A Unified Conceptual Framework for AI-Enhanced Reservoir Characterization and Predictive Field Decision Systems

• Author(s): Chibueze Marcellus Amadi; Oluwaseyi Ayotunde Akano; Alexander Onwumere
• Paper ID: 1716525
• Page: 637-653
• Published Date: 31-12-2023
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 7 Issue 6 December-2023

Abstract

The integration of artificial intelligence (AI) into reservoir characterization and field decision systems has significantly transformed exploration and production strategies in the oil and gas industry. This paper presents a comprehensive review of AI-enhanced reservoir characterization and predictive field decision systems, focusing on their methodologies, architectures, and practical implications for improving subsurface understanding and operational efficiency. Traditional reservoir characterization techniques often rely on deterministic models and manual interpretation of geological, geophysical, and petrophysical data, which can be time-consuming and prone to uncertainty. In contrast, AI-driven approaches leverage machine learning, deep learning, and data-driven analytics to process large volumes of heterogeneous data, including seismic surveys, well logs, and production data, enabling more accurate and automated reservoir modeling. The review examines the application of advanced AI techniques such as convolutional neural networks for seismic interpretation, recurrent neural networks for production forecasting, and clustering algorithms for facies classification. It also explores the integration of digital twin technologies and real-time data analytics for predictive decision-making in field operations. By combining data assimilation, uncertainty quantification, and optimization algorithms, AI-enhanced systems provide dynamic and adaptive decision support for reservoir management, including well placement, enhanced oil recovery strategies, and production optimization. Furthermore, the paper highlights challenges associated with data quality, model interpretability, and system integration, emphasizing the need for robust governance frameworks and explainable AI techniques. Emerging trends such as edge computing, cloud-based analytics, and hybrid physics-informed machine learning models are also discussed as key enablers of next-generation reservoir management systems. This review contributes to the field by synthesizing current advancements and identifying research gaps, providing a structured foundation for developing intelligent, scalable, and data-driven reservoir characterization and decision systems that enhance operational performance and resource recovery.

Keywords

Reservoir Characterization, Artificial Intelligence in Oil and Gas, Predictive Field Decision Systems, Seismic Data Analytics, Digital Twin Technology, Production Optimization.

Citations

IRE Journals:
Chibueze Marcellus Amadi, Oluwaseyi Ayotunde Akano, Alexander Onwumere "A Unified Conceptual Framework for AI-Enhanced Reservoir Characterization and Predictive Field Decision Systems" Iconic Research And Engineering Journals Volume 7 Issue 6 2023 Page 637-653

IEEE:
Chibueze Marcellus Amadi, Oluwaseyi Ayotunde Akano, Alexander Onwumere "A Unified Conceptual Framework for AI-Enhanced Reservoir Characterization and Predictive Field Decision Systems" Iconic Research And Engineering Journals, 7(6)