Calculate Well Trajectory from Different Data Sources¶
This notebook shows examples of how to calculate the well trajectory from different data sources. Currently accepting data from CSV, dataframe, dictionary, and json.
The data from csv and dataframe must be in a certain format commonly seen in directional surveys. The dictionary and json must be in a format specific to the library.
[13]:
from welltrajconvert.wellbore_trajectory import *
from welltrajconvert.data_source import *
Get Wellbore Trajectory object¶
[14]:
path = './'
get_files is a great function for accessing your data in a folder. You can access all data in a specific folder or data with a specific extension. It returns said data in a list of items.
[3]:
# use get_files to get all the files in the data directory
file_paths = get_files(path, folders='data')
#gives a path lib path to all your files in the folder of choice
file_paths.items
[3]:
[WindowsPath('data/wellbore_survey.csv'),
WindowsPath('data/wellbore_survey.json'),
WindowsPath('data/wellbore_survey_many.csv'),
WindowsPath('data/well_export.json')]
From Dict¶
[18]:
json_path = get_files(path, folders='data', extensions='.json')
json_path.items[0]
with open(json_path.items[0]) as json_file:
json_data = json.load(json_file)
json_file.close()
#show the dict keys present in the dict.
json_data.keys()
[18]:
dict_keys(['wellId', 'md', 'inc', 'azim', 'surface_latitude', 'surface_longitude'])
[4]:
# call DataSource.from_dictionary and input the dict data
my_data = DataSource.from_dictionary(json_data)
#view the dict data dataclass object
my_data.data
#create a wellboreTrajectory object
dev_obj = WellboreTrajectory(my_data.data)
#view the object
dev_obj.deviation_survey_obj
#calculate the survey points along the wellbore for the object
dev_obj.calculate_survey_points()
#serialize the data
json_ds = dev_obj.serialize()
# view the json in a df
json_ds_obj = json.loads(json_ds)
df_min_curve = pd.DataFrame(json_ds_obj)
df_min_curve.head()
[4]:
| wellId | md | inc | azim | tvd | e_w_deviation | n_s_deviation | dls | surface_latitude | surface_longitude | longitude_points | latitude_points | zone_number | zone_letter | x_points | y_points | surface_x | surface_y | isHorizontal | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | well_C | 0.00 | 0.00 | 227.11 | 0.000000 | 0.000000e+00 | 0.000000e+00 | 0.000000 | 29.908294 | 47.688521 | 47.688521 | 29.908294 | 38 | R | 759587.934440 | 3.311662e+06 | 759587.93444 | 3.311662e+06 | Vertical |
| 1 | well_C | 35.00 | 0.00 | 227.11 | 35.000000 | -1.484001e-17 | -1.378531e-17 | 0.000000 | 29.908294 | 47.688521 | 47.688521 | 29.908294 | 38 | R | 759587.934440 | 3.311662e+06 | 759587.93444 | 3.311662e+06 | Vertical |
| 2 | well_C | 774.81 | 0.46 | 227.11 | 774.802052 | -2.175842e+00 | -2.021203e+00 | 0.062178 | 29.908294 | 47.688521 | 47.688514 | 29.908289 | 38 | R | 759587.271243 | 3.311661e+06 | 759587.93444 | 3.311662e+06 | Vertical |
| 3 | well_C | 800.00 | 0.13 | 163.86 | 799.991684 | -2.241984e+00 | -2.117474e+00 | 1.312323 | 29.908294 | 47.688521 | 47.688514 | 29.908289 | 38 | R | 759587.251083 | 3.311661e+06 | 759587.93444 | 3.311662e+06 | Vertical |
| 4 | well_C | 900.00 | 0.57 | 230.43 | 899.989571 | -2.593878e+00 | -2.543311e+00 | 0.439221 | 29.908294 | 47.688521 | 47.688512 | 29.908288 | 38 | R | 759587.143826 | 3.311661e+06 | 759587.93444 | 3.311662e+06 | Vertical |
From DF¶
If you have a df and want to calculate its metadata use the following workflow. Just enter in the column names and calculate its survey points.
