`Project`

The Project class lets us handle groups of wells. It is really just a list of Well objects.



In [1]:

    
from welly import Project



In [2]:

    
!ls *.LAS









    



P-129_out.LAS  P-130_out.LAS  P-135_out.LAS  P-135_time.LAS



In [3]:

    
p = Project.from_las("P-*.LAS")









    



4it [00:03,  1.14it/s]



In [4]:

    
len(p)









    Out[4]:





4



In [5]:

    
p









    Out[5]:




Index UWI Data Curves
0 100/N14A/11E05 18 curves CALI, DT, NPHI_SAN, NPHI_LIM, NPHI_DOL, DPHI_LIM, DPHI_SAN, DPHI_DOL, M2R9, M2R6, M2R3, M2R2, M2R1, GR, SP, PEF, DRHO, RHOB
1 Long = 63* 45'24.460  W 24 curves CALI, HCAL, PEF, DT, DTS, DPHI_SAN, DPHI_LIM, DPHI_DOL, NPHI_SAN, NPHI_LIM, NPHI_DOL, RLA5, RLA3, RLA4, RLA1, RLA2, RXOZ, RXO_HRLT, RT_HRLT, RM_HRLT, DRHO, RHOB, GR, SP
2 -999.25 22 curves CALI, DT, DTS, DPHI_SAN, DPHI_LIM, DPHI_DOL, NPHI_SAN, NPHI_LIM, NPHI_DOL, AMF, AF90, AF10, AF20, AF60, AF30, RXOZ, GR, POTA, THOR, URAN, SP, RHOB
3 3 curves SONIC_DESPIKED, RHOB_DESPIKED, TWT

Typical, the UWIs are a disaster. Let's ignore this for now.

The Project is really just a list-like thing:



In [6]:

    
for w in p:
    print(w.uwi)









    



100/N14A/11E05
Long = 63* 45'24.460  W
-999.25

Plot curves from several wells



In [7]:

    
%matplotlib inline
import matplotlib.pyplot as plt



In [8]:

    
alias = {'GR': ['GR', 'GRC', 'NGT']}



In [13]:

    
fig, axs = plt.subplots(figsize=(7, 14), ncols=len(p))

for i, (ax, w) in enumerate(zip(axs, p)):
    gr = w.get_curve('GR', alias=alias)
    if gr is not None:
        ax = gr.plot(ax=ax)
    ax.set_title("GR for\n{}".format(w.uwi))
    
plt.show()

Export data to pandas DataFrame

This makes a DataFrame using a dual index of UWI and Depth.



In [17]:

    
keys = ['GR', 'DT']

df = p.df(keys=keys, alias=alias)



In [21]:

    
df.describe()









    Out[21]:







  
    
      
      GR
      DT
    
  
  
    
      count
      43068.000000
      40840.000000
    
    
      mean
      99.688700
      70.503936
    
    
      std
      33.663215
      11.057708
    
    
      min
      3.894070
      40.583134
    
    
      25%
      76.377998
      63.458676
    
    
      50%
      106.260002
      68.167877
    
    
      75%
      125.804001
      74.454033
    
    
      max
      267.940430
      134.012634



In [22]:

    
df.loc['100/N14A/11E05'].DT.plot()









    Out[22]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f022e2c6898>



In [23]:

    
x = df.loc['100/N14A/11E05'].index
idx = df.loc['100/N14A/11E05'].loc[(28<x) & (x<30)].index
idx









    Out[23]:





Float64Index([28.099999999999547,  28.19999999999954, 28.299999999999535,
               28.39999999999953, 28.499999999999524,  28.59999999999952,
              28.699999999999513, 28.799999999999507,   28.8999999999995,
              28.999999999999496,  29.09999999999949, 29.199999999999484,
               29.29999999999948, 29.399999999999473, 29.499999999999467,
               29.59999999999946, 29.699999999999456,  29.79999999999945,
              29.899999999999444,  29.99999999999944],
             dtype='float64', name='Depth')

Quality

We can make some tests of curve quality.



In [24]:

    
import welly.quality as q



In [25]:

    
tests = {
#    'Each': [q.no_gaps],
    'GR': [q.no_monotonic, q.no_flat, q.all_positive],
    'DT': [q.all_positive, q.all_between(50, 200)],
}



In [26]:

    
from IPython.display import HTML



In [28]:

    
HTML(p.curve_table_html(keys=['CALI', 'GR', 'DT', 'SP'], tests=tests, alias=alias))









    Out[28]:




Idx UWI Data Passing CALI GR DT SP
% 3/4 wells 3/4 wells 3/4 wells 3/4 wells
0 100/N14A/11E05 4/18 curves 100 CALI⬤

8.90 in GR⬤

103.74 gAPI DT⬤

74.90 us/ft SP⬤

101.60 mV 1 Long = 63* 45'24.460  W 4/24 curves 40 CALI⬤

7.97 in GR⬤

78.99 gAPI DT⬤

63.08 us/ft SP⬤

52.47 mV 2 -999.25 4/22 curves 100 CALI⬤

6.42 in GR⬤

133.38 gAPI DT⬤

63.38 us/ft SP⬤

-416.47 mV 3 0/3 curves – ⬤

 ⬤

 ⬤

 ⬤



In [ ]:

Index	UWI	Data	Curves
0	100/N14A/11E05	18 curves	CALI, DT, NPHI_SAN, NPHI_LIM, NPHI_DOL, DPHI_LIM, DPHI_SAN, DPHI_DOL, M2R9, M2R6, M2R3, M2R2, M2R1, GR, SP, PEF, DRHO, RHOB
1	*Long = 63 45'24.460 W**	24 curves	CALI, HCAL, PEF, DT, DTS, DPHI_SAN, DPHI_LIM, DPHI_DOL, NPHI_SAN, NPHI_LIM, NPHI_DOL, RLA5, RLA3, RLA4, RLA1, RLA2, RXOZ, RXO_HRLT, RT_HRLT, RM_HRLT, DRHO, RHOB, GR, SP
2	-999.25	22 curves	CALI, DT, DTS, DPHI_SAN, DPHI_LIM, DPHI_DOL, NPHI_SAN, NPHI_LIM, NPHI_DOL, AMF, AF90, AF10, AF20, AF60, AF30, RXOZ, GR, POTA, THOR, URAN, SP, RHOB
3		3 curves	SONIC_DESPIKED, RHOB_DESPIKED, TWT

	GR	DT
count	43068.000000	40840.000000
mean	99.688700	70.503936
std	33.663215	11.057708
min	3.894070	40.583134
25%	76.377998	63.458676
50%	106.260002	68.167877
75%	125.804001	74.454033
max	267.940430	134.012634

Idx	UWI	Data	Passing	CALI	GR	DT	SP
			%	3/4 wells	3/4 wells	3/4 wells	3/4 wells
0	100/N14A/11E05	4/18 curves	100	CALI ⬤ 8.90 in	GR ⬤ 103.74 gAPI	DT ⬤ 74.90 us/ft	SP ⬤ 101.60 mV	1	Long = 63* 45'24.460 W	4/24 curves	40	CALI ⬤ 7.97 in	GR ⬤ 78.99 gAPI	DT ⬤ 63.08 us/ft	SP ⬤ 52.47 mV	2	-999.25	4/22 curves	100	CALI ⬤ 6.42 in	GR ⬤ 133.38 gAPI	DT ⬤ 63.38 us/ft	SP ⬤ -416.47 mV	3		0/3 curves	–	⬤	⬤	⬤	⬤