic search strategy of genetic algorithm is changed to a semi-stochastic search or based on knowledge. It is shown that the number of reservoir simulation runs is decreased and the convergence of the proposed hybrid genetic is faster than the simple genetic.</OtherAbstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Optimal Well Placement</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Reservoir Modeling and Simulation Based on Streamline Technique</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Genetic Algorithm</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Producer and Injector Wells</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://pr.ripi.ir/article_612_f76a89f0cb91bc419542ce9fa43902dc.pdf</ArchiveCopySource>
</Article>
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