AI-Driven Approaches to Enhancing Reservoir Management: Predictive Modeling Techniques for Long-Term Production Forecasting

EasyChair Preprint 14469

Abstract

The advent of artificial intelligence (AI) has brought significant advancements in various industries, including the oil and gas sector. This paper explores the potential of AI-driven approaches in enhancing reservoir management, focusing on predictive modeling techniques for long-term production forecasting. By leveraging AI algorithms, such as machine learning and deep learning, operators can gain deeper insights into reservoir behavior, optimize production strategies, and improve decision-making processes. The paper also discusses the challenges associated with implementing AI in reservoir management, including data quality, model interpretability, and computational requirements. Through a comprehensive analysis, this study aims to provide a detailed understanding of how AI can revolutionize reservoir management for long-term production forecasting.

Keyphrases: : AI in Reservoir Management, Data Quality in AI, High Performance Computing, Long-Term Production Forecasting, Model Interpretability, deep learning, machine learning, neural networks, oil and gas industry, predictive modeling

@booklet{EasyChair:14469,
  author    = {Kayode Sheriffdeen},
  title     = {AI-Driven Approaches to Enhancing Reservoir Management: Predictive Modeling Techniques for Long-Term Production Forecasting},
  howpublished = {EasyChair Preprint 14469},
  year      = {EasyChair, 2024}}