نوع مقاله : مقاله پژوهشی
نویسندگان
گروه مدیریت فناوری اطلاعات، دانشکده مدیریت و حسابداری، دانشگاه علامه طباطبایی، تهران، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Intelligent Decision Support Systems (IDSS) have become increasingly essential for enhancing prediction accuracy, resource planning, and operational stability in the oil and gas sector. Traditional linear regression–based models often fail to capture nonlinear patterns inherent in petroleum operational datasets, resulting in reduced forecasting reliability. Recent research highlights the importance of hybrid approaches that integrate statistical and machine learning techniques to improve predictive performance and support upstream decision-making.This study proposes a hybrid model combining Linear Regression (LR), Random Forest (RF), and Artificial Neural Network (ANN) algorithms to enhance oil production prediction accuracy and reduce production instability. The model was evaluated using 9,208 operational records from the Azadegan oil field. Results demonstrate a 17% reduction in prediction error and a 15% decrease in production instability, confirming the superiority of hybrid predictive models for upstream decision support. These findings align with prior studies emphasizing the effectiveness of AI-driven hybrid systems in optimizing petroleum production forecasting.
کلیدواژهها [English]
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