[5]:
# FROM DF
csv_path = get_files(path, folders='data', extensions='.csv')
csv_path.items[0]
df = pd.read_csv(csv_path.items[0],sep=',')
df.head()
[5]:
| wellId | md | inc | azim | surface_latitude | surface_longitude | |
|---|---|---|---|---|---|---|
| 0 | well_C | 0.00 | 0.00 | 227.11 | 29.908294 | 47.688521 |
| 1 | well_C | 35.00 | 0.00 | 227.11 | 29.908294 | 47.688521 |
| 2 | well_C | 774.81 | 0.46 | 227.11 | 29.908294 | 47.688521 |
| 3 | well_C | 800.00 | 0.13 | 163.86 | 29.908294 | 47.688521 |
| 4 | well_C | 900.00 | 0.57 | 230.43 | 29.908294 | 47.688521 |
[6]:
# call the from_df method, fill in the parameters with the column names
my_data = DataSource.from_df(df, wellId_name='wellId',md_name='md',inc_name='inc',azim_name='azim',
surface_latitude_name='surface_latitude',surface_longitude_name='surface_longitude')
#view the dict data dataclass object
my_data.data
#create a wellboreTrajectory object
dev_obj = WellboreTrajectory(my_data.data)
#view the object
dev_obj.deviation_survey_obj
#calculate the survey points along the wellbore for the object
dev_obj.calculate_survey_points()
#serialize the data
json_ds = dev_obj.serialize()
# view the json in a df
json_ds_obj = json.loads(json_ds)
df_min_curve = pd.DataFrame(json_ds_obj)
df_min_curve.head()
[6]:
| wellId | md | inc | azim | tvd | e_w_deviation | n_s_deviation | dls | surface_latitude | surface_longitude | longitude_points | latitude_points | zone_number | zone_letter | x_points | y_points | surface_x | surface_y | isHorizontal | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | well_C | 0.00 | 0.00 | 227.11 | 0.000000 | 0.000000e+00 | 0.000000e+00 | 0.000000 | 29.908294 | 47.688521 | 47.688521 | 29.908294 | 38 | R | 759587.934440 | 3.311662e+06 | 759587.93444 | 3.311662e+06 | Vertical |
| 1 | well_C | 35.00 | 0.00 | 227.11 | 35.000000 | -1.484001e-17 | -1.378531e-17 | 0.000000 | 29.908294 | 47.688521 | 47.688521 | 29.908294 | 38 | R | 759587.934440 | 3.311662e+06 | 759587.93444 | 3.311662e+06 | Vertical |
| 2 | well_C | 774.81 | 0.46 | 227.11 | 774.802052 | -2.175842e+00 | -2.021203e+00 | 0.062178 | 29.908294 | 47.688521 | 47.688514 | 29.908289 | 38 | R | 759587.271243 | 3.311661e+06 | 759587.93444 | 3.311662e+06 | Vertical |
| 3 | well_C | 800.00 | 0.13 | 163.86 | 799.991684 | -2.241984e+00 | -2.117474e+00 | 1.312323 | 29.908294 | 47.688521 | 47.688514 | 29.908289 | 38 | R | 759587.251083 | 3.311661e+06 | 759587.93444 | 3.311662e+06 | Vertical |
| 4 | well_C | 900.00 | 0.57 | 230.43 | 899.989571 | -2.593878e+00 | -2.543311e+00 | 0.439221 | 29.908294 | 47.688521 | 47.688512 | 29.908288 | 38 | R | 759587.143826 | 3.311661e+06 | 759587.93444 | 3.311662e+06 | Vertical |
From CSV¶
If you have a csv and want to calculate its metadata use the following workflow. Just enter in the column names and calculate its survey points.
