The purpose of this paper is to present a new approach for selecting the good heavy oil reservoirs to develop preferentially, which can avoid the huge economical loss resulted from wrong decision.
A new method of ranking the development priority of heavy oil reservoir is present, in which the neural network is applied for the first time to acquire reservoir parameters' weights through training samples and the genetic algorithm is used to optimize the joint weighs of neurons in case that neural network falling into local minimum. Additionally, the paper establishes subordinate function of every parameter. Eventually, comprehensive evaluation values of all heavy oil reservoirs are obtained.
The method can ensure the veracity and creditability of the parameters' weights, avoid the randomicity brought by experts.
Accessibility of the data of many heavy oil reservoirs is the main limitation.
A very useful and new method for the decision makers of heavy oil reservoirs development.
The new approach of ranking the development priority of heavy oil reservoir based on the neural network and the genetic algorithm. The paper is aimed at the leaders who manage the development of heavy oil reservoirs.
Wu, X., Shi, J., Chen, F. and Wang, Y. (2009), "Application of neural network combined genetic algorithm to rank the development priority of heavy oil reservoirs", Kybernetes, Vol. 38 No. 10, pp. 1684-1692. https://doi.org/10.1108/03684920910994042Download as .RIS
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