def smooth_log(log, window=1500):
"""
Parameters
----------
log : Log object
log to smooth
window : scalar
window size of the median filter
Returns
-------
smoothed log : Log object
smoothed log
"""
data = np.array(log.data)
depth = np.array(log.depth)
mask = np.isfinite(data)
func = interp1d(depth[mask], data[mask])
interp_data = func(depth[log.start_idx: log.stop_idx])
new_data = np.array(data)
new_data[log.start_idx: log.stop_idx] = interp_data
smoothed = smooth(new_data[log.start_idx: log.stop_idx], window_len=window//2, window='flat')
# using half the window length in order to be consistent with opendtect
smooth_data = np.array(data)
smooth_data[log.start_idx: log.stop_idx] = smoothed
log_smooth = Log()
log_smooth.name = log.name + "_sm"
log_smooth.units = log.units
log_smooth.descr = log.descr
log_smooth.depth = log.depth
log_smooth.data = smooth_data
return log_smooth
def truncate_log(log, top, bottom):
"""
Remove unreliable values in the top and bottom section of well log
Parameters
----------
log : Log object
top, bottom : scalar
depth value
Returns
-------
trunc_log : Log object
"""
depth = np.array(log.depth)
data = np.array(log.data)
if top != 0:
mask = depth < top
data[mask] = np.nan
if bottom != 0:
mask = depth > bottom
data[mask] = np.nan
trunc_log = Log()
trunc_log.name = log.name + '_trunc'
trunc_log.units = log.units
trunc_log.descr = log.descr
trunc_log.depth = depth
trunc_log.data = data
return trunc_log
def obp_well(den_log, kb=41, wd=82, rho_w=1.01):
"""
Compute Overburden Pressure for a Log
Parameters
----------
den_log : Log
density log (extrapolated)
kb : scalar
kelly bushing elevation in meter
wd : scalar
from sea level to sea bottom (a.k.a mudline) in meter
rho_w : scalar
density of sea water - depending on the salinity of sea water
(1.01-1.05g/cm3)
Returns
-------
out : Log
Log containing overburden pressure in mPa
"""
depth = np.array(den_log.depth)
rho = np.array(den_log.data)
obp = overburden_pressure(
depth, rho, kelly_bushing=kb, depth_w=wd, rho_w=rho_w)
obp_log = Log()
obp_log.depth = depth
obp_log.data = obp
obp_log.name = "log_obp"
obp_log.descr = "Overburden_Pressure"
obp_log.units = "MPa"
return obp_log
def local_average(log, rad=10):
"""upscale data using local averaging
Parameters
----------
data : Log()
log data to be upscaled
rad : int
local radius, data within this radius will be represented by a single value
Returns
-------
new_log : Log()
upscaled log data
"""
data = np.array(log.data)
mask = np.isfinite(data)
index = np.where(mask)
start = index[0][0]
end = index[0][-1]+1
interval = data[start: end]
index_toadd = []
data_toadd = []
step = rad*2+1
n = len(interval)
for i in range(0, n, step):
seg = interval[i: i+step]
new_mask = np.isfinite(seg)
if len(seg[new_mask]) > rad+1:
data_toadd.append(np.mean(seg[new_mask]))
index_toadd.append(start+i+rad)
data_toadd, index_toadd = np.array(data_toadd), np.array(index_toadd)
new_data = np.full_like(data, np.nan)
new_data[index_toadd] = data_toadd
new_log = Log()
new_log.depth = log.depth
new_log.data = new_data
return new_log
def interpolate_log(log):
"""
Log curve interpolation
"""
depth = np.array(log.depth)
data = np.array(log.data)
mask = np.isfinite(data)
func = interp1d(depth[mask], data[mask])
interp_data = func(depth[log.start_idx: log.stop_idx])
data[log.start_idx: log.stop_idx] = interp_data
interp_log = Log()
interp_log.name = log.name + '_interp'
interp_log.units = log.units
interp_log.descr = log.descr
interp_log.depth = depth
interp_log.data = data
return interp_log
def normal_log(vel_log, a, b):
"""
Returns
-------
Log
normal velocity log
"""
normal_vel = normal(np.array(vel_log.depth), a, b)
mask = np.isnan(np.array(vel_log.data))
normal_vel[mask] = np.nan
log = Log()
log.depth = np.array(vel_log.depth)
log.data = normal_vel
log.name = 'normal_vel_log'
