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首页» 过刊浏览» 2020» Vol.5» Issue(4) 520-530     DOI : 10.3969/j.issn.2096-1693.2021.01.045
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采用改进的 FCM 聚类算法进行储层优势通道分级
鲁春华,姜汉桥,李杰,尤诚程,成宝洋,李俊键
中国石油大学(北京)油气资源与探测国家重点实验室,北京 102249
A classification method for reservoir thief zones based on an improved FCM clustering algorithm
LU Chunhua, JIANG Hanqiao, LI Jie, YOU Chengcheng, CHENG Baoyang, LI Junjian
State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum-Beijing, Beijing 102249, China

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摘要  井间示踪剂技术被认为是目前识别优势通道最直接、最准确的方法之一,但在稠油油藏中,示踪剂产 出曲线多呈抛物线型单峰形状,曲线之间差别小,优势通道发育级别难以判断;同时因为模糊C均值(Fuzzy C-means, FCM)聚类算法需要事先指定聚类数目,使得其对示踪剂曲线分类问题适应性较差。针对上述问题,本 文提出了改进的FCM聚类算法,解决了单峰型示踪剂曲线难以分类的问题。首先,通过引进决策图来确定聚类 数,避免FCM算法在选取聚类数时的盲目性;然后,选取见剂速度、峰值浓度、回采率等示踪剂浓度曲线的特 征参数,对见剂井进行聚类,确定优势通道发育级别和主控因素;最后,利用洛伦兹系数和现场泡沫驱应用效 果,验证分级的合理性。研究结果表明:目标油田发育三级优势通道,一级优势通道属于均质~相对均质储层, 其见剂速度小于 1.65 m/d,高渗层渗透率小于 3259 mD,同时回采率小于 0.13%,地层系数小于 1341 mD·m; 二级优势通道属于相对均质~非均质储层,见剂速度在 1.65~2.17 m/d,渗透率 3259~8383 mD;同时回采率在 0.13%~0.21%,地层系数介于 1341~3194 mD·m;三级优势通道属于非均质~严重非均质储层,见剂速度大于 2.17 m/d,高渗层渗透率大于 8383 mD,回采率大于 0.21%,地层系数大于 3194 mD·m。现场实践表明:优势通 道越发育,泡沫驱治理效果越好。该分类结果对现场调剖堵水具有指导意义。
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关键词 : 示踪剂;改进的FCM聚类算法;浓度曲线;优势通道分级;洛伦兹系数
Abstract
The technique of inter-well tracer testing is considered to be one of the most direct and accurate methods to identify thief zones in reservoirs. However, in heavy oil reservoirs, tracer breakthrough curves are mostly parabolic and unimodal, resulting in small differences between curves and difficulty in determining the level of thief zones. Because of the need to specify the number of clusters in advance, the Fuzzy C-means (FCM) algorithm is not suitable for the problem of unimodal tracer breakthrough curve classification. To this end, an improved FCM clustering algorithm is proposed in this paper to solve the problem      of classifying unimodal tracer breakthrough curves. First, a decision graph is introduced to determine the cluster number to avoid the blindness of FCM algorithm in selecting the cluster number. Then, the characteristic parameters of tracer curves, such as migration velocity, peak concentration and recovery rate, were selected to cluster the wells that have tracer data to determine the development levels of the thief zones. At the same time, the main factors of the development levels of thief zones are determined based on the tracer curves history matching and clustering results. Finally, the rationality of classification results is verified by their Lorenz coefficient and the application effects of foam flooding in the oilfield. The results show that the tracer migration      velocity is positively correlated with the permeability under the semi-logarithmic coordinate. The recovery rate is positively      correlated with the formation coefficient in a Cartesian coordinate system. The target oilfield has developed three levels of thief      zones. The original geological conditions of the light thief zones are homogeneous to relatively homogeneous, with a migration      velocity less than 1.65 m/d, the corresponding permeability is less than 3259 mD, and the recovery rate is less than 0.13     %     since      the formation coefficient is less than 1341 mD·m. The moderate thief zones are relatively homogeneous to heterogeneous, with a      migration velocity between 1.65 and 2.17m/d, a corresponding permeability of between 3259 mD and 8383 mD, and the recovery      rate is between 0.13     %     and 0.21     %     as the formation coefficient is between 1341 mD·m and 3194 mD·m. The severe thief zones are      heterogeneous to severely heterogeneous, with a migration velocity and permeability greater than 2.17 m/d and 8384 mD, respec     tively, and a recovery rate greater than 0.21     %     with the formation coefficient greater than 3194 mD·m. Field practice shows that      the more developed the thief zone, the better the foam flooding treatment effect. The research results have guiding significance      for profile control and water plugging in oilfields.  


Key words: tracer; improved FCM clustering algorithm; breakthrough curves; thief zones classification; Lorenz coefficient
收稿日期: 2020-12-30     
PACS:    
基金资助:
通讯作者: junjian@cup.edu.cn
引用本文:   
LU Chunhua, JIANG Hanqiao, LI Jie, YOU Chengcheng, CHENG Baoyang, LI Junjian. A classification method for reservoir thief zones based on an improved FCM clustering algorithm. Petroleum Science Bulletin, 2020, 04: 520-530.
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