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考虑爬坡约束的油井间抽批调度问题

郎劲 唐立新

郎劲, 唐立新. 考虑爬坡约束的油井间抽批调度问题. 自动化学报, 2019, 45(2): 388-397. doi: 10.16383/j.aas.c150332
引用本文: 郎劲, 唐立新. 考虑爬坡约束的油井间抽批调度问题. 自动化学报, 2019, 45(2): 388-397. doi: 10.16383/j.aas.c150332
LANG Jin, TANG Li-Xin. Batch Scheduling Problem of Oil Well Considering Ramping Constraints in Oilfield Production. ACTA AUTOMATICA SINICA, 2019, 45(2): 388-397. doi: 10.16383/j.aas.c150332
Citation: LANG Jin, TANG Li-Xin. Batch Scheduling Problem of Oil Well Considering Ramping Constraints in Oilfield Production. ACTA AUTOMATICA SINICA, 2019, 45(2): 388-397. doi: 10.16383/j.aas.c150332

考虑爬坡约束的油井间抽批调度问题

doi: 10.16383/j.aas.c150332
基金项目: 

国家重点研发计划资助项目 2016YFB0901900

流程工业综合自动化国家重点实验室基础研究项目 2013ZCX02

111引智基地 B16009

国家自然科学基金重点国际合作项目 71520107004

详细信息
    作者简介:

    郎劲  智能工业数据解析与优化教育部重点实验室(东北大学)讲师.主要研究方向为机组调度, 能源建模与优化.E-mail:langjin@ise.neu.edu.cn

    通讯作者:

    唐立新  东北大学工业与系统工程研究所教授.主要研究方向为生产批调度, 库存控制与供应链计划, 生产过程操作优化.本文通信作者.E-mail:lixintang@mail.neu.edu.cn

Batch Scheduling Problem of Oil Well Considering Ramping Constraints in Oilfield Production

Funds: 

National Key Research and Development Program of China 2016YFB0901900

State Key Laboratory of Synthetical Automation for Process Industrial Fundamental Research Funds 2013ZCX02

the 111 Project B16009

the Major International Joint Research Project of the National Natural Science Foundation of China 71520107004

More Information
    Author Bio:

     Lecturer at the Key Laboratory of Data Analytics and Optimization for Smart Industry, Northeastern University. Her research interest covers scheduling, energy modelling, and optimization algorithms

    Corresponding author: TANG Li-Xin  Professor at the Institute of Industrial & Systems Engineering, Northeastern University. His research interest covers batching and scheduling of the production operations, inventory control and supply chain planning, and production-process operations optimization. Corresponding author of this paper
  • 摘要: 油井间抽批调度问题是确定未来给定计划期内油田井场间抽工作方式的油井各时间段的启停状态及采油量,在满足采油需求的情况下,考虑油井底部压力变化特征对油井开启的影响以及油井最小开关机时间和爬坡约束等生产工艺要求,使总的油井采油运行成本最小.针对油井数量多而导致大规模常规数学规划模型难以求解的困难,建立了基于批的混合整数规划模型.根据模型特点设计了基于变量分离的拉格朗日松弛算法(Lagrangian relaxation,LR)进行求解.针对常规动态规划方法求解分解后的带有爬坡约束的单机组子问题效率低的缺点,提出了用特征点代表同一阶段具有相同性质节点群的状态空间约简策略,使动态规划搜索节点的复杂度从On4)降到On2),显著提高了算法的搜索效率.通过大量随机产生的数值实验表明,提出的基于变量分离的LR算法,小规模问题与CPLEX获得的最优解接近,中大规模问题能够在合理的计算时间内获得高质量的解.
    1)  本文责任编委 宋士吉
  • 图  1  油井批聚合示意图

    Fig.  1  The process of well divided into batching

    图  2  $({{\tau }_{rn}},{{\tau }_{qn}})$和$T$的关系图

    Fig.  2  The diagram for the relationship between $({{\tau }_{rn}},{{\tau }_{qn}})$ and $T$

