Mohammad Mehrad

1.4k total citations · 1 hit paper
39 papers, 1.0k citations indexed

About

Mohammad Mehrad is a scholar working on Ocean Engineering, Mechanical Engineering and Mechanics of Materials. According to data from OpenAlex, Mohammad Mehrad has authored 39 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Ocean Engineering, 29 papers in Mechanical Engineering and 9 papers in Mechanics of Materials. Recurrent topics in Mohammad Mehrad's work include Hydraulic Fracturing and Reservoir Analysis (22 papers), Drilling and Well Engineering (20 papers) and Enhanced Oil Recovery Techniques (11 papers). Mohammad Mehrad is often cited by papers focused on Hydraulic Fracturing and Reservoir Analysis (22 papers), Drilling and Well Engineering (20 papers) and Enhanced Oil Recovery Techniques (11 papers). Mohammad Mehrad collaborates with scholars based in Russia, United Kingdom and Iran. Mohammad Mehrad's co-authors include Shadfar Davoodi, David A. Wood, Valeriy S. Rukavishnikov, Hamzeh Ghorbani, Hung Vo Thanh, Nima Mohamadian, Mohammed Al-Shargabi, Ahmad Ramezanzadeh, Mohammad Sabah and Zhenxue Dai and has published in prestigious journals such as SHILAP Revista de lepidopterología, Applied Energy and Expert Systems with Applications.

In The Last Decade

Mohammad Mehrad

39 papers receiving 991 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.

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
Mohammad Mehrad Russia	20	605	535	338	161	94	39	1.0k
Ahmed Farid Ibrahim Saudi Arabia	19	852 1.4×	584 1.1×	513 1.5×	190 1.2×	65 0.7×	137	1.2k
Dmitriy A. Martyushev Russia	22	762 1.3×	661 1.2×	583 1.7×	94 0.6×	64 0.7×	88	1.2k
Xingru Wu United States	18	758 1.3×	691 1.3×	388 1.1×	239 1.5×	117 1.2×	98	1.2k
Mehdi Mokhtari United States	17	756 1.2×	654 1.2×	546 1.6×	130 0.8×	251 2.7×	70	1.2k
Sadiq J. Zarrouk New Zealand	18	520 0.9×	686 1.3×	308 0.9×	539 3.3×	121 1.3×	73	1.7k
Shaobin Hu China	17	457 0.8×	251 0.5×	588 1.7×	133 0.8×	126 1.3×	50	979
Amirmasoud Kalantari Dahaghi United States	16	491 0.8×	386 0.7×	350 1.0×	118 0.7×	35 0.4×	36	748
Christopher J. Landry United States	15	394 0.7×	292 0.5×	290 0.9×	137 0.9×	27 0.3×	31	903
Temoor Muther United States	16	475 0.8×	367 0.7×	394 1.2×	114 0.7×	30 0.3×	34	732

Countries citing papers authored by Mohammad Mehrad

Since Specialization

Citations

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

Fields of papers citing papers by Mohammad Mehrad

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mohammad Mehrad

This figure shows the co-authorship network connecting the top 25 collaborators of Mohammad Mehrad. A scholar is included among the top collaborators of Mohammad Mehrad 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 Mohammad Mehrad. Mohammad Mehrad 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

Davoodi, Shadfar, et al.. (2025). Advanced Optimized Deep-Learning Model for Precise Evaluation of Subsurface Carbon Dioxide Trapping Efficiency. Energy & Fuels. 39(8). 3966–3992. 3 indexed citations

Davoodi, Shadfar, et al.. (2025). Machine-learning models for predicting CO2 solubility in various brine systems: implications for carbon geo-storage. Journal of Molecular Liquids. 435. 128122–128122. 2 indexed citations

Davoodi, Shadfar, Hung Vo Thanh, David A. Wood, et al.. (2024). Carbon dioxide storage and cumulative oil production predictions in unconventional reservoirs applying optimized machine-learning models. Petroleum Science. 22(1). 296–323. 8 indexed citations

Davoodi, Shadfar, et al.. (2024). Robust Machine Learning Predictive Models for Real-Time Determination of Confined Compressive Strength of Rock Using Mudlogging Data. Rock Mechanics and Rock Engineering. 57(9). 6881–6907. 10 indexed citations

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, 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

10.

Davoodi, Shadfar, et al.. (2024). Robust machine-learning model for prediction of carbon dioxide adsorption on metal-organic frameworks. Journal of Alloys and Compounds. 1010. 177890–177890. 15 indexed citations

11.

Davoodi, Shadfar, Hung Vo Thanh, David A. Wood, et al.. (2024). Committee machine learning: A breakthrough in the precise prediction of CO2 storage mass and oil production volumes in unconventional reservoirs. Geoenergy Science and Engineering. 245. 213533–213533. 4 indexed citations

12.

Davoodi, Shadfar, et al.. (2024). Combined deep-learning optimization predictive models for determining carbon dioxide solubility in ionic liquids. Journal of Industrial Information Integration. 41. 100662–100662. 8 indexed citations

13.

Davoodi, Shadfar, Hung Vo Thanh, David A. Wood, et al.. (2023). Machine-learning predictions of solubility and residual trapping indexes of carbon dioxide from global geological storage sites. Expert Systems with Applications. 222. 119796–119796. 73 indexed citations

14.

Davoodi, Shadfar, Hung Vo Thanh, David A. Wood, Mohammad Mehrad, & Valeriy S. Rukavishnikov. (2023). Combined machine-learning and optimization models for predicting carbon dioxide trapping indexes in deep geological formations. Applied Soft Computing. 143. 110408–110408. 40 indexed citations

15.

Ramezanzadeh, Ahmad, et al.. (2023). Estimation of geomechanical rock characteristics from specific energy data using combination of wavelet transform with ANFIS-PSO algorithm. Journal of Petroleum Exploration and Production Technology. 13(8). 1715–1740. 10 indexed citations

16.

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

17.

Davoodi, Shadfar, Mohammad Mehrad, David A. Wood, Hamzeh Ghorbani, & Valeriy S. Rukavishnikov. (2023). Hybridized machine-learning for prompt prediction of rheology and filtration properties of water-based drilling fluids. Engineering Applications of Artificial Intelligence. 123. 106459–106459. 41 indexed citations

18.

Mehrad, Mohammad, et al.. (2022). Prediction of permeability from well logs using a new hybrid machine learning algorithm. Petroleum. 9(1). 108–123. 46 indexed citations

19.

Mehrad, Mohammad, et al.. (2022). A new robust predictive model for lost circulation rate using convolutional neural network: A case study from Marun Oilfield. Petroleum. 9(3). 468–485. 18 indexed citations

20.

Mehrad, Mohammad, et al.. (2022). A novel approach to pore pressure modeling based on conventional well logs using convolutional neural network. Journal of Petroleum Science and Engineering. 211. 110156–110156. 26 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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