def imshow_sw(dat,seis_coord = None,well_files = None,horfiles = None,rockprop = None) :
minval = 0
maxval = np.max(dat)
fig, ax = plt.subplots()
divider = make_axes_locatable(ax)
cax = divider.append_axes('right', size='5%', pad=0.05)
if (seis_coord is None):
pos =ax.imshow(dat,interpolation='bicubic',cmap = 'nipy_spectral', aspect='auto',vmin = 0,vmax =maxval)
fig.colorbar(pos, cax=cax, orientation='vertical')
else:
#dist = calc_dist(seis_coord)[0]
tmin = seis_coord['tseis'][0]
tmax = seis_coord['tseis'][-1]
xmin = 0
xmax = dat.shape[1]*seis_coord['bins'][0]
pos = ax.imshow(dat,interpolation='bicubic',cmap = 'nipy_spectral', aspect='auto',vmin =0,vmax =maxval,\
extent = [xmin,xmax,tmax,tmin])
nwells = well_files.size
numm = 40
wellcoord = seis_coord['wellcoord']
for i in range(nwells):
wlog = mfun.load_obj(well_files[i])
rho = wlog.Rhob
if (rho is None):
raise Exception ('Density log does not exist')
wtmin = np.int(wlog.time[0])
wtmax = np.int(wlog.time[-1])
if(wtmin < tmin):
wtmin = tmin
elif( wtmax > tmax):
wtmax = tmax
zone = np.array([wtmin,wtmax], dtype = int)
rho = mfun.segment_logs(zone,wlog.time,rho)[0]
wxmin = xmin + wellcoord['indx'][i]*seis_coord['bins'][0]
wxmax = wxmin + numm
ai_mat = np.tile(rho,[numm,1]).T
ax.imshow(ai_mat,interpolation='bicubic',cmap = 'nipy_spectral', aspect='auto',vmin = 0,vmax =maxval,\
extent = [wxmin,wxmax,wtmax,wtmin])
if(horfiles is not None):
nhor = horfiles.size
indx = seis_coord['coord_indx']
hor_x = np.arange(xmin,xmax,seis_coord['bins'][0]) # temp
for xd in range(nhor):
hor = mfun.load_obj(horfiles[xd])
hor_y = hor.Z[indx]
ax.plot(hor_x,hor_y,'k')
fig.colorbar(pos, cax=cax, orientation='vertical')
ax.autoscale()
ax.set_xlabel('Distance(m)')
ax.set_ylabel('Time(ms)')
if (rockprop is None):
rockprop = 'Porosity'
ax.set_title(rockprop)
def read(self):
if (self.init_flag is None):
self.init()
tmp = mfun.load_obj(self.wavefiles[0])
self.data = mfun.cell(self.nwav, tmp.nang)
for ii in range(self.nwav):
self.wav[ii] = mfun.load_obj(self.wavefiles[ii])
self.nang = self.wav[ii].nang
self.wavename[ii] = self.wav[ii].wavename
for i in range(self.nang):
if (self.nang == 1):
self.data[ii, i] = self.wav[ii].data
else:
self.data[ii, i] = self.wav[ii].data[:, i]
def save_interp_mod(self):
print ('inside save_interp_mod->backgrundmodeling class')
print('To do : check if model directory exist and if not create it')
BKModelcoord ={}
if (self.coordName is None):
coord_bin,coord_XY = self.generate_coordinate()
BKModelcoord['Lines'] = coord_bin[:,0]
BKModelcoord['Traces'] = coord_bin[:,1]
else:
tmp = mfun.load_obj(self.coordName)
BKModelcoord['Lines'] = tmp['Lines']
BKModelcoord['Traces'] = tmp['Traces']
BKModelcoord['HorIndx'] = self.HorIndx
BKModelcoord['hor'] = self.hor
BKModelcoord['corr_indx'] = self.corr_indx
BKModelcoord['nLines'] = self.nLines
BKModelcoord['nTraces'] = self.nTraces
BKModelcoord['GEOM'] = self.GEOM
BKModelcoord['Grid'] = self.Grid
BKModelcoord['bin'] = self.bin
