Enhancing Oil Reserve Prediction with Recurrent Neural Networks: A Data-Driven Approach
DOI:
https://doi.org/10.54097/ad2wtg33Keywords:
Recurrent Neural Networks (RNNs), LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit), Oil Reserves Forecasting, Time-Series Analysis, Data Preprocessing, Feature Engineering.Abstract
"The oil and gas industry relies heavily on accurate reserve predictions for strategic decision-making. This study introduces a Recurrent Neural Network (RNN) model to forecast oil reserves, leveraging the network's ability to capture temporal dependencies within data. After reviewing traditional forecasting methods and their limitations, we present the theoretical foundations of RNNs, focusing on LSTM and GRU architectures. Our methodology involves rigorous data preprocessing and feature engineering, followed by the development of an RNN model specifically tuned for oil reserve forecasting. Experimental results indicate that our model outperforms conventional methods, as demonstrated through a detailed case study. We conclude with insights into the model's effectiveness and propose directions for future enhancements.".
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