APPLICATION OF CUBIC SPLINE FUNCTION IN GENETIC ALGORITHM FOR HORIZONTAL WELL LENGTH OPTIMIZATION IN RESERVOIR GAS X
Genetic Algorithm (GA) is intended to replace the conventional trial and error procedure which is usually done in reservoir simulations. In this study, the GA is used to find an optimal horizontal well length to obtain the maximum plateau time period of gas production. GA is a random search method b...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/19868 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Genetic Algorithm (GA) is intended to replace the conventional trial and error procedure which is usually done in reservoir simulations. In this study, the GA is used to find an optimal horizontal well length to obtain the maximum plateau time period of gas production. GA is a random search method based on natural evolution that needs existence of an objective function on its application. The objective function used is the result of curve fitting using cubic spline function of the plateau time with respect to the horizontal well length obtained from reservoir simulation. GA model is also applied to different directions to get the drilling direction that produces maximum plateau time period. Furthermore, the reduction of running data simulation is also done to show the least simulation process. <br />
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In the end, it can be concluded that the GA can provide results that are not much different when compared to the validation results using reservoir simulation that have a maximum error of plateau time of 7.22%. Furthermore, the 315 degree drilling is drilling direction that produces maximum plateau time. On the condition of 25 MMSCFD gas flow rate and the THP of 250 psia, still in the same direction, it was found that the data reduction to 18%, 30% and 50% of all simulation running data, giving the plateau time difference respectively 7.22%, 2.64% and 0.05%. While the difference in optimal length of horizontal well segment on the use of small portion of all simulation running data results in a maximum error rate reached 24.5%. This indicates that the GA can be applied in this optimization with the degree of difference that is acceptable and also sufficient to reduce the time needed to perform reservoir simulation. |
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