Adopting Simple & Advanced Genetic Algorithms as Optimization Tools for Increasing Oil Recovery & NPV in an Iraq Oil Field
- Wathiq Jassim Mohammed Al-Mudhafer (South Oil Company-Iraq) | Maytham Shaheed (The University of Manchester)
- Document ID
- Society of Petroleum Engineers
- SPE Middle East Oil and Gas Show and Conference, 25-28 September, Manama, Bahrain
- Publication Date
- Document Type
- Conference Paper
- 2011. Society of Petroleum Engineers
- 5.6.4 Drillstem/Well Testing, 3.1.6 Gas Lift, 5.7.5 Economic Evaluations, 1.6 Drilling Operations, 7.6.6 Artificial Intelligence, 5.1 Reservoir Characterisation, 5.1.5 Geologic Modeling, 5.6.5 Tracers, 5.5 Reservoir Simulation, 3.2.3 Hydraulic Fracturing Design, Implementation and Optimisation, 2.5.1 Fracture design and containment, 4.6 Natural Gas
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A Multidisciplinary study for increasing oil recovery has been made in the present paper. This work has been adopted in the Upper Sandstone member/Zubair formation in South Rumaila Oil Field. The work was achieved by using optimization techniques for determining the optimal future reservoir performance regarding to infill drilling. Two different methods of Genetic Algorithm used to optimize the number and locations of infill wells. The first method is simple adaptive genetic algorithm and the second one is the breeder adaptive. The main parameters depended in this study is the cumulative oil production obtained from the output of reservoir simulation software. These two methods of GA depend on using the objective function of Net Present Value (NPV) as economic analysis. The optimal number of infill wells is three wells that have maximum cumulative oil production and maximum value of NPV. The same results from two GA methods have been obtained. The locations of these optimal infill wells located in the crest of the oil field.
Production and management of oil and gas in today's highly competitive environment require the use of high tech tools. These tools provide the means by which the cost of exploration, production, and management of hydrocarbon resources may be reduced. Engineers find themselves in a never ending race to catch up with new advancements in information technologies. Employing computers in the workplace, incorporating sophisticated simulation models in decision-making processes, and digital control and monitoring of equipment that were regarded as state of the art only a few years ago, are now normal day-to-day procedures. The phrase "Advanced Technologies" has a highly dynamic meaning. In recent years, Genetic Algorithm, Neural Networks and Fuzzy Logic set theory with its application in artificial intelligence has assumed the new meaning of the phrase "Advanced Technologies." These tools are providing engineers and scientists with the foundation upon which intelligent machines can be developed.
In the present study, only Genetic Algorithm has been adopted to increase oil recovery for the main pay in South Rumaila Oil Field. GA offers an efficient search method and can be used as powerful optimization tools introduced by John Holland in 1975(1). Potential solutions generated randomly (population in terms of GAs consist of a number of individuals represented by chromosomes) to a problem compete with each other in order to achieve increasingly better results by applying a set of operators: Selection, Crossover (Recombination), and Mutation. These operators mimic the genetic reproduction in biological sense similar to Darwin's theory of Natural Selection (1).
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