Predıctıon of produced oıl amount usıng randomızed artıfıcıal neural networks

Oil is one of the cornerstones of the global energy sector, and oil production holds vital importance for countries in terms of economic development and energy security. Accurately predicting production levels in oilfield wells plays a critical role in enhancing operational efficiency and optimizing costs. However, traditional forecasting methods fail to adequately address the complexities of production processes and environmental variations. Today, artificial intelligence techniques, such as Artificial Neural Networks (ANNs), have the potential to provide higher accuracy and efficiency in oil production forecasting.This study aims to compare the predictive performance of models like RNN, ELM, and RVFL by utilizing real production data from the S3 Field in the Batman Region and to contribute to AI-based applications in the energy sector. Furthermore, another significant aspect of this study is its emphasis on how accurate predictions not only yield economic benefits but also pave the way for developing strategies that support environmental sustainability. In this context, the potential of AI-based models in the energy sector is evaluated. If you have further requests or would like me to continue building the paper, let me know!