The Evolving Landscape of Oil and Gas Chemicals: Convergence of Artificial Intelligence and Chemical-Enhanced Oil Recovery in the Energy Transition Toward Sustainable Energy Systems and Net-Zero Emissions

Journal Title: Journal of Data Science and Intelligent Systems - Year 2024, Vol 2, Issue 2

Abstract

Chemical-enhanced oil recovery (EOR) is a field of study that can gain significantly from artificial intelligence (AI), addressing uncertainties such as mobility control, interfacial tension reduction, wettability alteration, and emulsifications. The primary objective of this paper is to introduce an integrated framework for AI and chemical EOR for energy harvest operations. Central emphasis is placed on the energy transition, with the aim of expediting the development of cleaner energy harvesting systems and attaining the goal of net-zero emission. To do so, we present how the energy transition is changing the manufacturing of the chemicals for EOR application. For this, the uncertainty associated with materials' design and critical role of the simulators for transferring the laboratory experiences into full-field implementations is discussed. The concept of digitalization and its impact on energy companies are highlighted. The role of digital twin in simulators integration is discussed, emphasizing how increased data access can help design more tolerant chemicals for harsh reservoir environments using real-time data. Also, we discuss how the chemical suppliers, research institutes, startups, and field operators can benefit from self-leaning and robotic laboratories for chemicals manufacturing. Moreover, this paper explores how including AI perspectives can improve our understanding of developing chemical formulations by blending hybrid capabilities. This approach contributes to making energy production more sustainable and aligning with the goal of zero emissions. A workflow is presented to demonstrate how the integration of AI and chemical EOR can be used for both hydrocarbon production and other energy transition operations, such as carbon capture, utilization and storage, hydrogen storage, and geothermal reservoirs. The outcome of this paper stands as a pioneering effort that uniquely addresses these challenges for both academia and the industry and can open many additional doors and identify topics requiring further investigations.

Authors and Affiliations

Alireza Bigdeli, Mojdeh Delshad

Keywords

Related Articles

Bootstrap Methods for Canonical Correlation Analysis of Functional Data

The bootstrap method is a very general resampling procedure for investigating the distributional property of statistics. In this paper, we present two bootstrap methods with the aim of studying the functional canonical c...

Detection of Facial Mask Using Deep Learning Classification Algorithm

Deep learning is an algorithm that works by representing data in layers of learning layers so that the representation becomes more meaningful. "Deep" in deep learning means that deep learning begins layers of sequential...

Correlation Filters in Machine Learning Algorithms to Select Demographic and Individual Features for Autism Spectrum Disorder Diagnosis

Autism spectrum disorder is currently considered one of the main neurodevelopmental disorders with predominant characteristics of difficulty in social communication and cognitive skills, and limited and repetitive patter...

Customer Segmentation Using Machine Learning Model: An Application of RFM Analysis

Machine learning (ML) encompasses a diverse array of both supervised and unsupervised techniques that facilitate prediction, classification, and anomaly detection. Among the many fields of application for such techniques...

Advancing Bridge Structural Health Monitoring: Insights into Knowledge-Driven and Data-Driven Approaches

Structural health monitoring (SHM) is increasingly being used in the field of bridge engineering, and the technology for monitoring bridges has undergone a radical change. It has evolved from the initial local monitoring...

Download PDF file
  • EP ID EP752179
  • DOI 10.47852/bonviewJDSIS42022111
  • Views 14
  • Downloads 0

How To Cite

Alireza Bigdeli, Mojdeh Delshad (2024). The Evolving Landscape of Oil and Gas Chemicals: Convergence of Artificial Intelligence and Chemical-Enhanced Oil Recovery in the Energy Transition Toward Sustainable Energy Systems and Net-Zero Emissions. Journal of Data Science and Intelligent Systems, 2(2), -. https://europub.co.uk/articles/-A-752179