---
title: Machine Learning for Gas-Liquid Flow
url: https://www.ml-quant.com/papers/ssrn/4988520/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4988520
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4988520
featured: 2024-10-17
citations: unknown
topic: ML & AI Methods
---


# Machine Learning for Gas-Liquid Flow

The article assesses various machine learning frameworks for multiphase flow in oil and gas production to enhance prediction accuracy.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4988520
- Identifier: SSRN 4988520
- Released: 2024-10-15
- First featured: Quant Letter No. 70 (2024-10-17): https://www.ml-quant.com/issues/2024-10-17/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: ML & AI Methods

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