Today, I continued to improve the second paper.
Summary:
I finish all improvement before 'case study'. There are now two parts left to be improved, which are 'case study' and 'conclusion'.
Tomorrow, I will try to finish all the rest part.
8/30/2017
Improve the second paper
Today, I improved part of the second paper.
Summary:
I will try to finish it on Thursday or Friday.
Tomorrow, I will continue to improve the second paper.
Summary:
I will try to finish it on Thursday or Friday.
Tomorrow, I will continue to improve the second paper.
8/29/2017
some ideas after reading papers combined with your suggestions
Today, I thought of some ideas after reading papers combined with your suggestions.
Summary:
1. CMIS: in other papers, people combine different models into CMIS like fuzzy-logic, neuro-fuzzy and ANN. Since ANN performs the best among selected models, I think one improvement may be to use different ANN models in CMIS instead of various models. For now, I have not found how to realize it in MATLAB. I will continue to look for it.
2. one way to use Vp/Vs is to predict Vp and Vp/Vs together instead of predicting Vp and Vs together. From yesterday's work, it is more accurate to predict Vp than Vs, so predicting Vp and Vp/Vs together may be a good choice.
Tomorrow, I will start to improve the second paper.
Summary:
1. CMIS: in other papers, people combine different models into CMIS like fuzzy-logic, neuro-fuzzy and ANN. Since ANN performs the best among selected models, I think one improvement may be to use different ANN models in CMIS instead of various models. For now, I have not found how to realize it in MATLAB. I will continue to look for it.
2. one way to use Vp/Vs is to predict Vp and Vp/Vs together instead of predicting Vp and Vs together. From yesterday's work, it is more accurate to predict Vp than Vs, so predicting Vp and Vp/Vs together may be a good choice.
Tomorrow, I will start to improve the second paper.
8/28/2017
predict sonic logs with basic ANN models
Today, I predicted sonic logs with basic ANN models.
Summary:
There are 13 inputs and 2 outputs (compressional and shear).
one hidden layer with 8 neurons
R2: 0.8570, 0.8184
NRMSE: 0.0631, 0.0673
two hidden layers with 9 and 5 neurons
R2: 0.8619, 0.8247
NRMSE: 0.0620, 0.0661
Asoodeh et al. (2011) improved accuracy using CMIS compared with ANN. But it is just 0.16% for compressional velocity and 0.63% for shear velocity with R2.
In our case, the accuracy is also improved by adding one more hidden layers. It is 0.57% for compressional velocity and 0.77% for shear velocity with R2.
The result shows that both of two methods improve little in accuracy and there is no need to use CMIS. Adding one more hidden layer is more efficient.
Tomorrow, I will search papers to see if I can find new methods.
Summary:
There are 13 inputs and 2 outputs (compressional and shear).
one hidden layer with 8 neurons
R2: 0.8570, 0.8184
NRMSE: 0.0631, 0.0673
two hidden layers with 9 and 5 neurons
R2: 0.8619, 0.8247
NRMSE: 0.0620, 0.0661
Asoodeh et al. (2011) improved accuracy using CMIS compared with ANN. But it is just 0.16% for compressional velocity and 0.63% for shear velocity with R2.
In our case, the accuracy is also improved by adding one more hidden layers. It is 0.57% for compressional velocity and 0.77% for shear velocity with R2.
The result shows that both of two methods improve little in accuracy and there is no need to use CMIS. Adding one more hidden layer is more efficient.
Tomorrow, I will search papers to see if I can find new methods.
8/25/2017
Possible improvement from former research
Today, I thought about possible improvement from former research about predicting sonic logs.
Summary:
1. from papers I read yesterday, most predictive models use Vp as inputs with other conventional logs to predict Vs. Most empirical equations predict Vp and Vs together with limited accuracy.
2. There is one paper where Vp and Vs are predicted together with CMIS. There are three different models in CMIS including ANN, FL (fuzzy logic) and NF (Neuro-Fuzzy) models. The accuracy of CMIS is a little higher than ANN and much higher than FL and NF. So maybe there is no need to use CMIS.
Possible improvements:
1. Just use ANN to predict Vp and Vs together instead of CMIS
2. improve the performance of ANN and obtain higher accuracy than CMIS with less time (contain more inputs, more hidden layers, predict one of Vs and Vp first and predict the other next to get higher accuracy, select the best training function)
Next week, I will see if it is possible to use ANN to obtain higher accuracy than CMIS.
Summary:
1. from papers I read yesterday, most predictive models use Vp as inputs with other conventional logs to predict Vs. Most empirical equations predict Vp and Vs together with limited accuracy.
2. There is one paper where Vp and Vs are predicted together with CMIS. There are three different models in CMIS including ANN, FL (fuzzy logic) and NF (Neuro-Fuzzy) models. The accuracy of CMIS is a little higher than ANN and much higher than FL and NF. So maybe there is no need to use CMIS.
Possible improvements:
1. Just use ANN to predict Vp and Vs together instead of CMIS
2. improve the performance of ANN and obtain higher accuracy than CMIS with less time (contain more inputs, more hidden layers, predict one of Vs and Vp first and predict the other next to get higher accuracy, select the best training function)
Next week, I will see if it is possible to use ANN to obtain higher accuracy than CMIS.
