Rasool Khosravanian

646 total citations
23 papers, 462 citations indexed

About

Rasool Khosravanian is a scholar working on Ocean Engineering, Mechanical Engineering and Mechanics of Materials. According to data from OpenAlex, Rasool Khosravanian has authored 23 papers receiving a total of 462 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Ocean Engineering, 9 papers in Mechanical Engineering and 2 papers in Mechanics of Materials. Recurrent topics in Rasool Khosravanian's work include Drilling and Well Engineering (19 papers), Oil and Gas Production Techniques (14 papers) and Reservoir Engineering and Simulation Methods (14 papers). Rasool Khosravanian is often cited by papers focused on Drilling and Well Engineering (19 papers), Oil and Gas Production Techniques (14 papers) and Reservoir Engineering and Simulation Methods (14 papers). Rasool Khosravanian collaborates with scholars based in Iran, Norway and United Kingdom. Rasool Khosravanian's co-authors include David A. Wood, Mohammad Sabah, Bernt S. Aadnøy, Mohammad Anemangely, Mohsen Talebkeikhah, Jaber Taheri-Shakib, Tron Golder Kristiansen, Jafar Qajar, Tomasz Wiktorski and Adil Rasheed and has published in prestigious journals such as SHILAP Revista de lepidopterología, Energies and Journal of Petroleum Science and Engineering.

In The Last Decade

Rasool Khosravanian

21 papers receiving 453 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Rasool Khosravanian Iran 11 347 204 70 65 65 23 462
Yunfeng Xu China 10 175 0.5× 173 0.8× 87 1.2× 29 0.4× 49 0.8× 24 331
Mohammad Sabah Iran 8 360 1.0× 296 1.5× 85 1.2× 90 1.4× 46 0.7× 14 485
S. Mohaghegh United States 16 573 1.7× 448 2.2× 110 1.6× 34 0.5× 40 0.6× 30 673
Amin Amirlatifi United States 10 104 0.3× 100 0.5× 77 1.1× 34 0.5× 82 1.3× 36 367
Carl Horst Albrecht Brazil 12 238 0.7× 123 0.6× 45 0.6× 90 1.4× 40 0.6× 33 399
Yasin Hajizadeh United Kingdom 12 387 1.1× 287 1.4× 50 0.7× 11 0.2× 42 0.6× 28 457
Morteza Haghighat Sefat United Kingdom 12 305 0.9× 213 1.0× 28 0.4× 14 0.2× 45 0.7× 38 431
Emad A. El-Sebakhy Saudi Arabia 11 200 0.6× 128 0.6× 114 1.6× 22 0.3× 87 1.3× 21 423
Mehdi Shahbazian Iran 10 123 0.4× 138 0.7× 41 0.6× 129 2.0× 65 1.0× 37 424
Timur Bikmukhametov Norway 4 166 0.5× 89 0.4× 80 1.1× 28 0.4× 32 0.5× 5 326

Countries citing papers authored by Rasool Khosravanian

Since Specialization
Citations

This map shows the geographic impact of Rasool Khosravanian'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 Rasool Khosravanian with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rasool Khosravanian more than expected).

Fields of papers citing papers by Rasool Khosravanian

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Rasool Khosravanian. 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 Rasool Khosravanian. The network helps show where Rasool Khosravanian may publish in the future.

Co-authorship network of co-authors of Rasool Khosravanian

This figure shows the co-authorship network connecting the top 25 collaborators of Rasool Khosravanian. A scholar is included among the top collaborators of Rasool Khosravanian 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 Rasool Khosravanian. Rasool Khosravanian is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Cao, Jie, et al.. (2025). Optimizing Drilling Operations in Real-Time: The Role of Digital Twins in Reducing Risks and Enhancing Performance. SPE/IADC International Drilling Conference and Exhibition.
3.
Svendsen, Karianne, et al.. (2025). Automated Computer Vision System for Real-Time Detection of Drilled Cuttings and Cavings. SPE/IADC International Drilling Conference and Exhibition. 3 indexed citations
4.
Tabib, Mandar, et al.. (2024). Anomaly detection in multivariate time series of drilling data. Geoenergy Science and Engineering. 237. 212778–212778. 10 indexed citations
5.
Aadnøy, Bernt S., et al.. (2023). An approach for optimization of controllable drilling parameters for motorized bottom hole assembly in a specific formation. Results in Engineering. 20. 101548–101548. 10 indexed citations
6.
Ambrus, Adrian, et al.. (2023). Improving predictive models for rate of penetration in real drilling operations through transfer learning. Journal of Computational Science. 72. 102100–102100. 10 indexed citations
7.
Qajar, Jafar, et al.. (2023). Selection of Optimal Well Trajectory Using Multi-Objective Genetic Algorithm and TOPSIS Method. Arabian Journal for Science and Engineering. 48(12). 16831–16855. 7 indexed citations
8.
Cayeux, Eric, et al.. (2023). A New Paradigm for Automatic Well Path Generation Using Multidisciplinary Constraints. SPE Annual Technical Conference and Exhibition. 1 indexed citations
9.
Khosravanian, Rasool, et al.. (2021). Application of mathematical and machine learning models to predict differential pressure of autonomous downhole inflow control devices. ADVANCES IN GEO-ENERGY RESEARCH. 5(4). 386–406. 17 indexed citations
10.
Sabah, Mohammad, et al.. (2019). A machine learning approach to predict drilling rate using petrophysical and mud logging data. Earth Science Informatics. 12(3). 319–339. 106 indexed citations
11.
Taheri-Shakib, Jaber, et al.. (2019). Experimental investigation of rock-solvent interaction on the properties of carbonate reservoir rock. Journal of Petroleum Science and Engineering. 181. 106246–106246. 9 indexed citations
12.
Sabah, Mohammad, et al.. (2018). Application of an Adaptive Neuro-fuzzy Inference System and Mathematical Rate of Penetration Models to Predicting Drilling Rate. SHILAP Revista de lepidopterología. 22 indexed citations
13.
14.
Khosravanian, Rasool, et al.. (2018). A comparative study of several metaheuristic algorithms for optimizing complex 3-D well-path designs. Journal of Petroleum Exploration and Production Technology. 8(4). 1487–1503. 27 indexed citations
15.
Khosravanian, Rasool, et al.. (2018). Chemical removal of organic precipitates deposition from porous media: Characterizing adsorption and surface properties. Journal of Petroleum Science and Engineering. 175. 200–214. 11 indexed citations
16.
Khosravanian, Rasool & Bernt S. Aadnøy. (2016). Optimization of casing string placement in the presence of geological uncertainty in oil wells: Offshore oilfield case studies. Journal of Petroleum Science and Engineering. 142. 141–151. 10 indexed citations
17.
Khosravanian, Rasool & David A. Wood. (2016). Selection of high-rate gas well completion designs applying multi-criteria decision making and hierarchy methods. Journal of Natural Gas Science and Engineering. 34. 1004–1016. 5 indexed citations
18.
Wood, David A. & Rasool Khosravanian. (2015). Exponential utility functions aid upstream decision making. Journal of Natural Gas Science and Engineering. 27. 1482–1494. 9 indexed citations
19.
Khosravanian, Rasool, et al.. (2015). 3-D well path design using a multi objective genetic algorithm. Journal of Natural Gas Science and Engineering. 27. 219–235. 48 indexed citations
20.
Wood, David A., et al.. (2014). Designing and optimizing deviated wellbore trajectories using novel particle swarm algorithms. Journal of Natural Gas Science and Engineering. 21. 1184–1204. 73 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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