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In [1]:



    


import pyfas as fa
import pandas as pd
import matplotlib.pyplot as plt

OLGA tpl files, examples and howto

For an tpl file the following methods are available:

  • filter_data - return a filtered subset of trends
  • extract - extract a single trend variable
  • to_excel - dump all the data to an excel file

The usual workflow should be:

  1. Load the correct tpl
  2. Select the desired variable(s)
  3. Extract the results or dump all the variables to an excel file
  4. Post-process your data in Excel or in the notebook itself

Tpl loading

To load a specific tpl file the correct path and filename have to be provided:





In [2]:



    


tpl_path = '../../pyfas/test/test_files/'
fname = '11_2022_BD.tpl'
tpl = fa.Tpl(tpl_path+fname)

Trend selection

A tpl file may contain hundreds of trends, in particular for complex networks. For this reason a filtering method is quite useful. A trend can be specified in an OLGA input files in differnet ways, the identification of a single trend may be not trivial.
The easiest way is to filter all the trends using patters, the command tpl.filter_trends("PT") filters all the pressure trends (or better, all the trends with "PT" in the description, if you have defined a temperature trend in the position "PTTOPSIDE", for example, this trend will be selected too). The resulting python dictionaly will have a unique index for each filtered trend that can be used to identify the interesting trend(s). In case of an emply pattern all the available trends will be reported.





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tpl.filter_data('PT')



    


















    
Out[3]:








{9: "PT 'POSITION:' 'EXIT' '(PA)' 'Pressure'\n",
 37: "PT 'POSITION:' 'BOTTOMHOLE' '(PA)' 'Pressure'\n",
 38: "PT 'POSITION:' 'TUBINGHEAD' '(PA)' 'Pressure'\n",
 39: "PT 'POSITION:' 'DC6' '(PA)' 'Pressure'\n",
 40: "PT 'POSITION:' 'DC7' '(PA)' 'Pressure'\n",
 41: "PT 'POSITION:' 'DC8' '(PA)' 'Pressure'\n",
 42: "PT 'POSITION:' 'DC9' '(PA)' 'Pressure'\n",
 43: "PT 'POSITION:' 'RBM' '(PA)' 'Pressure'\n",
 44: "PT 'POSITION:' 'EXIT' '(PA)' 'Pressure'\n"}

or





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tpl.filter_data("'POSITION:' 'EXIT'")



    


















    
Out[4]:








{3: "GLT 'POSITION:' 'EXIT' '(KG/S)' 'Total liquid mass flow'\n",
 4: "GLTHL 'POSITION:' 'EXIT' '(KG/S)' 'Mass flow rate of oil'\n",
 5: "GLTWT 'POSITION:' 'EXIT' '(KG/S)' 'Mass flow rate of water excluding vapour'\n",
 6: "GLWVT 'POSITION:' 'EXIT' '(KG/S)' 'Total mass flow rate of water including Vapour'\n",
 7: "GT 'POSITION:' 'EXIT' '(KG/S)' 'Total mass flow'\n",
 8: "HOL 'POSITION:' 'EXIT' '(-)' 'Holdup (liquid volume fraction)'\n",
 9: "PT 'POSITION:' 'EXIT' '(PA)' 'Pressure'\n",
 10: "QLT 'POSITION:' 'EXIT' '(M3/S)' 'Total liquid volume flow'\n",
 11: "TM 'POSITION:' 'EXIT' '(C)' 'Fluid temperature'\n",
 12: "UL 'POSITION:' 'EXIT' '(M/S)' 'Average liquid film velocity'\n",
 20: "HOL 'POSITION:' 'EXIT' '(-)' 'Holdup (liquid volume fraction)'\n",
 28: "HOLWT 'POSITION:' 'EXIT' '(-)' 'Water volume fraction'\n",
 36: "ID 'POSITION:' 'EXIT' '(-)' 'Flow regime: 1=Stratified, 2=Annular, 3=Slug, 4=Bubble.'\n",
 44: "PT 'POSITION:' 'EXIT' '(PA)' 'Pressure'\n",
 52: "Q2 'POSITION:' 'EXIT' '(W/M2-C)' 'Overall heat transfer coefficient'\n",
 60: "TM 'POSITION:' 'EXIT' '(C)' 'Fluid temperature'\n",
 68: "TU 'POSITION:' 'EXIT' '(C)' 'Ambient temperature'\n",
 76: "TWS 'POSITION:' 'EXIT' '(C)' 'Inner wall surface temperature'\n",
 84: "QGST 'POSITION:' 'EXIT' '(SM3/S)' 'Gas volume flow at standard conditions'\n",
 92: "QOST 'POSITION:' 'EXIT' '(SM3/S)' 'Oil volume flow at standard conditions'\n",
 100: "QWST 'POSITION:' 'EXIT' '(SM3/S)' 'Water volume flow at standard conditions'\n",
 108: "AL 'POSITION:' 'EXIT' '(-)' 'Void (gas volume fraction)'\n",
 116: "GG 'POSITION:' 'EXIT' '(KG/S)' 'Gas mass flow'\n",
 124: "GLT 'POSITION:' 'EXIT' '(KG/S)' 'Total liquid mass flow'\n",
 132: "GT 'POSITION:' 'EXIT' '(KG/S)' 'Total mass flow'\n",
 140: "DTHYD 'POSITION:' 'EXIT' '(C)' 'Difference between hydrate and section temperature'\n"}

