Understanding reservoir behaviour through an integrated geological and engineering study

Barr, Duncan

doi:10.7186/bgsm32199203

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…To eliminate this hidden danger, this paper proposes an FZI method that considers pore throat parameters (Figure 4). [13]. Qiu and Mu proposed that the flow unit is a part of the internal architectural structure of a sand body, and a concept is a reservoir unit with the same seepage and water flooding characteristics caused by boundary constraints, discontinuous barrier layers, various sedimentary microinterfaces, small faults, and permeability differences within an oil sand body [14,15].…”

Section: Influence Of Pore Structure On Permeabilitymentioning

confidence: 99%

A Method for Evaluating Reservoir Permeability Based on Machine Learning Flow Unit Index

Cheng,

Zhao,

Gao

et al. 2023

Lithosphere

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The H formation of the Y gas field in the X depression belongs to a low-permeability tight sandstone reservoir affected by sedimentation, diagenesis, and cementation. The lithology and pore structure of the target layer are complex, with strong physical heterogeneity and complex pore-permeability relationships. Conventional core pore permeability regression and nuclear magnetic resonance software-defined radio methods do not satisfy the requirements for precise evaluation in terms of permeability calculation accuracy. Based on the principle of the flow zone index (FZI) method, this study analyzed the influence of pore structure on permeability and extracted three pore structure characterization parameters, namely, the maximum pore throat radius (Rmax), displacement pressure (Pd), and average throat radius (R), from the mercury injection capillary pressure curve. The relationship between the FZI and pore structure is clarified. Therefore, the FZI in this area can characterize the permeability differences within different flow units. Based on the flow unit theory, a refined evaluation model for three types of reservoirs was established in the study area. By analyzing the response characteristics and correlation of conventional logging curves using machine learning, three optimization combination curves were selected, and a multiparameter fitting equation for the FZI was established, which was applied to predict the permeability of new wells. The results showed that the calculated permeability was highly consistent with the core analysis results, thereby providing a theoretical basis for the precise evaluation of low-permeability tight reservoirs.

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Section: Influence Of Pore Structure On Permeabilitymentioning

confidence: 99%

A Method for Evaluating Reservoir Permeability Based on Machine Learning Flow Unit Index

Cheng,

Zhao,

Gao

et al. 2023

Lithosphere

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Predicting reservoir quality during exploration: lithic grains, porosity and permeability in Tertiary clastic rocks of the South China Sea basin

Worden

Mayall²,

Evans

1997

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Tertiary sandstones in the Gulf of Thailand, Malay and South China Sea basins have been supplied with sediment rich in pelitic metamorphic rock fragments, weathered basic igneous rock fragments and micaceous rock fragments by similar drainage systems. We have analysed data from the Gulf of Thailand and Malay basins to predict reservoir quality in the South China Sea basin. These Tertiary sandstones contain 20–40% lithic rock fragments that are rich in clay minerals. There are good correlations between both porosity and permeability and the total clay mineral content. They undergo rapid loss of porosity with depth at a rate commensurate with sandstones bearing 20–40% ductile grains. The relationship between porosity and permeability is also strongly influenced by the presence of ductile grains. The rapid loss of permeability with porosity is a result of ductile grains being squeezed between the rigid grains, blocking pore-throats and leaving isolated, ineffective porosity. Mineral cements seem to be unimportant modifiers of porosity or permeability. The rapid loss of porosity with depth and the decrease in permeability with porosity leads to low permeabilities at shallow depths relative to many other hydrocarbon provinces (e.g. North Sea, Gulf of Mexico, etc.) which have lower ductile grain contents. We have used modelled compaction curves for sandstones with 20 and 40% ductile grains to predict a porosity value for any given depth. We have also used sandstones of 100 and 300 μm grain size to predict permeability for a given porosity. The combination of these predictions can be used to predict permeability as a function of depth of burial. The choice of ductile content and grain size will depend upon the conditions of deposition. The main positive reservoir quality anomaly may result from overpressure generation before or during the final phase of burial.

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Understanding reservoir behaviour through an integrated geological and engineering study

Cited by 2 publications

References 2 publications

A Method for Evaluating Reservoir Permeability Based on Machine Learning Flow Unit Index

A Method for Evaluating Reservoir Permeability Based on Machine Learning Flow Unit Index

Predicting reservoir quality during exploration: lithic grains, porosity and permeability in Tertiary clastic rocks of the South China Sea basin

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