SUN Lichun, LIU Jia, LI Na, LI Xinze, WEN Heng. Main controlling factors of production and reasonable fracturing scale optimization of deep coalbed methane wells in Shenfu block, Ordos Basin[J]. PETROLEUM GEOLOGY & EXPERIMENT, 2025, 47(1): 43-53. doi: 10.11781/sysydz2025010043
Citation: SUN Lichun, LIU Jia, LI Na, LI Xinze, WEN Heng. Main controlling factors of production and reasonable fracturing scale optimization of deep coalbed methane wells in Shenfu block, Ordos Basin[J]. PETROLEUM GEOLOGY & EXPERIMENT, 2025, 47(1): 43-53. doi: 10.11781/sysydz2025010043

Main controlling factors of production and reasonable fracturing scale optimization of deep coalbed methane wells in Shenfu block, Ordos Basin

doi: 10.11781/sysydz2025010043
  • Received Date: 2024-10-08
  • Rev Recd Date: 2024-11-26
  • Publish Date: 2025-01-28
  • The production of deep coalbed methane (CBM) wells in the Shenfu block of Ordos Basin varies greatly, with a lack of understanding of the main controlling factors. To further reveal the production patterns in deep CBM wells, identify key factors affecting well production capacity, and guide the efficient development of deep CBM resources in the Shenfu block, this study analyzed the production dynamic characteristics of typical CBM wells in the area based on basic geological data, production data, and previous research results. Through individual well comparisons and overall trend analysis, the main controlling factors influencing CBM well production in the Shenfu block were identified. The results showed that the gas content and fracturing scale have the greatest impact on production. Pearson's multivariate correlation regression analysis was used to quantitatively evaluate the impact of various factors on production capacity. The factors affecting post-fracturing production capacity of deep CBM wells, ranked by correlation, are as follows: gas content > fracturing sand amount > construction flow rate > fracturing fluid volume > structural depth > thickness. Under certain geological conditions, a reasonable fracturing scale is key to the efficient development of deep CBM wells. An integrated "geological reservoir, fracturing, and economic evaluation" approach was adopted, with economic benefits as the objective. Numerical simulation was conducted to study the coupled optimization of well spacing and fracturing scale. The results determined that the optimal well spacing for the Shenfu block was 300 m, the optimal cluster spacing was 20 m, and the optimal fracture half-length was 120 m. These findings provide technical support for the efficient development of deep CBM resources in the Shenfu block.

     

  • All authors declare no relevant conflict of interests.
    The study was designed by SUN Lichun and LI Na. The dynamic analysis was completed by LI Xinze and WEN Heng. The manuscript was drafted and revised by LIU Jia and LI Xinze. All authors have read the final version of the paper and consented to its submission.
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    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 9.3 %其他: 9.3 %其他: 0.8 %其他: 0.8 %Boydton: 0.6 %Boydton: 0.6 %Luleå: 0.2 %Luleå: 0.2 %Prineville: 1.9 %Prineville: 1.9 %Springfield: 0.6 %Springfield: 0.6 %上海: 3.0 %上海: 3.0 %东营: 0.2 %东营: 0.2 %中卫: 0.4 %中卫: 0.4 %克拉玛依: 0.4 %克拉玛依: 0.4 %北京: 8.9 %北京: 8.9 %北博滕省: 1.1 %北博滕省: 1.1 %北卡罗来纳州: 0.6 %北卡罗来纳州: 0.6 %十堰: 0.2 %十堰: 0.2 %南京: 1.1 %南京: 1.1 %南基伍省: 0.8 %南基伍省: 0.8 %南通: 0.2 %南通: 0.2 %厦门: 0.2 %厦门: 0.2 %吕梁: 0.6 %吕梁: 0.6 %嘉兴: 0.4 %嘉兴: 0.4 %埃德蒙顿: 0.2 %埃德蒙顿: 0.2 %墨西哥城: 1.1 %墨西哥城: 1.1 %大庆: 1.3 %大庆: 1.3 %天津: 1.1 %天津: 1.1 %安康: 0.2 %安康: 0.2 %宣城: 0.8 %宣城: 0.8 %常州: 0.2 %常州: 0.2 %常德: 0.2 %常德: 0.2 %平顶山: 0.2 %平顶山: 0.2 %广州: 0.6 %广州: 0.6 %廊坊: 0.2 %廊坊: 0.2 %延安: 0.2 %延安: 0.2 %弗吉尼亚州: 1.1 %弗吉尼亚州: 1.1 %张家口: 9.7 %张家口: 9.7 %成都: 1.1 %成都: 1.1 %扬州: 0.8 %扬州: 0.8 %无锡: 2.7 %无锡: 2.7 %昆明: 0.2 %昆明: 0.2 %晋中: 0.2 %晋中: 0.2 %杭州: 0.4 %杭州: 0.4 %武汉: 1.1 %武汉: 1.1 %泰州: 0.2 %泰州: 0.2 %洛阳: 0.2 %洛阳: 0.2 %济宁: 0.2 %济宁: 0.2 %淄博: 0.4 %淄博: 0.4 %深圳: 1.1 %深圳: 1.1 %温州: 1.5 %温州: 1.5 %漯河: 1.1 %漯河: 1.1 %眉山: 0.2 %眉山: 0.2 %石家庄: 3.2 %石家庄: 3.2 %秦皇岛: 0.2 %秦皇岛: 0.2 %芒廷维尤: 19.0 %芒廷维尤: 19.0 %苏州: 0.2 %苏州: 0.2 %荆州: 0.6 %荆州: 0.6 %蚌埠: 0.2 %蚌埠: 0.2 %衡阳: 0.4 %衡阳: 0.4 %襄阳: 0.2 %襄阳: 0.2 %西宁: 5.3 %西宁: 5.3 %西安: 1.5 %西安: 1.5 %谢利夫: 0.2 %谢利夫: 0.2 %贵阳: 0.6 %贵阳: 0.6 %达州: 1.3 %达州: 1.3 %运城: 0.6 %运城: 0.6 %遵义: 0.2 %遵义: 0.2 %邯郸: 0.6 %邯郸: 0.6 %郑州: 0.8 %郑州: 0.8 %重庆: 1.3 %重庆: 1.3 %锦州: 1.1 %锦州: 1.1 %长沙: 0.4 %长沙: 0.4 %阿什本: 0.6 %阿什本: 0.6 %阿尔图纳: 0.6 %阿尔图纳: 0.6 %马鞍山: 0.2 %马鞍山: 0.2 %黄冈: 0.2 %黄冈: 0.2 %其他其他BoydtonLuleåPrinevilleSpringfield上海东营中卫克拉玛依北京北博滕省北卡罗来纳州十堰南京南基伍省南通厦门吕梁嘉兴埃德蒙顿墨西哥城大庆天津安康宣城常州常德平顶山广州廊坊延安弗吉尼亚州张家口成都扬州无锡昆明晋中杭州武汉泰州洛阳济宁淄博深圳温州漯河眉山石家庄秦皇岛芒廷维尤苏州荆州蚌埠衡阳襄阳西宁西安谢利夫贵阳达州运城遵义邯郸郑州重庆锦州长沙阿什本阿尔图纳马鞍山黄冈

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      沈阳化工大学材料科学与工程学院 沈阳 110142

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