Invited Session Wed.1.MA 550

Wednesday, 10:30 - 12:00 h, Room: MA 550

Cluster 18: Optimization in energy systems [...]

Optimization in the oil \& natural gas industry


Chair: Bruno Flach and Luiz Augusto Barroso



Wednesday, 10:30 - 10:55 h, Room: MA 550, Talk 1

Jorge Passamani Zubelli
Evaluation of LNG contracts with cancellation options

Coauthors: Vincent Guigues, Claudia Sagastizabal


For gas companies, liquefied natural gas (LNG) appears as a convenient complement to their own natural gas resources. A proper mix of natural gas and LNG contracts allows a gas company not only to diversify its portfolio, but to better hedge risk. In the gas sector, risk concerns are related to volatility of gas prices and also to the obligation of providing a faultless delivery, especially to "uninterruptible'' clients, usually crucial customers for the business.
LNG contracts offer the possibility to ship LNG loads at dates and volumes specified in the contract. Each shipment can be totally or partly cancelled, possibly paying a fee. Cancellation fees naturally increase as the shipment forecasted date approaches. Therefore, for a given LNG contract it is important to determine which loads might be cancelled. The introduction of LNG
contracts in a gas portfolio brings into consideration two challenging issues. First, a company needs to determine a set of acceptable prices for the contracts. Second, on the basis of each contract's features, a company needs to determine an optimal portfolio. We address these two points being complex and intertwined.



Wednesday, 11:00 - 11:25 h, Room: MA 550, Talk 2

Bjarne Foss
Real-time production optimization based on decomposition techniques

Coauthor: Vidar Gunnerud


This presentation focusses on Dantzig-Wolfe decomposition for real-time optimization of oil and gas systems with a decentralized structure. The idea is to improve computational efficiency and transparency of a solution. The contribution lies in the application of the Dantzig-Wolfe method which allows us to efficiently decompose an optimization problem into parts. Moreover, we show how the algorithm can be parallelized for even higher efficiency. The nonlinear system is modeled by piecewise linear models with the added benefit that error bounds can be computed. In this context alternative parameterizations are discussed.
The properties of the method are studied by applying it to realistic models of petroleum fields. The examples lend themselves to a decomposition strategy due to the layout and structure of the wells and pipeline systems. The first model resembles the Troll west oil rim, a huge gas and oil field on the Norwegian Continental shelf. Decision variables are allocation of production between wells and routing of well streams. A second case also includes allocation of supply gas for gas lift. This is based on an on-going study of an offshore field outside Brazil.



Wednesday, 11:30 - 11:55 h, Room: MA 550, Talk 3

Bruno Flach
An MIQP approach to the determination of analogous reservoirs

Coauthors: Davi Valladao, Bianca Zadrozny


Oil companies are constantly faced with decision under uncertainty problems related to the analysis of potential investments in target reservoirs. Often, the amount of information on these prospects is relatively scarce and a common adopted strategy is having specialists determine analogous reservoirs - i.e., those for which plenty of data is available and are believed to be similar to the target - as a way to estimate unknown parameters and evaluate production forecasts. Machine learning algorithms, such as k-nearest-neighbors (KNN), may  also be applied in this context but the quality of their results is instrinsically related with the definition of a distance metric that defines the similarity between the target reservoir and those stored in a database. To this end, our work focuses on the determination of an optimal distance function - in the sense of minimizing the error in the prediction of a given property or attribute obtained with the computed analogues - by formulating it as a mixed integer quadratic programming (MIQP) problem. Computational results on the application of different solution algorithms to a realistic large-scale problems will be discussed.


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