scholarly journals Multi-Objective Optimization of Microemulsion Flooding for Chemical Enhanced Oil Recovery

2018 ◽  
Vol 73 ◽  
pp. 4 ◽  
Author(s):  
Mohammad Saber Karambeigi ◽  
Ali Haghighi Asl ◽  
Masoud Nasiri
Keyword(s):  
Enhanced Oil Recovery ◽  
Oil Recovery ◽  
Unconventional Reservoirs ◽  
Residual Oil ◽  
Multi Objective Optimization ◽  
Microemulsion Formulation ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Single Objective

Microemulsion flooding is one of the most effective methods of Chemical Enhanced Oil Recovery (CEOR), particularly for the production of residual oil trapped in unconventional reservoirs. A critical step for successful application of this technique is to achieve a suitable formulation. Previous studies have almost focused on the technical aspects while considering both practical and economic matters as conflicting objectives has been neglected. In the present paper, the formulation of microemulsion is optimized based on the trade-off between scientific and financial responses using a hybrid workflow in which experimental design and artificial intelligence methodologies are composed. To appraise the efficiency of developed algorithm, a challenge case study is first evaluated and compared to previous approaches. Thereafter, the second case is examined in which a newly developed formulation of microemulsion for high temperature carbonate reservoirs is optimized. The outcomes of this multi-attribute workflow are compared to a single-objective algorithm. The results indicate the outstanding performance of the proposed approach for multi-objective optimization of microemulsion formulation. Eventually, the possible concerns regarding the application of microemulsion flooding in unconventional reservoirs are discussed.

Multi-Objective Pricing Optimization for a High-Speed Rail Network Under Competition

2019 ◽  
Vol 2673 (7) ◽  
pp. 215-226 ◽  
Author(s):  
Huizhuo Cao ◽  
Xuemei Li ◽  
Vikrant Vaze ◽  
Xueyan Li
Keyword(s):  
High Speed ◽  
Programming Model ◽  
Pareto Frontier ◽  
Multiple Objectives ◽  
Multi Objective Optimization ◽  
High Speed Rail ◽  
Multi Objective ◽  
Trade Offs ◽  
Conflicting Objectives ◽  
Single Objective

Multi-objective pricing of high-speed rail (HSR) passenger fares becomes a challenge when the HSR operator needs to deal with multiple conflicting objectives. Although many studies have tackled the challenge of calculating the optimal fares over railway networks, none of them focused on characterizing the trade-offs between multiple objectives under multi-modal competition. We formulate the multi-objective HSR fare optimization problem over a linear network by introducing the epsilon-constraint method within a bi-level programming model and develop an iterative algorithm to solve this model. This is the first HSR pricing study to use an epsilon-constraint methodology. We obtain two single-objective solutions and four multi-objective solutions and compare them on a variety of metrics. We also derive the Pareto frontier between the objectives of profit and passenger welfare to enable the operator to choose the best trade-off. Our results based on computational experiments with Beijing–Shanghai regional network provide several new insights. First, we find that small changes in fares can lead to a significant improvement in passenger welfare with no reduction in profitability under multi-objective optimization. Second, multi-objective optimization solutions show considerable improvements over the single-objective optimization solutions. Third, Pareto frontier enables decision-makers to make more informed decisions about choosing the best trade-offs. Overall, the explicit modeling of multiple objectives leads to better pricing solutions, which have the potential to guide pricing decisions for the HSR operators.

