Mohammed Al-Shargabi

1.7k total citations · 4 hit papers
42 papers, 1.1k citations indexed

About

Mohammed Al-Shargabi is a scholar working on Ocean Engineering, Mechanical Engineering and Civil and Structural Engineering. According to data from OpenAlex, Mohammed Al-Shargabi has authored 42 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Ocean Engineering, 17 papers in Mechanical Engineering and 9 papers in Civil and Structural Engineering. Recurrent topics in Mohammed Al-Shargabi's work include Drilling and Well Engineering (18 papers), Hydraulic Fracturing and Reservoir Analysis (14 papers) and Tunneling and Rock Mechanics (9 papers). Mohammed Al-Shargabi is often cited by papers focused on Drilling and Well Engineering (18 papers), Hydraulic Fracturing and Reservoir Analysis (14 papers) and Tunneling and Rock Mechanics (9 papers). Mohammed Al-Shargabi collaborates with scholars based in Russia, United Kingdom and Saudi Arabia. Mohammed Al-Shargabi's co-authors include Shadfar Davoodi, David A. Wood, Valeriy S. Rukavishnikov, Konstantin M. Minaev, Mohammad Mehrad, Dhafer Al Shehri, Hung Vo Thanh, Chin‐Tser Huang, Adel Rajab and Abdul Samad Ismail and has published in prestigious journals such as PLoS ONE, Langmuir and Journal of Cleaner Production.

In The Last Decade

Mohammed Al-Shargabi

40 papers receiving 1.0k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Review of technological progress in carbon dioxide capture, storage, and utilization

2023 169 citations Shadfar Davoodi, Mohammed Al-Shargabi et al. Gas Science and Engineering profile →
Experimental and field applications of nanotechnology for enhanced oil recovery purposes: A review

2022 114 citations Shadfar Davoodi, Mohammed Al-Shargabi et al. Fuel profile →
Carbon Dioxide Applications for Enhanced Oil Recovery Assisted by Nanoparticles: Recent Developments

2022 114 citations Mohammed Al-Shargabi, Shadfar Davoodi et al. profile →
Carbon dioxide sequestration through enhanced oil recovery: A review of storage mechanisms and technological applications

2024 63 citations Shadfar Davoodi, Mohammed Al-Shargabi et al. Fuel profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Mohammed Al-Shargabi Russia	15	641	512	231	169	158	42	1.1k
Valeriy S. Rukavishnikov Russia	20	814 1.3×	677 1.3×	353 1.5×	254 1.5×	193 1.2×	41	1.4k
Konstantin M. Minaev Russia	12	532 0.8×	422 0.8×	160 0.7×	110 0.7×	157 1.0×	25	818
Mobeen Murtaza Saudi Arabia	25	1.2k 1.9×	889 1.7×	334 1.4×	163 1.0×	336 2.1×	115	1.7k
Javed Akbar Khan Malaysia	17	515 0.8×	334 0.7×	259 1.1×	161 1.0×	38 0.2×	59	875
Ismail Mohd Saaid Malaysia	18	589 0.9×	308 0.6×	258 1.1×	168 1.0×	43 0.3×	79	964
Yanling Wang China	16	419 0.7×	305 0.6×	200 0.9×	60 0.4×	38 0.2×	50	772
Khalil Shahbazi Iran	21	1.2k 1.8×	916 1.8×	381 1.6×	90 0.5×	412 2.6×	106	1.5k
Dexin Liu China	17	592 0.9×	265 0.5×	198 0.9×	88 0.5×	146 0.9×	58	1.1k
Abdelbaki Benmounah Algeria	17	406 0.6×	330 0.6×	133 0.6×	34 0.2×	373 2.4×	61	896
Sonny Irawan Malaysia	17	681 1.1×	457 0.9×	136 0.6×	49 0.3×	290 1.8×	118	1.0k

Countries citing papers authored by Mohammed Al-Shargabi

Since Specialization

Citations

This map shows the geographic impact of Mohammed Al-Shargabi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mohammed Al-Shargabi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mohammed Al-Shargabi more than expected).

Fields of papers citing papers by Mohammed Al-Shargabi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mohammed Al-Shargabi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mohammed Al-Shargabi. The network helps show where Mohammed Al-Shargabi may publish in the future.

Co-authorship network of co-authors of Mohammed Al-Shargabi

This figure shows the co-authorship network connecting the top 25 collaborators of Mohammed Al-Shargabi. A scholar is included among the top collaborators of Mohammed Al-Shargabi based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mohammed Al-Shargabi. Mohammed Al-Shargabi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Davoodi, Shadfar, et al.. (2025). Predicting water-based drilling fluid filtrate volume in close to real time from routine fluid property measurements. Petroleum. 11(2). 174–187. 1 indexed citations

Pan, Fang, et al.. (2025). Microfluidic Fabrication of Peptide-Functionalized Poly(lactic-co-glycolic acid) Nanoparticles for Targeted Curcumin Delivery in Breast Cancer. Langmuir. 41(29). 19514–19525. 2 indexed citations

Al-Shargabi, Mohammed, et al.. (2025). Prediction of oil production rate in multiple wells of a producing field applying combined deep–learning and optimization techniques. Fuel. 406. 136847–136847.

