Thickness, porosity, and permeability prediction: comparative studies and application of the geostatistical modeling in an Oil field

Zhao, Shan; Zhou, Yang; Wang, Mengyuan; Xin, Xiaying; Chen, Fang

doi:10.1186/2193-2697-3-7

Cited by 18 publications

(12 citation statements)

References 18 publications

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“…The existing methods (SCHA and the polynomial) require regular and more closely spaced data to produce accurate models (Torta, 2020;Bonito et al, 2021). The OK method can fit various types of data distributions such as random, clustered, and anisotropic (Webster and Oliver, 2008;Zhao et al, 2014). Ordinary kriging applies differently weight between clustered with the different number of data set.…”

Section: Discussionmentioning

confidence: 99%

How Accurate Geomagnetic Regional Modeling Using Ordinary Kriging For Clustered Distributed Data?: New Insight From Indonesian Archipelago

Syirojudin

Haryono

Ahadi

2021

Preprint

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Indonesia relies only on the limited number of repeat station networks due to the archipelago setting with the extensive sea with the clustery distributed pattern. This paper explored geostatistical modeling to overcome that typical data characteristic. The modeling used repeat station data from the 1985 to 2015 epoch. The research used ordinary kriging (OK) compared to the Spherical Cap Harmonic Analysis (SCHA) and Polynomial. The results show that the root means square error (RMSE) of each declination, inclination, and total intensity vary among epochs. OK method for declination component produces smaller average RMSE (7.67 minutes) than SCHA (9.26 minutes) and Polynomial (7.97minutes). For the inclination component, OK has an average RMSE of 9.55 minutes, smaller than SCHA (10.05) but slightly higher than Polynomial (9.36 minutes). For the total intensity component, OK produce an average RMSE of 63.58 nT, smaller than SCHA (82.24 nT) and Polynomial (68.97 nT). The finding shows that the kriging method can be a promising method to model the regional geomagnetic field, especially in the area of limited available data and clustered distributed data.

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Section: Discussionmentioning

confidence: 99%

How Accurate Geomagnetic Regional Modeling Using Ordinary Kriging For Clustered Distributed Data?: New Insight From Indonesian Archipelago

Syirojudin

Haryono

Ahadi

2021

Preprint

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“…Unlike other interpolation techniques such as inverse distance weighting, through the application of geostatistics it is possible to minimise the effect of smoothing out important features of the material through the retention of hard 'known' data (Zhao et al 2014). This increases the usefulness of the technique for modelling patterns of spatial variability in geological structures.…”

Section: Geostatistics and Permeabilitymentioning

confidence: 99%

Comparative investigation of the spatial distribution of past weathering impacts on sandstone masonry

2019

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Inspection of the deterioration of both natural outcrops and historical built stone facades reveals that patterns of disruption are not uniform, indicating the influence of material and environmental properties. Sandstones have a spatial variability in weathering response. For this reason, geostatistical techniques have been applied to studies of sandstone properties, mineralogical and structural, that influence susceptibility to decay. For this study, a comparative analysis of permeability data acquired from a 'quarry fresh' stone and a weathered sample is undertaken.The depositional processes that resulted in the formation of sandstone also create structures of spatial variation such as laminations or larger bedding planes within the material. Using geostatistical techniques, the permeability variance observed within 'quarry fresh' blocks of sandstone indicates the subsampling of larger geological structures when cut from the quarry face. Once blocks have been emplaced within a building they will be subjected to the increased stress of the urban environment.These conditions result in exacerbated weathering through processes such as salt and chemical weathering. Weathering can alter material properties such as permeability with the creation of a secondary permeability produced by the opening pores and alteration of pore connectivity. Through the application of geostatistics in the analysis of permeability data observed from the weathered block, variography reveals the presence of smaller scale structures. This suggests that alteration of the sandstone's permeability has led to the creation of new weathering structures within the stone, overwriting the initial sedimentary structures. These weathering related structures will affect both the magnitude and spatial distribution of permeability within the weathered block. The interconnected pathways of permeability, created by past weathering, facilitate the migration of moisture and salts in solution through the substrate. This work has demonstrated that through a geostatistical approach with the application of co-kriging, these potential moisture pathways can be identified and visualised. Highlights (3-5, 85 Characters Each) Structures can be observed within the variance of permeability using geostatistics. Observed variogram structures indicate the block is sampling sedimentary features. Alteration of permeability through weathering overwrites sedimentary structures. Co-Kriging is used to model conduits for moisture movement through a weathered block.

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“…The geologic model of the training image includes lithofacies assemblages from seismic sections, well log and production data as constraints and the petrophysical model which consist of the parameters of each facie (Caers 2002;Gonzalez and Reeves 2007). Akin to the deterministic approach, hard data points are usually conserved where it exists and have been interpreted and soft data where they are useful (Zhou et al 2014;Wilson et al 2011). In contrast to the deterministic approach, geostatistics provides several plausible outcomes (Zarei et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Stochastic modelling of spatial variability of petrophysical properties in parts of the Niger Delta Basin, southern Nigeria

Ebong

Akpan

Ekwok

2019

J Petrol Explor Prod Technol

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Three-dimensional models of petrophysical properties were constructed using stochastic methods to reduce ambiguities associated with estimates for which data is limited to well locations alone. The aim of this study is to define accurate and efficient petrophysical property models that best characterize reservoirs in the Niger Delta Basin at well locations and predicting their spatial continuities elsewhere within the field. Seismic data and well log data were employed in this study. Petrophysical properties estimated for both reservoirs range between 0.15 and 0.35 for porosity, 0.27 and 0.30 for water saturation, and 0.10 and 0.25 for shale volume. Variogram modelling and calculations were performed to guide the distribution of petrophysical properties outside wells, hence, extending their spatial variability in all directions. Transformation of pillar grids of reservoir properties using sequential Gaussian simulation with collocated cokriging algorithm yielded equiprobable petrophysical models. Uncertainties in petrophysical property predictions were performed and visualized based on three realizations generated for each property. The results obtained show reliable approximations of the geological continuity of petrophysical property estimates over the entire geospace.

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Thickness, porosity, and permeability prediction: comparative studies and application of the geostatistical modeling in an Oil field

Cited by 18 publications

References 18 publications

How Accurate Geomagnetic Regional Modeling Using Ordinary Kriging For Clustered Distributed Data?: New Insight From Indonesian Archipelago

How Accurate Geomagnetic Regional Modeling Using Ordinary Kriging For Clustered Distributed Data?: New Insight From Indonesian Archipelago

Comparative investigation of the spatial distribution of past weathering impacts on sandstone masonry

Stochastic modelling of spatial variability of petrophysical properties in parts of the Niger Delta Basin, southern Nigeria

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