A new method to production prediction for the shale gas reservoir

被引:8
|
作者
Li, Ting [1 ,2 ,3 ]
Tan, Yongsheng [4 ,5 ]
Ahmad, Faraj A. [3 ]
Liu, Haiyan [6 ]
机构
[1] Yangtze Univ, Petr Engn Coll, Wuhan, Peoples R China
[2] Chengdu Univ Technol, State Key Lab Oil & Gas Reservoir Geol & Exploit, Chengdu, Peoples R China
[3] Colorado Sch Mines, Petr Engn Dept, Golden, CO 80401 USA
[4] Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan, Hubei, Peoples R China
[5] Univ Chinese Acad Sci, Beijing, Peoples R China
[6] Ningbo Fengcheng Adv Energy Mat Res Inst, Ningbo, Peoples R China
关键词
BP neural-network; controlling factors; principal component analysis; production analysis; shale gas;
D O I
10.1080/15567036.2020.1779876
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
It is a common phenomenon that shale gas production differs significantly among wells, and it is difficult to find a relationship between engineering variables and gas rate by utilizing a simple plotting of production data against well and treatment variables. Data such as completion method, flow back, and gas production have been collected for 180 shale gas wells. Firstly, the collected data were standardized, and then related or dependent variables were removed by gray correlation analysis. Finally, principal component analysis and BP Neural-network methods are applied separately to analyze the collected data. It is the first-of-its-kind study that was conducted to define the relationship between gas production and engineering parameters for Fuling shale gas reservoir in China. By use of principal component analysis, it is concluded that the number of stages, the lateral length, the distance between clusters, and the average volume of slick-water are the main factors that impact shale gas production. The results showed that stage numbers account for 42.9% of the total variance, while the horizontal length accounts for about 21.4%; therefore, these two factors account for 64.3% among all of the factors. However, the next important factors are the average distance between clusters and the average volume of slick-water, both account for 24.1% of the total variance. The importance of each variable was also defined by the BP Neural-network method. The number of stages and the horizontal length are still the two major factors, and both account for 52%. The volume of slick-water and the proppant size of 40/70 mesh for both ceramic and sand are considered the second important factor and both account for 37%. This implies that shale gas well configuration and treatment parameters are the dominant factors for gas production in shale reservoirs. Therefore, in order to enhance gas production, it is crucial to increase the number of stages and horizontal length firstly and to raise the volume of slick-water by using 40/70 mesh for both ceramic and sand. However, another driver to accelerate production is to decrease the distance of clusters. These measures will be helpful to improve production in the future shale gas development of Fuling reservoir. This study is believed to be the first-of-its-kind to define the relationship between gas production and engineering parameters for Fuling shale gas reservoir in China.
引用
收藏
页码:9856 / 9869
页数:14
相关论文
共 50 条
  • [41] Gas Production Prediction Model of Volcanic Reservoir Based on Data-Driven Method
    Zhang, Haijie
    Pu, Junwei
    Zhang, Li
    Deng, Hengjian
    Yu, Jihao
    Xie, Yingming
    Tong, Xiaochang
    Man, Xiangjie
    Liu, Zhonghua
    ENERGIES, 2024, 17 (21)
  • [42] Physics-informed data-driven shale gas well production prediction method
    Ren, Wenxi
    Duan, Youjing
    Guo, Jianchun
    Tian, Zhuhong
    Zeng, Fanhui
    Luo, Yang
    Natural Gas Industry, 2024, 44 (09) : 127 - 139
  • [43] Research on Production Prediction Method of Multi-stage Fractured Shale Gas Horizontal Well
    Yin R.
    Zhang S.
    Applied Mathematics and Nonlinear Sciences, 2024, 9 (01)
  • [44] PETROPHYSICAL CHARACTERISTICS AND RESERVOIR PREDICTION OF SHALE GAS IN CHANGNING BLOCK SICHUAN BASIN
    Zhang, Jing
    Yang, Rongjun
    Hao, Tao
    Zhang, Jianxin
    Du, Bingyi
    Chen, Tao
    Liu, Junying
    FRESENIUS ENVIRONMENTAL BULLETIN, 2022, 31 (01): : 517 - 524
  • [45] The prediction of shale gas reservoir parameters through a multilayer transfer learning network
    Wang, Min
    Guo, XinPing
    Tang, HongMing
    Yu, WeiMing
    Zhao, Peng
    Shi, XueWen
    GEOPHYSICAL JOURNAL INTERNATIONAL, 2023, 234 (02) : 1463 - 1475
  • [46] A New Image Processing Workflow for the Detection of Quartz Types in Shales: Implications for Shale Gas Reservoir Quality Prediction
    Guo, Sen
    Misch, David
    Sachsenhofer, Reinhard F.
    Zhu, Yanming
    Tang, Xin
    Bai, Weichen
    MINERALS, 2022, 12 (08)
  • [47] Artificial neural network prediction models for Montney shale gas production profile based on reservoir and fracture network parameters
    Viet Nguyen-Le
    Shin, Hyundon
    ENERGY, 2022, 244
  • [48] Production simulation and prediction of fractured horizontal well with complex fracture network in shale gas reservoir based on unstructured grid
    Xiao, Hongsha
    Chen, Man
    Jing, Cui
    Zhao, Huiyan
    Wang, Keren
    FRONTIERS IN EARTH SCIENCE, 2023, 11
  • [49] An adaptive mesh method for shale gas reservoir with complex fracture networks
    Mi L.
    Jiang H.
    Hu X.
    Li J.
    Hu X.
    Zhou Y.
    Jia Y.
    Yan B.
    Shiyou Xuebao/Acta Petrolei Sinica, 2019, 40 (02): : 197 - 206
  • [50] Time series modeling for production prediction of shale gas wells
    Niu, Wente
    Lu, Jialiang
    Zhang, Xiaowei
    Sun, Yuping
    Zhang, Jianzhong
    Cao, Xu
    Li, Qiaojing
    Wu, Bo
    GEOENERGY SCIENCE AND ENGINEERING, 2023, 231