def optimize_bowers_trace(depth_tr, vel_tr, obp_tr, hydro_tr,
depth_upper, depth_lower):
es_data = np.array(obp_tr) - np.array(hydro_tr)
depth = np.array(depth_tr)
mask = depth < depth_lower
mask *= depth > depth_upper
vel_interval = np.array(vel_tr)[mask]
es_interval = es_data[mask]
vel_to_fit = pick_sparse(vel_interval, 3)
es_to_fit = pick_sparse(es_interval, 3)
popt, _ = curve_fit(virgin_curve, es_to_fit, vel_to_fit)
a, b = popt
return a, b
def optimize_bowers_trace(depth_tr, vel_tr, obp_tr, hydro_tr, depth_upper,
depth_lower):
es_data = np.array(obp_tr) - np.array(hydro_tr)
depth = np.array(depth_tr)
mask = depth < depth_lower
mask *= depth > depth_upper
vel_interval = np.array(vel_tr)[mask]
es_interval = es_data[mask]
vel_to_fit = pick_sparse(vel_interval, 3)
es_to_fit = pick_sparse(es_interval, 3)
popt, _ = curve_fit(virgin_curve, es_to_fit, vel_to_fit)
a, b = popt
return a, b
def optimize_nct_trace(depth, vel, fit_start, fit_stop, pick=True):
mask = depth > fit_start
mask *= depth < fit_stop
mask *= np.isfinite(vel)
depth_interval = np.array(depth)[mask]
vel_interval = np.array(vel)[mask]
if pick is True:
depth_to_fit = pick_sparse(depth_interval, 3)
vel_to_fit = pick_sparse(vel_interval, 3)
else:
depth_to_fit = depth_interval
vel_to_fit = vel_interval
dt = vel_to_fit**(-1)
log_dt = np.log(dt)
popt, _ = curve_fit(normal_dt, depth_to_fit, log_dt)
a, b = popt
return a, b
def optimize_nct_trace(depth, vel, fit_start, fit_stop, pick=True):
mask = depth > fit_start
mask *= depth < fit_stop
mask *= np.isfinite(vel)
depth_interval = np.array(depth)[mask]
vel_interval = np.array(vel)[mask]
if pick is True:
depth_to_fit = pick_sparse(depth_interval, 3)
vel_to_fit = pick_sparse(vel_interval, 3)
else:
depth_to_fit = depth_interval
vel_to_fit = vel_interval
dt = vel_to_fit**(-1)
log_dt = np.log(dt)
popt, _ = curve_fit(normal_dt, depth_to_fit, log_dt)
a, b = popt
return a, b
def optimize_bowers_virgin(well, vel_log, obp_log, upper, lower,
pres_log='loading', mode='nc', nnc=5):
"""
Optimizer for Bowers loading curve
Parameters
----------
well : Well
vel_log : Log or str
Log object or well log name stored in well
obp_log : Log or str
Log object or well log name stored in well
upper : float or str
upper bound of nct, depth value or horizon name
lower : float or str
lower bound of nct, depth value or horizon name
pres_log : Log or str
Log object storing measured pressure value or Pressure name
stored in well
mode : {'nc', 'pres', 'both'}
which pressure to use for optimization,
- 'nc' : points on NCT
- 'pres' : points in pres_log
- 'both' : both of them
nnc : int
number of points to pick on NCT
Returns
-------
a, b : tuple of floats
optimized bowers loading curve coefficients
rms_err : float
root mean square error of pressure
"""
if isinstance(upper, (bytes, str)):
depth_upper = well.params['horizon'][upper]
else:
depth_upper = upper
if isinstance(upper, (bytes, str)):
depth_lower = well.params['horizon'][lower]
else:
depth_lower = lower
if isinstance(vel_log, (bytes, str)):
vel_log = well.get_log(vel_log)
if isinstance(obp_log, (bytes, str)):
obp_log = well.get_log(obp_log)
if isinstance(pres_log, (bytes, str)):
# pres_log = well.get_loading_pressure()
pres_log = well.get_pressure(pres_log)
depth = np.array(obp_log.depth)
nct_vel_to_fit = []
nct_es_to_fit = []
pres_vel_to_fit = []
pres_es_to_fit = []
if mode == 'nct' or mode == 'both':
nct_es_data = np.array(obp_log.data) - np.array(well.hydrostatic)
nct_mask = depth < depth_lower
nct_mask *= depth > depth_upper
nct_mask *= depth < vel_log.stop