Davoodi, Shadfar, et al.. (2024). Underground hydrogen storage: A review of technological developments, challenges, and opportunities. Applied Energy. 381. 125172–125172. 20 indexed citations

Davoodi, Shadfar, et al.. (2024). Geothermal energy recovery from abandoned petroleum wells: A review of the challenges and opportunities. Sustainable Energy Technologies and Assessments. 68. 103870–103870. 15 indexed citations

Davoodi, Shadfar, Mohammed Al-Shargabi, David A. Wood, Mohammad Mehrad, & Valeriy S. Rukavishnikov. (2024). Carbon dioxide sequestration through enhanced oil recovery: A review of storage mechanisms and technological applications. Fuel. 366. 131313–131313. 63 indexed citations breakdown →

Davoodi, Shadfar, et al.. (2024). Hybrid Machine-Learning Model for Accurate Prediction of Filtration Volume in Water-Based Drilling Fluids. Applied Sciences. 14(19). 9035–9035. 3 indexed citations

Davoodi, Shadfar, Evgeny Burnaev, David A. Wood, Mohammed Al-Shargabi, & Mohammad Mehrad. (2024). An integrated intelligent approach to the determination of drilling fluids’ solid content. Colloids and Surfaces A Physicochemical and Engineering Aspects. 707. 135906–135906. 2 indexed citations

Davoodi, Shadfar, Mohammed Al-Shargabi, David A. Wood, Konstantin M. Minaev, & Valeriy S. Rukavishnikov. (2023). Modified-starch applications as fluid-loss reducers in water-based drilling fluids: A review of recent advances. Journal of Cleaner Production. 434. 140430–140430. 27 indexed citations

10.

Davoodi, Shadfar, Mohammed Al-Shargabi, David A. Wood, & Valeriy S. Rukavishnikov. (2023). A comprehensive review of beneficial applications of viscoelastic surfactants in wellbore hydraulic fracturing fluids. Fuel. 338. 127228–127228. 59 indexed citations

11.

Davoodi, Shadfar, Mohammed Al-Shargabi, David A. Wood, & Valeriy S. Rukavishnikov. (2023). Recent advances in polymers as additives for wellbore cementing applications: A review. Fuel. 357. 129692–129692. 32 indexed citations

12.

Al-Shargabi, Mohammed, et al.. (2023). A Novel Automated Model for Evaluation of the Efficiency of Hole Cleaning Conditions during Drilling Operations. Applied Sciences. 13(11). 6464–6464. 9 indexed citations

13.

Al-Shargabi, Mohammed, et al.. (2023). A Developed Robust Model and Artificial Intelligence Techniques to Predict Drilling Fluid Density and Equivalent Circulation Density in Real Time. Sensors. 23(14). 6594–6594. 10 indexed citations

14.

Davoodi, Shadfar, Mohammed Al-Shargabi, David A. Wood, Valeriy S. Rukavishnikov, & Konstantin M. Minaev. (2023). Review of technological progress in carbon dioxide capture, storage, and utilization. Gas Science and Engineering. 117. 205070–205070. 169 indexed citations breakdown →

15.

Davoodi, Shadfar, Hung Vo Thanh, David A. Wood, et al.. (2023). Machine-learning models to predict hydrogen uptake of porous carbon materials from influential variables. Separation and Purification Technology. 316. 123807–123807. 53 indexed citations

16.

Namoun, Abdallah, et al.. (2019). A Learning Outcome Inspired Survey Instrument for Assessing the Quality of Continuous Improvement Cycle. International Journal of Information and Communication Technology Education. 15(2). 108–129. 7 indexed citations

17.

Al-Shargabi, Mohammed. (2019). Decision Support Approach for Enhancing Course Learning Outcomes Achievement and Accuracy of Continuous Improvement. International Journal of Advanced Trends in Computer Science and Engineering. 8(1.3). 320–326. 1 indexed citations

18.

Al-Shargabi, Mohammed, et al.. (2016). CNT‐PAC(カーボンナノチューブ‐粉末活性炭)複合材料による水からのカドミウム除去官能化の影響. NANO. 11(1). 7. 3 indexed citations

19.

Al-Shargabi, Mohammed, et al.. (2016). Enhancing the Quality of Service for Real Time Traffic over Optical Burst Switching (OBS) Networks with Ensuring the Fairness for Other Traffics. PLoS ONE. 11(9). e0161873–e0161873. 5 indexed citations

20.

Al-Shargabi, Mohammed & Abdul Samad Ismail. (2008). 2008 International Symposium on High Capacity Optical Networks and Enabling Technologies, HONET 2008. 4 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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