SECONDARY WAVE VELOCITY PREDICTION THROUGH ROCK PHYSICS AND EMPIRICAL APPROACH
<p align="justify">The secondary wave velocity (Vs) is one of the most important parts of the well data. However, it is often found data that a well does not have Vs data or even having unreliable Vs data, for example when the Vs obtained results in a negative Poisson’s Ratio valu...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/30729 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | <p align="justify">The secondary wave velocity (Vs) is one of the most important parts of the well data. However, it is often found data that a well does not have Vs data or even having unreliable Vs data, for example when the Vs obtained results in a negative Poisson’s Ratio value or too high Poisson’s Ratio value in hydrocarbons bearing rocks. Some reservoirs have deltaic facies; thus, a lot of rocks will have clay content. Clay content will form shaly sand or sandy shale rock. Reservoirs that are formed do not always have good qualities such as clean sand, and it is possible that the composites cointain clay content. This study used real data to make a model of several lithology like clean sand and shaly sand that represents the behavior of the secondary wave velocity trend occurring. The data were modeled using rock physics methods such as Hashin-Strikman lower bound and Hertz-Mindlin. The model made requires Vpdry, Vsdry, clay content XRD, grain density, and total porosity data. The model is a representative for the real data in general, it is used as the reference model to see the suitability of some empirical method in predicting the secondary wave velocity. The prediction of secondary wave velocity using rock physics modeling is quite complex, thus this study performed comparisons of several empirical methods such as Greenberg-Castagna, Vernik, Williams, Dvorkin, and Krief, which are simpler to predict secondary wave velocities corresponding to forward modeling as well as real data. The results showed that the simplest and most robust method to predict Vs are Greenberg-Castagna (1992) and Vernik (2002), both showed small errors. Greenberg-Castagna’s formulation can predict Vs better than any methods because they accommodate the fraction for both composites, sand and shale. Whereas, Vernik’s formulation only accomodate Vp as the input and yet still gave one of the best result among any other methods.<p align="justify"> |
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