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Article
Peer-Review Record

Numerical Simulation Study of Heavy Oil Production by Using In-Situ Combustion

Processes 2019, 7(9), 621; https://doi.org/10.3390/pr7090621
by Zhao Yang 1,*, Shuang Han 1 and Hongji Liu 2
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Processes 2019, 7(9), 621; https://doi.org/10.3390/pr7090621
Submission received: 17 July 2019 / Revised: 26 August 2019 / Accepted: 27 August 2019 / Published: 14 September 2019
(This article belongs to the Special Issue Chemical Process Design, Simulation and Optimization)

Round 1

Reviewer 1 Report

This paper requires significant editing as well as improvements to the quality and readability of the many figures for which the text and line type is not legible.  Also, there are significant translation improvements that are needed throughout the text.  For example the word "rhythm" is used (see tables 15, 16, for example) when I believe the authors are referring to the rock heterogeneity.  This is likely an interesting case history but translation issues significantly hinder its readability.  Also, the amount of general information related to fire-flooding and steam flooding could be reduced to allow greater focus on the specifics of the Du66 project.  

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report

The paper outlines integration of numerical simulation and experimental study in in-situ combustion. Since, heavy oil recovery is a challenging procedure these kind of techniques can assist in better understanding of the process and trying to improve future production from the thermal reservoirs. This paper can help to understand physics behind the in-situ combustion process. 
Here are my recommendations:
 Some literature on heavy oil reservoir regarding SAGD process and Polymer Flooding also needs to be added. Authors may like to use the following references to make the literature review more comprehensive:

   (a) Amirian, E., Fedutenko, E., Yang, C., Chen, Z., & Nghiem, L. (2018). Artificial Neural Network Modeling and Forecasting of Oil Reservoir Performance. In Applications of Data Management and Analysis (pp. 43-67). Springer, Cham

   (b) Fedutenko, E., Nghiem, L., Yang, C., Chen, T., & Seifi, M. (2019, March 29). Artificial Neural Network Modeling of Compaction-Dilation Data for Unconventional Oil Reservoirs. Society of Petroleum Engineers. doi:10.2118/193811-MS

Good luck



Author Response

Please see the attachment.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Manuscript revision shows significant improvement - technical content and grammatical.

Minor grammatical errors still exist but the cheif concern is inconsistent use of Du 66 Block, Du 66 block, du 66 Block, du 66 

Also, the word "rhythm" continues to be used.  This is almost certainly a translation issue; suggest finding more appropriate term for "positive rhytm case" (e.g. line 1729, 1833, etc (30 cases in all).  For Stratigraphic "Rhythm" in line 1729 and following siggest using Stratigraphic Layering or Stratigraphic Interval or Stratigraphic Zone, etc...  

Author Response

请参阅附件。

Author Response File: Author Response.docx

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