replot the figures and build method 2

Today, I replotted the figures and build method 2

Summary:

I will send you by the email.

Next week, I will build method 3 and start to write the second paper.

plot figures and tables in the outline

Today, I plotted figures and tables in the outline.

Summary:

I will send you by the email.

Tomorrow, I will finish plotting figures and tables for one well in three methods.

design the distribution examples of flags

Today, I designed the distribution examples of flags.

Summary:

I will send you by the email.

Tomorrow, I will continue to build the three models for one well.

finish preparing for the results of the second paper

Today, I finished preparing for the results of the second paper.

Summary:

I will send you by the email.

Tomorrow, I may start to write the second paper.

finish the task of abernathy well

Today, I follow your instructions to compare the three wells and finish the abernathy well.

Summary:

I have finished the abernathy well. I will send it to your by email.

Tomorrow, I will finish compare the rest two wells.

do some data preprocessing to improve the model

Today, I did not figure out how to improve the model. I just delete 66/4359 outliers to obtain a slightly better result.

Summary:

First, I changed the order of predicting these 8 outputs. I changed the order of perm 4 and perm 3 so that both orders of cond and perm are 2134, which is more reasonable. 8 changed models are shown as follows.

ANN1: log data   to   cond 2;
ANN2: log data + cond 2   to   cond 1;
ANN3: log data + cond 2 1   to   cond 3;
ANN4: log data + cond 2 1 3   to   cond 4;
ANN5: log data + cond 2 1 3 4   to   perm 2;
ANN6: log data + cond 2 1 3 4 + perm 2  to   perm 1;
ANN7: log data + cond 2 1 3 4 + perm 2 1   to   perm 3;
ANN8: log data + cond 2 1 3 4 + perm 2 1 3   to   perm 4.

The result improved 8 R2 of testing data in the above order are 0.91, 0.92, 0.88, 0.87, 0.71, 0.70, 0.65, 0.61 in the above order.

5 of them are higher, 2 of them are the same and just 1 is lower, which is perm 4.

For now, I predict 1 output and add predicted values into next model's inputs to train and test the next model. I plan to build all eight models with original data first and use predicted data to calculate the outputs directly next week. I think it may help.

Next week, I plan to do as the above idea.

predict 8 outputs one by one to get better results

Today, I predicted 8 outputs one by one to get a better result than predicting them together.

Summary:

The following two methods are inspired from honoring relationship between 4 cond and 4 perm.

First, I build two models. The first is to use log data to predict  4 cond. The second is to use 4 cond to predict 4 perm.
The prediction accuracy of 4 cond is good, 4 R2 of testing data are 0.80, 0.90, 0.86, 0.86.
But using 4 predicted cond to predict 4 perm is not good. 4 R2 of testing data are 0.54, 0.65, 0.56, 0.47.

Second, I build eight models.
I predict 8 outputs together first for ten times and average the R2 results. The accuracy from top to bottom is cond 2 1 3 4 and perm 2 1 4 3.
ANN1: log data   to   cond 2;
ANN2: log data + cond 2   to   cond 1;
ANN3: log data + cond 2 1   to   cond 3;
ANN4: log data + cond 2 1 3   to   cond 4;
ANN5: log data + cond 2 1 3 4   to   perm 2;
ANN6: log data + cond 2 1 3 4 + perm 2  to   perm 1;
ANN7: log data + cond 2 1 3 4 + perm 2 1   to   perm 4;
ANN8: log data + cond 2 1 3 4 + perm 2 1 4   to   perm 3.

The result improved. 8 R2 of testing data in the above order are 0.89, 0.91, 0.88, 0.87, 0.71, 0.64, 0.64, 0.65.

Tomorow, I will see if I can improve prediction accuracy by other methods.