The same outpout can be reported as a pandas dataframe:





In [5]:



    


pd.DataFrame(tpl.filter_data('PT'), index=("Trends",)).T



    


















    
Out[5]:











  
    

      
      Trends
    

  
  
    

      9
      PT 'POSITION:' 'EXIT' '(PA)' 'Pressure'\n
    

    

      37
      PT 'POSITION:' 'BOTTOMHOLE' '(PA)' 'Pressure'\n
    

    

      38
      PT 'POSITION:' 'TUBINGHEAD' '(PA)' 'Pressure'\n
    

    

      39
      PT 'POSITION:' 'DC6' '(PA)' 'Pressure'\n
    

    

      40
      PT 'POSITION:' 'DC7' '(PA)' 'Pressure'\n
    

    

      41
      PT 'POSITION:' 'DC8' '(PA)' 'Pressure'\n
    

    

      42
      PT 'POSITION:' 'DC9' '(PA)' 'Pressure'\n
    

    

      43
      PT 'POSITION:' 'RBM' '(PA)' 'Pressure'\n
    

    

      44
      PT 'POSITION:' 'EXIT' '(PA)' 'Pressure'\n

The view_trends method provides the same info better arranged:





In [18]:



    


tpl.view_trends('PT')



    


















    
Out[18]:











  
    

      
      Index
      Variable
      Position
      Unit
      Description
    

    

      Filter: PT
      
      
      
      
      
    

  
  
    

      0
      37
      PT
      POSITION - BOTTOMHOLE
      PA
      Pressure
    

    

      1
      38
      PT
      POSITION - TUBINGHEAD
      PA
      Pressure
    

    

      2
      39
      PT
      POSITION - DC6
      PA
      Pressure
    

    

      3
      40
      PT
      POSITION - DC7
      PA
      Pressure
    

    

      4
      9
      PT
      POSITION - EXIT
      PA
      Pressure
    

    

      5
      41
      PT
      POSITION - DC8
      PA
      Pressure
    

    

      6
      43
      PT
      POSITION - RBM
      PA
      Pressure
    

    

      7
      44
      PT
      POSITION - EXIT
      PA
      Pressure
    

    

      8
      42
      PT
      POSITION - DC9
      PA
      Pressure

Dump to excel

To dump all the variables in an excel file use tpl.to_excel() If no path is provided an excel file with the same name of the tpl file is generated in the working folder. Depending on the tpl size this may take a while.

Extract a specific variable

Once you know the variable(s) index you are interested in (see the filtering paragraph above for more info) you can extract it (or them) and use the data directly in python.

