FULL-WAVEFORM INVERSION USING ACOUSTIC LOGGING DATA

Acoustic logging is the principal method for measuring the elastic wave velocities of formation rock around the borehole. The measured velocities around the borehole provide useful geophysical and mechanical information for petroleum engineers. Therefore, it is necessary to guarantee the accuracy of the elastic wave velocities measured from the acoustic logging data. Conventional methods process the acoustic logging data without considering the radial velocity variations around the borehole, thus may provide incorrect rocks’ velocities and miss many useful rock properties. Ray-tracing tomography is currently applied for obtaining the velocity structures around the borehole, which is an inversion method using the travel time of elastic waves.

FWI (full-waveform inversion) was developed for building the seismic velocity model using seismic data. Since FWI could utilize the information in the waveform, FWI is a promising method to build the velocity model with higher resolution than ray-tracing tomography. Both seismic data and acoustic logging data are quantified using the same elastic wave equations. Therefore, FWI using the acoustic logging data has the potential to obtain the velocity model around the borehole with higher resolution.

A feature of borehole acoustic logging data is the existence of strong borehole guided waves. During the field data tests, it was found that matching the whole waveform of the calculated data and the observed field data is challenging. Therefore, the field data application of FWI is applied using only the waveform of the first arrived P waves.

Although synthetic data and field data tests prove the feasibility of FWI using the first arrived P waves of acoustic logging data, the velocity model inverted by the FWI only slightly improves than ray-tracing tomography. Information in the whole waveform should be utilized to achieve high-resolution results. Therefore, checking the approximations made in numerical simulation through experiments and theoretical analysis is necessary.

The thesis contains six chapters to illustrate the products of this Ph.D. research of FWI using acoustic logging data. The first chapter briefly introduces necessary background knowledge, including the importance of the underground velocity model, acoustic logging, and FWI. The second chapter is composed of two parts: a detailed literature review of methods for processing the acoustic logging data; then a thesis road map highlighting the current state of the art of the subject matter and justifying the needs of the current research. The third chapter first elaborates on the derivation of the FWI algorithm in the 2D cylindrical coordinates, then examines the algorithm through synthetic acoustic logging data tests. The fourth chapter illustrates the applicability of the FWI algorithm using field acoustic logging data. In addition, the difficulties of matching the whole waveform of the field and synthetic data are shown in this chapter. The fifth chapter focuses on analyzing the factors that may affect the waveform of the simulated acoustic logging data through the methods of the analytical solution. The sixth chapter concludes the research and proposes future works.

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