nct_vel_interval = np.array(vel_log.data)[nct_mask]
nct_es_interval = nct_es_data[nct_mask]
nct_vel_to_fit = np.array(pick_sparse(nct_vel_interval, nnc))
nct_es_to_fit = np.array(pick_sparse(nct_es_interval, nnc))
if mode == 'pres' or mode == 'both':
vel = list()
obp = list()
pres = list()
for dp in pres_log.depth:
idx = np.searchsorted(depth, dp)
vel.append(vel_log.data[idx])
obp.append(obp_log.data[idx])
vel, obp, pres = np.array(vel), np.array(obp), np.array(pres_log.data)
es = obp - pres
pres_vel_to_fit = vel
pres_es_to_fit = es
vel_to_fit = np.append(nct_vel_to_fit, pres_vel_to_fit)
es_to_fit = np.append(nct_es_to_fit, pres_es_to_fit)
popt, _ = curve_fit(virgin_curve, es_to_fit, vel_to_fit)
a, b = popt
es_predicted = invert_virgin(vel_to_fit, a, b)
rms_err = rmse(es_to_fit, es_predicted)
return a, b, rms_err
def optimize_bowers_virgin(well,
vel_log,
obp_log,
upper,
lower,
pres_log='loading',
mode='nc',
nnc=5):
"""
Optimizer for Bowers loading curve
Parameters
----------
well : Well
vel_log : Log or str
Log object or well log name stored in well
obp_log : Log or str
Log object or well log name stored in well
upper : float or str
upper bound of nct, depth value or horizon name
lower : float or str
lower bound of nct, depth value or horizon name
pres_log : Log or str
Log object storing measured pressure value or Pressure name
stored in well
mode : {'nc', 'pres', 'both'}
which pressure to use for optimization,
- 'nc' : points on NCT
- 'pres' : points in pres_log
- 'both' : both of them
nnc : int
number of points to pick on NCT
Returns
-------
a, b : tuple of floats
optimized bowers loading curve coefficients
rms_err : float
root mean square error of pressure
"""
if isinstance(upper, (bytes, str)):
depth_upper = well.params['horizon'][upper]
else:
depth_upper = upper
if isinstance(upper, (bytes, str)):
depth_lower = well.params['horizon'][lower]
else:
depth_lower = lower
if isinstance(vel_log, (bytes, str)):
vel_log = well.get_log(vel_log)
if isinstance(obp_log, (bytes, str)):
obp_log = well.get_log(obp_log)
if isinstance(pres_log, (bytes, str)):
# pres_log = well.get_loading_pressure()
pres_log = well.get_pressure(pres_log)
depth = np.array(obp_log.depth)
nct_vel_to_fit = []
nct_es_to_fit = []
pres_vel_to_fit = []
pres_es_to_fit = []
if mode == 'nct' or mode == 'both':
nct_es_data = np.array(obp_log.data) - np.array(well.hydrostatic)
nct_mask = depth < depth_lower
nct_mask *= depth > depth_upper
nct_mask *= depth < vel_log.stop
nct_vel_interval = np.array(vel_log.data)[nct_mask]
nct_es_interval = nct_es_data[nct_mask]
nct_vel_to_fit = np.array(pick_sparse(nct_vel_interval, nnc))
nct_es_to_fit = np.array(pick_sparse(nct_es_interval, nnc))
if mode == 'pres' or mode == 'both':
vel = list()
obp = list()
pres = list()
for dp in pres_log.depth:
idx = np.searchsorted(depth, dp)
vel.append(vel_log.data[idx])
obp.append(obp_log.data[idx])
vel, obp, pres = np.array(vel), np.array(obp), np.array(pres_log.data)
es = obp - pres
pres_vel_to_fit = vel
pres_es_to_fit = es
vel_to_fit = np.append(nct_vel_to_fit, pres_vel_to_fit)
es_to_fit = np.append(nct_es_to_fit, pres_es_to_fit)
popt, _ = curve_fit(virgin_curve, es_to_fit, vel_to_fit)
a, b = popt
es_predicted = invert_virgin(vel_to_fit, a, b)
rms_err = rmse(es_to_fit, es_predicted)
return a, b, rms_err
def plot_bowers_vrigin(ax,
a,
b,
well,
vel_log,
obp_log,
upper,
lower,
pres_log='loading',
mode='nc',
nnc=5):
if isinstance(upper, (bytes, str)):
depth_upper = well.params['horizon'][upper]
else:
depth_upper = upper
if isinstance(upper, (bytes, str)):
depth_lower = well.params['horizon'][lower]