    图  3  动态规划状态转移图

    Fig.  3  Dynamic programming state transition diagram

    图  4  三个约束对油井启停的影响

    Fig.  4  The influence of three constraints on the start-up/shut-down

    表  1  与油井相关的参数的生成范围

    Table  1  Range of values for parameters corresponding to oil wells

    参数(单位) 下限 上限
    $x_{i}^{\min }$ (barrel) 30 100
    $x_{i}^{\max }$ (barrel) 800 1 200
    $p_{i}^{\max } $ (psia) 6 000 7 000
    $p_{i}^{\min }$ (psia) 2 500 3 500
    $p^{\rm given}$ (psia) 6 000 700
    $T_{i}^{\rm off}$ (h) 1 6
    $T_{i}^{\rm on}$ (h) 1 6
    $T_{i0}^{{}}$ (h) $-$8 8
    ${{S}_{i}}$ ($) 70 150
    ${{c}_{1i}}$ 0.10 0.20
    ${{c}_{2i}}$ 0.02 0.65
    ${{\alpha }_{1i}}$ 40 70
    ${{a}_{2i}}$ 0.02 0.30
    ${{\gamma }_{i}}$ 9.00 9.30
    $D_{1t}^{{}}$ (barrel) 500 1 500
    $D_{2t}^{{}}$ (barrel) 500 1 500
    下载: 导出CSV

    表  2  模型与算法的性能比较结果

    Table  2  Computational results of the LR algorithm and the CPLEX solver

    规模 CPLEX LR-CPLEX LD-CPLEX LD
    $T \times N$ $AR_1$ (单机) $AR_2$ (批) $AR_3$ (单机) $AR_4$ (批) $AR_5$ (单机) $AR_6$ (批) $AR_7$ (单机) $AR_8$ (批)
    $24 \times 10$ 1.00000 1.01340 1.00442 1.01340 1.00440 1.01339 1.00467 1.01340
    $24 \times 30$ 1.00000 1.00463 1.01224 1.00885 1.01201 1.00885 1.01224 1.00885
    $24 \times 50$ 1.00000 1.00224 1.01004 1.01388 1.01004 1.01258 1.00999 1.01388
    $24 \times 70$ 1.00000 1.00113 1.01075 1.00761 1.01049 1.00762 1.01124 1.00762
    $24 \times 90$ 1.00000 1.00078 1.01140 1.01146 1.01089 1.01119 1.01107 1.01160
    $24 \times 100$ 1.00000 1.00037 1.01330 1.01016 1.01178 1.01020 1.01196 1.01016
    $24 \times 300$ 1.02071 1.00000 1.01223 1.01016 1.01195 1.01008 1.01209 1.01059
    $24 \times 500$ 1.08078 1.00000 1.01254 1.01145 1.01243 1.01131 1.01249 1.01156
    $24 \times 700$ 1.12516 1.00000 1.01278 1.01191 1.01256 1.01152 1.01257 1.01154
    $24 \times 900$ 1.15323 1.00000 1.01217 1.01129 1.01477 1.01133 1.01181 1.01124
    AVE 1.03799 1.00225 1.01119 1.01102 1.01113 1.01081 1.01101 1.01104
    下载: 导出CSV

    表  3  模型与算法的计算时间比较

    Table  3  Computational time of the proposed model and algorithm

    规模 CPLEX LR-CPLEX LD-CPLEX LD
    $T\times N$ 单机 单机 单机 单机
    $24 \times 10$ 120.44 0.03 9.62 1.19 14.37 2.59 0.15 0.05
    $24 \times 30$ 247.45 1.25 27.64 2.50 36.82 3.77 0.17 0.03
    $24 \times 50$ 3 600 26.65 48.07 4.23 68.40 105.18 0.34 0.05
    $24 \times 70$ 3 600 359.45 72.52 6.06 96.70 11.65 0.42 0.05
    $24 \times 90$ 3 600 788.54 97.53 7.70 287.47 216.25 0.49 0.05
    $24 \times 100$ 3 600 342.16 116.42 8.75 1 074.90 866.45 0.51 0.06
    $24 \times 300$ 3 600 3 600 557.95 28.69 789.05 241.23 1.70 0.17
    $24 \times 500$ 3 600 3 600 1 273.16 53.08 1 810.21 183.56 2.80 0.28
    $24 \times 700$ 3 600 3 600 2 329.22 78.07 3 250.06 307.01 3.93 0.39
    $24 \times 900$ 3 600 3 600 3 597 105.56 3 600 734.70 5.07 0.50
    AVE 2 916.79 1 591.81 812.91 29.58 1 102.8 267.24 1.56 0.16
    下载: 导出CSV
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  • 收稿日期:  2015-05-25
  • 录用日期:  2016-06-30
  • 刊出日期:  2019-02-20

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