Unlocking the Power of Artificial Intelligence: Building Digital Twins with Classification Algorithms for Optimized Geothermal Drilling
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Keywords:
Geothermal Energy, Digital Twin, Artificial Intelligence, Drilling, Classification AlgorithmsAbstract
Geothermal energy has emerged as a promising renewable energy source due to its
sustainability for long-term power generation. Effective drilling practices are crucial for the successful
utilization of geothermal resources, as they directly impact the productivity and operational efficiency of
geothermal systems. However, optimizing drilling operations in the geothermal sector presents unique
challenges due to the complex subsurface conditions and the need for continuous monitoring and
optimization. In recent years, the integration of artificial intelligence (AI) technologies has shown great
promise in improving various industrial processes. The energy sector, including geothermal energy, has
started leveraging AI techniques to enhance operational efficiency, reduce costs, and minimize
environmental impacts. Among the diverse AI methods, machine learning algorithms have gained
significant attention for their ability to analyze large datasets, extract patterns, and generate predictive
models. This paper focused on the potential benefits and challenges of utilizing AI, particularly the
classification algorithms, in the context of geothermal drilling. Such as Extreme Gradient Boosting
Machine (XGBoost), Light Gradient Boosting Machine (LightGBM) are a powerful machine learning
algorithms known for its effectiveness in handling structured datasets and achieving high predictive
accuracy.
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