Autor: |
Yangfeng Ren, Baoping Lu, Shuangjin Zheng, Kai Bai, Lin Cheng, Hao Yan, Gan Wang |
Jazyk: |
angličtina |
Rok vydání: |
2023 |
Předmět: |
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Zdroj: |
Geofluids, Vol 2023 (2023) |
Druh dokumentu: |
article |
ISSN: |
1468-8123 |
DOI: |
10.1155/2023/6645604 |
Popis: |
ROP is an important index to evaluate the efficiency of oil and gas drilling. In order to accurately predict the ROP of an oilfield in Xinjiang working area, a ROP prediction model based on the historical drilling data of this working area was established based on stacking ensemble learning. This model integrates the K-nearest neighbor algorithm and support vector machine algorithm by stacking ensemble strategy and uses genetic algorithm to optimize model parameters, forming a new method of ROP prediction suitable for this oilfield. The prediction results show that the accuracy of ROP prediction by this method is up to 92.5%, and the performance is stable, which can provide reference for the optimization of drilling parameters in this oilfield and has specific guiding significance for improving the efficiency of drilling operations. |
Databáze: |
Directory of Open Access Journals |
Externí odkaz: |
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