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

EasyChair Preprint 14469

8 pagesDate: August 15, 2024

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

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@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}}
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