Quantifying the Model of Uncertainty and Risk Using Sequential Indicator Simulation

Author(s):  
Andrew S. Rogowski
2007 ◽  
Vol 87 (5) ◽  
pp. 551-563
Author(s):  
Carol Luca ◽  
Bing C Si ◽  
Richard E Farrell

Petroleum hydrocarbon (PHC) contamination is one of the most common contaminants in soils and remediation of PHC-contaminated sites requires methods for characterizing the spatial distribution of PHC on a site. Few studies have compared the performance of indicator kriging (IK) and sequential indicator simulation (SIS) in site characterization of petroleum-contaminated sites, or the application of these methods given the fraction based guidelines. The objectives of this study were to determine if IK and SIS indicate similar contaminated areas and to examine how the probability of exceeding thresholds changes when multiple fractions are considered simultaneously. An abandoned refinery near Kamsack, Saskatchewan, characterized by clay-textured soils was sampled and analyzed for PHC fractions (F2 and F3). The probability of a location exceeding a fraction’s remediation criteria was determined using IK and SIS. Based on critical probability thresholds, IK indicated a greater area was contaminated by F2 (6.3%) and F3 (0.8%) than SIS (4.5 and 0.6%, respectively). When the remediation criteria for both F2 and F3 were considered simultaneously, “dependent” and “independent” cases were examined. The dependent case assumed perfect correlation and used the maximum probability of either F2 or F3 as the new estimate. The independent case assumed no correlation and evaluated the probability of F2 > 2500 mg kg–1 or F3 > 6600 mg kg–1. The dependent case resulted in a smaller contaminated area than the independent case in both IK and SIS. On this site the differences between the two methods were small, although IK did smooth the distribution. Key words: Sequential indicator simulation, indicator kriging, geostatics, petroleum hydrocarbon contamination, uncertainty


2015 ◽  
Vol 8 (10) ◽  
pp. 8449-8459 ◽  
Author(s):  
Mona Sojdehee ◽  
Iraj Rasa ◽  
Nima Nezafati ◽  
Mansour Vosoughi Abedini ◽  
Nasser Madani ◽  
...  

2017 ◽  
Vol 07 (07) ◽  
pp. 133-148 ◽  
Author(s):  
Jussara de Oliveira Ortiz ◽  
Carlos Alberto Felgueiras ◽  
Eduardo Celso Gerbi Camargo ◽  
Camilo Daleles Rennó ◽  
Manoel Jimenez Ortiz

2017 ◽  
Vol 4 (1) ◽  
pp. 146-156
Author(s):  
Carlos Portilla ◽  
Richard Baque ◽  
Alamir Álvarez ◽  
Otto Vera ◽  
Carlos Malavé ◽  
...  

 La construcción de un modelo geoestadístico a partir de la integración de datos geofísicos, registros de pozos, información litológica, facies interpretadas, topes estratigráficos, coordenadas de pozos, surveys, wellheaders,los cuales son esenciales para determinar la ubicación de potenciales yacimientos de hidrocarburos y poder determinar el volumen de petróleo original en el sitio (POES). Para lo cual se recopilará información de campo para crear una base de datos la misma que se ingresará a la plataforma de trabajo Openworks, que a su vez, serán cargados a sesiones individuales en el Software Decision Space Geoscience (DSG), lo cual permitirá crear modelos de las diferentes realizaciones geoestadísticas que consisten en modelos de facies utilizando los algoritmos de Simulación Secuencial Indicadora y Simulación Plurigaussiana, además tomando en cuenta las propiedades petrofísicas nos permitirá seleccionar la que más se asemeje a la realidad geológica del campo y de esta manera realizar la estimación de reservas. Los métodos de simulación numérica de yacimientos que se aplican en el software DSG permiten generar datos en las zonas que no cuentan con información, a partir de técnicas o algoritmos de interpolación para cada modelo que se generará, los cuales serán estudiados en el presente proyecto. Abstract The objective of this research is to build a geostatistical model from the integration of geophysical data, logging wells and lithology information to determine the location of potential hydrocarbon deposits and know the POES of field. For which we have to collect information about field according to coordinates of wells, surveys, wellheaders, logging wells, facies interpreted stratigraphic tops and a database to be input in the platform OpenWorks, those data will be charged in individual sessions in the Software Decision Space Geoscience (DSG), which will create models of different Geostatistical realizations such as models of facies using the Sequential Indicator Simulation  and Plurigaussian Simulation algorithms, also the petrophysical properties let to select the most similar model of facies  with the real geological models  of the field and let to determine  the reserves estimation of field. The numerical reservoir simulation  methods applied in the DSG software can generate data in areas that do not have information,  from techniques or interpolation algorithms for each model that will be generated, those methods will be studied in this project. 


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