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Abstrak :
ABSTRACT
Subsurface models of lithology are often poorly constrained due to the lack of dense well control. Although limited in vertical resolution, high-quality threedimensional (3-D) seismic data usually provide valuable information regarding the lateral variations of lithology. In this thesis, I will show how Bayesian approach can be used to generate seismically constrained models of lithology. Unlike cokriging-based simulation methods, this method does not rely on a generalized linear regression model, which is inadequate when combining discrete variables, such as lithology indicator; and continuous variables, such as seismic attributes. This method uses a Bayesian updating rule to construct a posterior probability distribution function of lithoclasses by using a priori information from well data and the seismic likelihood to constrain the 3-D geological scenarios produced by geostatistical technique, which is then sampled sequentially at all points in space to generate a set of realizations. The realizations define alternative, equiprobable lithologic models. The methodology was applied to delineate productive reservoir zone in Boonsville, Texas. To achieve better result in the Bayesian Sequential Indicator Simulation, I used acoustic impedance obtained from a seismic inversion process as the attribute to constrain the simulation. It is expected that by using this attribute, the separation of the litho-class conditional distribution will be well defined and at the same time minimizing the overlaps between the two distributions. The lithology classification obtained from BSIS is then integrated with the result of the seismic inversion to clearly delineate the productive zone in the field.

Abstrak :
Efektivitas Wayfinding adalah kesuksesan interaksi antara faktor manusia dan faktor lingkungan yang mampu membuat seseorang berhasil berpindah dari posisi sekarang ke posisi yang ingin dituju dengan waktu yang sesuai dengan kebutuhan. Saat ini proses tersebut belum dimodelkan untuk menggambarkan hubungan dari kesuksesan efektivitas wayfinding tersebut. Penelitian ini bertujuan untuk memodelkan komplek sistem dari aktivita wayfinding dengan menggunakan Bayesian Network dan model tersebut menyesuaikan dengan factor faktor yang di aplikasikan di Terminal 2 Bandara Soekarno Hatta. Model menjelaskan bahwa faktor manusia memiliki dampak yang lebih besar dari faktor lingkungan dalam mempengaruhi efektivitas wayfinding. Untuk Faktor manusia sendiri faktor yang paling berpengaruh adalah previous familiarity diikuti dengan cognitive&spatial skill. Model ini juga memprediksi bahwa navigation pathway memiliki pengaruh lebih besar dari terminal design dalam memberikan dampak pada faktor lingkungan. ...... Wayfinding's effectiveness is the success of interactions between human factors and environmental factors that can make a person succeed in moving from the current position to the position they want to reach with the time that suits their needs. At present the process has not been modeled to illustrate the relationship between the success of the effectiveness of the wayfinding. This study aims to model the system complex of wayfinding activites using the Bayesian Network and the model adapts to the factor factors applied at Terminal 2 of Soekarno Hatta Airport. The model explains that human factors have a greater impact on environmental factors in influencing the effectiveness of wayfinding. For the human factor itself, the most influential factor is the previous familiarity followed by cognitive & spatial skills. This model also predicts that the navigation pathway has a greater influence than terminal design to have an impact on environmental factors.