Thickness prediction of seismic multi-attributes sand based on association rules and random forests

Qu Zhipeng; Wang Fangfang; Zhang Yunyin; Li Xiaochen

doi:10.19509/j.cnki.dzkq.2021.0314

Volume 40 Issue 3

May 2021

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Article Navigation > Bulletin of Geological Science and Technology > 2021 > 40(3): 211-218

Qu Zhipeng, Wang Fangfang, Zhang Yunyin, Li Xiaochen. Thickness prediction of seismic multi-attributes sand based on association rules and random forests[J]. Bulletin of Geological Science and Technology, 2021, 40(3): 211-218. doi: 10.19509/j.cnki.dzkq.2021.0314

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Qu Zhipeng, Wang Fangfang, Zhang Yunyin, Li Xiaochen. Thickness prediction of seismic multi-attributes sand based on association rules and random forests[J]. Bulletin of Geological Science and Technology, 2021, 40(3): 211-218. doi: 10.19509/j.cnki.dzkq.2021.0314

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Thickness prediction of seismic multi-attributes sand based on association rules and random forests

doi: 10.19509/j.cnki.dzkq.2021.0314

Received Date: 25 May 2020

Abstract

Abstract

Seismic attributes analysis technique is an important tool for sand thickness prediction. Due to the varieties of seismic attributes, the best seismic attributes need to be optimized before the seismic attributes analysis technique is applied to reduce the repeatability and redundancy of the attributes. Therefore, we present an improved random forest regression algorithm combined with associate rules for sand thickness prediction (AR-RFR). Although random forest regression algorithm(RFR) is powerful for the problem characterized for nonlinearity and high dimension in reservoir prediction, it cannot solve attribute reduction problems. The associate rules can find the non-linear relationship among the multi-attributes and can reduce some redundant attributes by means of Chi-squared Test. We apply ordinary RFR, AR-RFR and Neural network regression algorithm combined with associate rules(AR-BP) to a synthetic geological model and a real dataset. The results prove that the selection attributes from associate rules is more efficient than that from random forest. Compared to the drilled wells, AR-RFR has higher precision than RFR and AR-BP. And AR-RFR also can improve the lateral distribution of sand bodies. The method proposed is able to choose efficient seismic attributes and improve prediction of sand thickness.
- seismic attribute selection,
- association rule,
- frequent pattern tree,
- random forest,
- reservoir prediction

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References(30)