log.descr = "Velocity_normal"
log.units = "m/s"
return log
def extrapolate_log_traugott(den_log, a, b, kb=0, wd=0):
"""
Extrapolate density log using Traugott equation
"""
density_trend = traugott_trend(np.array(den_log.depth), a, b, kb=kb, wd=wd)
extra_log = Log()
extra_log.name = den_log.name + "_ex"
extra_log.units = den_log.units
extra_log.descr = "Density_extra"
extra_log.depth = np.array(den_log.depth)
new_data = np.full_like(density_trend, np.nan)
new_data[:den_log.start_idx] = density_trend[:den_log.start_idx]
old_data = np.array(den_log.data)
new_data[den_log.start_idx:] = old_data[den_log.start_idx:]
extra_log.data = new_data
return extra_log
def hydrostatic_well(depth, kb=0, wd=0, rho_f=1., rho_w=1.):
"""
Returns
-------
Log
Hydrostatic pressure as a Log
"""
hydrostatic = hydrostatic_pressure(depth,
kelly_bushing=kb,
depth_w=wd,
rho_f=rho_f,
rho_w=rho_w)
hydro_log = Log()
hydro_log.depth = np.array(depth)
hydro_log.data = hydrostatic
hydro_log.name = 'log_hydro'
hydro_log.descr = "Hydrostatic_Pressure"
hydro_log.units = "MPa"
return hydro_log
def hydrostatic_well(depth, kb=0, wd=0, rho_f=1., rho_w=1.):
"""
Returns
-------
Log
Hydrostatic pressure as a Log
"""
hydrostatic = hydrostatic_pressure(
depth, kelly_bushing=kb, depth_w=wd, rho_f=rho_f, rho_w=rho_w)
hydro_log = Log()
hydro_log.depth = np.array(depth)
hydro_log.data = hydrostatic
hydro_log.name = 'log_hydro'
hydro_log.descr = "Hydrostatic_Pressure"
hydro_log.units = "MPa"
return hydro_log
def upscale_log(log, freq=20):
"""
downscale a well log with a lowpass butterworth filter
"""
depth = np.array(log.depth)
data = np.array(log.data)
mask = np.isfinite(data)
func = interp1d(depth[mask], data[mask])
interp_data = func(depth[log.start_idx: log.stop_idx])
nyq = 10000 / 2
dw = freq / nyq
b, a = butter(4, dw, btype='low', analog=False)
filtered = filtfilt(b, a, interp_data, method='gust')
downscale_data = np.array(data)
downscale_data[log.start_idx: log.stop_idx] = filtered
log_downscale = Log()
log_downscale.name = log.name + "_downscale_" + str(freq)
log_downscale.units = log.units
log_downscale.descr = log.descr
log_downscale.depth = log.depth
log_downscale.data = downscale_data
return log_downscale
def normal_log(vel_log, a, b):
"""
Returns
-------
Log
normal velocity log
"""
normal_vel = normal(np.array(vel_log.depth), a, b)
mask = np.isnan(np.array(vel_log.data))
normal_vel[mask] = np.nan
log = Log()
log.depth = np.array(vel_log.depth)
log.data = normal_vel
log.name = 'normal_vel_log'
log.descr = "Velocity_normal"
log.units = "m/s"
return log
def obp_well(den_log, kb=41, wd=82, rho_w=1.01):
"""
Compute Overburden Pressure for a Log
Parameters
----------
den_log : Log
density log (extrapolated)
kb : scalar
kelly bushing elevation in meter
wd : scalar
from sea level to sea bottom (a.k.a mudline) in meter
rho_w : scalar
density of sea water - depending on the salinity of sea water
(1.01-1.05g/cm3)
Returns
-------
out : Log
Log containing overburden pressure in mPa
"""
depth = np.array(den_log.depth)
rho = np.array(den_log.data)
obp = overburden_pressure(depth,
rho,
kelly_bushing=kb,
depth_w=wd,
rho_w=rho_w)
obp_log = Log()
obp_log.depth = depth
obp_log.data = obp
obp_log.name = "log_obp"
obp_log.descr = "Overburden_Pressure"
obp_log.units = "MPa"
return obp_log
def shale(log, vsh_log, thresh=0.35):
"""
Discern shale intervals
log : Log
log to discern
vsh_log : Log
shale volume log
thresh : scalar
percentage threshold, 0 < thresh < 1
"""
shale_mask = np.isfinite(vsh_log.depth)
shale_mask[vsh_log.start_idx: vsh_log.stop_idx] = True
mask_thresh = np.array(vsh_log.data) < thresh
mask = shale_mask * mask_thresh
data = np.array(log.data)
data[mask] = np.nan
log_sh = Log()
log_sh.name = log.name + "_sh"
log_sh.units = log.units
log_sh.descr = log.descr
log_sh.depth = log.depth
log_sh.data = data
return log_sh