8/24/2017
literature review of sonic logs
Today, I did literature review of sonic logs.
Summary:
There are two main kinds of methods of predicting Vp and Vs.
1. data from lab measurements
1.1. multiple regression
1.1.1. Inputs: NPHI, RHOB, GR, LLD(deep laterolog, resistivity), Vp
Outputs: Vs
1.1.2. Inputs: NPHI, RHOB, Vp
Outputs: Vs
1.2. ANN
Inputs: NPHI, RHOB, Vp
Outputs: Vs
2. data from well logs
Xu-White model
In conclusion, following logs can be selected as inputs and outputs for our research.
Inputs: GR, AT10, AT90, DPHZ, NPOR, PEFZ, RHOZ, VCL
Outputs: DTCO, DTSM
In the evening, more literature review are done for predicting sonic logs.
In Oregon Basin, Wyoming, Iverson and Walker (1992), empirical equations
inputs: shale content (from GR), lithology and porosity (nuclear logs)
outputs: shear and compressional transit times
sand/shale mixture, Dvorkin and Gutierrez (2001), empirical equations
inputs: mineral properties, rock porosity and shale volume
outputs: Vp and Vs
South Texas clastics, offshore gulf of mexico clastics, Greenberg and Castagna (1992), empirical equations
inputs: lothology, porosity, water saturation, Vp
outputs: Vs
Asmari formation (carbonate reservoir rock of Iranian oil field), Asoodeh (2011), CMIS (committee machine with intelligent sustems)
inputs: NPHI, RHOB, Rt, Vsh
outputs:Vp, Vs, stoneley wave velocity
sandstone reservoir of Carnarvon Basin, Australia, Rezaee et al. (2006), CMIS (committee machine with intelligent sustems)
inputs: NPHI, Vp, GR, RHOB, Rlld
outputs: Vs
carbonate reservoir in Iran, Maleki et al. (2014), empirical correlations and AI methods (SVM, ANN)
inputs: Vp, RHOZ, GR, PHIT, Rt, HCAL
outputs: Vs
Tomorrow, I will start to build ANN for DTCO and DTSM.
Summary:
There are two main kinds of methods of predicting Vp and Vs.
1. data from lab measurements
1.1. multiple regression
1.1.1. Inputs: NPHI, RHOB, GR, LLD(deep laterolog, resistivity), Vp
Outputs: Vs
1.1.2. Inputs: NPHI, RHOB, Vp
Outputs: Vs
1.2. ANN
Inputs: NPHI, RHOB, Vp
Outputs: Vs
2. data from well logs
Xu-White model
In conclusion, following logs can be selected as inputs and outputs for our research.
Inputs: GR, AT10, AT90, DPHZ, NPOR, PEFZ, RHOZ, VCL
Outputs: DTCO, DTSM
In the evening, more literature review are done for predicting sonic logs.
In Oregon Basin, Wyoming, Iverson and Walker (1992), empirical equations
inputs: shale content (from GR), lithology and porosity (nuclear logs)
outputs: shear and compressional transit times
sand/shale mixture, Dvorkin and Gutierrez (2001), empirical equations
inputs: mineral properties, rock porosity and shale volume
outputs: Vp and Vs
South Texas clastics, offshore gulf of mexico clastics, Greenberg and Castagna (1992), empirical equations
inputs: lothology, porosity, water saturation, Vp
outputs: Vs
Asmari formation (carbonate reservoir rock of Iranian oil field), Asoodeh (2011), CMIS (committee machine with intelligent sustems)
inputs: NPHI, RHOB, Rt, Vsh
outputs:Vp, Vs, stoneley wave velocity
sandstone reservoir of Carnarvon Basin, Australia, Rezaee et al. (2006), CMIS (committee machine with intelligent sustems)
inputs: NPHI, Vp, GR, RHOB, Rlld
outputs: Vs
carbonate reservoir in Iran, Maleki et al. (2014), empirical correlations and AI methods (SVM, ANN)
inputs: Vp, RHOZ, GR, PHIT, Rt, HCAL
outputs: Vs
Tomorrow, I will start to build ANN for DTCO and DTSM.
8/23/2017
finish some tasks
Today, I finished some tasks.
Summary:
1. finish compile BHP and Hess dataset.
2. check that VPVS=DTSM/DTCO
I check it in Hess well and it is true. I did not find VPVS in BHP well. I think it also means that VPVS is obtained by DTSM/DTCO.
3. do some research on sonic logs. There are some applications for them:
quality control of transit-time logs
refinement of porosity predictions
determination of lithology
improvement of fracture detection
improvement of cement bond evaluation
Tomorrow, I will learn more about sonic logs and start to predict them by conventional logs.
Summary:
1. finish compile BHP and Hess dataset.
2. check that VPVS=DTSM/DTCO
I check it in Hess well and it is true. I did not find VPVS in BHP well. I think it also means that VPVS is obtained by DTSM/DTCO.
3. do some research on sonic logs. There are some applications for them:
quality control of transit-time logs
refinement of porosity predictions
determination of lithology
improvement of fracture detection
improvement of cement bond evaluation
Tomorrow, I will learn more about sonic logs and start to predict them by conventional logs.
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