Global Research Trends on Shale Gas from 2010–2020 Using a Bibliometric Approach
Bibliographic record
Abstract
In the last few decades, shale gas resources have attracted much global attention as potential sources for clean and affordable energy. Due to this fact, coupled with the increasing energy shortfall, shale gas has become an increasingly attractive energy prospect from both an environmental and economic perspective. This development has led to the rapid growth in the number of researchers and publications in the field of shale gas. Although there are few review articles on the state of research on shale gas, the literature lacked a bibliometric analysis. This study is intended to fill the research gap by carrying out a bibliometric analysis of 9247 shale gas articles that were published between 2010 and 2020. The Web of Science database was used to collect the data. The analysis was performed to identify the most productive authors, institutions, countries, and sources, and to visualize existing collaborations as well as provide valuable information which could form the basis for establishing future collaboration. The analysis results revealed that Li J has the highest number of publications on shale gas whereas Loucks RG is the most cited author. The top three countries with the highest number of publications in shale gas research are China, USA, and Canada, while the China University of Petroleum (Beijing), China University of Geosciences, and Southwest Petroleum University China were the three top institutions with the highest number of publications. Fuel, International Journal of Coal Geology, and Marine and Petroleum Geology are the journals with the highest number of published articles on shale gas. The keyword analysis indicated that shale gas, hydraulic fracturing, pore structure, permeability, adsorption, kinetics, pyrolysis, organic matter, thermal maturity, and numerical simulation are the predominant research topics. This showed the multi-dimensional and multi-faceted character of the shale gas field. Besides, it appeared to be an exciting topic for further study that is based on a detailed evaluation of the shale gas literature. In fact, shale gas, hydraulic fracturing (fracking), CO2 sequestration, kinetic, gas adsorption, diffusion, and simulation are becoming emerging research hotspots. The bibliometric analysis that was presented in this study has revealed valuable information about the most active institutions and countries, and the most influential authors in the field of shale gas which could form the basis for establishing future collaboration. Furthermore, it can help researchers to understand the global research trend in shale gas as well as provide references for establishing future research directions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.093 | 0.145 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".