Analisa Sensitivitas Beberapa Parameter yang Berpengaruh Pada Hybrid-asp Flooding Dengan Menggunakan Simulator CMG Cmost 2020

Harahap, Rio Akbar (2023) Analisa Sensitivitas Beberapa Parameter yang Berpengaruh Pada Hybrid-asp Flooding Dengan Menggunakan Simulator CMG Cmost 2020. Other thesis, Universitas Islam Riau.

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Abstract

The EOR method or advanced oil recovery enhancement is the latest effort to increase oil production in old fields that have entered the tertiary stage and are experiencing a decline in production. For this reason, advanced dewatering technology is applied to old oil wells that still have a lot of oil remaining in the reservoir. One of the widely used EOR methods is chemical injection. One of the widely chemical injection is Hybrid-ASP Flooding. An important step to obtain optimal results in Hybrid-ASP flooding is that it is necessary to carry out sensitivity tests of design variables such as injection volume and concentration of the chemical to be injected to increase oil recovery. In this final project research, the authors will conduct a sensitivity test of 6 parameters to be tested, namely Bottom Hole Pressure, Flow Rate Injection, Surfactant Concentration, Adsorption, Reservoir Salinity, and Hybrid Alkali (Na2CO3+NH4OH). It is necessary to test the sensitivity of several parameters to be injected in order to increase the oil recovery factor. Sensitivity analysis was carried out to find out how important the influence of the key parameters on the output in the form of oil recovery factor. The relationship between several parameters depends on changes and modifications of these parameters. Then create a dataset design of 500 using CMOST 2020 and Recovery Factor as output. From the results of investigations conducted on RSM, the most influential parameter in Hybrid-ASP Flooding is Hybrid¬-Alkali, followed by Bottom Hole Pressure Producer as the top parameter affecting oil recovery during the Hybrid-ASP Flooding process. From the running results, it shows that the R value is higher than 0.85, which means that there is a match between the data and the model. The results of the investigation also show that Sensitivity Analysis CMOST should be considered for use in performing significant parameter analysis with an accuracy level of 0.942. 2

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