from .equations import *
import pandas as pd
from math import degrees
from .well import Well, define_section
from numpy import linspace
[docs]def load(data, **kwargs):
"""
Load an existing wellpath.
Parameters
----------
data:
Excel file, dataframe or list of dictionaries.
Must contain at least md, inclination and azimuth. Can also contain tvd, northing and easting.
Keyword Args
------------
set_start: dict, None
set initial point in m {'north': 0, 'east': 0}.
change_azimuth: float, int, None
add specific degrees to azimuth values along the entire well.
set_info: dict, None
dict, {'dlsResolution', 'wellType': 'onshore'|'offshore', 'units': 'metric'|'english'}.
inner_pts: num
include certain amount of inner points between survey stations.
Returns
-------
well: well object
A wellpath object with 3D position
"""
# Settings
set_start = kwargs.get('set_start', None)
change_azimuth = kwargs.get('change_azimuth', None)
set_info = kwargs.get('set_info', None)
inner_pts = kwargs.get('inner_points', 0)
info = {'dlsResolution': 30, 'wellType': 'offshore', 'units': 'metric'}
initial_point = {'north': 0, 'east': 0}
base_data = False
data_initial = None
processed = False
# PROCESSING DATA
if isinstance(set_info, dict):
for param in set_info: # changing default values
if param in info:
info[param] = set_info[param]
if isinstance(set_start, dict):
for x in set_start: # changing default values
if x in initial_point:
initial_point[x] = set_start[x]
if isinstance(data, pd.DataFrame):
base_data = True
data_initial = data.copy()
data.dropna(axis=1, how='all', inplace=True)
data.dropna(inplace=True)
data = solve_key_similarities(data)
data = data.to_dict('records')
processed = True
if ".xlsx" in data:
base_data = True
data = pd.read_excel(data) # open excel file with pandas
data_initial = data.copy()
data.dropna(axis=1, how='all', inplace=True)
data.dropna(inplace=True)
data = solve_key_similarities(data)
data = data.to_dict('records')
processed = True
if ".csv" in data:
base_data = True
data = pd.read_csv(data) # open csv file with pandas
data_initial = data.copy()
data.dropna(axis=1, how='all', inplace=True)
data.dropna(inplace=True)
data = solve_key_similarities(data)
data = data.to_dict('records')
processed = True
if type(data[0]) is dict:
if not processed:
data = solve_key_similarities(data)
md = [x['md'] for x in data]
inc = [x['inc'] for x in data]
az = [x['azi'] for x in data]
else: # if data is not a list of dicts, but a list of lists
md, inc, az = data[:3]
# DEALING WITH NAN-DATA
for x, y in enumerate(md): # change values to numbers if are strings
if type(y) == str:
md[x] = float(y.split(",", 1)[0])
inc[x] = float(inc[x].split(",", 1)[0])
az[x] = float(az[x].split(",", 1)[0])
# GENERAL CHANGE IN AZIMUTH
if change_azimuth is not None:
for a in range(len(az)):
az[a] += change_azimuth
# CREATING TRAJECTORY POINTS
trajectory = [{'md': 0, 'inc': 0, 'azi': 0, 'dl': 0, 'tvd': 0, 'sectionType': 'vertical', 'pointType': 'survey'}]
trajectory[-1].update(initial_point)
inner_pts += 2
if md[0] != 0:
md = [0] + md
inc = [0] + inc
az = [0] + az
for idx, md in enumerate(md):
if md > 0:
dogleg = calc_dogleg(inc[idx-1], inc[idx], az[idx-1], az[idx])
point = {'md': md, 'inc': inc[idx], 'azi': az[idx],
'north': calc_north(trajectory[-1]['north'], trajectory[-1]['md'], md,
trajectory[-1]['inc'], inc[idx],
trajectory[-1]['azi'], az[idx],
dogleg),
'east': calc_east(trajectory[-1]['east'], trajectory[-1]['md'], md,
trajectory[-1]['inc'], inc[idx],
trajectory[-1]['azi'], az[idx],
dogleg),
'tvd': calc_tvd(trajectory[-1]['tvd'], trajectory[-1]['md'], md,
trajectory[-1]['inc'], inc[idx], dogleg),
'dl': degrees(dogleg),
'pointType': 'survey'
}
point['sectionType'] = define_section(point, trajectory[-1])
p1 = trajectory[-1]
if inner_pts > 2:
dl_unit = point['dl'] / (inner_pts - 1)
condition = sin(radians(p1['inc'])) * sin(radians(point['inc'])) * sin(
radians(point['azi'] - trajectory[-1]['azi']))
if condition != 0:
md_segment = linspace(p1['md'], point['md'], inner_pts)[1:-1]
count = 1
for new_md in md_segment:
dl_new = dl_unit * count
inner_point = {'md': new_md, 'dl': dl_unit}
inner_pt_calcs(inner_point, p1, point, dl_sv=dl_new)
count += 1
trajectory.append(inner_point)
point['dl'] = dl_unit
trajectory.append(point)
well = Well({'trajectory': trajectory, 'info': info})
if base_data:
well._base_data = data_initial
return well
def solve_key_similarities(data):
md_similarities = ['MD', 'md(ft)', 'md(m)', 'MD(m)', 'MD(ft)',
'measureddepth', 'MeasuredDepth',
'measureddepth(m)', 'MeasuredDepth(m)',
'measureddepth(ft)', 'MeasuredDepth(ft)']
inc_similarities = ['Inclination', 'inclination', 'Inc', 'Incl', 'incl',
'inclination(°)', 'Inclination(°)', 'Incl(°)',
'incl(°)', 'Inc(°)', 'inc(°)', 'INC', 'INC(°)', 'INCL',
'INCL(°)', 'Inc(deg)', 'inc(deg)']
azi_similarities = ['az', 'az(°)',
'Az', 'Az(°)',
'AZ', 'AZ(°)',
'Azi', 'Azi(°)',
'azi(°)',
'AZI', 'AZI(°)',
'Azimuth', 'Azimuth(°)',
'azimuth', 'azimuth(°)',
'Azi(deg)', 'azi(deg)']
tvd_similarities = ['TVD', 'TVD (m)', 'TVD (ft)', 'TVD(m)', 'TVD(ft)',
'tvd (m)', 'tvd (ft)', 'tvd(m)', 'tvd(ft)']
north_similarities = ['NORTH', 'NORTH(m)', 'NORTH(ft)',
'North', 'North(m)', 'North(ft)',
'Northing(m)', 'Northing(ft)'
'N/S(m)', 'N/S(ft)',
'Ns(m)', 'Ns(ft)']
east_similarities = ['EAST', 'EAST(m)', 'EAST(ft)',
'East', 'East(m)', 'East(ft)',
'Easting(m)', 'Easting(ft)'
'E/W(m)', 'E/W(ft)',
'Ew(m)', 'Ew(ft)']
possible_keys = [md_similarities,
tvd_similarities,
inc_similarities,
azi_similarities,
north_similarities,
east_similarities]
correct_keys = ['md', 'tvd', 'inc', 'azi', 'north', 'east']
true_key = 0
for i in possible_keys:
for x in i:
if isinstance(data, pd.DataFrame):
data.columns = data.columns.str.replace(' ', '')
if x in data.columns:
data.rename(columns={x: correct_keys[true_key]}, inplace=True)
else:
if x.replace(' ', '') in data[0]:
for point in data:
point[correct_keys[true_key]] = point[x]
true_key += 1
return data