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首頁» 過刊瀏覽» 2020» Vol.5» Issue(4) 520-530???? DOI : 10.3969/j.issn.2096-1693.2021.01.045
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采用改進(jìn)的 FCM 聚類算法進(jìn)行儲(chǔ)層優(yōu)勢通道分級
魯春華,姜漢橋,李杰,,尤誠程,,成寶洋,李俊鍵
中國石油大學(xué)(北京)油氣資源與探測國家重點(diǎn)實(shí)驗(yàn)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? 井間示蹤劑技術(shù)被認(rèn)為是目前識別優(yōu)勢通道最直接、最準(zhǔn)確的方法之一,但在稠油油藏中,,示蹤劑產(chǎn) 出曲線多呈拋物線型單峰形狀,,曲線之間差別小,優(yōu)勢通道發(fā)育級別難以判斷,;同時(shí)因?yàn)槟:鼵均值(Fuzzy C-means, FCM)聚類算法需要事先指定聚類數(shù)目,,使得其對示蹤劑曲線分類問題適應(yīng)性較差。針對上述問題,,本 文提出了改進(jìn)的FCM聚類算法,,解決了單峰型示蹤劑曲線難以分類的問題。首先,,通過引進(jìn)決策圖來確定聚類 數(shù),,避免FCM算法在選取聚類數(shù)時(shí)的盲目性;然后,,選取見劑速度,、峰值濃度、回采率等示蹤劑濃度曲線的特 征參數(shù),,對見劑井進(jìn)行聚類,,確定優(yōu)勢通道發(fā)育級別和主控因素;最后,,利用洛倫茲系數(shù)和現(xiàn)場泡沫驅(qū)應(yīng)用效 果,,驗(yàn)證分級的合理性。研究結(jié)果表明:目標(biāo)油田發(fā)育三級優(yōu)勢通道,,一級優(yōu)勢通道屬于均質(zhì)~相對均質(zhì)儲(chǔ)層,, 其見劑速度小于 1.65 m/d,高滲層滲透率小于 3259 mD,,同時(shí)回采率小于 0.13%,,地層系數(shù)小于 1341 mD·m; 二級優(yōu)勢通道屬于相對均質(zhì)~非均質(zhì)儲(chǔ)層,,見劑速度在 1.65~2.17 m/d,,滲透率 3259~8383 mD,;同時(shí)回采率在 0.13%~0.21%,,地層系數(shù)介于 1341~3194 mD·m;三級優(yōu)勢通道屬于非均質(zhì)~嚴(yán)重非均質(zhì)儲(chǔ)層,,見劑速度大于 2.17 m/d,,高滲層滲透率大于 8383 mD,回采率大于 0.21%,,地層系數(shù)大于 3194 mD·m?,F(xiàn)場實(shí)踐表明:優(yōu)勢通 道越發(fā)育,泡沫驅(qū)治理效果越好,。該分類結(jié)果對現(xiàn)場調(diào)剖堵水具有指導(dǎo)意義,。
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關(guān)鍵詞 : 示蹤劑,;改進(jìn)的FCM聚類算法,;濃度曲線;優(yōu)勢通道分級,;洛倫茲系數(shù)
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 ????
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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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