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| 一种强非均质性油藏自动历史拟合混合求解方法 |
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张凯1,马小鹏1,王增飞2,刘凡3,4,马玮5,姚军1
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(1.中国石油大学(华东)石油工程学院,山东青岛 266580;2.北京中油瑞飞信息技术有限公司,北京 100007;3.中海油研究总院,北京 100010;4.海洋石油高效开发国家重点实验室,北京 100028;5.胜利油田油气井下作业中心,山东东营 257001)
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| 摘要: |
| 针对强非均质性油藏,提出一种强非均质性油藏自动历史拟合混合求解方法。混合由两部分组成,一是主成分分析方法(PCA)与离散余弦变换方法(DCT)混合,对先验模型进行特征提取能够在降低数据维数的同时保留更为全面的地质特征,提高历史拟合的鲁棒性;二是人工蜂群算法(ABC)与同步扰动随机近似算法(SPSA)混合,利用人工蜂群算法良好的全局搜索模式改进同步扰动随机近似算法对目标函数进行数值求解,提高算法的求解速度与精度。最后应用提出方法进行强非均质油藏模型自动历史拟合。结果表明:PCA与DCT方法混合能够有效提取强非均质油藏的地质特征,反演结果接近真实油藏模型。该研究成果可为复杂非均质油气藏地质特征的识别研究提供理论支持。 |
| 关键词: 强非均质性油藏 主成分分析 离散余弦变换 改进同步扰动随机近似算法 |
| DOI：10.3969/j.issn.1673-5005.2018.05.010 |
| 分类号::TE 31 |
| 文献标识码:A |
| 基金项目:国家自然科学基金项目(51722406,61573018);山东省自然科学基金项目(JQ201808,ZR2015EL014)；国家重大专项(2016ZX05025001-006)；中央高校基本科研业务费专项(18CX02097A,17CX05002A) |
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| A hybrid method for solving automated production history match of reservoirs with strong heterogeneity |
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ZHANG Kai1, MA Xiaopeng1, WANG Zengfei2, LIU Fan3,4, MA Wei5, YAO Jun1
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(1.School of Petroleum Engineering in China University of Petroleum(East China), Qingdao 266580, China;2.CNPC Beijing Richfit Information Technology Company Limited, Beijing 100007, China;3.CNOOC Research Institute, Beijing 100010, China;4.State Key Laboratory of Offshore Oil Exploitation, Beijing 100028, China;5.Oil and Gas Underground Operation Center, Shengli Oilfield, Dongying 257001, China)
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| Abstract: |
| Many sandstone oil and gas reservoirs are features with large reservoir span and strong heterogeneity, and long-term water flooding can make their geological condition more complicated. In this study, a hybrid method of automatic history matching is proposed for strong heterogeneity reservoirs. The new method consists of two parts. The first part includes a combination of principal component analysis (PCA) and a discrete cosine transform (DCT) method, in which the features of the prior models can be extracted with reduced data dimension and preserving the comprehensive geological characters and improving the robustness of history matching. In the second part, an artificial bee colony algorithm (ABC) is combined with a stochastic perturbation and stochastic approximation algorithm (SPSA), and the improved algorithm is applied to improve the accuracy and the speed of the solution. In a case study, the proposed method is applied to a strong non-homogeneous reservoir model. The results show that The PCA combined with DCT method can extract the main geological features of strong heterogeneous reservoir, which is close to the real geological model. The method can provide theoretical support for the study and identification of complex heterogeneous oil and gas reservoirs. |
| Key words: strong heterogeneity reservoir principal component analysis method discrete cosine transform method simultaneous perturbation stochastic approximation |
