Prediction of petroleum resource abundance based on artificial neural network method: a case study of third member of Paleogene Shahejie Formation in Wenliu area of Dongpu Sag, Bohai Bay Basin

YANG Zijie; CHEN Dongxia; WANG Qiaochu; WANG Fuwei; LI Sha; TIAN Ziye; CHEN Shumin; ZHANG Wanrong; YAO Dongsheng; WANG Yuchao

doi:10.11781/sysydz202402428

Volume 46 Issue 2

Mar. 2024

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Article Navigation > PETROLEUM GEOLOGY & EXPERIMENT > 2024 > 46(2): 428-440

YANG Zijie, CHEN Dongxia, WANG Qiaochu, WANG Fuwei, LI Sha, TIAN Ziye, CHEN Shumin, ZHANG Wanrong, YAO Dongsheng, WANG Yuchao. Prediction of petroleum resource abundance based on artificial neural network method: a case study of third member of Paleogene Shahejie Formation in Wenliu area of Dongpu Sag, Bohai Bay Basin[J]. PETROLEUM GEOLOGY & EXPERIMENT, 2024, 46(2): 428-440. doi: 10.11781/sysydz202402428

Citation:

YANG Zijie, CHEN Dongxia, WANG Qiaochu, WANG Fuwei, LI Sha, TIAN Ziye, CHEN Shumin, ZHANG Wanrong, YAO Dongsheng, WANG Yuchao. Prediction of petroleum resource abundance based on artificial neural network method: a case study of third member of Paleogene Shahejie Formation in Wenliu area of Dongpu Sag, Bohai Bay Basin[J]. PETROLEUM GEOLOGY & EXPERIMENT, 2024, 46(2): 428-440. doi: 10.11781/sysydz202402428

Citation:

YANG Zijie, CHEN Dongxia, WANG Qiaochu, WANG Fuwei, LI Sha, TIAN Ziye, CHEN Shumin, ZHANG Wanrong, YAO Dongsheng, WANG Yuchao. Prediction of petroleum resource abundance based on artificial neural network method: a case study of third member of Paleogene Shahejie Formation in Wenliu area of Dongpu Sag, Bohai Bay Basin[J]. PETROLEUM GEOLOGY & EXPERIMENT, 2024, 46(2): 428-440. doi: 10.11781/sysydz202402428

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Prediction of petroleum resource abundance based on artificial neural network method: a case study of third member of Paleogene Shahejie Formation in Wenliu area of Dongpu Sag, Bohai Bay Basin

doi: 10.11781/sysydz202402428

YANG Zijie^{1, 2
,},
CHEN Dongxia^{1, 2
,
,},
WANG Qiaochu^{1, 2},
WANG Fuwei^{1, 2},
LI Sha^{1, 2},
TIAN Ziye^{1, 2},
CHEN Shumin^{1, 2},
ZHANG Wanrong^{1, 2},
YAO Dongsheng^{1, 2},
WANG Yuchao^{1, 2}

1.
College of Geosciences, China University of Petroleum(Beijing), Beijing 102249, China
2.
National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum(Beijing), Beijing 102249, China

Received Date: 2023-04-26
Rev Recd Date: 2024-02-06
Publish Date: 2024-03-28

Abstract

Abstract

The abundance of petroleum resource is influenced by various factors and involves complex parameters and extensive data. Consequently, traditional geostatistical methods often lack precision in quantitative prediction. To address this issue, this study focuses on the third member of Paleogene Shahejie Formation (member Es³) in the Wenliu area of the Dongpu Sag and utilizes a multi-layer perceptron neural network (MLP) for predicting petroleum resource abundance and employed the Boosting ensemble learning algorithm to optimize the prediction model. The MLP and MLP-Boosting algorithm models were test on 66 sample groups, yielding correlation coefficients of 0.789 and 0.989 for the training set, 0.618 and 0.825 for the validation set and 0.689 and 0.845 for the test set. The analysis identified effective thickness, average permeability and effective porosity are the most significant geological factors influencing petroleum resource abundance, with importance coefficients of 33.93%, 20.12% and 19.53%, respectively. Other factors such as trap area, surface crude oil density and sedimentary facies assignment were found to be less influential. Overall, the Boosting ensemble learning algorithm significantly enhanced the prediction accuracy of the multi-layer perceptron model, offering valuable insights for target optimization, exploration planning and petroleum resource evaluation in other blocks in the sag.
- machine learning,
- neural network,
- prediction model,
- resource abundance,
- Dongpu Sag,
- Bohai Bay Basin