[7]:
csv_path = get_files(path, folders='data', extensions='.csv')
my_data = DataSource.from_csv(csv_path.items[0], wellId_name='wellId',md_name='md',inc_name='inc',azim_name='azim',
surface_latitude_name='surface_latitude',surface_longitude_name='surface_longitude')
#view the dict data dataclass object
my_data.data
#create a wellboreTrajectory object
dev_obj = WellboreTrajectory(my_data.data)
#view the object
dev_obj.deviation_survey_obj
#calculate the survey points along the wellbore for the object
dev_obj.calculate_survey_points()
#serialize the data
json_ds = dev_obj.serialize()
# view the json in a df
json_ds_obj = json.loads(json_ds)
df_min_curve = pd.DataFrame(json_ds_obj)
df_min_curve.head()
[7]:
| wellId | md | inc | azim | tvd | e_w_deviation | n_s_deviation | dls | surface_latitude | surface_longitude | longitude_points | latitude_points | zone_number | zone_letter | x_points | y_points | surface_x | surface_y | isHorizontal | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | well_C | 0.00 | 0.00 | 227.11 | 0.000000 | 0.000000e+00 | 0.000000e+00 | 0.000000 | 29.908294 | 47.688521 | 47.688521 | 29.908294 | 38 | R | 759587.934440 | 3.311662e+06 | 759587.93444 | 3.311662e+06 | Vertical |
| 1 | well_C | 35.00 | 0.00 | 227.11 | 35.000000 | -1.484001e-17 | -1.378531e-17 | 0.000000 | 29.908294 | 47.688521 | 47.688521 | 29.908294 | 38 | R | 759587.934440 | 3.311662e+06 | 759587.93444 | 3.311662e+06 | Vertical |
| 2 | well_C | 774.81 | 0.46 | 227.11 | 774.802052 | -2.175842e+00 | -2.021203e+00 | 0.062178 | 29.908294 | 47.688521 | 47.688514 | 29.908289 | 38 | R | 759587.271243 | 3.311661e+06 | 759587.93444 | 3.311662e+06 | Vertical |
| 3 | well_C | 800.00 | 0.13 | 163.86 | 799.991684 | -2.241984e+00 | -2.117474e+00 | 1.312323 | 29.908294 | 47.688521 | 47.688514 | 29.908289 | 38 | R | 759587.251083 | 3.311661e+06 | 759587.93444 | 3.311662e+06 | Vertical |
| 4 | well_C | 900.00 | 0.57 | 230.43 | 899.989571 | -2.593878e+00 | -2.543311e+00 | 0.439221 | 29.908294 | 47.688521 | 47.688512 | 29.908288 | 38 | R | 759587.143826 | 3.311661e+06 | 759587.93444 | 3.311662e+06 | Vertical |
From CSV with multiple wells¶
If you have a file with multiple wells appened one after another you can use this simple function to break them up by wellid, run it through welltrajconvert and calculate each wells metadata. You can then convert the list of dicts into a dataframe.
[8]:
def from_multiple_wells_to_dict(df: DataFrame, wellId_name: Optional[str] = None, md_name: Optional[str] = None,
inc_name: Optional[str] = None, azim_name: Optional[str] = None,
surface_latitude_name: Optional[str] = None,
surface_longitude_name: Optional[str] = None,
surface_x_name: Optional[str] = None,
surface_y_name: Optional[str] = None):
"""
takes a df of mulitple well deviation surveys in a typical columnar fashion and calculates their survey points.
Then appends them to a list of dicts.
:parameter: df
:returns: list of dict
"""
