A bibliometric approach for analyzing the potential role of waste-derived nanoparticles in the upstream oil and gas industry

Panes, Prince; Macariola, Mary Antonette; Niervo, Christian; Maghanoy, Andrew Gem; Garcia, Karl Patrick; Ignacio, Jonathan Jared

doi:10.1016/j.clet.2022.100468

Cited by 6 publications

(4 citation statements)

References 64 publications

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“…This study is focused extensively on journal articles published in journals indexed by the Scopus database [66] until 24 March 2022. The Scopus database covers nearly 36,377 titles from approximately 11,678 publishers and 34,346 peer-reviewed journals [67][68][69]. The study is limited to works published in scientific journals by researchers utilising the Scopus database even though [70] mentioned the importance of exploring multiple databases for relevant material, this approach has drawbacks.…”

Section: Methodsmentioning

confidence: 99%

“…Uncertainty is fundamental in research but is especially prevalent in current assessments of numerical flood models. Due to data, methodology, and theory discrepancies, the accuracy of results from these models has been threatened, as concluded by [69,107]. Calibration decreases model uncertainty by seeking accurate parameters to ensure the model's accuracy.…”

Section: Review On the Flood Prediction Toolsmentioning

confidence: 99%

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Know to Predict, Forecast to Warn: A Review of Flood Risk Prediction Tools

Antwi-Agyakwa

Afenyo

Angnuureng

2023

Water

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Flood prediction has advanced significantly in terms of technique and capacity to achieve policymakers’ objectives of accurate forecast and identification of flood-prone and impacted areas. Flood prediction tools are critical for flood hazard and risk management. However, numerous reviews on flood modelling have focused on individual models. This study presents a state-of-the-art review of flood prediction tools with a focus on analyzing the chronological growth of the research in the field of flood prediction, the evolutionary trends in flood prediction, analysing the strengths and weaknesses of each tool, and finally identifying the significant gaps for future studies. The article conducted a review and meta-analysis of 1101 research articles indexed by the Scopus database in the last five years (2017–2022) using Biblioshiny in r. The study drew an up-to-date picture of the recent developments, emerging topical trends, and gaps for future studies. The finding shows that machine learning models are widely used in flood prediction, while Probabilistic models like Copula and Bayesian Network (B.N.) play significant roles in the uncertainty assessment of flood risk, and should be explored since these events are uncertain. It was also found that the advancement of the remote sensing, geographic information system (GIS) and cloud computing provides the best platform to integrate data and tools for flood prediction. However, more research should be conducted in Africa, South Africa and Australia, where less work is done and the potential of the probabilistic models in flood prediction should be explored.

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Section: Methodsmentioning

confidence: 99%

Section: Review On the Flood Prediction Toolsmentioning

confidence: 99%

Know to Predict, Forecast to Warn: A Review of Flood Risk Prediction Tools

Antwi-Agyakwa

Afenyo

Angnuureng

2023

Water

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“…This information could then be used to identify potential international collaborators. 141,142 6.4. Expertise Gaps and Potential International Collaborations.…”

Section: Major Nano-eor Field Trialsmentioning

confidence: 99%

“…For example, the bibliometric analysis could be used to identify countries and institutions that are leading the way in nano-EOR research. This information could then be used to identify potential international collaborators. , …”

Section: Aspects Of the Fieldmentioning

confidence: 99%

Nanotechnology Impact on Chemical-Enhanced Oil Recovery: A Review and Bibliometric Analysis of Recent Developments

Hosny,

Zahran,

Abotaleb

et al. 2023

ACS Omega

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Oil and gas are only two industries that could change because of nanotechnology, a rapidly growing field. The chemical-enhanced oil recovery (CEOR) method uses chemicals to accelerate oil flow from reservoirs. New and enhanced CEOR compounds that are more efficient and eco-friendly can be created using nanotechnology. One of the main research areas is creating novel nanomaterials that can transfer EOR chemicals to the reservoir more effectively. It was creating nanoparticles that can be used to change the viscosity and surface tension of reservoir fluids and constructing nanoparticles that can be utilized to improve the efficiency of the EOR compounds that are already in use. The assessment also identifies some difficulties that must be overcome before nanotechnology-based EOR can become widely used in industry. These difficulties include the requirement for creating mass-producible, cost-effective nanomaterials. There is a need to create strategies for supplying nanomaterials to the reservoir without endangering the formation of the reservoir. The requirement is to evaluate the environmental effects of CEOR compounds based on nanotechnology. The advantages of nanotechnology-based EOR are substantial despite the difficulties. Nanotechnology could make oil production more effective, profitable, and less environmentally harmful. An extensive overview of the most current advancements in nanotechnology-based EOR is provided in this paper. It is a useful resource for researchers and business people interested in this area. This review’s analysis of current advancements in nanotechnology-based EOR shows that this area is attracting more and more attention. There have been a lot more publications on this subject in recent years, and a lot of research is being done on many facets of nanotechnology-based EOR. The scientometric investigation discovered serious inadequacies in earlier studies on adopting EOR and its potential benefits for a sustainable future. Research partnerships, joint ventures, and cutting-edge technology that consider assessing current changes and advances in oil output can all benefit from the results of our scientometric analysis.

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