Integrating Stacking Velocities With Sonic Log Velocities Bill

Integrating Stacking Velocities With Sonic Log Velocities Bill Curry SEP AGM 2001

Motivation • Synthetic seismograms created from sonic logs rarely match actual seismic data • While velocity measurements are made in numerous locations within seismic surveys by logging, the data is not optimally used in processing Bill Curry Stanford Exploration Project AGM 2001 bill@sep. stanford. edu

Time-Depth Mis-tie Bill Curry Stanford Exploration Project AGM 2001 bill@sep. stanford. edu

Another Time-Depth Mis-tie Bill Curry Stanford Exploration Project AGM 2001 bill@sep. stanford. edu

Comparison of Measurements Sonic Log Velocity • High-frequency sampling • Small spatial area of investigation • Measured with high frequency waves (1 -5 k. Hz) • Measured w. r. t. depth Bill Curry Seismic Velocity • Low-frequency sampling • Large spatial area of investigation • Measured with low frequency waves (1100 Hz) • Measured w. r. t. twoway travel time Stanford Exploration Project AGM 2001 bill@sep. stanford. edu

Properties of the Desired Model RMS Sonic Log vs. Velocity Scan Sonic Log Vrms Bill Curry • Honor the low frequency information of the seismic data • Would stack the seismic data as well as an interpreted RMS model Stanford Exploration Project AGM 2001 bill@sep. stanford. edu

Properties of the Desired Model • Honor the high frequency content of the well log, including depth information • Would produce a realistic synthetic seismogram that matches the data Bill Curry Stanford Exploration Project AGM 2001 Well Log Vp bill@sep. stanford. edu

Least-Squares Fitting Goals Fits the model to the high frequencies of the log data, retains positions in depth Matches the model’s RMS velocity to that of the seismic data Bill Curry m = desired interval velocity model (in depth) K = RMS inversion weighting (Mask or Stack Power) R = RMS operator (depth to time) Stanford Exploration Project AGM 2001 bill@sep. stanford. edu

The Fitting Goals at Work RMS Sonic Log vs. Velocity Scan Well Log Sonic Log Vrms Vp Bill Curry Stanford Exploration Project AGM 2001 bill@sep. stanford. edu

Alternate Method of Inversion Regularization with nonstationary PEF imparts character of log to model Matches the model’s RMS velocity to that of the seismic data Bill Curry Stanford Exploration Project AGM 2001 bill@sep. stanford. edu

Justification Vp + Bill Curry • Schlumberger used a low frequency shift to match sonic logs to a VSP, resulting in a “check-shot corrected sonic” • I’m assuming that the effects of the acquisition geometry and anisotropy are low frequency Stanford Exploration Project AGM 2001 bill@sep. stanford. edu

Stay Tuned… • Complete inversion, varying linearization techniques • Compare estimated velocity to that from VSPs • Invert for 3 -D interval velocity using corrected sonic logs as boundary conditions Bill Curry Stanford Exploration Project AGM 2001 bill@sep. stanford. edu