This paper describes the use of artificial neural networks in exploring field development strategies in conjunction with various recovery schemes. In reservoir engineering applications, the field development process is considered to be highly challenging due to inherent time constraints, lack of sufficient data, and the presence of several degrees of freedom that need to be taken into consideration. In this paper, the respective roles of the important neural network parameters with relevance to recovery scenarios under consideration are examined. The overall objective, in these recovery scenarios, is to increase the rate of oil recovery under specified GOR and WOR constraints. The increase in the oil recovery rate can be achieved by implementing an infill-drilling program or by introducing an improved recovery technique such as water or gas injection. In both cases, one of the critical decisions is to determine the locations of the new wells that need to be drilled.

This paper investigates the potential use of artificial neural networks as an effective tool in identifying the optimum well locations in field development studies for various recovery schemes. The efficiency and accuracy of an artificial neural network are controlled by various parameters that are specific to a given network topology. Two of the artificial neural network parameters that control the robustness of the entire process include the learning constant, and the number of middle layer neurons. Another issue that needs to be addressed is the hidden danger stemming from the over-training of a neural network. Over-training a neural network causes the network to memorize rather than learn. A training session that concludes with memorization rather than learning will cause the artificial neural network to generate biased answers during the prediction phase. Suggestions are made in this paper to avoid the occurrence of memorization during the training process. By developing a good understanding of the roles of these key artificial neural network parameters, one will be better equipped to successfully implement the proposed hybrid (soft and hard) computing methodologies to field development studies.

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