# group by wellId, ensures this will work with single well or mulitple.
grouped = df.groupby(wellId_name)
# initialize empty dict and list
#appended_df = pd.DataFrame()
dict_list = []
# loop through groups converting them to the proper dict format
for name, group in grouped:
group.reset_index(inplace=True, drop=True)
if surface_latitude_name is not None and surface_longitude_name is not None:
well_obj = DataSource.from_df(group, wellId_name=wellId_name, md_name=md_name,
inc_name=inc_name, azim_name=azim_name,
surface_latitude_name=surface_latitude_name,
surface_longitude_name=surface_longitude_name)
if surface_x_name is not None and surface_y_name is not None:
well_obj = DataSource.from_df(group, wellId_name=wellId_name, md_name=md_name,
inc_name=inc_name, azim_name=azim_name,
surface_x_name=surface_x_name,
surface_y_name=surface_y_name)
well_obj = WellboreTrajectory(well_obj.data)
well_obj.calculate_survey_points()
json_ds = well_obj.serialize()
dict_list.append(json_ds)
res = dict_list
return res
[9]:
# FROM df with mulitple wells
csv_path = get_files(path, folders='data', extensions='.csv')
csv_path.items[1]
df = pd.read_csv(csv_path.items[1])
# well id unique
print(df['wellId'].unique())
df.head()
['well_C' 'well_A' 'well_B']
[9]:
| wellId | md | inc | azim | surface_latitude | surface_longitude | |
|---|---|---|---|---|---|---|
| 0 | well_C | 0.00 | 0.00 | 227.11 | 29.908294 | 47.688521 |
| 1 | well_C | 35.00 | 0.00 | 227.11 | 29.908294 | 47.688521 |
| 2 | well_C | 774.81 | 0.46 | 227.11 | 29.908294 | 47.688521 |
| 3 | well_C | 800.00 | 0.13 | 163.86 | 29.908294 | 47.688521 |
| 4 | well_C | 900.00 | 0.57 | 230.43 | 29.908294 | 47.688521 |
[10]:
# call function, fill in column name params
dicts = from_multiple_wells_to_dict(df,wellId_name='wellId',md_name='md',inc_name='inc',azim_name='azim',
surface_latitude_name='surface_latitude',surface_longitude_name='surface_longitude')
#dicts[:]
[11]:
def list_of_dicts_to_df(dict_list):
"""takes a list of dicts and converts to a appended df"""
appended_df = pd.DataFrame()
for i in dict_list:
json_ds_obj = json.loads(i)
df_well_obj = pd.DataFrame(json_ds_obj)
appended_df = appended_df.append(df_well_obj)
return appended_df
[12]:
# convert a list of dicts into a df
df = list_of_dicts_to_df(dicts)
df.head()
[12]:
| wellId | md | inc | azim | tvd | e_w_deviation | n_s_deviation | dls | surface_latitude | surface_longitude | longitude_points | latitude_points | zone_number | zone_letter | x_points | y_points | surface_x | surface_y | isHorizontal | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | well_A | 0.0000 | 0.00 | 227.11 | 0.000000 | 0.000000e+00 | 0.000000e+00 | 0.000000 | 29.908294 | 47.688521 | 47.688521 | 29.908294 | 38 | R | 759587.932586 | 3.311662e+06 | 759587.932586 | 3.311662e+06 | Vertical |
| 1 | well_A | 36.7500 | 0.00 | 227.11 | 36.750000 | -1.558201e-17 | -1.447458e-17 | 0.000000 | 29.908294 | 47.688521 | 47.688521 | 29.908294 | 38 | R | 759587.932586 | 3.311662e+06 | 759587.932586 | 3.311662e+06 | Vertical |
| 2 | well_A | 813.5505 | 0.46 | 227.11 | 813.542155 | -2.284634e+00 | -2.122263e+00 | 0.059217 | 29.908294 | 47.688521 | 47.688513 | 29.908289 | 38 | R | 759587.236230 | 3.311661e+06 | 759587.932586 | 3.311662e+06 | Vertical |
| 3 | well_A | 840.0000 | 0.13 | 163.86 | 839.991268 | -2.354083e+00 | -2.223348e+00 | 1.249832 | 29.908294 | 47.688521 | 47.688513 | 29.908289 | 38 | R | 759587.215062 | 3.311661e+06 | 759587.932586 | 3.311662e+06 | Vertical |
| 4 | well_A | 945.0000 | 0.57 | 230.43 | 944.989049 | -2.723571e+00 | -2.670477e+00 | 0.418306 | 29.908294 | 47.688521 | 47.688512 | 29.908287 | 38 | R | 759587.102442 | 3.311661e+06 | 759587.932586 | 3.311662e+06 | Vertical |
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