Geologically Consistent History Matching Honouring Rock Types Driven by Adjoint Method

2020 ◽  
Author(s):  
Bettina Jenei ◽  
Leonhard Ganzer ◽  
Hussein Almuallim ◽  
Roman Manasipov
Keyword(s):  
History Matching ◽  
Adjoint Method ◽  
Rock Types

Full Field History Matching For Chemical Flooding With The Adjoint Method

2013 ◽  
Author(s):  
Sippe G Douma ◽  
Issa M Abu-Shiekah ◽  
Zakariya Yahya Kindi
Keyword(s):  
History Matching ◽  
Adjoint Method ◽  
Chemical Flooding ◽  
Full Field

scholarly journals Numerical Simulation Modeling of Carbonate Reservoir Based on Rock Type

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Peiqing Lian ◽  
Cuiyu Ma ◽  
Bingyu Ji ◽  
Taizhong Duan ◽  
Xuequn Tan
Keyword(s):  
History Matching ◽  
Water Saturation ◽  
Rock Type ◽  
Carbonate Reservoir ◽  
Reservoir Rock ◽  
Column Height ◽  
Fluid Distribution ◽  
Type Model ◽  
Type Curves ◽  
Rock Types

There are many types of carbonate reservoir rock spaces with complex shapes, and their primary pore structure changes dramatically. In order to describe the heterogeneity of K carbonate reservoir, equations of porosity, permeability and pore throat radii under different mercury injection saturations are fitted, and it shows that 30% is the best percentile. R30 method is presented for rock typing, and six rock types are divided according to R30 value of plugs. The porosity-permeability relationship is established for each rock type, and their relevant flow characteristics of each rock type have been studied. Logs are utilized to predict rock types of noncored wells, and a three-dimensional (3D) rock type model has been established based on the well rock type curves and the sedimentary facies constraint. Based on the relationship between J function and water saturation, the formula of water saturation, porosity, permeability, and oil column height can be obtained by multiple regressions for each rock type. Then, the water saturation is calculated for each grid, and a 3D water saturation model is established. The model can reflect the formation heterogeneity and the fluid distribution, and its accuracy is verified by the history matching.

Fast and Efficient Sensitivity Calculation Using Adjoint Method for 3 Phase Field-Scale History Matching

2009 ◽  
Author(s):  
Ramez Masoud Azmy ◽  
Ahmed Mohamed Daoud ◽  
Khaled Abdlel-Fattah ◽  
Mohamed H.M. Sayyouh
Keyword(s):  
Phase Field ◽  
History Matching ◽  
Adjoint Method ◽  
Field Scale ◽  
Sensitivity Calculation

Correlation of Pore Structure and Permeability by SEM-Image Analysis

1990 ◽  
Vol 48 (1) ◽  
pp. 428-429
Author(s):  
C. A. Callender ◽  
Wm. C. Dawson ◽  
J. J. Funk
Keyword(s):  
Image Analysis ◽  
Pore Structure ◽  
Imaging System ◽  
Pore Space ◽  
Simulation Models ◽  
Image Analyzer ◽  
Imaging Data ◽  
Sem Images ◽  
Thin Sections ◽  
Rock Types

The geometric structure of pore space in some carbonate rocks can be correlated with petrophysical measurements by quantitatively analyzing binaries generated from SEM images. Reservoirs with similar porosities can have markedly different permeabilities. Image analysis identifies which characteristics of a rock are responsible for the permeability differences. Imaging data can explain unusual fluid flow patterns which, in turn, can improve production simulation models.Analytical SchemeOur sample suite consists of 30 Middle East carbonates having porosities ranging from 21 to 28% and permeabilities from 92 to 2153 md. Engineering tests reveal the lack of a consistent (predictable) relationship between porosity and permeability (Fig. 1). Finely polished thin sections were studied petrographically to determine rock texture. The studied thin sections represent four petrographically distinct carbonate rock types ranging from compacted, poorly-sorted, dolomitized, intraclastic grainstones to well-sorted, foraminiferal,ooid, peloidal grainstones. The samples were analyzed for pore structure by a Tracor Northern 5500 IPP 5B/80 image analyzer and a 80386 microprocessor-based imaging system. Between 30 and 50 SEM-generated backscattered electron images (frames) were collected per thin section. Binaries were created from the gray level that represents the pore space. Calculated values were averaged and the data analyzed to determine which geological pore structure characteristics actually affect permeability.

Next-Generation Workflow for Multi-level Assisted History Matching: Visualization and Collaboration

2014 ◽  
Author(s):  
G. A. Carvajal ◽  
M. Maucec ◽  
A. Singh ◽  
A. Mahajan ◽  
J. Dhar ◽  
...  
Keyword(s):  
History Matching ◽  
Next Generation ◽  
Assisted History Matching ◽  
Multi Level
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