Evaluation of Skin Factor and Non-Darcy Effects in UndergroundGas Storage Reservoirs

In underground gas storage (UGS) reservoirs, deliverability and velocity of gas flow toward thewell is very high and rate-dependent pseudo skin may be a big part of the total skin factor aroundthe wellbore. Therefore, accurate determination of non-Darcy factor, D, can be very important inexact prediction of rate-independent skin (or true skin) factor and deliverability of the well. Multiratetests provide reasonable estimates of reservoir parameters such as true skin factor and non-Darcy factor. However, running a multi rate test is much more expensive and time consuming thansingle rate tests. Especially in the case of UGS reservoirs, running a multi stage test can be risky,as these reservoirs are usually designed for supply of energy in cold months of the year and anyinterruption in constant production of gas for running multi rate tests can be critical. Therefore,the use of these tests should be minimized in analysis of UGS reservoirs. The objective of this studyis to use back-propagation neural network (BPN) in prediction of non-Darcy factor in some UGSreservoirs by using reservoir properties. Then, based on the proposed correlation and analysis ofsingle rate tests, the reservoir parameters, i.e. non-Darcy factor and true skin factor for each wellwere calculated. The results indicate that the presented artificial neural network (ANN) isappropriate to estimate skin factor in these reservoirs

Keywords:

underground gas storage reservoir , artificial neural network , non-Darcy factor