scholarly journals An efficient approach for optimizing full field development plan using Monte-Carlo simulation coupled with Genetic Algorithm and new variable setting method for well placement applied to gas condensate field in Vietnam

2016 ◽  
Vol 35 (1) ◽  
pp. 75-102 ◽  
Author(s):  
Jongyoung Jun ◽  
Joomyung Kang ◽  
Daein Jeong ◽  
Haeseon Lee
Keyword(s):  
Genetic Algorithm ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Optimization Procedure ◽  
Gas Condensate ◽  
Dew Point ◽  
Development Plan ◽  
Well Placement ◽  
Full Field ◽  
Field Development

This paper presents an efficient technique to optimize a gas condensate field development plan under economic uncertainties. Many studies have been conducted to optimize development plan but mostly limited to oil field under fixed economic environments and required huge number of simulation runs. It is proved that black oil model can be a reasonable alternative of compositional model to complete field development optimization within acceptable period when reservoir pressure is higher enough than dew point pressure. This study implements Monte-Carlo simulation to Genetic Algorithm to assess economic uncertainties while optimization procedure is being performed and to avoid duplicating whole optimization procedure by changing economic assumptions. An idea for setting optimization variables for well placement is also introduced to reduce required number of simulation runs. A real field application confirms that the technique can be applied to optimize a gas condensate field with contractual gas sales obligation, and the idea plays a key role to find the optimized solution with limited resources by reducing the number of simulation runs required during the optimization procedure. The proposed technique can be applied to optimize not only full field development plan but also reservoir management plan and it will be helpful to improve economics of all kinds of E&P projects under lots of uncertainties.

scholarly journals A Genetic Algorithm Integrated with Monte Carlo Simulation for the Field Layout Design Problem

2018 ◽  
Vol 73 ◽  
pp. 24 ◽  
Author(s):  
Leonardo de Pádua Agripa Sales ◽  
Anselmo Ramalho Pitombeira-Neto ◽  
Bruno de Athayde Prata
Keyword(s):  
Genetic Algorithm ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Design Problem ◽  
Oil And Gas ◽  
Probabilistic Approach ◽  
Layout Design ◽  
Gas Production ◽  
Oil And Gas Production ◽  
Field Development

Oil and gas production is moving deeper and further offshore as energy companies seek new sources, making the field layout design problem even more important. Although many optimization models are presented in the revised literature, they do not properly consider the uncertainties in well deliverability. This paper aims at presenting a Monte Carlo simulation integrated with a genetic algorithm that addresses this stochastic nature of the problem. Based on the results obtained, we conclude that the probabilistic approach brings new important perspectives to the field development engineering.

Full Field Development Plan Optimization Integrating Subsurface reservoirs uncertainties for Green Field Offshore Abu Dhabi, UAE

2012 ◽  
Author(s):  
Said Meziani ◽  
Mohamed Sayed Ibrahem ◽  
Khalil Al-Hossani ◽  
Tarek Mohamed Matarid ◽  
Bader Saif Al Badi
Keyword(s):  
Development Plan ◽  
Abu Dhabi ◽  
Full Field ◽  
Field Development ◽  
Plan Optimization

Project Scheduling Problem with Stochastic Resource-Dependent Activity Duration

2013 ◽  
Vol 483 ◽  
pp. 607-610 ◽  
Author(s):  
Chun Jie Zhong ◽  
Ying Yu ◽  
Yun Lang Jia
Keyword(s):  
Genetic Algorithm ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Project Scheduling ◽  
Point Method ◽  
Scheduling Problem ◽  
Resource Constrained ◽  
Activity Duration ◽  
Dependent Activity ◽  
Project Scheduling Problem

A resource-constrained project scheduling problem with stochastic resource-dependent activity durations is presented in this paper,and the two-point method is employed to simulate the uncertain property.Furthermore a genetic algorithm combined with this method is provided to solve the problem. Compared with the results from the genetic with Monte Carlo simulation, the proposed method is verified to be effective and more efficient.

Full-Field Development Plan "The Big Picture - Creating the Future from the Future and Success through Real Synergy !H

2009 ◽  
Author(s):  
Ashraf Al-Saiid Keshka ◽  
Jorge Salgado Gomes ◽  
Maher Mahmoud Kenawy ◽  
Hafez H. Hafez ◽  
Sharif Al Olama ◽  
...  
Keyword(s):  
Development Plan ◽  
Full Field ◽  
Field Development ◽  
The Future ◽  
Big Picture

Gas/Condensate Field Development Plan by Means of Numerical Compositional Simulation

2010 ◽  
Author(s):  
Victor Alexei Huerta Quinones ◽  
Alex Fernando Lanchimba ◽  
Peter Colonomos
Keyword(s):  
Gas Condensate ◽  
Development Plan ◽  
Compositional Simulation ◽  
Field Development

scholarly journals Optimal sampling in a noisy genetic algorithm for risk-based remediation design

2003 ◽  
Vol 5 (1) ◽  
pp. 11-25 ◽  
Author(s):  
Gayathri Gopalakrishnan ◽  
Barbara S. Minsker ◽  
David E. Goldberg
Keyword(s):  
Genetic Algorithm ◽  
Monte Carlo Simulation ◽  
Genetic Algorithms ◽  
Monte Carlo ◽  
Fitness Function ◽  
Sampling Strategies ◽  
Robust Designs ◽  
Highly Efficient ◽  
Efficient Sampling

A groundwater management model has been developed that predicts human health risks and uses a noisy genetic algorithm to identify promising risk-based corrective action (RBCA) designs. Noisy genetic algorithms are simple genetic algorithms that operate in noisy environments. The noisy genetic algorithm uses a type of noisy fitness function (objective function) called the sampling fitness function, which utilises Monte-Carlo-type sampling to find robust designs. Unlike Monte Carlo simulation modelling, however, the noisy genetic algorithm is highly efficient and can identify robust designs with only a few samples per design. For hydroinformatic problems with complex fitness functions, however, it is important that the sampling be as efficient as possible. In this paper, methods for identifying efficient sampling strategies are investigated and their performance evaluated using a case study of a RBCA design problem. Guidelines for setting the parameter values used in these methods are also developed. Applying these guidelines to the case study resulted in highly efficient sampling strategies that found RBCA designs with 98% reliability using as few as 4 samples per design. Moreover, these designs were identified with fewer simulation runs than would likely be required to identify designs using trial-and-error Monte Carlo simulation. These findings show considerable promise for applying these methods to complex hydroinformatic problems where substantial uncertainty exists but extensive sampling cannot feasibly be done.

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