References

[1]	Chopra S, Marfurt K J J G. Seismic attributes: A historical perspective[J]. Geophysics, 2005, 70(5): 3-28. doi: 10.1190/1.2098670
[2]	Leiphart D J, Hart B S. Comparison of linear regression and a probabilistic neural network to predict porosity from 3D seismic attributes in Lower Brushy Canyon channeled sandstones, southeast New Mexico[J]. Geophysics, 2001, 66(5): 1349-1358. doi: 10.1190/1.1487080
[3]	Hampson D P, Schuelke J S, Quirein J A. Use of multiattribute transforms to predict log properties from seismic data[J]. Geophysics, 2001, 66(1): 220-236. doi: 10.1190/1.1444899
[4]	万里明, 吴均, 卢军凯, 等. 基于Adam-神经网络的致密砂岩脆性评价方法: 以南堡凹陷高北边坡为例[J]. 地质科技通报, 2020, 39(2): 94-103. http://dzkjqb.cug.edu.cn/CN/abstract/abstract9978.shtml Wan L M, Wu J, Lu J K, et al. Brittleness evaluation method of tight sandstone based on Adam-neural network: A case study of a block in Gaobei Slope, Nanpu Sag[J]. Bulletin of Geological Science and Technology, 2020, 39(2): 94-103(in Chinese with English abstract). http://dzkjqb.cug.edu.cn/CN/abstract/abstract9978.shtml
[5]	Na'imi S R, Shadizadeh S R, Riahi M A, et al. Estimation of reservoir porosity and water saturation based on seismic attributes using support vector regression approach[J]. Journal of Applied Geophysics, 2014, 107: 93-101. doi: 10.1016/j.jappgeo.2014.05.011
[6]	宋建国, 高强山, 李哲. 随机森林回归在地震储层预测中的应用[J]. 石油地球物理勘探, 2016, 51(6): 1202-1211. https://www.cnki.com.cn/Article/CJFDTOTAL-SYDQ201606021.htm Song J G, Gao Q S, Li Z. Application of random forests for regression to seismic reservoir prediction[J]. Oil Geophysical Prospecting, 2016, 51(6): 1202-1211(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-SYDQ201606021.htm
[7]	Ao Y, Li H, Zhu L, et al. Identifying channel sand-body from multiple seismic attributes with an improved random forest algorithm[J]. Journal of Petroleum Science and Engineering, 2019, 173: 781-792. doi: 10.1016/j.petrol.2018.10.048
[8]	闫星宇, 顾汉明, 肖逸飞, 等. XGBoost算法在致密砂岩气储层测井解释中的应用[J]. 石油地球物理勘探, 2019, 54(2): 447-455. https://www.cnki.com.cn/Article/CJFDTOTAL-SYDQ201902024.htm Yan X Y, Gu H M, Xiao Y F, et al. XGBoost algorithm applied in the interpretation of tight-sand gas reservoir on well logging data[J]. Oil Geophysical Prospecting, 2019, 54(2): 447-455(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-SYDQ201902024.htm
[9]	付超, 林年添, 张栋, 等. 多波地震深度学习的油气储层分布预测案例[J]. 地球物理学报, 2018, 61(1): 293-303. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWX201801026.htm Fu C, Lin N T, Zhang D, et al. Prediction of reservoirs using multi-component seismic data and the deep learning method[J]. Chinese Journal of Geophysics (in Chinese with English abstract), 2018, 61(1): 293-303(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-DQWX201801026.htm
[10]	马瑶, 赵江南. 机器学习方法在矿产资源定量预测应用研究进展[J]. 地质科技通报, 2021, 40(1): 132-141. http://dzkjqb.cug.edu.cn/CN/abstract/abstract10099.shtml Ma Y, Zhao J N. Advances in the application of machine learning methods in mineral prospectivity mapping[J]. Bulletin of Geological Science and Technology, 2021, 40(1): 132-141(in Chinese with English abstract). http://dzkjqb.cug.edu.cn/CN/abstract/abstract10099.shtml
[11]	郭天颂, 张菊清, 韩煜, 等. 基于粒子群优化支持向量机的延长县滑坡易发性评价[J]. 地质科技情报, 2019, 38(3): 236-243. https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ201903025.htm Guo T S, Zhang J Q, Han Y, et al. Evaluation of landslide susceptibility in Yanchang County based on particle swarm optimization-based support vector machine[J]. Geological Science and Technology Information, 2019, 38(3): 236-243(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ201903025.htm
[12]	印兴耀, 周静毅. 地震属性优化方法综述[J]. 石油地球物理勘探, 2005, 40(4): 482-489. doi: 10.3321/j.issn:1000-7210.2005.04.027 Yin X Y, Zhou J Y. Summary of optimum methods of seismic attributes[J]. Oil Geophysical Prospecting, 2005, 40(4): 482-489(in Chinese with English abstract). doi: 10.3321/j.issn:1000-7210.2005.04.027
[13]	王晓阳, 桂志先, 高刚, 等. K-L变换地震属性优化及其在储层预测中的应用[J]. 石油天然气学报, 2008, 30(3): 96-98. doi: 10.3969/j.issn.1000-9752.2008.03.022 Wang X Y, Gui Z X, Gao G, et, al. Seismic attribute optimization and its application in reservoir prediction by using K-L transform[J]. Journal of Oil and Gas Technology, 2008, 30(3): 96-98. doi: 10.3969/j.issn.1000-9752.2008.03.022
[14]	Singh Y. Lithofacies detection through simultaneous inversion and principal component attributes[J]. The Leading Edge, 2007, 26(12): 1568-1575. doi: 10.1190/1.2821944
[15]	Roden R, Smith T, Sacrey D. Geologic pattern recognition from seismic attributes: Principal component analysis and self-organizing maps[J]. Interpretation, 2015, 3(4): 59-83. https://library.seg.org/journal/inteio
