Yapay zeka algoritmalarını kullanarak petrol ve gaz üretim tahminlerinin iyileştirilmesi

The energy industry heavily relies on accurate oil and gas production forecasting, which is crucial in optimizing resource allocation, production planning, and operational efficiency. Predicting future production rates is fundamental to the petroleum industry, enabling businesses to enhance the extraction and refinement of fuels such as gasoline and diesel while minimizing risks and maximizing profits. Reliable forecasting models help stakeholders make informed decisions regarding investment strategies, infrastructure development, and regulatory compliance. As global energy demands continue to rise, the need for precise, data-driven forecasting methodologies becomes increasingly important to ensure stability and sustainability in the petroleum sector. Traditionally, forecasting the future production of energy-rich natural gas and oil has been a widely studied topic in petroleum engineering. Due to their similar extraction processes and market demands, these two resources are often analyzed together. Historically, researchers have relied on conventional techniques such as decline curve analysis and numerical reservoir simulation to estimate future production levels. While these approaches have been widely used, they suffer from several critical limitations. The high computational costs, the extensive time required to complete the simulations, and the dependence on multiple assumptions often result in unreliable and inconsistent predictions. Additionally, these traditional models struggle to adapt to the complex, nonlinear nature of oil and gas production, particularly when dealing with fluctuating reservoir conditions, market dynamics, and external environmental factors. As a result, there is a growing need for more advanced, efficient, and accurate predictive methodologies. In recent years, artificial intelligence (AI) has emerged as a transformative tool in the energy sector, offering the potential to enhance the speed, accuracy, and adaptability of production forecasting. AI-based approaches have significantly reduced the computational time required for forecasting while improving prediction accuracy through automated feature selection, pattern recognition, and model optimization. By leveraging machine learning, ensemble learning, and deep learning techniques, AI-driven models can effectively process large volumes of historical production data to identify trends and make precise forecasts. The integration of AI into petroleum engineering has enabled more efficient decision-making processes, optimized resource utilization, and minimized uncertainties in production planning. This thesis aims to enhance the accuracy and efficiency of oil and gas production forecasting by implementing a robust AI-driven predictive framework. The proposed system employs a combination of machine learning, ensemble learning, and deep learning techniques to develop highly accurate predictive models. By leveraging past production data, the system aims to generate precise future outcome predictions, enabling companies and stakeholders to improve strategic planning, optimize resource allocation, and mitigate market risks. The research focuses on evaluating the performance of multiple AI models to determine the most effective methodology for forecasting oil and gas production with minimal error rates. Eleven different methodologies were used in the proposed system. These consist of five machine learning models: Decision Tree Regressor (DTR), Random Forest Regressor (RFR), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), and Gradient Boosting Regressor (GBR), as well as three ensemble learning models: Bagging, Boosting, and Stacking. Additionally, there are three models in deep learning: Artificial Neural Network (ANN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). By incorporating these methodologies, the system ensures that multiple aspects of data-driven forecasting are considered, thereby improving robustness and overall performance. The results of this thesis demonstrate that ensemble learning techniques, particularly the stacking model, offer superior predictive performance compared to standalone machine learning or deep learning models. The ensemble-based approaches significantly reduced error rates in key evaluation metrics, including Mean Absolute Error (MAE) and Mean Squared Error (MSE). Among all the models tested, the stacking model achieved the highest level of accuracy, with an R-squared value of 99%, indicating its exceptional ability to capture complex patterns and dependencies in oil and gas production data. This finding highlights the effectiveness of stacking as a model integration technique, which combines the predictive power of multiple base learners to produce a highly accurate final prediction. Beyond academic contributions, this thesis has significant practical implications for the petroleum industry. The integration of AI-driven forecasting models into oil and gas production workflows can lead to substantial improvements in decision-making efficiency, cost reduction, and risk management. By utilizing intelligent predictive analytics, industry professionals can make data-driven investment decisions, optimize drilling and extraction strategies, and improve overall operational performance. Furthermore, AI-based models provide greater adaptability to changing reservoir conditions and external economic factors, ensuring more reliable and sustainable production forecasts in the long term. The ability to dynamically adjust to real-time production data, external market fluctuations, and environmental constraints makes AI-powered forecasting a critical component of the future energy sector. The adoption of AI-based forecasting methodologies has the potential to revolutionize the petroleum industry, ensuring more precise and reliable production planning in an increasingly complex and dynamic energy market. Additionally, continued advancements in AI and data science will enable the integration of real-time monitoring systems, improving responsiveness to changing market demands and environmental conditions. Generating highly accurate and adaptive predictions will be crucial in meeting global energy needs while promoting efficiency and sustainability in the oil and gas sector. Furthermore, the expansion of AI applications in the energy sector may open avenues for automation, enhanced reservoir management, and predictive maintenance, further optimizing extraction and refining processes. Future research will focus on incorporating additional real-time operational parameters, expanding the dataset to include diverse geological formations, and developing hybrid AI architectures that combine the strengths of multiple methodologies to further enhance forecasting performance. Overall, this thesis provides a comprehensive framework for AI-driven oil and gas production forecasting, demonstrating the immense potential of integrating machine learning, deep learning, and ensemble learning techniques to achieve highly accurate and efficient predictions. The continued evolution of AI methodologies will play a crucial role in shaping the future of energy production, ensuring more sustainable, efficient, and intelligent resource management strategies in the petroleum industry.