Presentation Profile
What are the Recent Advances in the Last Three Years Using AI and ML that Can Revolutionize the Petroleum Testing Laboratory?
Currently Scheduled: 10/14/2026 - 1:00 PM - 2:00 PM
Room: Exhibit Hall Entrance
Main Author
Raj Shah - Koehler Instrument Company, Inc.
- Arya Patil - Koehler Instrument Company, Inc.
- Gavin Cunningham - Koehler Instrument Company, Inc.
Abstract:
Petroleum testing laboratories are prevalent in regulating the usage of petroleum, a large part of the world’s energy supply. However, the high demand for petroleum suggests that it needs to be improved to maximize production efficiency and adapt to technological developments. Laboratories for fuel testing are separated into the upstream sector, the midstream sector, and the downstream sector; these sections ensure that there are no complications or dangers from using fuels in practice. Moreover, petroleum testing laboratories follow a refining process that ensures that the fuel meets regulations and safety standards. To ensure testing is efficient and effective, companies have begun testing new methods with automated technologies. Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) are being explored to shift the way petroleum labs use systems and digital methods to revolutionize performance in the oil and gas industry. AI can arguably be used to solve existing problems by rapidly processing data with higher accuracy compared to manual methods. Multiple prior studies have been conducted to understand how AI affects performance in operational, financial, and environmental domains. Furthermore, there are studies to understand how ML algorithms such as artificial neural networks (ANN) and partial least squares (PLS) can optimize the refining process and streamline operations. Although prior research has been done to understand the implications of AI and ML across the oil and gas industry, few studies have been directed to explore the direct implications of smart systems in petroleum testing laboratories. The purpose of this study is to more specifically understand how recent technological advancements improve laboratory testing and accuracy. Thereby, the study aims to answer the following question: What are the recent advances in the last three years using AI and ML that can revolutionize the petroleum testing laboratory? Research has shown that using AI/ML for composition analysis, contamination detection, predictive modelling, automated image analysis, computer vision, process optimization, and data management fields shows strong potential for improvements in cost, efficiency, accuracy, and speed for petroleum testing.
