Enhancing Rate of Penetration Prediction in Drilling Operations: A Data Stream Framework Approach
SSRN, ISSN: 1556-5068
2024
- 109Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
In the oil and gas industry, drilling operations serve as a cornerstone in the quest for sustainable hydrocarbon production. Among the myriad factors shaping drilling success, the Rate of Penetration (ROP) through subsurface rock formations emerges as a pivotal parameter. Precise prediction and optimization of ROP assume heightened significance, given their profound impact on cost efficiency, safety protocols, and the overarching aim of minimizing environmental footprints. Predictive machine learning algorithms utilize data transformations to capture intricate patterns, offering flexibility in optimizing drilling operations. While machine learning algorithms consistently yield favorable results, optimizing ROP prediction warrants careful consideration of ROP data dynamics, including concept drift—a phenomenon wherein the underlying distribution of ROP data changes over time. We advocate addressing ROP prediction within the data stream framework to accommodate high-speed data, frequent model updating, and effective handling of concept drift. Our comparative analysis between traditional machine learning methods and online learning algorithms demonstrates a notable improvement. Employing suitable data stream algorithms led to a significant 63% reduction in absolute error rate and a substantial 77% decrease in processing time, on average.
Bibliographic Details
Elsevier BV
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