基于数据挖掘算法低渗透油田水淹井治理效果建模

来源:用户上传      作者:袁凯涛，焦卫华，周亚茹，张瑞，张仙伟

　　摘 要：由于已有方法在低B透油田水淹井治理效果建模过程中，未挖掘水淹井的地质参数以及开发时间数据，导致水质达标率、水驱动用程度以及水淹井综合利用率大幅度下降，提出了一种基于数据挖掘算法低渗透油田水淹井治理效果建模方法。定量分析低渗透水油田水淹井的适应性以及储层的非均匀质性等相关因素，研究不同因素对水淹井的影响范围程度，结合分析结果组建低渗透油田水淹井治理模型。通过数据挖掘算法挖掘低渗透油田水淹井的地质参数和开发时间数据，利用遗传算法优化模型参数。结合油田实际情况，选取能够反映低渗透油田水淹井治理效果的主要指标，并制定各个指标的评价标准和获取指标权重，进而构建一套完成的治理效果评价体系，通过评价体系完成治理效果评价和分析。结果表明，所提方法能够全面提升水质达标率、水驱动用程度以及水淹井综合利用率，获取满意的治理效果。
　　关键词：数据挖掘算法;低渗透;油田;水淹井;治理效果建模
　　中图分类号：TP391 文献标识码：A 文章编号：1001-5922（2021）12-0083-05
　　Modeling of Treatment Effect of Water-immerse Well in Low Permeability Oilfield Based on Data Mining
　　Yuan Kaitao1， Jiao Weihua1， ZhouYaru1， Zhang Rui1， Zhang Xianwei2
　　（1.Dingbian oil production plant of Yanchang Oil field Co.， Ltd.， Dingbian 718699， China;
　　2.Schoolof Computer， Xi an ShiyouUniversity， Xi an 710065， China）
　　Abstract：In the process of modeling of treatment effect of water-immerse well in low permeability oilfield， the geological parameters and development time data are not mined， which leads to the significant decline of water quality standard rate， water drive production degree and comprehensive utilization rate of water-immerse wells. Therefore， a modeling method based on data mining algorithm is proposed. Quantitative analysis of the adaptability of water-immerse wells in low permeability oilfield and the heterogeneity of reservoir and other related factors is carried out to study the influence range of different factors on water-immerse wells. Combined with the analysis results， the water-immerse well governance model in low permeability oilfield is established. The data mining algorithm is adopted to mine geological parameters and development time data， and the genetic algorithm is used to model optimize parameters. Combined with the actual situation of the oilfield， this paper selects the main indicators that can reflect the treatment effect of water flooded wells in low permeability oilfield， formulates the evaluation standard of each indicator and obtains the index weight， so as to construct a set of completed treatment effect evaluation system. Thus the treatment effect evaluation and analysis are completed. The results show that the proposed method can comprehensively improve the water quality standard rate， water drive production degree and comprehensive utilization rate of water-immerse wells to obtain satisfactory treatment effect.
　　Key words：Data mining algorithm; Low permeability; Oi field; Water-immerse well; Treatment effect modeling
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