Multistage stochastic programming approach for offshore oilfield infrastructure planning under production sharing agreements and endogenous uncertainties
Identifieur interne : 005207 ( Main/Exploration ); précédent : 005206; suivant : 005208Multistage stochastic programming approach for offshore oilfield infrastructure planning under production sharing agreements and endogenous uncertainties
Auteurs : Vijay Gupta [États-Unis] ; Ignacio E. Grossmann [États-Unis]Source :
- Journal of petroleum science & engineering [ 0920-4105 ] ; 2014.
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- Pascal (Inist)
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Abstract
The paper presents a new optimization model and solution approach for the investment and operations planning of offshore oil and gas field infrastructure. As compared to the conventional models where either fiscal rules or uncertainty in the field parameters is considered, the proposed model is the first one in the literature that includes both of these complexities in an efficient manner. In particular, a tighter formulation for the production sharing agreements based on our recent work, and a perfect positive or negative correlation among the endogenous uncertain parameters (field size, oil deliverability, water-oil ratio and gas-oil ratio) is considered to reduce the total number of scenarios in the resulting multistage stochastic formulation. To solve the large instances of the problem, a Lagrangean decomposition approach allowing parallel solution of the scenario subproblems is implemented in the GAMS grid computing environment. Computational results on a variety of oilfield development planning examples are presented to illustrate the efficiency of the model and the proposed solution approach.
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Grossmann</name><affiliation wicri:level="4"><inist:fA14 i1="01"><s1>Department of Chemical Engineering, Carnegie Mellon University</s1><s2>Pittsburgh, PA 15213</s2><s3>USA</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14><country>États-Unis</country><placeName><settlement type="city">Pittsburgh</settlement><region type="state">Pennsylvanie</region></placeName><orgName type="university">Université Carnegie-Mellon</orgName></affiliation></author></analytic><series><title level="j" type="main">Journal of petroleum science & engineering</title><title level="j" type="abbreviated">J. pet. sci. eng.</title><idno type="ISSN">0920-4105</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">Journal of petroleum science & engineering</title><title level="j" type="abbreviated">J. pet. sci. eng.</title><idno type="ISSN">0920-4105</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Constraint</term><term>Exploration</term><term>Production sharing</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Contrainte</term><term>Exploration</term><term>Partage de la production</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The paper presents a new optimization model and solution approach for the investment and operations planning of offshore oil and gas field infrastructure. As compared to the conventional models where either fiscal rules or uncertainty in the field parameters is considered, the proposed model is the first one in the literature that includes both of these complexities in an efficient manner. In particular, a tighter formulation for the production sharing agreements based on our recent work, and a perfect positive or negative correlation among the endogenous uncertain parameters (field size, oil deliverability, water-oil ratio and gas-oil ratio) is considered to reduce the total number of scenarios in the resulting multistage stochastic formulation. To solve the large instances of the problem, a Lagrangean decomposition approach allowing parallel solution of the scenario subproblems is implemented in the GAMS grid computing environment. Computational results on a variety of oilfield development planning examples are presented to illustrate the efficiency of the model and the proposed solution approach.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Pennsylvanie</li></region><settlement><li>Pittsburgh</li></settlement><orgName><li>Université Carnegie-Mellon</li></orgName></list><tree><country name="États-Unis"><region name="Pennsylvanie"><name sortKey="Gupta, Vijay" sort="Gupta, Vijay" uniqKey="Gupta V" first="Vijay" last="Gupta">Vijay Gupta</name></region><name sortKey="Grossmann, Ignacio E" sort="Grossmann, Ignacio E" uniqKey="Grossmann I" first="Ignacio E." last="Grossmann">Ignacio E. Grossmann</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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