WOA (Whale Optimization Algorithm) Optimizes Elman Neural Network Model to Predict Porosity Value in Well Logging Curve
Round 1
Reviewer 1 Report
A very well written manuscript with some added value in improving prediction accuracy in the field of logging curve property prediction using WOA Elman neural networks.
The manuscript needs some improvements.
The state of the art must be separated from the target description that follows after it in two different paragraphs. Also the description of target must be further elaborated and be more specific.
Figure 3 is hard to read and some parts of it need to enlarged.
Provide further analysis and describe better the way the results are being presented in figures 4 and 5. Provide concise descriptions of the visual representations included in them.
In figures 7 & 8 some data fall out of scale and especially in figure 8 not all data are being shown. Please correct that.
The conclussions section is more of a short summary rather than a highlight of conclussions and also you should consider adding further work and discuss the impact and added value of your presented work.
Author Response
Dear reviewer, thank you very much for your review. I have revised your valuable suggestions. Please have a look.
The existing technology and the subsequent research objectives have been modified into two parts.
We have roughened the coordinates of the coordinate axis in Figure 3, which can make the figure more clearly displayed.
In Figure 4 and figure 5, we have described the results in detail.
In Figure 7 and figure 8, we reset the scale so that the results can be displayed in the article.
Conclusion we rewrite it to increase the next work and the impact of this work.
Reviewer 2 Report
Porosity is a vital parameter for oil reservoirs. It is necessary to take cores for indoor test in order to accurately obtain the porosity value of cores, this process consumes a lot of resources. This paper introduces the method of machine learning to predict the porosity by using logging curves. This paper creatively develops a WOA (whale optimization algorithm) optimized Elman neural network model to predict porosity through some logging parameters.
The topic of the paper is relevant. But I think it is necessary to describe the methodology in more detail and divide the conclusions into several points.
Author Response
Dear reviewer, thank you very much for your review. I have revised your valuable suggestions. Please have a look.
Thank you for your replacement methodology. We repeat the Elman neural network and WOA whale algorithm based on the original text of the article. I'm sorry that we can't change this part. Later, we found that we didn't show a relatively complete methodology in the correlation mapping method, so we supplemented the correlation mapping method.
We have divided the conclusion into several points. Please have a look.
Author Response File: 
Author Response.docx
