<abstract xmlns="http://www.w3.org/1999/xhtml"><p>A geostatistical approach was used to model porosity of OBA field in onshore area of Niger Delta using simulation technique. The objective is to understand the spatial distribution of porosity and characterize the degree of heterogeneity of underlying formation. Porosity data from twenty-two wells were loaded into <italic>SGeMS</italic> software. Univariate statistical analysis, experimental semivariogram and Sequential Gaussian Simulation (SGS) were applied on the data. The data was close to normal approximation of Gaussian based of the results of univariate statistics. However, to construct and model horizontal and vertical semivariograms, the data was log-normalized to reduce the coefficient of variation and to get good fit of the model. Parametric semivariogram model shows the range of 72–6480 m, nugget effect of 0.006 and sills of 0.0095, 0.0099 and 0.0111. Six realizations were generated using SGS algorithm and the results suggest that any one of the realizations can independently represents the true picture of the subsurface geology within the study area. Ranking of realizations shows realization 6 as the best and realization 2 as the lowest. This model could be used as an initial condition for simulation of flow.</p></abstract>

A geostatistical approach was used to model porosity of OBA field in onshore area of Niger Delta using simulation technique. The objective is to understand the spatial distribution of porosity and characterize the degree of heterogeneity of underlying formation. Porosity data from twenty-two wells were loaded into SGeMS software. Univariate statistical analysis, experimental semivariogram and Sequential Gaussian Simulation (SGS) were applied on the data. The data was close to normal approximation of Gaussian based of the results of univariate statistics. However, to construct and model horizontal and vertical semivariograms, the data was log-normalized to reduce the coefficient of variation and to get good fit of the model. Parametric semivariogram model shows the range of 72–6480 m, nugget effect of 0.006 and sills of 0.0095, 0.0099 and 0.0111. Six realizations were generated using SGS algorithm and the results suggest that any one of the realizations can independently represents the true picture of the subsurface geology within the study area. Ranking of realizations shows realization 6 as the best and realization 2 as the lowest. This model could be used as an initial condition for simulation of flow.

Geostatistical modeling of porosity data in ‘oba’ field, onshore Niger Delta

Materials and Geoenvironment

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A geostatistical approach was used to model porosity of OBA field in onshore area of Niger Delta using simulation technique. The objective is to understand the...

Geostatistical modeling of porosity data in ‘oba’ field, onshore Niger Delta

Publié en ligne: 06 août 2018

Pages: 21 - 34

Reçu: 30 juil. 2017

Accepté: 08 oct. 2017

DOI: https://doi.org/10.2478/rmzmag-2018-0005

Mots clés
geostatistics, stochastic, simulation, porosity, semivariogram

© 2017 Oluseun Adetola Sanuade et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Geostatistical modeling of porosity data in ‘oba’ field, onshore Niger Delta

Publié en ligne: 06 août 2018

Pages: 21 - 34

Reçu: 30 juil. 2017

Accepté: 08 oct. 2017

DOI: https://doi.org/10.2478/rmzmag-2018-0005

Mots clésgeostatistics, stochastic, simulation, porosity, semivariogram

© 2017 Oluseun Adetola Sanuade et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
geostatistics, stochastic, simulation, porosity, semivariogram