THE CERTAINTY IN UNCERTAINTY: Quantifying coreflood data errors

2021 ◽  
Vol 2021 ◽  
Author(s):  
Steffen Berg ◽  
◽  
Evren Unsal

Multiphase flow in porous media systems is a critical element of many processes in the energy industry. The characteristics of the simultaneous flow of the immiscible phases can be quantified using relative permeability relations. In geoscience applications, these relations are determined in coreflooding studies that often comprise coreflood tests of oil–water mixtures performed on centimetre-scale rock samples. The outcomes of these are subject to uncertainty, which ultimately influences how accurately the parameters from small-scale tests translate to the upscaled estimations. To assess this uncertainty, Shell researchers have developed an inverse modelling workflow for the uncertainty analysis of relative permeability functions derived from coreflood tests. The results suggest that, even at a small scale, the uncertainty can be significant.

Fuel ◽  
2016 ◽  
Vol 176 ◽  
pp. 222-236 ◽  
Author(s):  
Abbas Zeinijahromi ◽  
Rouhi Farajzadeh ◽  
J. (Hans) Bruining ◽  
Pavel Bedrikovetsky

Author(s):  
Huijun Zhao ◽  
Xiang Ding ◽  
Pengfei Yu ◽  
Yun Lei ◽  
Xiaofei Lv ◽  
...  

2012 ◽  
Vol 616-618 ◽  
pp. 964-969 ◽  
Author(s):  
Yue Yang ◽  
Xiang Fang Li ◽  
Ke Liu Wu ◽  
Meng Lu Lin ◽  
Jun Tai Shi

Oil and water relative permeabilities are main coefficients in describing the fluid flow in porous media; however, oil and water relative permeability for low - ultra low perm oil reservoir can not be obtained from present correlations. Based on the characteristics of oil and water flow in porous media, the model for calculating the oil and water relative permeability of low and ultra-low perm oil reservoirs, which considering effects of threshold pressure gradient and capillary pressure, has been established. Through conducting the non-steady oil and water relative permeability experiments, oil and water relative permeability curves influenced by different factors have been calculated. Results show that: the threshold pressure gradient more prominently affects the oil and water relative permeability; capillary pressure cannot influence the water relative permeability but only the oil relative permeability. Considering effects of threshold pressure gradient and capillary pressure yields the best development result, and more accordant with the flow process of oil and water in low – ultra low perm oil reservoirs.


2021 ◽  
Vol 73 (01) ◽  
pp. 43-43
Author(s):  
Subodh Gupta

The enhanced-oil-recovery (EOR) literature produced in the past several months was dominated by reservoir modeling and characterization; flood enhancements; machine learning; and, more notably, relative permeability estimation. This last one needs to be under-stood further. Relative permeability characterizes flow in porous media, the understanding and manipulating of which is key to the success of EOR. Our fascination with the topic during the last 75 years, therefore, is understandable. Starting with a seminal paper from W.R. Purcell in 1949, we have more than 12,000 articles in the SPE collection alone on relative permeability estimation, of which more than 400 were published in the last year. Mechanics of motion is also important to mankind, but we do not have literature piling up on Newton’s laws of motion. Is it that we haven’t understood flow in porous media yet, or is it because the subject matter is so complex? The truth, perhaps, lies somewhere in between. Flow in pores that are randomly sized, randomly connected, and have variable chemical makeup is, by nature, complex and mathematically unmanageable. Relative permeability has been our attempt to lend its aggregate behavior some sense of manageability. This has always been done in a fit-for-purpose manner. What is fit for one context, however, may not be so for the others, and this uncertainty continues to churn out theses, antitheses, and syntheses. Expectedly, even more such activity arises with every improvement in computing capabilities, as is once again evident with advances in machine learning—new tools to handle old problems. That is where my first pick is for you, with some additional suggested readings in references that follow. Data analytics does much more than estimate relative permeability, and the second paper abridged here uses it to predict a flood performance. To allow a break from data science, the third paper chosen deals with the important topic of electromagnetics as applied to reservoir characterization and heating. I hope you find these to be useful and interesting reads.


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