scholarly journals A Fractal Simulation Method for Simulating the Resource Abundance of Oil and Gas and Its Application

Author(s):  
Qiulin Guo ◽  
Hongjia Ren ◽  
Xiaozhi Wu ◽  
Zhuangxiaoxue Liu ◽  
Yanzhao Wei ◽  
...  

AbstractIn this study, a fractal simulation method for simulating resource abundance is proposed based on the evaluation results of the exploration risk and prediction technology for the spatial distribution of oil and gas resources at home and abroad. In addition, a key technical workflow for simulating resource abundance was developed. Furthermore, the model for predicting resource abundance has been modified, and the objective functions for conditional simulation have been improved. A series of prediction technologies for predicting the spatial distribution of oil and gas resources have been developed, and the difficulties in visualizing the exploration risks and predicting the spatial distribution of oil and gas resources have been solved. Prediction technologies have been applied to the Jurassic Sangonghe Formation in the hinterland of the Junggar Basin, and good results have been obtained. The results indicated that within the known area, taking the known abundance as the constraint condition, the coincidence rate of the simulated quantities of the original model and the improved model with the actual reserves reached 99.98% after the conditional simulation, indicating that the conditional simulation was effective. In addition, with the improved model, the predicted remaining resources are 0.7899$$\times 10^{8}$$ × 10 8 t, which is 65% of the discovered reserves, and the original model predicts that the remaining resources are 3.5033$$\,\times \,10^{8}$$ × 10 8 t, which is 2.89 times greater than the discovered reserves. Compared with the area in the middle stage of exploration, the results of the improved model are more consistent, and the results of the original model are obviously larger, indicating that the improved model has a good predictive effect for the unknown area. Finally, according to the risk probability and remaining resource distribution, the favorable areas for exploration were optimized as follows: the neighborhood of the triangular area formed by Well Lunan1, Well Shimo1, and Well Shi008, the area near Well Mo11, the area east of Well Mo5, the area west of Well Pen7, the area southwest of Well Shidong1, and the surroundings, as well as the area north of Well Fang2. The application results show that these prediction technologies are effective and can provide important reference and decision-making for resource evaluation and target optimization.

2021 ◽  
Vol 9 ◽  
Author(s):  
Siwei Meng ◽  
Dongxu Li ◽  
Qi Wang ◽  
Jiaping Tao ◽  
Jian Su ◽  
...  

Shale fracturing evaluation is of great significance to the development of shale oil and gas resources, but the commonly used shale evaluation methods (e.g., the method using the brittleness index based on mineral composition or elastic parameters) have certain limitations. Fractures and beddings affecting fracturing are not considered in these methods. Therefore, it is necessary to develop a new method to evaluate fracturing more comprehensively. The samples used in this research were taken from four typical continental shale basins of China, namely the Bohai Bay Basin, the Ordos Basin, the Songliao Basin, and the Junggar Basin. From a microscopic point of view, a three-parameter evaluation method involving multi-dimensional factors has been developed based on the nanoindentation method. Then, the fracturing coefficient K2 is obtained by combining the ratio β of the fracture indentation to the total indentation and the uneven coefficient m. After that, the fracability coefficient K3 is the ratio of the elastic modulus parallel to bedding to that perpendicular to bedding. Finally, the correlation between fracability coefficients K1, K2, and K3 is used to evaluate the overall fracturing performance of shale. The results of this evaluation method are in good agreement with the actual fracturing performance. It can be concluded that this method is highly reliable and practical and well worthy of promoted applications.


2019 ◽  
Vol 44 (11) ◽  
pp. 5220-5229 ◽  
Author(s):  
Yangyang Tian ◽  
Yu Tian ◽  
Zhanqing Qu ◽  
Tiankui Guo ◽  
Yongmin Shi ◽  
...  

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Hongjia Ren ◽  
Xianchang Wang ◽  
Hongbo Ren ◽  
Qiulin Guo

Effectively predicting the spatial distribution of oil and gas contributes to delineating promising target areas for further exploration. Determining the location of hydrocarbon is a complex and uncertain decision problem. This paper proposes a method for predicting the spatial distribution of oil and gas resource based on Bayesian network. In this method, qualitative dependency relationship between the hydrocarbon occurrence and key geologic factors is obtained using Bayesian network structure learning by integrating the available geoscience information and the current exploration results and then using Bayesian network topology structure to predict the probability of hydrocarbon occurrence in the undiscovered area; finally, the probability map of hydrocarbon-bearing is formed by interpolation method. The proposed method and workflow are further illustrated using an example from the Carboniferous Huanglong Formation (C2hl) in the eastern part of the Sichuan Basin in China. The prediction results show that the coincidence rate between the results of 248 known exploration wells and the predicted results reaches 89.5%, and it has been found that the gas fields are basically located in the high value area of the hydrocarbon-bearing probability map. The application results show that the Bayesian network method can effectively predict the spatial distribution of oil and gas resources, thereby reducing exploration risks, optimizing exploration targets, and improving exploration benefits.


Fact Sheet ◽  
2017 ◽  
Author(s):  
Christopher J. Potter ◽  
Christopher J. Schenk ◽  
Marilyn E. Tennyson ◽  
Timothy R. Klett ◽  
Stephanie B. Gaswirth ◽  
...  

2016 ◽  
Vol 42 (1) ◽  
pp. 266-270
Author(s):  
A. Kasaeva ◽  
◽  
Z. Bіrіmzhanova ◽  
A. Rysmagambetova ◽  
◽  
...  

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