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

2018 ◽  
Vol 65 (1) ◽  
pp. 21-34
Author(s):  
Oluseun Adetola Sanuade ◽  
Akindeji Opeyemi Fajana ◽  
Abayomi Adesola Olaojo ◽  
Kehinde David Oyeyemi ◽  
Joel Olayide Amosun
Keyword(s):  
Niger Delta ◽  
Normal Approximation ◽  
Sequential Gaussian Simulation ◽  
Initial Condition ◽  
Geostatistical Modeling ◽  
Semivariogram Model ◽  
Subsurface Geology ◽  
Geostatistical Approach ◽  
Univariate Statistics ◽  
Gaussian Simulation

AbstractA 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.

scholarly journals Spatial variability of selected physicochemical parameters within peat deposits in small valley mire: a geostatistical approach

Geologos ◽  
2014 ◽  
Vol 20 (4) ◽  
pp. 269-288 ◽  
Author(s):  
Dominik Pawłowski ◽  
Daniel Okupny ◽  
Wojciech Włodarski ◽  
Tomasz Zieliński
Keyword(s):  
Spatial Variability ◽  
Sequential Gaussian Simulation ◽  
Mineral Matter ◽  
Geostatistical Methods ◽  
Peat Deposits ◽  
Geostatistical Approach ◽  
Through Flow ◽  
Input Sample ◽  
Gaussian Simulation ◽  
Small Valley

Abstract Geostatistical methods for 2D and 3D modelling spatial variability of selected physicochemical properties of biogenic sediments were applied to a small valley mire in order to identify the processes that lead to the formation of various types of peat. A sequential Gaussian simulation was performed to reproduce the statistical distribution of the input data (pH and organic matter) and their semivariances, as well as to honouring of data values, yielding more ‘realistic’ models that show microscale spatial variability, despite the fact that the input sample cores were sparsely distributed in the X-Y space of the study area. The stratigraphy of peat deposits in the Ldzań mire shows a record of long-term evolution of water conditions, which is associated with the variability in water supply over time. Ldzań is a fen (a rheotrophic mire) with a through-flow of groundwater. Additionally, the vicinity of the Grabia River is marked by seasonal inundations of the southwest part of the mire and increased participation of mineral matter in the peat. In turn, the upper peat layers of some of the central part of Ldzań mire are rather spongy, and these peat-forming phytocoenoses probably formed during permanent waterlogging.

scholarly journals GEOSTATISTICAL MODELLING OF RESERVOIR QUALITY OVER'BRIGHT' FIELD,NIGER DELTA.

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
James Sunday Abe ◽  
M. T Olowokere ◽  
P. P. Enikanselu
Keyword(s):  
Niger Delta ◽  
Effective Porosity ◽  
Sequential Gaussian Simulation ◽  
Reservoir Quality ◽  
Statistical Tool ◽  
Lp Norm ◽  
Data Resolution ◽  
Net To Gross ◽  
Gaussian Simulation

The quality of any hydrocarbon-bearing reservoir is vital for a successful exploitation work.. The reservoir quality is a function of its petrophysical parameters. Hence the need to model these properties geostatistically in order to determine the quality away from well locations.Composite logs for four wells and 3-D seismic data were used for the analysis. A reservoir named Sand X was mapped and correlated across wells 1 through 4. The four reservoir quality indicators - Effective porosity, permeability, volume of shale and net-to-gross-  were estimated and modelled across the field. Sequential Gaussian simulation algorithm was employed to distribute these properties stochastically away from well locations and five realizations were generated. The volume of shale varied from 0.025 (Well 1, second realization) to 0.18(Well 2, first realization). The net-to-gross varied from 0.81 to 0.96 in wells 3 and 4 respectively, for the third realization, while the effective porosity varied from 0.125 to 0.295 for the fifth realization in Wells 3 and 4 respectively. The permeability is above 5000mD at all the existing well locations.These realizations were ranked using Lp norm statistical tool to pick the best for further evaluation. The reservoir quality deduced from the analyzed indicators was favourably high across the reservoir.The application of geostatistics has laterally enhanced the log data resolution away from established well locations.

scholarly journals Characterization and geostatistical modeling of reservoirs in ‘Falad’ field, Niger Delta, Nigeria

