A new predrilling reservoir permeability prediction model and its application

Chen, Guosong; Meng, Yuanlin; Huan, Jinlai; Wang, Youchun; Xiao, Ling; Zhang, Lei; Feng, Dan

doi:10.1016/j.petrol.2021.110086

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…Modern approaches to permeability prediction in the interwell space are based on consideration of seismic trends using machine learning and statistical methods [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

High Permeability Streak Identification and Modelling Approach for Carbonate Reef Reservoir

Shirinkin,

Kochnev,

Krivoshchekov

et al. 2024

Energies

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Reef reservoirs are characterised by a complex structure of void space, which is a combination of intergranular porosity, fractures, and vuggy voids distributed chaotically in the carbonate body in different proportions. This causes great uncertainty in the distribution of porosity and permeability properties in the reservoir volume, making field development a complex and unpredictable process associated with many risks. High densities of carbonate secondary alterations can lead to the formation of zones with abnormally high porosity and permeability—high permeability streaks or super-reservoirs. Taking into account super-reservoirs in the bulk of the deposit is necessary in the dynamic modelling of complex-structure reservoirs because it affects the redistribution of filtration flows and is crucial for reservoir management. This paper proposes a method for identifying superreservoirs by identifying enormously high values of porosity and permeability from different-scale study results, followed by the combination and construction of probabilistic curves of superreservoirs. Based on the obtained curves, three probabilistic models of the existence of a superreservoir were identified: P10, P50, and P90, which were further distributed in the volume of the reservoir and on the basis of which new permeability arrays were calculated. Permeability arrays were simulated in a dynamic model of the Alpha field. The P50 probabilistic model showed the best history matching after one iteration.

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“…Modern approaches to permeability prediction in the interwell space are based on consideration of seismic trends using machine learning and statistical methods [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

High Permeability Streak Identification and Modelling Approach for Carbonate Reef Reservoir

Shirinkin,

Kochnev,

Krivoshchekov

et al. 2024

Energies

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“…Some works present a more comprehensive allowance for heterogeneity and reservoir permeability prediction [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Factoring Permeability Anisotropy in Complex Carbonate Reservoirs in Selecting an Optimum Field Development Strategy

Krivoshchekov¹,

Kochnev²,

Kozyrev³

et al. 2022

Energies

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Current methods of oil and gas field development design rely on reservoir simulation modeling. A reservoir simulation model is a tool to reproduce field development processes and forecast production data. Reservoir permeability is one of the basic properties that determines fluid flow. From existing approaches, the porosity and permeability values should be consistent with petrophysical correlations obtained from core sample tests in the course of development of an absolute permeability cube in the reservoir simulation model. For carbonate reservoirs with complex pore space structure and fractures, the petrophysical correlations are often unstable. To factor in the fluid flow in a fractured rock system, dual-medium models are developed, allowing for matrix and fracture components. Yet in this case, the degree of uncertainty only increases with the introduction of a new parameter: a cross-flow index of fluid migration from matrix to fracture, which is only determined indirectly by results of fluid flow studies conducted in the initial development period, and therefore most often is adaptive. Clearly, for well-studied fields there is an extensive data pool drawn on research findings: core studies, well logging, well flow testing, flowmetry, special well-logging methods (FMI, Sonic Scanner, etc.); the dual-medium model development for such reservoirs is fairly well-founded and supported by actual studies. However, at the start of the field development, the data are incomplete, which renders qualitative dual-medium modeling impossible. This paper proposes an approach to factor in the target’s permeability anisotropy at an early development stage through the integration of well, core and 3D seismic surveys. The reservoir was classified into pore space types, to which different petrophysical correlations were assigned to develop a permeability array, and relative phase permeabilities were studied. The fluid flow model was history-matched with allowance for permeability anisotropy and rock types. Comparative calculations were conducted on the resulting model to select the optimum development strategy for the target.

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A new method for dynamic predicting porosity and permeability of low permeability and tight reservoir under effective overburden pressure based on BP neural network

Jiang¹,

Chen²,

Xing³

et al. 2023

Geoenergy Science and Engineering

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A new predrilling reservoir permeability prediction model and its application

Cited by 3 publications

References 18 publications

High Permeability Streak Identification and Modelling Approach for Carbonate Reef Reservoir

High Permeability Streak Identification and Modelling Approach for Carbonate Reef Reservoir

Factoring Permeability Anisotropy in Complex Carbonate Reservoirs in Selecting an Optimum Field Development Strategy

A new method for dynamic predicting porosity and permeability of low permeability and tight reservoir under effective overburden pressure based on BP neural network

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