ReviewSensors (Basel, Switzerland)2024
Tools, Technologies and Frameworks for Digital Twins in the Oil and Gas Industry: An In-Depth Analysis.
Review in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Evolutionary Digital Twin for Oil and Gas Pipelines: A Cognitive Multi-Agent Framework with Continuous Feedback Learning.Sensors (Basel, Switzerland) · 2026Article
- Leveraging FMMEA for Digital Twin Development: A Case Study on Intelligent Completion in Oil and Gas.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
The digital twin (DT), which involves creating a virtual replica of a physical asset or system, has emerged as a transformative set of tools across various industries. In the oil and gas (O&G) industry, the development of DTs represents a significant evolution in how companies manage complex operations, enhance safety, and optimize decision-making processes. Despite these significant advancements, the underlying tools, technologies, and frameworks for developing DTs in O&G applications remain non-standardized and unfamiliar to many O&G practitioners, highlighting the need for a systematic literature review (SLR) on the topic. Thus, this paper offers an SLR of the existing literature on DT development for O&G from 2018 onwards, utilizing Scopus and Web of Science Core Collection. We provide a comprehensive overview of this field, demonstrate how it is evolving, and highlight standard practices and research opportunities in the area. We perform broad classifications of the 98 studies, categorizing the DTs by their development methodologies, implementation objectives, data acquisition, asset digital development, data integration and preprocessing, data analysis and modeling, evaluation and validation, and deployment tools. We also include a bibliometric analysis of the selected papers, highlighting trends and key contributors. Given the increasing number of new DT developments in O&G and the many new technologies available, we hope to provide guidance on the topic and promote knowledge production and growth concerning the development of DTs for O&G.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.