Publication de 6 numéros par an
ISSN Imprimer: 2152-5080
ISSN En ligne: 2152-5099
Indexed in
COMPARISON OF RISK ANALYSIS METHODOLOGIES IN A GEOSTATISTICAL CONTEXT: MONTE CARLO WITH JOINT PROXY MODELS AND DISCRETIZED LATIN HYPERCUBE
RÉSUMÉ
During the development of petroleum fields, uncertainty quantification is essential to base decisions. Several methods are presented in the literature, but its choice must agree with the complexity of the case study to ensure reliable results at minimum computational costs. In this study, we compared two risk analysis methodologies applied to a complex reservoir model comprising a large set of geostatistical realizations: (1) a generation of scenarios using the discretized Latin hypercube sampling technique combined with geostatistical realizations (DLHG) and (2) a generation of scenarios using the Monte Carlo sampling technique combined with joint proxy models, entitled the joint modeling method (JMM). For a reference response, we assessed risk using the pure Monte Carlo sampling combined with flow simulation using a very high sampling number. We compared the methodologies, looking at the (1) accuracy of the results, (2) computational cost, (3) difficulty in the application, and (4) limitations of the methods. Our results showed that both methods are reliable but revealed limitations in the JMM. Due to the way the JMM captures the effect of a geostatistical uncertainty, the number of required flow simulation runs increased exponentially and became unfeasible to consider more than 10 realizations. The DLHG method showed advantages in such a context, namely, because it generated precise results from less than half of the flow simulation runs, the risk curves were computed directly from the flow simulation results (i.e., a proxy model was not needed), and incorporated hundreds of geostatistical realizations. In addition, this method is fast, straightforward, and easy to implement.
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Schiozer Denis José, de Souza dos Santos Antonio Alberto, de Graça Santos Susana Margarida, von Hohendorff Filho João Carlos, Model-based decision analysis applied to petroleum field development and management, Oil & Gas Science and Technology – Revue d’IFP Energies nouvelles, 74, 2019. Crossref
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Borges Jéssica Ferreira, de Paula Valdeir Francisco, Evangelista Francisco, Bezerra Luciano Mendes, Reliability and uncertainty quantification of the net section tension capacity of cold-formed steel angles with bolted connections considering shear lag, Advances in Structural Engineering, 24, 7, 2021. Crossref
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Mahjour Seyed Kourosh, Mendes da Silva Luís Otávio, Meira Luis Augusto Angelotti, Coelho Guilherme Palermo, de Souza dos Santos Antonio Alberto, Schiozer Denis José, Evaluation of unsupervised machine learning frameworks to select representative geological realizations for uncertainty quantification, Journal of Petroleum Science and Engineering, 209, 2022. Crossref
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Mahjour Seyed Kourosh, Santos Antonio Alberto Souza, Correia Manuel Gomes, Schiozer Denis José, Developing a workflow to select representative reservoir models combining distance-based clustering and data assimilation for decision making process, Journal of Petroleum Science and Engineering, 190, 2020. Crossref
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Mirzaei-Paiaman Abouzar, Santos Susana M.G., Schiozer Denis J., A review on closed-loop field development and management, Journal of Petroleum Science and Engineering, 201, 2021. Crossref