All authors disclose no relevant conflict of interests.
The study was designed by YANG Zijie, CHEN Dongxia, WANG Qiaochu, WANG Fuwei, LI Sha and TIAN Ziye. The prediction of model data was made by YANG Zijie, CHEN Shumin, ZHANG Wanrong, YAO Dongsheng and WANG Yuchao. The manuscript was drafted and revised by YANG Zijie, CHEN Dongxia, WANG Qiaochu and WANG Fuwei. All the authors have read the last version of paper and consented for submission.

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References(30)

References

[1]	柳广弟, 刘成林, 郭秋麟. 油气资源评价[M]. 北京: 石油工业出版社, 2018. LIU Guangdi, LIU Chenglin, GUO Qiulin. Oil and gas resources evaluation[M]. Beijing: Petroleum Industry Press, 2018.
[2]	张蔚, 刘成林, 吴晓智, 等. 中国不同类型盆地油气资源丰度统计特征及预测模型[J]. 地质与勘探, 2019, 55(6): 1518-1527. https://www.cnki.com.cn/Article/CJFDTOTAL-DZKT201906018.htm ZHANG Wei, LIU Chenglin, WU Xiaozhi, et al. Statistical characteristics and prediction models for oil and gas resources abundance in different types of Chinese basins[J]. Geology and Exploration, 2019, 55(6): 1518-1527. https://www.cnki.com.cn/Article/CJFDTOTAL-DZKT201906018.htm
[3]	鄢琦, 周总瑛. 中国东部断陷盆地石油资源丰度统计模型的建立[J]. 石油实验地质, 2009, 31(3): 292-295. doi: 10.11781/sysydz200903292 YAN Qi, ZHOU Zongying. Establishment on oil resources abundance statistical model in East China rift basins[J]. Petroleum Geology & Experiment, 2009, 31(3): 292-295. doi: 10.11781/sysydz200903292
[4]	赵文智, 胡素云, 王红军, 等. 中国中低丰度油气资源大型化成藏与分布[J]. 石油勘探与开发, 2013, 40(1): 1-13. https://www.cnki.com.cn/Article/CJFDTOTAL-SKYK201301002.htm ZHAO Wenzhi, HU Suyun, WANG Hongjun, et al. Large-scale accumulation and distribution of medium-low abundance hydrocarbon resources in China[J]. Petroleum Exploration and Deve-lopment, 2013, 40(1): 1-13. https://www.cnki.com.cn/Article/CJFDTOTAL-SKYK201301002.htm
[5]	张金川, 金之钧, 郑浚茂. 深盆气资源量-储量评价方法[J]. 天然气工业, 2001, 21(4): 32-35. https://www.cnki.com.cn/Article/CJFDTOTAL-TRQG200104008.htm ZHANG Jinchuan, JIN Zhijun, ZHENG Junmao. Deep basin gas resource-reserve evaluation method[J]. Natural Gas Industry, 2001, 21(4): 32-35. https://www.cnki.com.cn/Article/CJFDTOTAL-TRQG200104008.htm
[6]	崔宝文, 赵莹, 张革, 等. 松辽盆地古龙页岩油地质储量估算方法及其应用[J]. 大庆石油地质与开发, 2022, 41(3): 14-23. https://www.cnki.com.cn/Article/CJFDTOTAL-DQSK202203002.htm CUI Baowen, ZHAO Ying, ZHANG Ge, et al. Estimation method and application for OOIP of Gulong shale oil in Songliao Basin[J]. Petroleum Geology & Oilfield Development in Daqing, 2022, 41(3): 14-23. https://www.cnki.com.cn/Article/CJFDTOTAL-DQSK202203002.htm
[7]	郭秋麟, 武娜, 闫伟, 等. 深层天然气资源评价方法[J]. 石油学报, 2019, 40(4): 383-394. https://www.cnki.com.cn/Article/CJFDTOTAL-SYXB201904001.htm GUO Qiulin, WU Na, YAN Wei, et al. An assessment method for deep gas resources[J]. Acta Petrolei Sinica, 2019, 40(4): 383-394. https://www.cnki.com.cn/Article/CJFDTOTAL-SYXB201904001.htm
