scholarly journals Fluid-Rock Characterization for NMR Well Logging and Special Core Analysis

2007 ◽  
Author(s):  
George Hirasaki ◽  
Kishore Mohanty
2015 ◽  
Vol 4 (2) ◽  
pp. 44-52
Author(s):  
Novia Rita ◽  
Tomi Erfando

Sebelum suatu model reservoir digunakan, terlebih dahulu dilakukan history matching atau menyesuaikan kondisi model dengan dengan kondisi reservoir. Salah satu parameter yang perlu disesuaikan adalah permeabilitas relatif. Untuk melakukan rekonstruksi nilai permeabilitas relatifnya dibutuhkan data SCAL (Special Core Analysis) dari sampel core. Langkah awal rekonstruksi adalah dengan melakukan normalisasi data permeabilitas relatif (kr) dan saturasi air (Sw) dari data SCAL yang berasal dari tiga sampel core. Setelah dilakukan nomalisasi, dilakukan denormalisasi data permeabilitas relatif yang akan dikelompokkan berdasarkan jenis batuannya. Setelah dilakukan history matching menggunakan black oil simulator, data denormalisasi tersebut belum sesuai dengan kondisi aktual. Selanjutnya digunakan persamaan Corey untuk rekonstruksi kurva permeabilitas relatifnya. Hasil dari persamaan tersebut didapat bahwa nilai kro dan krw jenis batuan 1 sebesar 0,25 dan 0,09 kemudian nilai kro dan krw untuk jenis batuan 2 sebesar 0,4 dan 0,2. Nilai permeabilitas dari persamaan Corey digunakan untuk melakukan history matching, hasilnya didapat kecocokan (matching) dengan keadaan aktual. Berdasarkan hasil simulasi, nilai produksi minyak aktualnya adalah 1.465.650 bbl sedangkan produksi dari simulasi adalah 1.499.000 bbl. Artinya persentase perbandingan aktual dan simulasinya adalah 1,14% yang dapat dikatakan cocok karena persentase perbedaannya di bawah 5%.


2020 ◽  
Vol 5 (3) ◽  
pp. 210-226 ◽  
Author(s):  
Abouzar Mirzaei-Paiaman ◽  
Seyed Reza Asadolahpour ◽  
Hadi Saboorian-Jooybari ◽  
Zhangxin Chen ◽  
Mehdi Ostadhassan

2014 ◽  
Author(s):  
N.S. Balushkina ◽  
G.A. Kalmykov ◽  
R.A. Khamidullin ◽  
V.S. Belokhin ◽  
N.I. Korobova ◽  
...  

SPE Journal ◽  
2019 ◽  
Vol 24 (03) ◽  
pp. 1234-1247 ◽  
Author(s):  
Shuangmei Zou ◽  
Ryan T. Armstrong

Summary Wettability is a major factor that influences multiphase flow in porous media. Numerous experimental studies have reported wettability effects on relative permeability. Laboratory determination for the impact of wettability on relative permeability continues to be a challenge because of difficulties with quantifying wettability alteration, correcting for capillary-end effect, and observing pore-scale flow regimes during core-scale experiments. Herein, we studied the impact of wettability alteration on relative permeability by integrating laboratory steady-state experiments with in-situ high-resolution imaging. We characterized wettability alteration at the core scale by conventional laboratory methods and used history matching for relative permeability determination to account for capillary-end effect. We found that because of wettability alteration from water-wet to mixed-wet conditions, oil relative permeability decreased while water relative permeability slightly increased. For the mixed-wet condition, the pore-scale data demonstrated that the interaction of viscous and capillary forces resulted in viscous-dominated flow, whereby nonwetting phase was able to flow through the smaller regions of the pore space. Overall, this study demonstrates how special-core-analysis (SCAL) techniques can be coupled with pore-scale imaging to provide further insights on pore-scale flow regimes during dynamic coreflooding experiments.


2014 ◽  
Vol 490-491 ◽  
pp. 468-472
Author(s):  
Ke Zeng ◽  
Zheng Zhou ◽  
Mei Ling Zhang

Based on the Putaohua groups in Yushulin oil field, and through the statiscics and analyses, weve found that the reservoir property of this area is in the range of specially low permeability level. So due to the low porosity and permeability, its necessary to do some reaearch on the parameters calculation method.This papers analysed the relationships between the physical property parameters such as porosity, permeability, shale content and the well logging responses such as AC, SP, GR, then we built the distribution rules histograms of each physical property parameter. And we got the distribution situations of the parameters of the oil groups. Through the multiple regression, we built the relationship formulas between the reservoir property parameters and the well logging responses by using the core analysis data of 53 test wells. Afetr comparing the parameters of calculation and the core analysis data, we found that the deviation is small, which meets the production requires of oil field.


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