Fracture Qualitative Identification Using Conventional Logging Data

2013 ◽  
Vol 316-317 ◽  
pp. 822-825
Author(s):  
Cui Ju Feng ◽  
Wei Lin Yan

Fractured reservoir evaluation is always a huge challenge for the oil exploration and development.The paper summarizes the response characteristics of fracture in conventional logging curves and gives 4 parameters which can identify fractures.Furthermore the paper proposes a comprehensive probability index of fracture which can integrate the 4 parameters above and indicate fracture develop rate qualitatively.In addition,the paper classified fracture developing level into three levels.Actual process shows that this method can indicate fracture develop rate,search fractured formation and guide actual production.

2013 ◽  
Vol 316-317 ◽  
pp. 826-829
Author(s):  
Cui Ju Feng ◽  
Wei Lin Yan

The output of fracture pool is over half of the entire outout of oil and gas,and fracture pool is one of the important fields of oil inhancing yield in 21st Century. Fractured reservoir evaluation is always a huge challenge for the oil exploration and development. Budart Group of Sudert district in Hailaer Basin is a reservoir that has very low porosity、very low permeability、double pore system and it is rich of fracture. The paper summarized Hailaer Basin Budart Group reservoir’s characteristics, especially fractures’s characteristics in conventional logs,fracture’s parameters,such as fracture density,dip,width and filling and illustrate the response of low angle fracture and high angle fracture in logs.


Minerals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 657
Author(s):  
Hongliang Wang ◽  
Zehua Zhang ◽  
Jintong Liang ◽  
Huimin Liu ◽  
Shige Shi

The successful development of shale gas and oil in North America has created considerable interest in shale. The analysis of genetic types, the sedimentary environment, and the mudstone development mechanism within sequences is critical for evaluating shale gas and oil exploration prospects, exploration favorable zones, and resource potential. This study focused on the shale of Shahejie Formation in Dongying Depression of Bohai Bay Basin. Shale lithofacies division, geochemical analysis, and well-log analysis were performed for a sedimentary environment and its related elemental response characteristics’ identification. Based on the results, we concluded that the sedimentary environment of the lake basin evolved from the saltwater lake to the ambiguous lake and then the open lake to the delta. In response, we observed gradually decreasing Sr/Ba and Ca/Mg ratios and increasing Rb/Ca and Fe/Mn ratios during the whole process during the reduction of the salinity and the decrease in PH value and sediments’ transport distance. The relationship between ratio elements and high-frequency sequences was initially established within the shale strata. Our results show that ratios of Sr/Ba and Ca/Mg ratios near the sequence boundary are relatively low, and ratios of Fe/Mn and Rb/Ca are relatively high, while ratios of Sr/Ba and Ca/Mg near the flooding surface are relatively high, and ratios of Fe/Mn and Rb/Ca are relatively low. Those features can be used as a marker for high-frequency sequence division of shale strata. Our results provided a new theoretical basis and technical method for shale gas and oil exploration and development.


2020 ◽  
Vol 84 ◽  
pp. 103674
Author(s):  
Xinhua Ma ◽  
Hongyan Wang ◽  
Shangwen Zhou ◽  
Ziqi Feng ◽  
Honglin Liu ◽  
...  

1990 ◽  
Author(s):  
Giovanni da Prat ◽  
Ricardo Jorquera ◽  
Suresh Puthigai

2021 ◽  
Author(s):  
Klemens Katterbauer ◽  
Alberto Marsala ◽  
Abdallah Al Shehri ◽  
Ali Yousif

Abstract 4th Industrial Revolution (4IR) technologies have assumed critical importance in the oil and gas industry, enabling data analysis and automation at unprecedented levels. Formation evaluation and reservoir monitoring are crucial areas for optimizing reservoir production, maximizing sweep efficiency and characterizing the reservoirs. Automation, robotics and artificial intelligence (AI) have led to tremendous transformations in these areas, in particular in subsurface sensing. We present a novel 4IR inspired framework for the real-time sensor selection for subsurface pressure and temperature monitoring, as well as reservoir evaluation. The framework encompasses a deep learning technique for sensor data uncertainty estimation, which is then integrated into an integer programming framework for the optimal selection of sensors to monitor the reservoir formation. The results are rather promising, showing that a relatively small numbers of sensors can be utilized to properly monitor the fractured reservoir structure.


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