[16]	倪艳. Isomap算法在地震属性参数降维中的应用[D]. 成都: 成都理工大学, 2007. Ni Y. Nonlinear dimensionality reduction of Isomap in the analysis of seismic attribute parameter data[D]. Chengdu: Chengdu University of Technology, 2007(in Chinese with English abstract).
[17]	Liu X F, Zheng X D, Xu G C, et al. Locally linear embedding-based seismic attribute extraction and applications[J]. Applied Geophysics, 2010, 7(4): 365-375. doi: 10.1007/s11770-010-0260-2
[18]	宋维琪, 刘江华, 王小马, 等. 预测油气的地震属性优化组合、灰关联分析技术[J]. 石油勘探与开发, 2002, 29(5): 34-36. doi: 10.3321/j.issn:1000-0747.2002.05.011 Song W Q, Liu J H, Wang X M, et al. Applying optimum combination of sciesmic attribute and gray correlotion analysis technology to the prediction of oil and gas reservoirs[J]. Petroleum Exploration and Development, 2002, 29(5): 34-36(in Chinese with English abstract). doi: 10.3321/j.issn:1000-0747.2002.05.011
[19]	何琰, 彭文, 殷军. 利用地震属性预测渗透率[J]. 石油学报, 2001, 22(6): 34-36. https://www.cnki.com.cn/Article/CJFDTOTAL-SYXB200106006.htm He Y, Peng W, Yin J. Permeability prediction by Seismic Attribute data[J]. Acta Petrolei Sinica, 2001, 22(6): 34-36(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-SYXB200106006.htm
[20]	谢东, 王永刚, 乐友喜, 等. 地震属性分析技术在子寅油田开发中的应用[J]. 石油物探, 2003, 42(1): 72-76. doi: 10.3969/j.issn.1000-1441.2003.01.016 Xie D, Wang Y G, Yue Y X, et al. Application of seismic attribute analysis technology in the production of Ziyin Oilfield[J]. Geophysical Prospecting for Petroleum, 2003, 42(1): 72-76(in Chinese with English abstract). doi: 10.3969/j.issn.1000-1441.2003.01.016
[21]	Dorrington K P, Link C A. Genetic-algorithm/neural-network approach to seismic attribute selection for well-log prediction[J]. Seg Technical Program Expanded Abstracts, 2004, 21(1): 212-221. http://scitation.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=SEGEAB000021000001001654000001&idtype=cvips&prog=normal
[22]	黄捍东, 赵迪, 张如伟, 等. 基于灰关联和遗传算法的地震属性优化方法[J]. 石油地球物理勘探, 2010, 45(3): 381-383. https://www.cnki.com.cn/Article/CJFDTOTAL-SYDQ201003010.htm Huang H D, Zhao D, Zhang R W, et al. Seismic attribute optimization method based on grey relational analysis and genetic algorithm[J]. Oil Geophysical Prospecting, 2010, 45(3): 381-383(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-SYDQ201003010.htm
[23]	武建华, 宋擒豹, 沈均毅, 等. 基于关联规则的特征选择算法[J]. 模式识别与人工智能, 2009, 22(2): 256-262. doi: 10.3969/j.issn.1003-6059.2009.02.013 Wu J H, Song Q B, Shen J Y, et al. Feature selection algorithm based on association rules[J]. Pattern Recognition and Artificial Intelligence, 2009, 22(2): 256-262(in Chinese with English abstract). doi: 10.3969/j.issn.1003-6059.2009.02.013
[24]	Chenxi H, Xin H, Yu F, et al. Sample imbalance disease classification model based on association rule feature selection[J]. Pattern Recognition Letters, 2020, 133. https://www.sciencedirect.com/science/article/pii/S0167865520300945
[25]	刘小珊, 罗文强, 李飞翱, 等. 基于关联规则的滑坡演化阶段判识指标[J]. 地质科技情报, 2014, 33(2): 160-164. doi: 10.3969/j.issn.1009-6248.2014.02.018 Liu X S, Luo W Q, Li F A, et al. Identification index of landslide evolution stage based on association rule[J]. Geological Science and Technology Information, 2014, 33(2): 160-164(in Chinese with English abstract). doi: 10.3969/j.issn.1009-6248.2014.02.018
[26]	袁照威. 基于机器学习与多信息融合的致密砂岩储层井震解释方法研究[D]. 北京: 中国地质大学(北京), 2017. Yuan Z W. Interpretation methods of tight sandstone reservoir with seismic data and well logs based on machine learning method and multi-information fusion[D]. Beijing: China University of Geosciences (Beijing), 2017.
[27]	Agrawal R. Mining association rules between sets of items in large databases[C]//Anon. Acm Sigmod International Conference on Management of Data. [S. l. ]: [s. n. ], 1993.
[28]	Agrwal R. Fast algorithms for mining association rules in large databases[C]//Anon. Proceedings of the international conference on very large data bases. [S. l. ]: [s. n. ], 1994.
[29]	Han J, Pei J, Yin Y, et al. Mining frequent patterns without candidate generation: A frequent-pattern tree approach[J]. Data Mining & Knowledge Discovery, 2004, 8(1): 53-87. http://doi.ieeecomputersociety.org/resolve?ref_id=doi:10.1023/B:DAMI.0000005258.31418.83&rfr_id=trans/td/2008/07/ttd2008070865.htm
[30]	Breiman L. Random forests[J]. Machine Learning, 2001, 45(1): 5-32. doi: 10.1023/A:1010933404324

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Thickness prediction of seismic multi-attributes sand based on association rules and random forests

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Thickness prediction of seismic multi-attributes sand based on association rules and random forests

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