This article presents an efficient approach to determine the optimal drilling location for maximizing the cumulative production without the need for a reservoir simulation, of which scheme is based on artificial neural network incorporating the productivity potential. A reservoir simulator can provide an accurate result, but is sometimes inefficient due to the enormous computing requirements. The typical artificial neural network scheme used in multiwell placement shows lower predictability as the size of the input data increases. This work introduces the productivity potential that merges various reservoir properties, such as the permeability, porosity, and saturation, and integrates it into an artificial neural network. The cumulative production is compared with the result of the reservoir simulator to determine the accuracy of the developed method. The efficiency of the conventional artificial neural network is improved by the proposed model, as well by using the productivity potential instead of a lot of separate inputs. The predictability is verified by determining the drilling location in the same way as that of the reservoir simulator in the case of a single infill well. The stability is confirmed by its ability to produce a reliable result even as the number of input data increases.
|Number of pages||13|
|Journal||Energy Sources, Part A: Recovery, Utilization and Environmental Effects|
|State||Published - Jan 2011|
Bibliographical noteFunding Information:
This work was supported by the Energy Efficiency & Resources of the Korea Institute of Energy Technology Evaluation and Planning (No. 2010201030001C) grant funded by the Ministry of Knowledge Economy and also a 2010 Research grant by Kangwon National University, Korea.
- artificial neural network
- infill drilling
- productivity potential
- well placement