Let's assume you are interested in the inlet pressure and the outlet temperature:





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tpl.view_trends('TM')



    


















    
Out[17]:











  
    

      
      Index
      Variable
      Position
      Unit
      Description
    

    

      Filter: TM
      
      
      
      
      
    

  
  
    

      0
      59
      TM
      POSITION - RBM
      C
      Fluid temperature
    

    

      1
      53
      TM
      POSITION - BOTTOMHOLE
      C
      Fluid temperature
    

    

      2
      54
      TM
      POSITION - TUBINGHEAD
      C
      Fluid temperature
    

    

      3
      55
      TM
      POSITION - DC6
      C
      Fluid temperature
    

    

      4
      56
      TM
      POSITION - DC7
      C
      Fluid temperature
    

    

      5
      57
      TM
      POSITION - DC8
      C
      Fluid temperature
    

    

      6
      58
      TM
      POSITION - DC9
      C
      Fluid temperature
    

    

      7
      11
      TM
      POSITION - EXIT
      C
      Fluid temperature
    

    

      8
      60
      TM
      POSITION - EXIT
      C
      Fluid temperature
    

  




















In [14]:



    


tpl.view_trends('PT')



    


















    
Out[14]:











  
    

      
      Index
      Variable
      Position
      Unit
      Description
    

    

      Filter: PT
      
      
      
      
      
    

  
  
    

      0
      37
      PT
      POSITION - BOTTOMHOLE
      PA
      Pressure
    

    

      1
      38
      PT
      POSITION - TUBINGHEAD
      PA
      Pressure
    

    

      2
      39
      PT
      POSITION - DC6
      PA
      Pressure
    

    

      3
      40
      PT
      POSITION - DC7
      PA
      Pressure
    

    

      4
      9
      PT
      POSITION - EXIT
      PA
      Pressure
    

    

      5
      41
      PT
      POSITION - DC8
      PA
      Pressure
    

    

      6
      43
      PT
      POSITION - RBM
      PA
      Pressure
    

    

      7
      44
      PT
      POSITION - EXIT
      PA
      Pressure
    

    

      8
      42
      PT
      POSITION - DC9
      PA
      Pressure

Our targets are:

variable 11 - TM 'POSITION:' 'EXIT' '(C)' 'Fluid temperature'

and

variable 38 - PT 'POSITION:' 'TUBINGHEAD' '(PA)' 'Pressure'\

Now we can proceed with the data extraction:





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# single trend extraction
tpl.extract(11)
tpl.extract(38)

# multiple trends extraction
tpl.extract(12, 37)

The tpl object now has the four trends available in the data attribute:





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tpl.data.keys()



    


















    
Out[11]:








dict_keys([11, 12, 37, 38])

while the label attibute stores the variable type as a dictionary:





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tpl.label



    


















    
Out[15]:








{11: 'TM POSITION: EXIT (C) Fluid temperature',
 12: 'UL POSITION: EXIT (M/S) Average liquid film velocity',
 37: 'PT POSITION: BOTTOMHOLE (PA) Pressure',
 38: 'PT POSITION: TUBINGHEAD (PA) Pressure'}

Data processing

The results available in the data attribute are numpy arrays and can be easily manipulated and plotted:





In [49]:



    


%matplotlib inline

pt_inlet = tpl.data[38]
tm_outlet = tpl.data[11]

fig, ax1 = plt.subplots(figsize=(12, 7));
ax1.grid(True)
p0, = ax1.plot(tpl.time/3600, tm_outlet)
ax1.set_ylabel("Outlet T [C]", fontsize=16)
ax1.set_xlabel("Time [h]", fontsize=16)

ax2 = ax1.twinx()
p1, = ax2.plot(tpl.time/3600, pt_inlet/1e5, 'r')
ax2.grid(False)
ax2.set_ylabel("Inlet P [bara]", fontsize=16)

ax1.tick_params(axis="both", labelsize=16)
ax2.tick_params(axis="both", labelsize=16)


plt.legend((p0, p1), ("Outlet T", "Inlet P"), loc=4, fontsize=16)
plt.title("Inlet P and Outlet T for case FC1", size=20);

Content source: gpagliuca/pyfas

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