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Predicting field production rates for waterflooding using a machine learning-based proxy model

Research article (Journal of Petroleum Science and Engineering, 2020) · cited 113× · AI/ML
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Summary

Predicting field production rates for waterflooding using a machine learning-based proxy model is a scholarly article[1].

Key Facts

  • Instance of: scholarly article[2]

References

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Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

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APA 4ort.xyz Knowledge Graph. (2026). Predicting field production rates for waterflooding using a machine learning-based proxy model. Retrieved October 19, 2026, from https://4ort.xyz/entity/predicting-field-production-rates-for-waterflooding-using-a-machine-learning-based-proxy-model
MLA “Predicting field production rates for waterflooding using a machine learning-based proxy model.” 4ort.xyz Knowledge Graph, 4ort.xyz, 19 Oct. 2026, https://4ort.xyz/entity/predicting-field-production-rates-for-waterflooding-using-a-machine-learning-based-proxy-model.
BibTeX @misc{4ortxyz_predicting-field-production-rates-for-waterflooding-using-a-machine-learning-based-proxy-model_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Predicting field production rates for waterflooding using a machine learning-based proxy model}}, year = {2026}, url = {https://4ort.xyz/entity/predicting-field-production-rates-for-waterflooding-using-a-machine-learning-based-proxy-model}, note = {Accessed: 2026-10-19}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Predicting field production rates for waterflooding using a machine learning-based proxy model — https://4ort.xyz/entity/predicting-field-production-rates-for-waterflooding-using-a-machine-learning-based-proxy-model (retrieved 2026-10-19)

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