Next Article in Journal
Numerical Study on the Effect of Enhanced Buffer Materials in a High-Level Radioactive Waste Repository
Next Article in Special Issue
Multi-Objective Optimization of CO2 Sequestration in Heterogeneous Saline Aquifers under Geological Uncertainty
Previous Article in Journal
Review of the Relationship between Reactive Oxygen Species (ROS) and Elastin-Derived Peptides (EDPs)
Previous Article in Special Issue
Research on Prediction of Movable Fluid Percentage in Unconventional Reservoir Based on Deep Learning
 
 
Article
Peer-Review Record

Quantitative Interpretation of TOC in Complicated Lithology Based on Well Log Data: A Case of Majiagou Formation in the Eastern Ordos Basin, China

Appl. Sci. 2021, 11(18), 8724; https://doi.org/10.3390/app11188724
by Shuiqing Hu 1, Haowei Zhang 2, Rongji Zhang 3, Lingxuan Jin 2 and Yuming Liu 2,4,*
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Appl. Sci. 2021, 11(18), 8724; https://doi.org/10.3390/app11188724
Submission received: 19 August 2021 / Revised: 8 September 2021 / Accepted: 14 September 2021 / Published: 18 September 2021
(This article belongs to the Special Issue Digital Technologies in the Petroleum Industry)

Round 1

Reviewer 1 Report

The manuscript needs revision of English syntax and definitions of some terms: "practical core", "subject lithology of core", "BP" (Is this back propagation or something else?), etc.

it requires some clarification of terms used.
it needs to be checked for English and missing definitions.

A suggested reference:

Intelligent Modeling Approaches in Petroleum Geosciences: Determining the Total Organic Carbon (TOC) in Marcellus Shale, NY State,

https://www.researchgate.net/publication/313905544_Intelligent_Modeling_Approaches_in_Petroleum_Geosciences_Determining_the_Total_Organic_Carbon_TOC_in_Marcellus_Shale_NY_State

 

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report

In my opinion, the article is characterized by a high substantive level, where the methodology in particular has been described in detail and accessible, which I congratulate the Authors.

More important remarks concern the inaccuracies as below:

In section 2 M1, 3, 5 layers are described as research target; in section 4.1.1 the equations for M3 and M5 are only given; in section 4.1.3 and in fig. 7, 10, 11 only M5 (6-7 ) are presented. For which data (layer's number)  interpretation was done with both methods: neural network and ΔlogR? Add this at the beginning.

Other minor comments:

  1. The legend in the Figure 1 is too small, what is LT1 marked on the red?
  2. Add the information about depth of M1-M5 occurrence in the fig.2, and line 86 - explain why only M1,M3, M5 were examined in this study.
  3. line 103 - how many TOC data were measured?
  4. line 114 - based on the curve shape can you point out the minimal layer thickness for which the curve response can be observed?
  5. line 144-147 - any examples on these results to add to citation?
  6. whether all correlations in the table 1 are statistically significant?
  7. line 180 - is the correlation R2 = 0.35 really high? Rather: the highest for the elements presented in fig.5, is just above R = 0.50...
  8. line 216-217 - what about TOC equation for Ma 1?
  9. fig.6 - too small axis labels
  10. fig.7 - move downward to the end of the 4.1.3 section
  11. line 229 - the results concern only formation Ma 5 showed on fig.7 (just a section)? or all of the examined formations?
  12. line 85, 325- add spaces
  13. line 340 - fig.10 is not a map, correct word, please
  14. In the discussion section, in the fig.11, I recommend to eliminate the information and the data which were not used in the calculations of both methods and add the description with method's name, maybe normalized values at one axis?
  15. References: capitalize each of author's name or none; remove "et al." if all the authors were named (e.g. [4-7])

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Back to TopTop