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Applying High Power Lasers in Perforating Oil and Gas Wells: Prediction of the Laser Power Loss During Laser Beam-Fluid Interaction by Using Artificial Neural Networks
R. Keshavarzi, R. Jahanbakhshi, H. Bayesteh A. Ghorbani and M. A. Shoorehdeli

In the petroleum industry perforating is a method of making holes through the casing opposite the production formation to allow the oil or gas to flow into the well. In the current explosive shaped charge perforation method there are some serious problems, such as producing debris, uncontrollable hole size and shape, compaction of rock formation in the area next to the tunnel and decreasing permeability. Recent advances in high power laser technology provide a new alternative to replace the current perforating gun. Due to the nature of oil and gas reservoirs, one of the challenges in laser perforation is the laser beam-fluid interaction that results in laser power loss (LPL). In this paper, feed-forward network with back-propagation and generalized regression neural networks have been developed to predict LPL in the laser beam-fluid interaction during laser perforation. Effective parameters in the laser-fluid interaction such as laser power, fluid viscosity and fluid thickness which are related to laboratory tests done by ytterbium-doped multi-clad fibre laser are the inputs and LPL is the output of the neural networks. The developed neural networks have shown high correlation coefficients with low error and the LPL for the laser beam-fluid interaction during laser perforation was predicted with high accuracy.

Keywords: Laser perforation, artificial neural networks, laser power loss (LPL), laser beam-fluid interaction, oil well, gas well

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