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Interpretable machine learning optimization (InterOpt) for operational parameters: A case study of highly-efficient shale gas development
Research article (Petroleum Science, 2023) · cited 13× · AI/ML
Interpretable machine learning optimization (InterOpt) for operational parameters: A case study of highly-efficient shale gas development
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Interpretable machine learning optimization (InterOpt) for operational parameters: A case study of highly-efficient shale gas development is a scholarly article[1].
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Interpretable machine learning optimization (InterOpt) for operational parameters: A case study of highly-efficient shale gas development's instance of is recorded as scholarly article[2].
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APA4ort.xyz Knowledge Graph. (2026). Interpretable machine learning optimization (InterOpt) for operational parameters: A case study of highly-efficient shale gas development. Retrieved May 24, 2026, from https://4ort.xyz/entity/interpretable-machine-learning-optimization-interopt-for-operational-parameters-a-case-study-of-highly-efficient-shale-g
MLA“Interpretable machine learning optimization (InterOpt) for operational parameters: A case study of highly-efficient shale gas development.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/interpretable-machine-learning-optimization-interopt-for-operational-parameters-a-case-study-of-highly-efficient-shale-g.
BibTeX@misc{4ortxyz_interpretable-machine-learning-optimization-interopt-for-operational-parameters-a-case-study-of-highly-efficient-shale-g_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Interpretable machine learning optimization (InterOpt) for operational parameters: A case study of highly-efficient shale gas development}}, year = {2026}, url = {https://4ort.xyz/entity/interpretable-machine-learning-optimization-interopt-for-operational-parameters-a-case-study-of-highly-efficient-shale-g}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Interpretable machine learning optimization (InterOpt) for operational parameters: A case study of highly-efficient shale gas development — https://4ort.xyz/entity/interpretable-machine-learning-optimization-interopt-for-operational-parameters-a-case-study-of-highly-efficient-shale-g (retrieved 2026-05-24)
