AI-Based Intelligent Decision-Making Platform for Operational Excellence in the Oil and Gas Industry

The integration of artificial intelligence (AI) in the oil and gas industry has revolutionized operational processes, enabling organizations to overcome complex challenges and improve efficiency. This study has explored the development and implementation of a suite of AI-based intelligent systems by CNPCNP Petroleum S.A. (CNPCNP) to enhance decision-making, optimize resource utilization, and address critical challenges in its Niger operations. By leveraging advanced AI technologies, including deep learning, business intelligence (BI), natural language processing (NLP), and robotic process automation (RPA), CNPCNP has developed the Intelligent Decision-Making Platform for finance, planning, human resources, security, and health management. The Intelligent Decision-Making Platform has provided an integrated governance and data intelligence system, connecting all the “silo” data and creating infinite intelligent analytical data models with very limited amount of code and “drag-and-drop” methods. This system offers real-time insights, streamline operations, and improve organizational performance. This paper serves as a reference for leveraging AI in resourcelimited and high-security environments, contributing to the broader discourse on digital transformation in the energy industry.

Authors

Institutions

Publication Details

Published
2025-04-11
DOI
https://doi.org/10.1109/ainit65432.2025.11035727
Citations
1
Primary Topic
Reservoir Engineering and Simulation Methods
Type
article
Field-Weighted Citation Impact
1.15
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AI-Based Intelligent Decision-Making Platform for Operational Excellence in the Oil and Gas Industry

Zong Tian, Xiang Ma, Xiao Dong He, Xiaoxiong Li et al.
1 citations
Reservoir Engineering and Simulation Methods
1.15
article

AI-Based Intelligent Decision-Making Platform for Operational Excellence in the Oil and Gas Industry

Zong Tian, Xiang Ma, Xiao Dong He, Xiaoxiong Li, Yin Wang, Siwei Peng
article en
1 citations

Abstract

The integration of artificial intelligence (AI) in the oil and gas industry has revolutionized operational processes, enabling organizations to overcome complex challenges and improve efficiency. This study has explored the development and implementation of a suite of AI-based intelligent systems by CNPCNP Petroleum S.A. (CNPCNP) to enhance decision-making, optimize resource utilization, and address critical challenges in its Niger operations. By leveraging advanced AI technologies, including deep learning, business intelligence (BI), natural language processing (NLP), and robotic process automation (RPA), CNPCNP has developed the Intelligent Decision-Making Platform for finance, planning, human resources, security, and health management. The Intelligent Decision-Making Platform has provided an integrated governance and data intelligence system, connecting all the “silo” data and creating infinite intelligent analytical data models with very limited amount of code and “drag-and-drop” methods. This system offers real-time insights, streamline operations, and improve organizational performance. This paper serves as a reference for leveraging AI in resourcelimited and high-security environments, contributing to the broader discourse on digital transformation in the energy industry.

China National Petroleum Corporation (China) (CN)
Peace, Justice and strong institutions
Openalex Percentile: Top 22%
Reservoir Engineering and Simulation Methods
1.15
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.