Multiphase Measurement, Soft Sensors, Digital Twins: A Systematic Literature Review

Luz Yamile Caicedo Chacón; Sebastian Roa Prada; Carlos Eduardo García Sánchez

doi:10.5753/jbcs.2026.6087

Authors

Luz Yamile Caicedo Chacón Universidad Autónoma de Bucaramanga https://orcid.org/0000-0002-2946-2298
Sebastian Roa Prada Universidad Autónoma de Bucaramanga https://orcid.org/0000-0002-1079-9798
Carlos Eduardo García Sánchez Corporación Centro de Desarrollo Tecnológico del Gas https://orcid.org/0000-0003-1543-1498

DOI:

https://doi.org/10.5753/jbcs.2026.6087

Keywords:

Digital twin, Machine Learning, Multiphase flow measurement, Data-driven models, soft computing techniques, Soft sensors

Abstract

Accurate multiphase flow measurement (MPFM) is essential in the oil and gas industry to optimize production, manage reservoirs, and ensure operational safety. Conventional MPFMs, such as Venturi, Coriolis, and positive displacement meters, remain costly and often unreliable under complex flow conditions, limiting their widespread application. In recent years, artificial intelligence (AI), soft sensors, and digital twins have emerged as promising alternatives to improve accuracy, reduce costs, and enable real-time monitoring. This paper presents a systematic review of multiphase measurement technologies, soft sensors, and digital twin applications in hydrocarbon production. Following a structured protocol, we analyze 150 publications from the past decade, addressing three research questions: (i) the current state, challenges, and limitations of MPFM technologies; (ii) the role of soft sensors and data-driven modeling, including statistical methods, machine learning algorithms, and hybrid physics-guided approaches; and (iii) methodological and industrial applications of digital twins in oil and gas operations. The review shows that while traditional MPFMs have reached technological maturity, their costs and operational constraints remain significant barriers. Soft sensors and AI-based methods offer high predictive capacity, although the challenges of interpretability and data quality persist. Digital twins demonstrate potential for integration of realtime monitoring and predictive analytics, but require clearer frameworks distinguishing theoretical models from industrial practice. In general, the findings highlight opportunities to advance multiphase measurement through the integration of AI, soft computing, and digital twin paradigms, and outline directions for future research in this field.

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