Summary:
First, I find all related papers and review their abstracts again.
Second, I write two parts of the outline, introduction and methods.
The following is my outline written today.
Outline
Top-level outline (sections)
1.
Introduction – nobody predict 6
parameters before. Some use 6 or 9 parameters to predict permeability or pore
types.
2.
Methods- build an ANN model to
predict 6 parameters
1.
Introduction – nobody predict 6
parameters before.
1.1.
Predict NMR T2
distribution can save time and money as well as know the characteristics of the
reservoir.
1.2.
Some use ANN and
other models to predict porosity, permeability, water saturation, TOC, etc. But
they did not predict NMR.
1.3.
Some use ANN and other models to predict data related to NMR such
as free fluid, irreducible water, effective porosity obtained from NMR.
1.4.
Some use ANN and
other models to predict bin porosities and T2 logarithmic mean values instead
of T2 distribution.
1.5.
6 parameters are
predicted in this paper. They are very close to T2 distribution.
2.
Methods - build an ANN model to
predict 6 parameters
2.1.
Select target depths
in Hess data, there are 7 different lithologies in target depths.
2.2.
Select 12 different
logging data
2.3.
Select 10 different inversion
data (QElan)
2.4.
Set one category for
different lithologies, there are 7 different lithologies.
2.5.
Set four different
categories according to the shape of T2 distribution. Each different categories
have 2 or 3 levels.
2.6.
Use k-nearest
neighbor method to predict four different categories, with best k=3.
2.7.
Fit T2 distribution
with normal distributions. Each normal distribution corresponds to 3
parameters. There are 6 parameters for each T2 distribution at each depth.
2.8.
Show the accuracy of
fitting
2.9.
Preprocess
data
2.10.
Build an ANN model
with 2 hidden layers
2.11.
Use R and matlab to
build the model, the results are similar but R takes much more time than
matlab. So matlab is recommended to build and test the model.
2.12.
Build an ANN model
with 2 hidden layers using matlab
2.13.
Show the accuracy of
the model
Tomorrow, I will try to finish the outline.
what is the title of your paper...
ReplyDeleteprediction of nmr t2 distribution by 6 parameters with an artificial neural netwok model
Deletewill you talk about the 64 bin prediction in this paper ?
ReplyDeletei prefer not to talk about 64 bins in this paper.
Delete2.1. Select target depths in Hess data, there are 7 different lithologies in target depths.
ReplyDeleteqill ou talk about the effect of 11 lithologies?
ok, i will talk about it. thank you for reminder.
Deletewrite the outline for results... following is an outline for results written by Sang..
ReplyDeleteAbstract
Chapter 1. Introduction
1.1. Background
1.2. Outline of the thesis
Chapter 2. Methodology
2.1. Asymptotic solution for diffusivity equation
2.2. Multistencils Fast Marching Method
2.3. Drainage Volume and Pressure Transient Analysis
Chapter 3. Results
3.1. Accuracy of MFM method
3.1.1. Singlestencil Fast Marching VS Multistencils Fast Marching
3.1.2. MFM model VS Texas A&M’s model
3.1.3. MFM model VS KAPPA analytical VS KAPPA numerical
3.2. Validation
3.2.1. Basic reservoir properties
3.2.2. Boundary condition
3.2.3. Effect of Fractures
3.2.4. Well interference
3.3. Application [Synthetic example]
Chapter 4. Conclusions
Ok, i will follow it.
Delete