Scale-Up Of Reservoir Model Relative Permeability Using A Global Method

1996 ◽  
Vol 11 (03) ◽  
pp. 149-157 ◽  
Author(s):  
D. Li ◽  
A.S. Cullick ◽  
L.W. Lake
1995 ◽  
Author(s):  
Naji Saad ◽  
A.S. Cullick ◽  
M.M. Honarpour

SPE Journal ◽  
2021 ◽  
pp. 1-18
Author(s):  
Farzad Bashtani ◽  
Mazda Irani ◽  
Apostolos Kantzas

Summary Improvements to more advanced tools, such as inflow control devices (ICDs), create a high drawdown regime close to wellbores. Gas liberation within the formation occurs when the drawdown pressure is reduced below the bubblepoint pressure, which in turn reduces oil mobility by reducing its relative permeability, and potentially reducing oil flow. The key input in any reservoir modeling to compare the competition between gas and liquid flow toward ICDs is the relative permeability of different phases. Pore-network modeling (PNM) has been used to compute the relative permeability curves of oil, gas, and water based on the pore structure of the formation. In this paper, we explain the variability of pore structure on its relative permeability, and for a similar formation and identical permeability, we explain how other factors, such as connectivity and throat radius distribution, can vary the characteristic curves. By using a boundary element method, we also incorporate the expected relative permeability and capillary pressure curves into the modeling. The results show that such variability in the pore network has a less than 10% impact on production gas rates, but its effect on oil production can be significant. Another important finding of such modeling is that providing the PNM-created relative permeabilities may provide totally different direction on setting the operational constraints. For example, in the case studied in this paper, PNM-created relative permeability curves suggest that a reduction of flowing bottomhole pressure (FBHP) increases the oil rate, but for the case modeled with a Corey correlation, changes in FBHP will not create any uplift. The results of such work show the importance of PNM in well completion design and probabilistic analysis of the performance, and can be extended based on different factors of the reservoir in future research. Although PNM has been widely used to study the multiphase flow in porous media in academia, the application of such modeling in reservoir and production engineering is quite narrow. In this study, we develop a framework that shows the general user the importance of PNM simulation and its implementation in day-to-day modeling. With this approach, the PNM can be used not just to provide relative permeability or capillary pressure curves on a core or pore- scale, but to preform simulations at the wellbore or reservoir scale as well to optimize the current completions.


1995 ◽  
Vol 47 (11) ◽  
pp. 980-986 ◽  
Author(s):  
M.M. Honarpour ◽  
A.S. Cullick ◽  
Naji Saad ◽  
N.V. Humphreys

SPE Journal ◽  
2016 ◽  
Vol 21 (05) ◽  
pp. 1899-1915 ◽  
Author(s):  
A. L. Compan ◽  
G. C. Bodstein ◽  
P.. Couto

Summary Many methods are used to group and classify rocks, most of them designed to be applied to all petrophysical properties, in general. Some authors, however, point out that these generic classifications may not be able to capture the complexity of the relative permeability property, resulting in highly spread relative permeability groups. These authors use correlations between petrophysical data and relative permeability curves to obtain two-phase dynamic rock types through a manual process. The present work describes a general, systematic, and semiautomatic methodology to determine the relative permeability classes (rock types) from relative permeability experimental data, by use of a clustering method associated with an optimization procedure. Clustering methods are able to classify elements according to their similarities in an n-dimensional space of characteristics, but they may not be able to produce clusters with little data scattering of relative permeability, because these clusters must be related to the rock characteristics available in the reservoir model. The method proposed in this investigation uses a clustering method to obtain groups of reservoir-model rock characteristics associated with an optimization method to deform the space of characteristics (clustering space) such that the clusters become representative dynamic classes with minimum spread of relative permeability curves. The results show a significant decrease of the data scattering found among the relative permeability curves within the groups, therefore reducing the uncertainty of the relative permeability used in reservoir simulations. In our methodology, the relative permeability boundaries of each group are also defined, such that every group has its own set of relative permeability curves with some uncertainty for the curve that represents the group. The associated confidence interval of each curve is evaluated with the Student's-t distribution to determine the relative permeability optimistic and pessimistic curves for each group. These boundaries allow optimistic and pessimist scenarios to be drawn.


Author(s):  
L.E. Murr ◽  
J.S. Dunning ◽  
S. Shankar

Aluminum additions to conventional 18Cr-8Ni austenitic stainless steel compositions impart excellent resistance to high sulfur environments. However, problems are typically encountered with aluminum additions above about 1% due to embrittlement caused by aluminum in solid solution and the precipitation of NiAl. Consequently, little use has been made of aluminum alloy additions to stainless steels for use in sulfur or H2S environments in the chemical industry, energy conversion or generation, and mineral processing, for example.A research program at the Albany Research Center has concentrated on the development of a wrought alloy composition with as low a chromium content as possible, with the idea of developing a low-chromium substitute for 310 stainless steel (25Cr-20Ni) which is often used in high-sulfur environments. On the basis of workability and microstructural studies involving optical metallography on 100g button ingots soaked at 700°C and air-cooled, a low-alloy composition Fe-12Cr-5Ni-4Al (in wt %) was selected for scale up and property evaluation.


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