PENYESUAIAN PERSAMAAN DEL-RIO UNTUK PERAMALAN KINERJA PRODUKSI MINYAK PADA LAPANGAN YANG DIIMPLEMENTASIKAN VIBROSEISMIK
The urge to increase oil production through Enhanced Oil Recovery in an economical and environmentally friendly manner has led many American, Russian and Indonesian researchers to study the vibration simulation method which is currently known as vibroseismik. Del Rio et al. (1998), inspired by the B...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/48969 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | The urge to increase oil production through Enhanced Oil Recovery in an economical and environmentally friendly manner has led many American, Russian and Indonesian researchers to study the vibration simulation method which is currently known as vibroseismik. Del Rio et al. (1998), inspired by the Biot equation, in his research succeeded in formulating a mathematical model that was able to predict the flow rate of fluid in a porous medium that was stimulated by vibrations (elastic waves). In Agung Ariya Wibowo's thesis (2019), using the equations and field data studied by Feryuma (2018), history matching was carried out in one field by varying the pressure difference. The results are shown to be better than previous studies with the production profile curve forming anticlines. These results prompted studies for the Del Rio equation with other field data. In its application, the Del Rio equation has a variable flow medium diameter (a) which is difficult to determine on a reservoir scale so that in research using the Del Rio equation requires a mathematical approach to determine the value of a.
This research have proven that the value of a and r are two variables that are the input of the Bessel function in the equation, so that the variable r which is the capillary porosity of the flow medium is observed for a mathematical approach as well. Because the variables has a significant effect on flow rate, permeability is chosen as another influential variable. The value a and r are obtained from the matching of the 6 field history and the permeability of each field for which the data is available, the correlation among variables can be determined using multi-variable regression. The result was great, The R-Square shows by the equation is 0.8925 and validations with field data A showed good results with 7% error. The minimum permeability value required for forecasting using this correlation is 293.28 mD. |
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