BKModelcoord['dt'] = self.dt
BKModelcoord['tmin'] = self.tmin
BKModelcoord['tmax'] = self.tmax
BKModelcoord['wellfiles'] = self.wellfiles
BKModelcoord['wellcoord'] = self.wellcoord
BKModelcoord['wellxy'] = self.wellxy
BKModelcoord['dlines'] = self.dlines
BKModelcoord['dtraces'] = self.dtraces
mfun.save_obj('models\BKModelcoord',BKModelcoord)
if (self.wlog == 'Vp'):
mfun.numpy_save(self.model,'models\VPinterp')
mfun.save_obj('models\\vpLOGS',self.LOGS) # original logs... no time axis
mfun.save_obj('models\\vplogss',self.logs) # resampled log on the seisimic grid
if (self.Algorithm):
mfun.save_obj('models\\VPmodcov',self.modvar)
elif (self.wlog == 'Vs'):
mfun.numpy_save(self.model,'models\VSinterp')
mfun.save_obj('models\\vsLOGS',self.LOGS)
mfun.save_obj('models\\vslogss',self.logs)
if (self.Algorithm):
mfun.save_obj('models\\VSmodcov',self.modvar)
elif (self.wlog == 'Rhob'):
mfun.numpy_save(self.model,'models\RHOinterp')
mfun.save_obj('models\\rhoLOGS',self.LOGS)
mfun.save_obj('models\\rhologss',self.logs)
if (self.Algorithm):
mfun.save_obj('models\\RHOmodcov',self.modvar)
def creatinglogsInGrid(self):
tmp = mfun.load_obj(self.coordName)
"""
BKModelcoord ={}
BKModelcoord['Lines'] = tmp['Lines']
BKModelcoord['Traces'] = tmp['Traces']
BKModelcoord['X'] = tmp['X']
BKModelcoord['Y'] = tmp['Y']
"""
XY = np.vstack((tmp['X'],tmp['Y']))
self.layered = np.zeros((self.nzones,self.nwells))
for i in range(self.nwells):
self.layered[:,i] = self.newlogs[i,1]
return XY.T
def load_hor(self,options = None):
print('-----loading horizons-----------')
if (options is None):
options = 'pristine'
# MAKING SURE THAT TOTAL THICKNESS IS MORE THAN FILTER LENGTH
period = round(1000/self.bkmodfreq)
FL = round(period/self.dt)
nhor =self.horfiles.size
horz = mfun.cell(nhor)
horm = mfun.cell(nhor)
for i in range(nhor):
m = mfun.load_obj(self.horfiles[i])
tmp = np.stack((m.Inline,m.Crossline,m.Z), axis = 1)
horz[i] = tmp
ns = horz[0][:,0].size
horm = horz
for ii in range(ns):
for i in range(nhor-1):
temp = horm[i+1][ii,2] - horm[i][ii,2]
if temp < (FL*self.dt):
horm[i+1][ii,2] = horm[i+1][ii,2] + (FL*self.dt) + 1
if (options == 'pristine'):
hor = horz
elif(options == 'modify'):
hor = horm
# recoordinating with seismic time sampling
for i in range(nhor):
temp = hor[i][:,2]
tmp = self.recoordzone(temp)
hor[i][:,2] = tmp
self.hor = hor
def loadhor(self,options = None):
if (options is None):
options = 'pristine'
Period = round(1000/self.bkmodfreq)
FL = round(Period/self.dt)
nhor = self.horfiles.size
horz = mfun.cell(nhor)
horm = mfun.cell(nhor)
for i in range(nhor):
m = mfun.load_obj(self.horfiles[i])
temp = np.concatenate(m.Inlne,m.Crossline,m.Z)
horz[i] = temp
nrm,ncm = horz.size_data
ns = nrm[0]
horm = horz
for ii in range(ns):
for i in range(nhor-1):
temp = horm[i+1][ii,2] - horm[i][ii,2]
if temp < (FL*self.dt):
horm[ii][ii,2] = horm[ii+1][ii,3] + (FL*self.dt) + 1
if (options == 'pristine'):
hor =horz
elif (options,'modify'):
