Drilling automation
This study explores the use of autoencoder models with convolutional neural networks to present a framework and prototype for early and accurate kick detection during offshore oilwell drilling.
This paper presents the first global application of autonomous drilling in deepwater and the journey to reach optimal drilling parameters, integrating proprietary tools from the project’s business partners.
Drilling experts recently shared candid views on what will be required for their segment of the upstream business to move to the next stage of development.
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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. -
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.
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