scholarly journals Integrated 3D modeling of Quaternary sediments in the Beijing Plain, based on a sequential indicator simulation

2019 ◽  
Vol 72 (Special issue) ◽  
pp. 3-17
Author(s):  
Xiao Ai ◽  
Baitao Sun ◽  
Xiangzhao Chen
Keyword(s):  
3D Modeling ◽  
Sequential Indicator Simulation ◽  
Quaternary Sediments ◽  
Beijing Plain ◽  
Indicator Simulation

Comparison of Sequential Indicator Simulation and Transition Probability Indicator Simulation Used to Model Clay Content in Microscale Surface Soil

Soil Science ◽  
2009 ◽  
Vol 174 (7) ◽  
pp. 395-402 ◽  
Author(s):  
Yong He ◽  
Kelin Hu ◽  
Baoguo Li ◽  
Deli Chen ◽  
Helen Charlotte Suter ◽  
...  
Keyword(s):  
Surface Soil ◽  
Transition Probability ◽  
Clay Content ◽  
Sequential Indicator Simulation ◽  
Indicator Simulation

scholarly journals Using sequential indicator simulation to assess the uncertainty of delineating heavy-metal contaminated soils

2004 ◽  
Vol 127 (2) ◽  
pp. 229-238 ◽  
Author(s):  
Kai-Wei Juang ◽  
Yue-Shin Chen ◽  
Dar-Yuan Lee
Keyword(s):  
Heavy Metal ◽  
Contaminated Soils ◽  
Sequential Indicator Simulation ◽  
Indicator Simulation

Assessing spatial distribution and joint uncertainty of TPH-fractions: Indicator kriging and sequential indicator simulation

2007 ◽  
Vol 87 (5) ◽  
pp. 551-563
Author(s):  
Carol Luca ◽  
Bing C Si ◽  
Richard E Farrell
Keyword(s):  
Spatial Distribution ◽  
Petroleum Hydrocarbon ◽  
Indicator Kriging ◽  
Hydrocarbon Contamination ◽  
Contaminated Sites ◽  
Sequential Indicator Simulation ◽  
Maximum Probability ◽  
Dependent Case ◽  
Indicator Simulation ◽  
Independent Case

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

Assessment of spatial uncertainty for delineating optimal soil sampling sites in rubber tree management using sequential indicator simulation

2016 ◽  
Vol 91 ◽  
pp. 231-237 ◽  
Author(s):  
Qing-Huo Lin ◽  
Hong Li ◽  
Bao-Guo Li ◽  
Peng-Tao Guo ◽  
Wei Luo ◽  
...  
Keyword(s):  
Rubber Tree ◽  
Soil Sampling ◽  
Spatial Uncertainty ◽  
Sequential Indicator Simulation ◽  
Tree Management ◽  
Indicator Simulation

Probabilistic modeling of mineralized zones in Daralu copper deposit (SE Iran) using sequential indicator simulation

2015 ◽  
Vol 8 (10) ◽  
pp. 8449-8459 ◽  
Author(s):  
Mona Sojdehee ◽  
Iraj Rasa ◽  
Nima Nezafati ◽  
Mansour Vosoughi Abedini ◽  
Nasser Madani ◽  
...  
Keyword(s):  
Probabilistic Modeling ◽  
Copper Deposit ◽  
Sequential Indicator Simulation ◽  
Indicator Simulation

Updating cover type maps using sequential indicator simulation

2003 ◽  
Vol 87 (2-3) ◽  
pp. 161-170 ◽  
Author(s):  
Steen Magnussen ◽  
Sytze de Bruin
Keyword(s):  
Sequential Indicator Simulation ◽  
Cover Type ◽  
Indicator Simulation

scholarly journals Spatial Modeling of Soil Lime Requirements with Uncertainty Assessment Using Geostatistical Sequential Indicator Simulation

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
Keyword(s):  
Spatial Modeling ◽  
Uncertainty Assessment ◽  
Sequential Indicator Simulation ◽  
Indicator Simulation

scholarly journals Simulación de Facies mediante un Modelo Geoestadístico integrado para el desarrollo de un campo petrolero ecuatoriano.

2017 ◽  
Vol 4 (1) ◽  
pp. 146-156
Author(s):  
Carlos Portilla ◽  
Richard Baque ◽  
Alamir Álvarez ◽  
Otto Vera ◽  
Carlos Malavé ◽  
...  
Keyword(s):  
Simulation Methods ◽  
Sequential Indicator Simulation ◽  
Similar Model ◽  
Data Logging ◽  
Geostatistical Model ◽  
Decision Space ◽  
Numerical Reservoir Simulation ◽  
Indicator Simulation ◽  
Para Determinar ◽  
Hydrocarbon Deposits

 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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