Application of geostatistics in facies modeling of Reservoir-E, “Hatch Field” offshore Niger Delta Basin, Nigeria

Abstract Lithofacies are very influential in the transmission of fluids within the reservoir. The objective of this study is to use geostatistical techniques of sequential indicator simulation (SISIM) a variogram-based algorithm (VBA), single normal equation simulation (SNESIM) and filter-based simu...

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Main Authors: H. T. Jika, K. M. Onuoha, C. I. P. Dim
Format: Article
Language:English
Published: SpringerOpen 2019-10-01
Series:Journal of Petroleum Exploration and Production Technology
Subjects:
Online Access:http://link.springer.com/article/10.1007/s13202-019-00788-1
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author H. T. Jika
K. M. Onuoha
C. I. P. Dim
author_facet H. T. Jika
K. M. Onuoha
C. I. P. Dim
author_sort H. T. Jika
collection DOAJ
description Abstract Lithofacies are very influential in the transmission of fluids within the reservoir. The objective of this study is to use geostatistical techniques of sequential indicator simulation (SISIM) a variogram-based algorithm (VBA), single normal equation simulation (SNESIM) and filter-based simulation (FILTERSIM) of multiple-point geostatistics (MPG) in developing realistic facies model. A reservoir sand package “Reservoir-E” was correlated across five wells in the field. Synthetic seismogram of well HT-1 was generated, and Horizon E picked on seismic section to produce time and depth surfaces of the reservoir. The conditional if statement to generate lithofacies was applied on the extracted volume of shale data within “Reservoir-E,” and the data were inputted in Stanford Geostatistics Modeling Software for facies modeling. The first realization from SISIM was converted to a training image used for MPG. Visually, the MPG algorithm of SNESIM and FILTERSIM produced realization that is substantially better and more realistic than the VBA of SISIM. The magnitude of correlation coefficients of algorithms was carried out using the mean and variance of realizations, the results revealed mean and variance magnitude of correlation coefficients between SISIM and SNESIM with 0.8933 and 0.9637, SISIM and FILTERSIM with 0.8639 and 0.5097 and SNESIM and FILTERSIM with 0.9717 and 0.8603. The results revealed a very good mean and variance magnitude of correlation coefficients between SISIM and SNESIM; good between SISIM and FILTERSIM; and very good mean and variance correlation coefficient between SNESIM and FILTERSIM. The qualitative interpretation of the model built with SNESIM and FILTERSIM clearly detects lithofacies in the field which makes them a better algorithm in facies modeling.
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spelling doaj.art-c78bbada86da40e2af41020a55b984e42022-12-22T02:05:24ZengSpringerOpenJournal of Petroleum Exploration and Production Technology2190-05582190-05662019-10-0110276978110.1007/s13202-019-00788-1Application of geostatistics in facies modeling of Reservoir-E, “Hatch Field” offshore Niger Delta Basin, NigeriaH. T. Jika0K. M. Onuoha1C. I. P. Dim2Department of Geology, University of NigeriaDepartment of Geology, University of NigeriaDepartment of Geology, University of NigeriaAbstract Lithofacies are very influential in the transmission of fluids within the reservoir. The objective of this study is to use geostatistical techniques of sequential indicator simulation (SISIM) a variogram-based algorithm (VBA), single normal equation simulation (SNESIM) and filter-based simulation (FILTERSIM) of multiple-point geostatistics (MPG) in developing realistic facies model. A reservoir sand package “Reservoir-E” was correlated across five wells in the field. Synthetic seismogram of well HT-1 was generated, and Horizon E picked on seismic section to produce time and depth surfaces of the reservoir. The conditional if statement to generate lithofacies was applied on the extracted volume of shale data within “Reservoir-E,” and the data were inputted in Stanford Geostatistics Modeling Software for facies modeling. The first realization from SISIM was converted to a training image used for MPG. Visually, the MPG algorithm of SNESIM and FILTERSIM produced realization that is substantially better and more realistic than the VBA of SISIM. The magnitude of correlation coefficients of algorithms was carried out using the mean and variance of realizations, the results revealed mean and variance magnitude of correlation coefficients between SISIM and SNESIM with 0.8933 and 0.9637, SISIM and FILTERSIM with 0.8639 and 0.5097 and SNESIM and FILTERSIM with 0.9717 and 0.8603. The results revealed a very good mean and variance magnitude of correlation coefficients between SISIM and SNESIM; good between SISIM and FILTERSIM; and very good mean and variance correlation coefficient between SNESIM and FILTERSIM. The qualitative interpretation of the model built with SNESIM and FILTERSIM clearly detects lithofacies in the field which makes them a better algorithm in facies modeling.http://link.springer.com/article/10.1007/s13202-019-00788-1Filter-based simulationSequential indicator simulationSingle normal equation simulationTraining image
spellingShingle H. T. Jika
K. M. Onuoha
C. I. P. Dim
Application of geostatistics in facies modeling of Reservoir-E, “Hatch Field” offshore Niger Delta Basin, Nigeria
Journal of Petroleum Exploration and Production Technology
Filter-based simulation
Sequential indicator simulation
Single normal equation simulation
Training image
title Application of geostatistics in facies modeling of Reservoir-E, “Hatch Field” offshore Niger Delta Basin, Nigeria
title_full Application of geostatistics in facies modeling of Reservoir-E, “Hatch Field” offshore Niger Delta Basin, Nigeria
title_fullStr Application of geostatistics in facies modeling of Reservoir-E, “Hatch Field” offshore Niger Delta Basin, Nigeria
title_full_unstemmed Application of geostatistics in facies modeling of Reservoir-E, “Hatch Field” offshore Niger Delta Basin, Nigeria
title_short Application of geostatistics in facies modeling of Reservoir-E, “Hatch Field” offshore Niger Delta Basin, Nigeria
title_sort application of geostatistics in facies modeling of reservoir e hatch field offshore niger delta basin nigeria
topic Filter-based simulation
Sequential indicator simulation
Single normal equation simulation
Training image
url http://link.springer.com/article/10.1007/s13202-019-00788-1
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AT cipdim applicationofgeostatisticsinfaciesmodelingofreservoirehatchfieldoffshorenigerdeltabasinnigeria