2021 ◽  
Author(s):  
Ayodele O. Falade ◽  
John O. Amigun ◽  
Yousif M. Makeen ◽  
Olatunbosun O. Kafisanwo
Keyword(s):  
Niger Delta ◽  
Structural Model ◽  
Water Saturation ◽  
Sequential Gaussian Simulation ◽  
Surface Generation ◽  
Geostatistical Modeling ◽  
Reservoir Properties ◽  
Field Development ◽  
Net To Gross ◽  
Well Data

AbstractThis research aims at characterizing and modeling delineated reservoirs in ‘Falad’ Field, Niger Delta, Nigeria, to mitigate the challenge caused by the heterogeneous nature of the reservoirs. Seismic and well log data were integrated, and geostatistics was applied to describe the reservoir properties of the interwell spaces within the study area. Four reservoirs, namely RES 1, RES 2, RES 3 and RES 4, were delineated and correlated across four wells. The reservoir properties {lithology, net to gross, porosity, permeability, water saturation} of all the delineated reservoirs mapped were determined, and two reservoirs with the best quality were picked for further analysis (surface generation and modeling) after ranking the reservoirs based on their quality. Structural interpretation of the field was carried, nine faults were mapped (F1—F9), and the fault polygon was generated. The structural model showed the area is structurally controlled with two of the major faults mapped (F1 and F3) oriented in the SW–NE direction while the other one (F4) is oriented in the NW–SE direction. A 3D grid was constructed using the surfaces of the delineated reservoirs and the reservoir properties were distributed stochastically using simple krigging method with sequential Gaussian simulation, sequential indicator simulation and Gaussian random function simulation algorithms. Geostatistical modeling used in this study has been able to give subsurface information in the areas deficient of well data as the estimated reservoir properties gotten from existing wells have been spatially distributed in the study area and will thus aid future field development while also they are used in identifying new prospect by combining property models with structural maps of the area.

Stochastic hydrogeological parameterisation and modelling of the Chalk of England

2020 ◽  
Author(s):  
Marco Bianchi ◽  
Andrew Hughes ◽  
Majdi Mansour ◽  
Johanna Michaela Scheidegger ◽  
Christopher Jackson
Keyword(s):  
Groundwater Flow ◽  
Deterministic Model ◽  
Sequential Gaussian Simulation ◽  
High Porosity ◽  
Pumping Tests ◽  
Geostatistical Approach ◽  
Non Linear ◽  
Hydrogeological Properties ◽  
Gaussian Simulation ◽  
T Distribution

<p>The Chalk is the most important regional aquifer in England supplying the majority of the groundwater used in the country. Traditionally, the Chalk has been interpreted as a dual-porosity aquifer consisting of a low-permeability, high-porosity matrix and a fracture component with associated relatively high secondary permeability, allowing groundwater flow. However, these two components alone cannot fully explain the groundwater flow regime and aquifer productivity indicating that the distribution of the hydrogeological properties is the result of more complex interplay of several regional and local factors. For instance, transmissivity generally exhibits a non-linear decline with depth controlled by variations in the spacing and aperture of the primary and secondary (solution) fractures. Topography is another important regional factor determining a spatial distribution of transmissivity (T) and storage coefficient (S) with generally higher values within valleys and lower values in the interfluves. The topographic factor is widely recognised, and it has been applied in several previous numerical modelling studies. However, these studies do not consider the local variability exhibited within an extensive dataset of more than 1000 pumping tests, while local adjustments of the initial topography-based T and S distributions are considered during the calibration step of the model. In this work, a hybrid geostatistical approach has been developed and applied for modelling the distribution of the hydrogeological properties of the Chalk. The approach combines, for the first time for the Chalk, local hard data from pumping tests with soft data accounting for the regional topographic trend. In particular, similar to the classic regression kriging approach, stochastic realisations of the T distribution in the unconfined region of the Chalk are generated from the combination of two components: 1) a non-linear deterministic model of the relationship between measured T values and the distance to valleys; 2) a sequential Gaussian simulation (SGS) component generating equally probable realizations of the residuals conditioned to the local data. Traditional conditional sequential Gaussian simulation was used instead to generate T and S spatial distributions in the confined region. To test the representativeness of the generated distributions, realisations of the hydrogeological parameters were considered for groundwater flow simulations based on a transient 2-D finite difference model coupled to a regional recharge model. Comparison between observed and simulated values for groundwater levels and river flows at reference locations showed a generally good agreement. The model was then used to quantify the importance of local hydrogeological data for improving model predictions versus alternative conceptualisations solely based on regional trends and model calibration.</p>

Sign in / Sign up

Export Citation Format

Share Document