else:
depth_lower = lower
if isinstance(vel_log, (bytes, str)):
vel_log = well.get_log(vel_log)
if isinstance(obp_log, (bytes, str)):
obp_log = well.get_log(obp_log)
if isinstance(pres_log, (bytes, str)):
# pres_log = well.get_loading_pressure()
pres_log = well.get_pressure(pres_log)
depth = np.array(obp_log.depth)
nct_vel_to_fit = []
nct_es_to_fit = []
pres_vel_to_fit = []
pres_es_to_fit = []
if mode == 'nct' or mode == 'both':
nct_es_data = np.array(obp_log.data) - np.array(well.hydrostatic)
nct_mask = depth < depth_lower
nct_mask *= depth > depth_upper
nct_mask *= depth < vel_log.stop
nct_vel_interval = np.array(vel_log.data)[nct_mask]
nct_es_interval = nct_es_data[nct_mask]
nct_vel_to_fit = np.array(pick_sparse(nct_vel_interval, nnc))
nct_es_to_fit = np.array(pick_sparse(nct_es_interval, nnc))
ax.scatter(nct_es_to_fit,
nct_vel_to_fit,
color='blue',
marker='d',
label='NCP')
if mode == 'pres' or mode == 'both':
vel = list()
obp = list()
pres = list()
for dp in pres_log.depth:
idx = np.searchsorted(depth, dp)
vel.append(vel_log.data[idx])
obp.append(obp_log.data[idx])
vel, obp, pres = np.array(vel), np.array(obp), np.array(pres_log.data)
es = obp - pres
pres_vel_to_fit = vel
pres_es_to_fit = es
ax.scatter(pres_es_to_fit,
pres_vel_to_fit,
color='purple',
marker='s',
label='measured')
es_curve = np.arange(0, 80, 1)
vel_curve = virgin_curve(es_curve, a, b)
ax.plot(es_curve, vel_curve, color='black', zorder=1, label="Loading")
ax.set(title="Loading Curve - {}".format(well.well_name),
xlabel="Effective Stress(MPa)",
ylabel="Velocity(m/s)")
ax.set_xlim(left=0)
ax.set_ylim(bottom=1500)
ax.legend(loc=4)
def plot_bowers_vrigin(ax, a, b, well, vel_log, obp_log, upper, lower,
pres_log='loading', mode='nc', nnc=5):
if isinstance(upper, (bytes, str)):
depth_upper = well.params['horizon'][upper]
else:
depth_upper = upper
if isinstance(upper, (bytes, str)):
depth_lower = well.params['horizon'][lower]
else:
depth_lower = lower
if isinstance(vel_log, (bytes, str)):
vel_log = well.get_log(vel_log)
if isinstance(obp_log, (bytes, str)):
obp_log = well.get_log(obp_log)
if isinstance(pres_log, (bytes, str)):
# pres_log = well.get_loading_pressure()
pres_log = well.get_pressure(pres_log)
depth = np.array(obp_log.depth)
nct_vel_to_fit = []
nct_es_to_fit = []
pres_vel_to_fit = []
pres_es_to_fit = []
if mode == 'nct' or mode == 'both':
nct_es_data = np.array(obp_log.data) - np.array(well.hydrostatic)
nct_mask = depth < depth_lower
nct_mask *= depth > depth_upper
nct_mask *= depth < vel_log.stop
nct_vel_interval = np.array(vel_log.data)[nct_mask]
nct_es_interval = nct_es_data[nct_mask]
nct_vel_to_fit = np.array(pick_sparse(nct_vel_interval, nnc))
nct_es_to_fit = np.array(pick_sparse(nct_es_interval, nnc))
ax.scatter(
nct_es_to_fit, nct_vel_to_fit, color='blue', marker='d',
label='NCP')
if mode == 'pres' or mode == 'both':
vel = list()
obp = list()
pres = list()
for dp in pres_log.depth:
idx = np.searchsorted(depth, dp)
vel.append(vel_log.data[idx])
obp.append(obp_log.data[idx])
vel, obp, pres = np.array(vel), np.array(obp), np.array(pres_log.data)
es = obp - pres
pres_vel_to_fit = vel
pres_es_to_fit = es
ax.scatter(
pres_es_to_fit, pres_vel_to_fit, color='purple', marker='s',
label='measured')
es_curve = np.arange(0, 80, 1)
vel_curve = virgin_curve(es_curve, a, b)
ax.plot(es_curve, vel_curve, color='black', zorder=1, label="Loading")
ax.set(title="Loading Curve - {}".format(well.well_name),
xlabel="Effective Stress(MPa)",
ylabel="Velocity(m/s)")
ax.set_xlim(left=0)
ax.set_ylim(bottom=1500)
ax.legend(loc=4)