[8]	徐旭辉, 周卓明, 宋振响, 等. 油气资源评价方法关键参数研究和资源分布特征: 以中国石化探区"十三五"资源评价为例[J]. 石油实验地质, 2023, 45(5): 832-843. doi: 10.11781/sysydz202305832 XU Xuhui, ZHOU Zhuoming, SONG Zhenxiang, et al. Methods and key parameters for oil and gas resource assessment and distribution characteristics of oil and gas resource: a case study of resource assessment of SINOPEC during the 13th Five-Year Plan period[J]. Petroleum Geology & Experiment, 2023, 45(5): 832-843. doi: 10.11781/sysydz202305832
[9]	凡玉梅. 未开发油气储量不确定性潜力评价方法[J]. 石油实验地质, 2022, 44(6): 1100-1104. doi: 10.11781/sysydz2022061100 FAN Yumei. Evaluation method for uncertain potential of undeveloped reserves[J]. Petroleum Geology & Experiment, 2022, 44(6): 1100-1104. doi: 10.11781/sysydz2022061100
[10]	柳广弟, 胡素云, 赵文智. 中国主要含油气盆地运聚单元石油资源丰度及其预测模型[J]. 石油勘探与开发, 2006, 33(6): 759-761. https://www.cnki.com.cn/Article/CJFDTOTAL-SKYK200606021.htm LIU Guangdi, HU Suyun, ZHAO Wenzhi. Oil resource abundance of petroleum plays in Chinese basins and its prediction model[J]. Petroleum Exploration and Development, 2006, 33(6): 759-761. https://www.cnki.com.cn/Article/CJFDTOTAL-SKYK200606021.htm
[11]	白琨琳, 赵迎冬. 油气资源评价中成因法分析与运聚系数取值模型研究[J]. 地质与勘探, 2021, 57(3): 656-666. https://www.cnki.com.cn/Article/CJFDTOTAL-DZKT202103019.htm BAI Kunlin, ZHAO Yingdong. Valuation model of the migration-accumulation coefficient in the genetic method for assessment of oil and gas resources[J]. Geology and Exploration, 2021, 57(3): 656-666. https://www.cnki.com.cn/Article/CJFDTOTAL-DZKT202103019.htm
[12]	GUO Qiulin, REN Hongjia, WU Xiaozhi, et al. A fractal simulation method for simulating the resource abundance of oil and gas and its application[J]. Mathematical Geosciences, 2022, 54(5): 873-901.
[13]	姚纪明, 于炳松, 车长波, 等. 组合法在塔里木盆地石油产量预测中的应用[J]. 自然资源学报, 2009, 24(5): 907-914. https://www.cnki.com.cn/Article/CJFDTOTAL-ZRZX200905019.htm YAO Jiming, YU Bingsong, CHE Changbo, et al. Application of a combination forecast model in the trend forecast of oil production in the Tarim Basin[J]. Journal of Natural Resources, 2009, 24(5): 907-914. https://www.cnki.com.cn/Article/CJFDTOTAL-ZRZX200905019.htm
[14]	ZHANG Guoyin, WANG Zhizhang, LI Huaji, et al. Permeability prediction of isolated channel sands using machine learning[J]. Journal of Applied Geophysics, 2018, 159: 605-615.
[15]	刘铁桩, 张清正, 李亚萍. 东濮凹陷天然气资源潜力、勘探方向及前景[J]. 西部探矿工程, 2001, 13(5): 45-46. https://www.cnki.com.cn/Article/CJFDTOTAL-XBTK200105024.htm LIU Tiezhu, ZHANG Qingzheng, LI Yaping. The natural gas resource potential and exploration direction and looking forward of Dongpu Sag[J]. West-China Exploration Engineering, 2001, 13(5): 45-46. https://www.cnki.com.cn/Article/CJFDTOTAL-XBTK200105024.htm
[16]	董潇阳. 东濮凹陷油气资源潜力与分布评价[D]. 北京: 中国石油大学(北京), 2018. DONG Xiaoyang. Potential and distribution of oil and gas resources in Dongpu Sag[D]. Beijing: China University of Petroleum (Beijing), 2018.