Multi-Objective Optimization in Multi-Attribute and Multi-Unit Combinatorial Reverse Auctions

2017 ◽  
Vol 26 (05) ◽  
pp. 1760016 ◽  
Author(s):  
Shubhashis Kumar Shil ◽  
Samira Sadaoui
Keyword(s):  
Quality Measures ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Solution Quality ◽  
Winner Determination ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Trading Constraints ◽  
Significant Superiority

This study introduces an advanced Combinatorial Reverse Auction (CRA), multi-units, multiattributes and multi-objective, which is subject to buyer and seller trading constraints. Conflicting objectives may occur since the buyer can maximize some attributes and minimize some others. To address the Winner Determination (WD) problem for this type of CRAs, we propose an optimization approach based on genetic algorithms that we integrate with our variants of diversity and elitism strategies to improve the solution quality. Moreover, by maximizing the buyer’s revenue, our approach is able to return the best solution for our complex WD problem. We conduct a case study as well as simulated testing to illustrate the importance of the diversity and elitism schemes. We also validate the proposed WD method through simulated experiments by generating large instances of our CRA problem. The experimental results demonstrate on one hand the performance of our WD method in terms of several quality measures, like solution quality, run-time complexity and trade-off between convergence and diversity, and on the other hand, it’s significant superiority to well-known heuristic and exact WD techniques that have been implemented for much simpler CRAs.

CO2-Based Enhanced Oil Recovery from Unconventional Reservoirs: A Case Study of the Bakken Formation

2014 ◽  
Author(s):  
G. Liu ◽  
J.A. Sorensen ◽  
J.R. Braunberger ◽  
R. Klenner ◽  
J. Ge ◽  
...  
Keyword(s):  
Enhanced Oil Recovery ◽  
Oil Recovery ◽  
Unconventional Reservoirs ◽  
Bakken Formation

From single-objective to multiple-objective multiple-rainfall events automatic calibration of urban storm water runoff models using genetic algorithms

2006 ◽  
Vol 54 (6-7) ◽  
pp. 57-64 ◽  
Author(s):  
F. di Pierro ◽  
S.-T. Khu ◽  
D. Savić
Keyword(s):  
Performance Criteria ◽  
Storm Water ◽  
Water Runoff ◽  
Flow Measurements ◽  
Rainfall Events ◽  
Multi Objective ◽  
Storm Water Runoff ◽  
Conflicting Objectives ◽  
Single Objective

The calibration of storm water runoff models is a complex task. Early attempts focused on the choice of a performance criterion function that could capture all the facets of the problem into a single-objective framework. Subsequently, the awareness that a good calibration must necessarily take into account conflicting objectives led to the adoption of more sophisticated multi-objective approaches. Only recently, the focus has shifted towards effective ways of exploiting the mounting information provided by the availability of many sets of concurrent rainfall and flow measurements. This paper revisits through a case study the transition just elucidated: the calibration of a SWMM model applied to a catchment in Singapore is tackled through a single-objective, a multi-objective and a multi-objective multiple-event (MOME) paradigm respectively. A new approach to support the latter is presented herein. It consists in formulating the problem of model calibration as a multi-objective problem with m×r objective functions, where m and r are the number of performance criteria and rainfall events respectively, that must be optimized simultaneously. Results suggest that the new MOME framework performs significantly better than the others tested on the case study presented.

Rational Design of Vehicles: I — Multi-Objective Optimization

2001 ◽  
Author(s):  
David M. Paulus ◽  
Richard A. Gaggioli
Keyword(s):  
Objective Function ◽  
Fuel Economy ◽  
Rational Design ◽  
Multiple Objectives ◽  
Relative Importance ◽  
Multi Objective Optimization ◽  
Straightforward Manner ◽  
Multi Objective ◽  
Single Objective

Abstract The customer for a vehicle typically has several desiderata, such as top speed, fuel economy, range, acceleration, .... Generally, these desiderata are conflicting. So, in order to deduce a single objective function, a means is needed for weighting (implicitly if not explicitly) the relative importance of these desiderata. That is, for weighting these “multiple objectives.” This paper presents a rational methodology for developing a single-objective function to be optimized during the design of a vehicle. The methodology does require answers from the customer(s) to a straightforward set of questions, referring to the desiderata. Based on the answers, the objective function follows, mathematically, in a straightforward manner. An application to a light, personal aircraft serves as a case study.

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