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Porosity is one of the key petrophysical parameters in evaluating reservoirs to optimize the production of oil and natural gas fields.
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An appropriate application of this method can help the reservoir engineer to optimize the reservoir management with low costs and high efficiency.
The approach has been used in the past to characterize reservoirs and to optimize hydrocarbon production.
A compositional reservoir simulator has been used to determine the effect of the gas injection process on reservoir production to optimize the oil recovery for the MIS field.
Compositional numerical simulations were conducted to identify the good injection site in the reservoir and to optimize the injection strategies.
The approach is automated and combines an economic package and existing numerical reservoir simulators to optimize the design of a selected EOR process using sensitivity analysis.
In this article, we used more than 4,000 simulation runs which were performed automatically in a reservoir simulator to optimize a cyclic production scenario with intelligent well completions.
Generating suitable connection weights of a reservoir network to optimize the learning of the read-out weights for a given task is not always easy.
This paper focuses on the use of geostatistical methods to map reservoir properties and combining these methods with numerical reservoir simulation techniques to optimize oil recovery from the reservoir by carrying out comparative analysis on several factors that influence production and waterflood performance in a case study.
In this method, the pressure of a reservoir is obtained based on the material balance equation by minimizing the difference between calculated and reservoir pressure in order to optimize reservoir parameters suchas OOIP, aquifer constant and water influx data calculated by the aquifer function.
Production efficiency from low permeability shale gas reservoirs requires techniques to optimize hydraulic fracture (HF) completions.
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