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Developed Material Balance Approach for Estimating Gas Initially in Place and Ultimate Recovery for Tight Gas Reservoirs
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Abstract<p>The gas material balance equation (MBE) has been widely used as a practical as well as a simple tool to estimate gas initially in place (GIIP), and the ultimate recovery (UR) factor of a gas reservoir. The classical form of the gas material balance equation is developed by considering the reservoir as a simple tank model, in which the relationship between the pressure/gas compressibility factor (p/z) and cumulative gas production (Gp) is generally appeared to be linear. This linear plot is usually extrapolated to estimate GIIP at zero pressure, and UR factor for a given abandonment pressure. While this assumption is reasonable to some extent for conventional reservoirs, this may incur significant error when applied for unconventional tight gas reservoirs. The implementation of multi-tank, compartmented reservoir models are reported to better represent the behaviour of tight gas reservoirs. This study focus to develop a simple numerical method to solve the MBE using the concept of multi-tank, compartmented reservoir model. A simple and practical computational tool is developed by solving the numerical model using False Position iterative method. The tool is applied to calculate GIIP and UR factor for an Australian tight gas field after validation of tool based on history matching. The results demonstrated that the developed tool can be used for the better estimation of GIIP and UR factor with better accuracy. The program can also be used as an efficient tool, especially in the case of homogenous tight gas reservoir, as an alternative to the reservoir simulation to understand the pressure decline behaviour with cumulative gas production; and to estimate GIIP and UR factor.</p>
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Publication Date
Sun Jun 01 2014
Journal Name
Journal Of Economics And Administrative Sciences
Design Sampling Plan when Life Time Follows Logistic Distribution
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Design sampling plan was and still one of most importance subjects because it give lowest cost  comparing with others, time live statistical distribution should be known to give best estimators for  parameters of sampling plan and get best sampling plan.

Research dell with design sampling plan when live time distribution follow Logistic distribution with () as location and shape parameters, using these information can help us getting (number of groups, sample size) associated with reject or accept the Lot

Experimental results for simulated data shows the least number of groups and sample size needs to reject or accept the Lot with certain probability of

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Publication Date
Fri Mar 01 2019
Journal Name
Applied Acoustics
Theoretical model of absorption coefficient of an inhomogeneous MPP absorber with multi-cavity depths
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