Date of Award


Document Type

Open Access Master's Thesis

Degree Name

Master of Science in Geophysics (MS)

Administrative Home Department

Department of Geological and Mining Engineering and Sciences

Advisor 1

Wayne D Pennington

Committee Member 1

Roohollah Askari

Committee Member 2

Mir Sadri-Sabet


This work consists of conventional petrophysical analysis and sonic response determination from empirical relations, rock physics modelling, and fluid substitution for six wells in the Moki formation of New Zealand’s Taranaki Basin. The project composed of three parts encompassing conventional log analysis, rock-physics modeling using empirical and theoretical approaches, and finally rock-physics modeling for shaley sands using semi-empirical adaptations to Gassmann fluid substitution. Finally, comparisons are made between results for the different wells, based on their depths and mineralogy.

The first part of this study is presented as a petrophysical analysis, including crossplot analysis, conventional 3-mineral identification approach, and determination of water saturation profile, applied to several wells. After petrophysical analysis, we concluded that the Moki Formation may have well sorted grain size distribution or/and may be less compacted in the Tui Field than in the Maari Field although the formation is located at greater depths in the Tui Field. Furthermore, crossplot analysis indicates that these two fields have different types of clay; this may help explain the different compaction trends.

In the second part of this study, empirical relations and rock physics modelling were used for sonic response prediction. The P-wave velocity was predicted from the empirical Wyllie’s Time Average equation and from the Kuster-Toksoz rock physics model. The S-wave velocity was predicted from the empirical Greenberg-Castagna relation and from the Kuster-Toksoz rock physics model.

After P-wave velocity predictions, we concluded that Kuster-Toksoz model works better in shale than Wyllie’s Time Average equation due to the bound water in shale structure. This situation causes to have higher density response and to predict higher P-wave velocities calculated from Wyllie’s Time Average equation in shale. Furthermore, less compaction in deeper depths causes to have higher porosity response; therefore, predicted P-wave velocities from Wyllie’s Time Average equation are lower than P-wave velocities derived from sonic log. The Kuster-Toksoz model optimizes parameters in order to obtain best fit with the observed P-wave velocity derived from sonic log since the P-wave results from Kuster-Toksoz fit well for each field. We note that the Greenberg-Castagna model predicts a lower S-wave velocity than the Kuster-Toksoz model, expect in the highest-velocity streaks where limestone is likely present. Because there is no recorded an S-wave log, the comparison is not reliable in this study.

In the last part of this study, Gassmann’s equation was modified by using effective porosity for the sand-shale mixture. In order to estimate sonic velocities for different saturations, two different fluid substitution approaches were used: Dvorkin et al. (2007) and Simm (2007). The two methods yielded very similar results.