Accurate measurement is essential to ensure safe and efficient operations in the oil and gas industry, but factors such as environmental conditions, aging equipment and human error can make maintaining high accuracy difficult.
Expert Babayeju Olusile believes machine learning (ML) offers a promising solution to improve measurement accuracy.
By leveraging data-driven insights, ML can improve monitoring and control systems. It can also analyze large amounts of data from various sensors and equipment to identify patterns and anomalies that may indicate potential problems.
Babayeju Olusile is a Shell SIFpro Certified Facilitator and graduated in Electrical and Electronics Engineering from the Obafemi Awolowo University, Ilefe. He is an active member of the Nigerian Society of Engineers (NSE) and a Certified Maintenance and Reliability Professional. Olusile is a practicing professional plant engineer with several years of experience in the fields of equipment engineering and process analysis.
According to Babaieju, the self-evolving nature of machine learning models makes it possible to effectively predict and plan maintenance timelines. “By continuously learning from new data, machine learning models can adapt to changing conditions and improve their accuracy over time. This proactive approach helps prevent equipment breakdowns, minimize downtime and optimize production processes. Additionally, machine learning can also help reduce maintenance costs by enabling predictive maintenance strategies,” he said.
Given the dynamic context of oil and gas extraction, the importance of accuracy cannot be overemphasized. Instrumentation accuracy is crucial in ensuring operational efficiency, safety, and regulatory compliance during the oil and gas exploration process. Environmental factors, equipment degradation, and human error can significantly impact the output of instrumentation systems, and machine learning models address these barriers.
“Instrumentation systems face various challenges in the harsh oil and gas environment. Machine learning, a subset of artificial intelligence, enables computers to identify patterns, anomalies and correlations in massive data sets. By leveraging advanced algorithms, machine learning goes beyond the limitations of traditional rule-based systems,” Babaeju said.
Based on experimental research, he believes that applying machine learning models to improve the accuracy of oil and gas exploration offers many benefits to implementing companies: “ML-enabled measurement accuracy has been proven to improve operational efficiency by reducing downtime associated with equipment failures. Predictive maintenance based on ML insights allows for timely intervention, ensuring smooth operations and optimal performance. By reducing unplanned downtime and optimizing maintenance schedules, companies can realize significant cost savings.”
Implementing ML to improve measurement accuracy in oil and gas extraction requires a well-defined strategy to ensure successful integration into existing workflows.
Babayeju believes that with proper planning, ML can be used to its full potential: “The goal and objective of using ML to improve measurement accuracy needs to be clearly defined. Specific areas or processes where ML can be applied to improve accuracy and efficiency need to be identified. Relevant data needs to be collected from measurement sensors and other sources. The data needs to be clean, labeled and properly formatted for training the ML model, and relevant features in the data that are most predictive of measurement accuracy need to be identified.”
“Unless your task requires a more complex model, choose an ML algorithm that balances complexity and performance, and consider simple models that are easy to interpret and maintain,” he added.
Accurate measurement is fundamental to ensuring safe, efficient, and reliable oil and gas operations, and Babayeju believes ML is a tool that should be leveraged and will ultimately lead to better outcomes for oil and gas companies.
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“The future is bright for leveraging machine learning to improve measurement accuracy in oil and gas extraction. Given the emergence of new technologies and their potential for major impact on the industry, it is essential that companies stay ahead by investing in research, data quality, collaboration, regulatory compliance and infrastructure.”
“In doing so, we can harness the full benefits of machine learning to drive innovation in oil and gas extraction.”