[17]	谈玉明, 李红磊, 张云献, 等. 东濮凹陷古近系优质烃源岩特征与剩余资源潜力分析[J]. 断块油气田, 2020, 27(5): 551-555. https://www.cnki.com.cn/Article/CJFDTOTAL-DKYT202005003.htm TAN Yuming, LI Honglei, ZHANG Yunxian, et al. Analysis to high quality source rock characteristics and residual resource potential in Dongpu Sag in Paleogene[J]. Fault-Block Oil & Gas Field, 2020, 27(5): 551-555. https://www.cnki.com.cn/Article/CJFDTOTAL-DKYT202005003.htm
[18]	余海波, 程秀申, 徐田武, 等. 东濮凹陷古近系构造特征及其对油气成藏的控制作用[J]. 油气地质与采收率, 2021, 28(3): 42-52. https://www.cnki.com.cn/Article/CJFDTOTAL-YQCS202103006.htm YU Haibo, CHENG Xiushen, XU Tianwu, et al. Paleogene tectonic characteristics and their controlling effect on hydrocarbon accumulation in Dongpu Sag[J]. Petroleum Geology and Reco-very Efficiency, 2021, 28(3): 42-52. https://www.cnki.com.cn/Article/CJFDTOTAL-YQCS202103006.htm
[19]	胡斌, 陈传浩, 王长征, 等. 东濮凹陷文留地区沙三中(Es_3中)遗迹化石与沉积环境[J]. 河南理工大学学报(自然科学版), 2017, 36(3): 40-46. https://www.cnki.com.cn/Article/CJFDTOTAL-JGXB201703007.htm HU Bin, CHEN Chuanhao, WANG Changzheng, et al. Trace fossils and sedimentary environments in the middle part of third member Shahejie Formation in Wenliu area, Dongpu Sag[J]. Journal of Henan Polytechnic University (Natural Science), 2017, 36(3): 40-46. https://www.cnki.com.cn/Article/CJFDTOTAL-JGXB201703007.htm
[20]	慕小水. 东濮凹陷文留地区含盐层系油气成藏机理与模式[D]. 北京: 中国地质大学(北京), 2011. MU Xiaoshui. Hydrocarbon reservoir formation mechanism and pattern for saline series in Wenliu area, Dongpu Depression[D]. Beijing: China University of Geosciences (Beijing), 2011.
[21]	刘宣威, 王学军, 李红磊, 等. 东濮凹陷古近系烃源岩特征及其形成环境分析[J]. 断块油气田, 2021, 28(4): 452-455. https://www.cnki.com.cn/Article/CJFDTOTAL-DKYT202104005.htm LIU Xuanwei, WANG Xuejun, LI Honglei, et al. Characteristics and formation environment analysis of Paleogene source rocks in Dongpu Depression[J]. Fault-Block Oil & Gas Field, 2021, 28(4): 452-455. https://www.cnki.com.cn/Article/CJFDTOTAL-DKYT202104005.htm
[22]	李浩, 王保华, 陆建林, 等. 东濮凹陷古近系页岩油富集地质条件与勘探前景[J]. 中国石油大学学报(自然科学版), 2021, 45(3): 33-41. https://www.cnki.com.cn/Article/CJFDTOTAL-SYDX202103004.htm LI Hao, WANG Baohua, LU Jianlin, et al. Geological characteristics and exploration prospects of Paleogene continental shale oil accumulation in Dongpu Sag, Bohai Bay Basin[J]. Journal of China University of Petroleum (Edition of Natural Science), 2021, 45(3): 33-41. https://www.cnki.com.cn/Article/CJFDTOTAL-SYDX202103004.htm
[23]	高晓红, 李兴奇. 主成分分析中线性无量纲化方法的比较研究[J]. 统计与决策, 2020, 36(3): 33-36. https://www.cnki.com.cn/Article/CJFDTOTAL-TJJC202003007.htm GAO Xiaohong, LI Xingqi. Comparative study on linear dimensionless methods in principal component analysis[J]. Statistics & Decision, 2020, 36(3): 33-36. https://www.cnki.com.cn/Article/CJFDTOTAL-TJJC202003007.htm
