IMPLEMENTATION OF GRADIENTLESS NEURAL NETWORK FOR GEOTHERMAL PRODUCTION WELL EVALUATION AND EARLY WARNING SYSTEM
One of the crucial things in managing geothermal production system is monitor the performance of each production well. To evaluate the performance of a production well, engineers takes up to days because of the complicated process. Therefore, engineers often find that the well's performance...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/49697 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | One of the crucial things in managing geothermal production system is monitor the
performance of each production well. To evaluate the performance of a production
well, engineers takes up to days because of the complicated process. Therefore,
engineers often find that the well's performance drops significantly when the well
was evaluated, this resulted in the effort to repair the well being very heavy, even
impossible to repaired. On the other hand, in terms of technology, there have been
many applications of Artificial Intelligence and Machine Learning in helping
human work. It would be very good if the Artificial Neural Network (ANN) could
be applied in the geothermal production system so that it could help in evaluating
the well performance more quickly. The algorithm that is often used in ANN is
gradient-descent, even though not all functions have a gradient. In this study, a
gradient-less ANN application will be analyzed in evaluating the performance of
geothermal wells. |
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