scholarly journals Study on Water Displacing Gas Relative Permeability Curves in Fractured Tight Sandstone Reservoirs Under High Pressure and High Temperature

ACS Omega ◽  
2020 ◽  
Vol 5 (13) ◽  
pp. 7456-7461
Author(s):  
Jianfen Du ◽  
Qi Liu ◽  
Ping Guo ◽  
Tongwen Jiang ◽  
Yuming Xiong ◽  
...  
2015 ◽  
Vol 42 (1) ◽  
pp. 92-96 ◽  
Author(s):  
Jianlong FANG ◽  
Ping GUO ◽  
Xiangjiao XIAO ◽  
Jianfen DU ◽  
Chao DONG ◽  
...  

Fuel ◽  
2021 ◽  
pp. 122389
Author(s):  
Mingyu Cai ◽  
Yuliang Su ◽  
Shiyuan Zhan ◽  
Derek Elsworth ◽  
Lei Li

1969 ◽  
Vol 23 ◽  
pp. 13-16 ◽  
Author(s):  
Tanni Abramovitz

Hydrocarbon-bearing Upper Jurassic sandstone reservoirs at depths of more than 5000 m may form a future exploration target in the Danish Central Graben (Fig. 1). The Upper Jurassic sandstone play in the Danish sector has historically been less successful than in the neighbouring Norwegian and British sectors of the North Sea. This is mainly due to poor reservoir quality of the sandstones. However, the discovery in 2001 of an oil accumulation at a depth of more than 5000 m in the Svane-1 well has triggered renewed interest in the Upper Jurassic High Temperature – High Pressure (HTHP) sandstone play in Danish waters. The Jurassic plays comprise sandstone reservoirs deposited in a variety of environments, ranging from fluvial to deep marine.


2020 ◽  
Author(s):  
JingJing Liu ◽  
JianChao Liu

<p>In recent years, China's unconventional oil and gas exploration and development has developed rapidly and has entered a strategic breakthrough period. At the same time, tight sandstone reservoirs have become a highlight of unconventional oil and gas development in the Ordos Basin in China due to its industrial and strategic value. As a digital representation of storage capacity, reservoir evaluation is a vital component of tight-oil exploration and development. Previous work on reservoir evaluation indicated that achieving satisfactory results is difficult because of reservoir heterogeneity and considerable risk of subjective or technical errors. In the data-driven era, this paper proposes a machine learning quantitative evaluation method for tight sandstone reservoirs based on K-means and random forests using high-pressure mercury-injection data. This method can not only provide new ideas for reservoir evaluation, but also be used for prediction and evaluation of other aspects in the field of oil and gas exploration and production, and then provide a more comprehensive parameter basis for “intelligent oil fields”. The results show that the reservoirs could be divided into three types, and the quantitative reservoir-evaluation criteria were established. This method has strong applicability, evident reservoir characteristics, and observable discrimination. The implications of these findings regarding ultra-low permeability and complex pore structures are practical.</p>


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