Prediction of Mud Weight Window Based on Geological Sequence Matching and a Physics-Driven Machine Learning Model for Pre-Drilling Article Swipe
Yuxin Chen
,
Ting Sun
,
Jin Yang
,
Xianjun Chen
,
Laiao Ren
,
Zhiliang Wen
,
Shu Jia
,
Wencheng Wang
,
Shuqun Wang
,
Mingxuan Zhang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/pr13072255
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/pr13072255
Accurate pre-drilling mud weight window (MWW) prediction is crucial for drilling fluid design and wellbore stability in complex geological formations. Traditional physics-based approaches suffer from subjective parameter selection and inadequate handling of multi-mechanism over-pressured formations, while machine learning methods lack physical constraints and interpretability. This study develops a novel physics-guided deep learning framework integrating rock mechanics theory with deep neural networks for enhanced MWW prediction. The framework incorporates three key components: first, a physics-driven layer synthesizing intermediate variables from rock physics calculations to embed domain knowledge while preserving interpretability; second, a geological sequence-matching algorithm enabling precise stratigraphic correlation between offset and target wells, compensating for lateral geological heterogeneity; third, a long short-term memory network capturing sequential drilling characteristics and geological structure continuity. Case study results from 12 wells in northwestern China demonstrate significant improvements over traditional methods: collapse pressure prediction error reduced by 40.96%, pore pressure error decreased by 30.43%, and fracture pressure error diminished by 39.02%. The proposed method successfully captures meter-scale pressure variations undetectable by conventional approaches, providing critical technical support for wellbore design optimization, drilling fluid formulation, and operational safety enhancement in challenging geological environments.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/pr13072255
- https://www.mdpi.com/2227-9717/13/7/2255/pdf?version=1752575713
- OA Status
- gold
- Cited By
- 1
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412454707
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4412454707Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/pr13072255Digital Object Identifier
- Title
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Prediction of Mud Weight Window Based on Geological Sequence Matching and a Physics-Driven Machine Learning Model for Pre-DrillingWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-07-15Full publication date if available
- Authors
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Yuxin Chen, Ting Sun, Jin Yang, Xianjun Chen, Laiao Ren, Zhiliang Wen, Shu Jia, Wencheng Wang, Shuqun Wang, Mingxuan ZhangList of authors in order
- Landing page
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https://doi.org/10.3390/pr13072255Publisher landing page
- PDF URL
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https://www.mdpi.com/2227-9717/13/7/2255/pdf?version=1752575713Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2227-9717/13/7/2255/pdf?version=1752575713Direct OA link when available
- Concepts
-
Window (computing), Drilling, Sequence (biology), Matching (statistics), Computer science, Geology, Artificial intelligence, Mechanical engineering, Engineering, Biology, Mathematics, Genetics, Statistics, World Wide WebTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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