[24]	郭秋麟, 任洪佳, 于京都, 等. 基于贝叶斯网络的油气勘探风险预测方法: 以准噶尔盆地腹部侏罗系三工河组为例[J]. 中国石油勘探, 2023, 28(1): 108-119. https://www.cnki.com.cn/Article/CJFDTOTAL-KTSY202301010.htm GUO Qiulin, REN Hongjia, YU Jingdu, et al. Prediction method of petroleum exploration risks based on Bayesian network: a case study of the Jurassic Sangonghe Formation in the hinterland of Junggar Basin[J]. China Petroleum Exploration, 2023, 28(1): 108-119. https://www.cnki.com.cn/Article/CJFDTOTAL-KTSY202301010.htm
[25]	姜福杰, 姜振学, 庞雄奇. 东营凹陷油气成藏体系的划分及定量评价[J]. 地球科学(中国地质大学学报), 2008, 33(5): 651-660. https://www.cnki.com.cn/Article/CJFDTOTAL-DQKX200805011.htm JIANG Fujie, JIANG Zhenxue, PANG Xiongqi. Division and quantitative evaluation of petroleum accumulation system in Dongying Sag[J]. Earth Science(Journal of China University of Geosciences), 2008, 33(5): 651-660. https://www.cnki.com.cn/Article/CJFDTOTAL-DQKX200805011.htm
[26]	ASSI K J, NAHIDUZZAMAN K M, RATROUT N T, et al. Mode choice behavior of high school goers: evaluating logistic regression and MLP neural networks[J]. Case Studies on Transport Policy, 2018, 6(2): 225-230.
[27]	KEARNS M J, VALIANT L G. Learning Boolean formulae or finite automata is as hard as factoring[R]. Cambridge: Harvard University, Center for Research in Computing Technology, Aiken Computation Laboratory, 1988.
[28]	陈凯, 朱钰. 机器学习及其相关算法综述[J]. 统计与信息论坛, 2007, 22(5): 105-112. https://www.cnki.com.cn/Article/CJFDTOTAL-TJLT200705022.htm CHEN Kai, ZHU Yu. A summary of machine learning and related algorithms[J]. Statistics & Information Forum, 2007, 22(5): 105-112. https://www.cnki.com.cn/Article/CJFDTOTAL-TJLT200705022.htm
[29]	吕传炳, 庞雄奇, 马奎友, 等. 渤海湾盆地束鹿凹陷"牙刷状"油藏成藏特征与模式[J]. 石油与天然气地质, 2022, 43(3): 566-581. https://www.cnki.com.cn/Article/CJFDTOTAL-SYYT202203007.htm LÜ Chuanbing, PANG Xiongqi, MA Kuiyou, et al. Characteristics and reservoiring patterns of "teeth-brush-shaped" oil pools in the Shulu Sag, Bohai Bay Basin[J]. Oil & Gas Geology, 2022, 43(3): 566-581. https://www.cnki.com.cn/Article/CJFDTOTAL-SYYT202203007.htm
[30]	张晓磊, 唐颖, 李卓奕, 等. 鄂尔多斯盆地西南缘延长组长8段低充注油藏成藏模式: 以环西-彭阳地区为例[J]. 西安石油大学学报(自然科学版), 2023, 38(1): 31-44. https://www.cnki.com.cn/Article/CJFDTOTAL-XASY202301004.htm ZHANG Xiaolei, TANG Ying, LI Zhuoyi, et al. Hydrocarbon accumulation patterns of Chang 8 low-charging oil reservoirs in southwestern margin of Ordos Basin: taking Huanxi-Pengyang area as an example[J]. Journal of Xi'an Shiyou University (Natural Science Edition), 2023, 38(1): 31-44. https://www.cnki.com.cn/Article/CJFDTOTAL-XASY202301004.htm

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Prediction of petroleum resource abundance based on artificial neural network method: a case study of third member of Paleogene Shahejie Formation in Wenliu area of Dongpu Sag, Bohai Bay Basin

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Prediction of petroleum resource abundance based on artificial neural network method: a case study of third member of Paleogene Shahejie Formation in Wenliu area of Dongpu Sag, Bohai Bay Basin

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