hor = horm
for i in range(nhor):
temp = hor[i][:,2]
temp = self.recoordzone(temp)
hor[i][:,2] = temp
self.hor = hor
def read(self):
if (self.well_flag is None):
self.init()
self.Inline = np.zeros(self.nwells)
self.Crossline = np.zeros(self.nwells)
self.Xloc = np.zeros(self.nwells)
self.Yloc = np.zeros(self.nwells)
for i in range(self.nwells):
self.wells[i] = mfun.load_obj(
self.wellnames[i]) # m is welldata class
self.wells[i].wellname = self.wellnames[i]
if (self.wells[i].Inline is not None):
self.Inline[i] = self.wells[i].Inline
if (self.wells[i].Crossline is not None):
self.Crossline[i] = self.wells[i].Crossline
if (self.wells[i].Xloc is not None):
self.Xloc[i] = self.wells[i].Xloc
if (self.wells[i].Yloc is not None):
self.Yloc[i] = self.wells[i].Yloc
def save_geodata(self,path):
geodata = {}
print('Run after VP, VS and Rho Models are saved on disk')
fp_vplogs = path + 'vplogss'
fp_vslogs = path + 'vslogss'
fp_rhologs = path + 'rhologss'
fp_vpLOGS = path + 'vpLOGS'
fp_vsLOGS = path + 'vsLOGS'
fp_rhoLOGS = path + 'rhoLOGS'
# This is the resampled log on the seisimic grid
# loaded into cells
geodata['Vplogs'] = mfun.load_obj(fp_vplogs)
geodata['Vslogs'] = mfun.load_obj(fp_vslogs)
geodata['rhologs'] = mfun.load_obj(fp_rhologs)
# This is the original log loaded into cells
geodata['VpLOGS'] = mfun.load_obj(fp_vpLOGS)
geodata['VsLOGS'] = mfun.load_obj(fp_vsLOGS)
geodata['rhoLOGS'] = mfun.load_obj(fp_rhoLOGS)
mfun.save_obj('models\geodata',geodata)
w19_19A.TD_MD = mat[:,0]
w19_19A.TD_X = mat[:,8]
w19_19A.TD_Y = mat[:,9]
# Deviation file
mat = w19_19A.read('well_files//volve//TD//HRS//15_9_19A_well_tie.txt',16)
w19_19A.MD_dev = mat[:,0]
w19_19A.Xdev = mat[:,8]
w19_19A.Ydev = mat[:,9]
fig, (ax1, ax2) = plt.subplots(2,1)
ax1.plot(w19_19A.MD,w19_19A.Vp)
w19_19A.apply_TD()
w19_19A_time = well()
w19_19A_time = mfun.load_obj('well_files//volve//time//w19A')
# debug
zone = np.zeros(2)
if (w19_19A.time [0] >= w19_19A_time.time[0] ):
zone[0] = w19_19A.time[0]
else:
zone[0] = w19_19A_time.time[0]
if (w19_19A.time [-1]<=w19_19A_time.time[-1] ):
zone[1] = w19_19A_time.time[-1]
else:
zone[1] = w19_19A.time[-1]
zone[0] = 2350
zone[1] = 2515
elif (upper.size == 0 and lower.size == 0):
zone = np.array([np.min(tseis), np.max(tseis)], dtype=int)
data, t = mfun.segment_logs(zone, LOGS[i, 0], LOGS[i, 3])
dat_out = data
else: #upper and lower zone zero
dat_out = np.concatenate(
(upperzone_data, LOGS[i, 3], lowerzone_data), axis=None)
logs[i, 0] = tseis
logs[i, 1] = LOGS[i, 1]
logs[i, 2] = LOGS[i, 2]
logs[i, 3] = dat_out
return logs
#----------------------------------------------------
if __name__ == "__main__":
import matplotlib.pyplot as plt
tseis = np.arange(0, 3601, 1)
data = mfun.load_obj('Poseidon_2')
plt.plot(data.Vs, data.time, 'k')
params = ctrendlogs(data.Vs, data.time, 'Vs')
#pred = fcompactntrend_vs(data.time,params)
pred = predict(params, 'Vp', data.Vs, tseis)
plt.plot(pred, data.time, 'ro')
plt.show()