Modeling of porosity by geostatistical methods

Ketteb, Rachid; Djeddi, Mabrouk; Kiche, Y.

doi:10.1007/s12517-019-4450-9

Cited by 3 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Geostatistic presents tools to analyze geological information for constructing 3D numerical models which are used to predict reservoir performance (Pyrcz and Deutsch 2014). The estimation methods (kriging-based) provide a reasonable porosity estimation by analyzing porosity from well log data with given trends (Ketteb et al 2019). Kriging and simulation require the use of either the covariance or the variogram.…”

Section: Introductionmentioning

confidence: 99%

Geostatistical modeling of porosity and evaluating the local and global distribution

GhojehBeyglou

2021

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

Porosity is one of the main variables needed for reservoir characterization. For this volumetric variable, there are many methods to simulate the spatial distribution. In this article, porosity was analyzed and modeled in the local and global distribution. For simulation, Sequential Gaussian simulation (SGS) and Gaussian Random Function (GRFS) were applied. Also, kriging was used to estimate the porosity at specific locations. The main purpose of this work was to investigate the porosity to compare geostatistical simulation and estimation methods in a sandstone reservoir as a real case study. First, the data sets were normalized by the Normal Scores Transformation (NST) and stratigraphic coordinate. The model of experimental variograms was fitted in the vertical and horizontal directions. For the simulation methods, 10 realizations were generated by each method. The Q-Q plots were calculated, and both sets of quintiles (Target Porosity Distribution versus Porosity realization) came from normal distributions with the following correlation coefficients: 0.93, 0.94 and 0.97 related to GRFS, SGS and Kriging, respectively. The extracted variograms from realizations showed that the kriging couldn’t reproduce the variograms with global distribution. For local validation, the cross-validation was evaluated and three wells were omitted. The re-estimation of porosity was considered at located well logs through the well sections window where the kriging had a better performance with minimum error to estimate porosity locally. Finally, the cross-sectional models were generated by each algorithm which showed that the simple kriging tries to produce smoother distribution, whereas conditional simulations (SGS and GRFS) try to represent more global-detailed sections.

show abstract

Section: Introductionmentioning

confidence: 99%

Geostatistical modeling of porosity and evaluating the local and global distribution

GhojehBeyglou

2021

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

show abstract

Stochastic seismic inversion and Bayesian facies classification applied to porosity modeling and igneous rock identification

Fernandes,

Teixeira,

Freire

et al. 2024

Petroleum Science

View full text Add to dashboard Cite

The Best Scenario for Geostatistical Modeling of Porosity in the Sarvak Reservoir in an Iranian Oil Field, Using Electrofacies, Seismic Facies, and Seismic Attributes

Mehdipour

Rabbani

Kadkhodaie

2023

SPE Reservoir Evaluation &Amp; Engineering

View full text Add to dashboard Cite

Summary The lateral and vertical variations in porosity significantly impact the reservoir quality and the volumetric calculations in heterogeneous reservoirs. With a case study from Iran’s Zagros Basin Sarvak reservoir in the Dezful Embayment, this paper aims to demonstrate an efficient methodology for distributing porosity. Four facies models (based on electrofacies analysis data and seismic facies) with different geostatistical algorithms were used to examine the effect of different facies types on porosity propagation. Both deterministic and stochastic methods are adopted to check the impact of geostatistical algorithms on porosity modeling in the static model. A total of 40 scenarios were run and validated for porosity distribution through a blind test procedure to check the reliability of the models. The study’s findings revealed high correlation values in the blind test data for all porosity realizations linked to seismic facies, ranging from 0.778 to 0.876. In addition, co-kriging to acoustic impedance (AI), as a secondary variable, increases the correlation coefficient in all related cases. Unlike deterministic algorithms, using stochastic methods reduces the uncertainty and causes the porosity model to have an identical histogram compared with the original data. This study introduced a comprehensive workflow for porosity distribution in the studied carbonate Sarvak reservoir, considering the electrofacies, and seismic facies, and applying different geostatistical algorithms. As a result, based on this workflow, simultaneously linking the porosity distribution to seismic facies, co-kriging to AI, and applying the sequential Gaussian simulation (SGS) algorithm result in the best spatial modeling of porosity.

show abstract

Modeling of porosity by geostatistical methods

Cited by 3 publications

References 10 publications

Geostatistical modeling of porosity and evaluating the local and global distribution

Geostatistical modeling of porosity and evaluating the local and global distribution

Stochastic seismic inversion and Bayesian facies classification applied to porosity modeling and igneous rock identification

The Best Scenario for Geostatistical Modeling of Porosity in the Sarvak Reservoir in an Iranian Oil Field, Using Electrofacies, Seismic Facies, and Seismic Attributes

Contact Info

Product

Resources

About