scholarly journals Evaluation of oil reservoir permeability reduction caused by oil-based mud

2018 ◽  
Vol 83 (6) ◽  
pp. 473-482
Author(s):  
Yoshihiro Miyagawa ◽  
Takumi Watanabe ◽  
Ryo Yamada ◽  
Nozomu Yoshida ◽  
Hideharu Yonebayashi
Keyword(s):  
Oil Reservoir ◽  
Permeability Reduction ◽  
Reservoir Permeability

scholarly journals Oil Reservoir Permeability Estimation from Well Logging Data Using Statistical Methods (A Case Study: South Pars Oil Reservoir)

2017 ◽  
Vol 3 (10) ◽  
pp. 831 ◽  
Author(s):  
Akbar Esmailzadeh ◽  
Sina Ahmadi ◽  
Reza Rooki ◽  
Reza Mikaeil
Keyword(s):  
Nonlinear Models ◽  
Accurate Determination ◽  
Well Logging ◽  
Oil Reservoir ◽  
Well Test ◽  
Sonic Log ◽  
Reservoir Permeability ◽  
Factor Log ◽  
Resistivity Log

Permeability is a key parameter that affects fluids flow in reservoir and its accurate determination is a significant task. Permeability usually is measured using practical approaches such as either core analysis or well test which both are time and cost consuming. For these reasons applying well logging data in order to obtaining petrophysical properties of oil reservoir such as permeability and porosity is common. Most of petrophysical parameters generally have relationship with one of well logged data. But reservoir permeability does not show clear and meaningful correlation with any of logged data. Sonic log, density log, neutron log, resistivity log, photo electric factor log and gamma log, are the logs which effect on permeability. It is clear that all of above logs do not effect on permeability with same degree. Hence determination of which log or logs have more effect on permeability is essential task. In order to obtaining mathematical relationship between permeability and affected log data, fitting statistical nonlinear models on measured geophysical data logs as input data and measured vertical and horizontal permeability data as output, was studied. Results indicate that sonic log, density log, neutron log and resistivity log have most effect on permeability, so nonlinear relationships between these logs and permeability was done.

The Tianjin geothermal field (north-eastern China): Water chemistry and possible reservoir permeability reduction phenomena

Geothermics ◽  
2008 ◽  
Vol 37 (4) ◽  
pp. 400-428 ◽  
Author(s):  
Angelo Minissale ◽  
Daniele Borrini ◽  
Giordano Montegrossi ◽  
Andrea Orlando ◽  
Franco Tassi ◽  
...  
Keyword(s):  
Water Chemistry ◽  
Eastern China ◽  
Geothermal Field ◽  
Permeability Reduction ◽  
Reservoir Permeability ◽  
North Eastern

scholarly journals Estimating Oil Reservoir Permeability and Porosity from Two Interacting Wells

2013 ◽  
Vol 45 (2) ◽  
pp. 144-153
Author(s):  
Sutawanir Sutawanir ◽  
◽  
Agus Yodi Gunawan ◽  
Anggita Septiani ◽  
Iskandar Fahmi ◽  
...  
Keyword(s):  
Oil Reservoir ◽  
Reservoir Permeability

scholarly journals Integrated Analysis of Permeability Reduction Caused by Polymer Retention for Better Understanding Polymer Transport

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
ByungIn Choi ◽  
Jinsuk Choi ◽  
Kun Sang Lee
Keyword(s):  
Flow Behavior ◽  
Polymer Concentration ◽  
Integrated Analysis ◽  
Permeability Reduction ◽  
Reservoir Permeability ◽  
Polymer Transport ◽  
Concentration Loss ◽  
Polymer Retention ◽  
Interpretation Method ◽  
Reduction Factors

Polymer retention is one of the most important factors to govern polymer propagation through porous media, determining whether successful or not. The focus of previous studies has been limited to polymer concentration loss caused by the retention; not only change in polymer concentration, but also reduction in reservoir permeability is the main issue for theoretical transport study. Due to the lack of accuracy of Langmuir isotherm describing the polymer retention mechanisms, this study proposes a new type of matching interpretation method to correlate the permeability reduction factors from experiments to permeability. In order to solve the problem of poorly matching results between estimation and observation, use of nonadsorptive constant conditionally selected in matching process was made. Based on the threshold permeability reduction factors, approximate critical permeability can be calculated to which nonadsorptive constant would be applied. Results showed significant improvements in the estimation of permeability reduction for both low and high permeability cores. In addition, effects of permeability reduction on polymer transport in field scale were analyzed using the proposed matching model. Thus, not only does this interpretation method help to evaluate prediction for accurate flow behavior, but also unwanted risk can be evaluated.

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