Drilling automation
This comprehensive review of stuck pipe prediction methods focuses on data frequency, approach to variable selection, types of predictive models, interpretability, and performance assessment with the aim of providing improved guidelines for prediction that can be extended to other drilling abnormalities, such as lost circulation and drilling dysfunctions.
New case studies highlight how artificial intelligence, advanced hardware, and innovative business models are enabling success in drilling automation.
This paper tests several commercial large language models for information-retrieval tasks for drilling data using zero-shot, in-context learning.
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This paper describes a number of system enhancements, including the ability to display and analyze not only the critical parameters of drilling hydraulics but also other information that allows different perspectives in considering the closed-loop system. -
The paper demonstrates the successful application of advanced automated managed-pressure-drilling (MPD) technologies on the Dover well close to Fort McMurray, Alberta, Canada.
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Our industry is involved in the development of several automated tools and processes to improve the quality of drilling operations.
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The vision of fully automated drilling rigs driven by big data gathered in real time looks so far off but there are people working on a road map to help the oil and gas industry find its way there some day.
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Drilling, completion, production, and general surveillance are all areas that benefit greatly from remote real-time analysis. However, several challenges to remote services exist, including communications issues, fear of job loss, and working outside one’s comfort zone. What is considered an important development goal for a business might be regarded as a threat to an…
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Recently, while preparing to present a seminar on deepwater-well-construction optimization, I tried hard to find a word or a phrase that could be seen as “the secret” for a safe and optimized drilling performance.
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Data-mining processes are fundamental in obtaining the predictive benefits of real-time systems and have been progressing from descriptive to predictive optimization methods.
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During the optimization process, the Castilla field became the model of management strategies for the rest of the fields in the company portfolio.
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Integration of technology applications is paramount in increasing the success rate of data delivery.
