Intelligent production optimization method for a low pressure and low productivity shale gas well附视频

被引:0
|
作者
ZHU Qikang [1 ,2 ]
LIN Botao [1 ]
YANG Guang [3 ]
WANG Lijia [4 ]
CHEN Man [5 ]
机构
[1] College of Artificial Intelligence, China University of Petroleum
[2] College of Safety and Ocean Engineering, China University of Petroleum
[3] College of Information Science and Engineering, China University of Petroleum
[4] Sichuan Shale Gas Exploration and Development CoLTD
[5] China National Petroleum Corporation Southwest Oil and Gas Field Company Sichuan Changning Natural Gas Development Co
关键词
D O I
暂无
中图分类号
TE37 [气田开发与开采];
学科分类号
摘要
Shale gas wells frequently suffer from liquid loading and insufficient formation pressure in the late stage of production. To address this issue, an intelligent production optimization method for low pressure and low productivity shale gas well is proposed. Based on the artificial intelligence algorithms, this method realizes automatic production and monitoring of gas well. The method can forecast the production performance of a single well by using the long short-term memory neural network and then guide gas well production accordingly, to fulfill liquid loading warning and automatic intermittent production. Combined with adjustable nozzle, the method can keep production and pressure of gas wells stable automatically, extend normal production time of shale gas wells, enhance automatic level of well sites, and reach the goal of refined production management by making production regime for each well. Field tests show that wells with production regime optimized by this method increased 15% in estimated ultimate reserve(EUR). Compared with the development mode of drainage after depletion recovery, this method is more economical and can increase and stabilize production effectively, so it has a bright application prospect.
引用
收藏
页码:886 / 894
页数:9
相关论文
共 15 条
  • [1] 基于三维分形裂缝模型的页岩气井智能化产能评价方法
    位云生
    王军磊
    于伟
    齐亚东
    苗继军
    袁贺
    刘楚溪
    [J]. 石油勘探与开发, 2021, 48 (04) : 787 - 796
  • [2] 四川盆地南部龙马溪组页岩气储集层地质特征及高产控制因素
    马新华
    谢军
    雍锐
    朱逸青
    [J]. 石油勘探与开发, 2020, 47 (05) : 841 - 855
  • [3] 威远页岩气田单井产能主控因素与开发优化技术对策
    马新华
    李熙喆
    梁峰
    万玉金
    石强
    王永辉
    张晓伟
    车明光
    郭伟
    郭为
    [J]. 石油勘探与开发, 2020, 47 (03) : 555 - 563
  • [4] 长宁页岩气田采气工艺实践与效果
    范宇
    岳圣杰
    李武广
    肖丹
    李小蓉
    向建华
    [J]. 天然气与石油, 2020, 38 (02) : 54 - 60
  • [5] 页岩气藏体积压裂水平井产能有限元数值模拟
    何易东
    任岚
    赵金洲
    李志强
    邓鹏
    [J]. 断块油气田, 2017, 24 (04) : 550 - 556
  • [6] 页岩气压裂水平井拟稳态阶段产能评价方法研究
    刘华
    胡小虎
    王卫红
    曾勇
    郭艳东
    [J]. 西安石油大学学报(自然科学版), 2016, (02) : 76 - 81
  • [7] 考虑水溶气的页岩气藏物质平衡方程及储量计算方法
    尚颖雪
    李晓平
    宋力
    [J]. 天然气地球科学, 2015, 26 (06) : 1183 - 1189
  • [8] 中扬子地区五峰组—龙马溪组页岩气储层及含气性特征
    邱小松
    杨波
    胡明毅
    [J]. 天然气地球科学, 2013, 24 (06) : 1274 - 1283
  • [9] 页岩气藏运移机制及数值模拟
    姚军
    孙海
    樊冬艳
    黄朝琴
    孙致学
    张国浩
    [J]. 中国石油大学学报(自然科学版), 2013, 37 (01) : 91 - 98
  • [10] Prediction of Shale-Gas Production at Duvernay Formation Using Deep-Learning Algorithm
    Lee, Kyungbook
    Lim, Jungtek
    Yoon, Daeung
    Jung, Hyungsik
    [J]. SPE JOURNAL, 2019, 24 (06): : 2423 - 2437