[1] Taud H, Martinez-Angeles R, Parrot J F, et al. Porosity estimation method by X-ray computed tomography[J]. Journal of Petroleum Science and Engineering, 2005, 47（3/4）： 209-217.
[2] Robin V, Sardini P, Mazurier A, et al. Effective porosity measurements of poorly consolidated materials using non-destructive methods[J]. Engineering Geology, 2016, 205： 24-29.
[3] 贾承造,邹才能,李建忠,等. 中国致密油评价标准、主要类型、基本特征及资源前景[J]. 石油学报,2012,33（3）：343-350.

Jia Chengzao, Zou Caineng, Li Jianzhong, et al. Assessment criteria, main types, basic features and resource prospects of the tight oil in China[J]. Acta Petrolei Sinica, 2012, 33（3）： 343-350.
[4] Fusi N, Martinez-Martinez J. Mercury porosimetry as a tool for improving quality of micro-CT images in low porosity carbonate rocks[J]. Engineering Geology, 2013, 166： 272-282.
[5] Jian K, Fu X H, Ding Y M, et al. Characteristics of pores and methane adsorption of low-rank coal in China[J]. Journal of Natural Gas Science and Engineering, 2015, 27： 207-218.
[6] Xiao L, Mao Z Q, Li G R, et al. Calculation of porosity from nuclear magnetic resonance and conventional logs in gas-bearing reservoirs[J]. Acta Geophysica, 2012, 60（4）： 1030-1042.
[7] Miah M I. Porosity assessment of gas reservoir using wireline log data： A case study of Bokabil Formation, Bangladesh[J]. Procedia Engineering, 2014, 90： 663-668.
[8] Reichstein M, Camps-Valls G, Stevens B, et al. Deep learning and process understanding for data-driven Earth system science[J]. Nature, 2019, 566（7743）： 195-204.
[9] 侯贤沐,王付勇,宰芸,等. 基于机器学习和测井数据的碳酸盐岩孔隙度与渗透率预测[J]. 吉林大学学报（地球科学版）,2022,52（2）：644-653.

Hou Xianmu, Wang Fuyong, Zai Yun, et al. Prediction of carbonate porosity and permeability based on machine learning and logging data[J]. Journal of Jilin University （Earth Science Edition）, 2022, 52（2）： 644-653.
[10] Nourani M, Alali N, Samadianfard S, et al. Comparison of machine learning techniques for predicting porosity of chalk[J]. Journal of Petroleum Science and Engineering, 2022, 209： 109853.
[11] Ahmadi M A, Chen Z X. Comparison of machine learning methods for estimating permeability and porosity of oil reservoirs via petro-physical logs[J]. Petroleum, 2019, 5（3）： 271-284.
[12] 项云飞,康志宏,郝伟俊,等. 基于线性回归与神经网络的储层参数预测复合方法[J]. 科学技术与工程,2017,17（31）：46-52.

Xiang Yunfei, Kang Zhihong, Hao Weijun, et al. A composite method of reservoir parameter prediction based on linear regression and neural network[J]. Science Technology and Engineering, 2017, 17（31）： 46-52.
[13] 段友祥,王言飞,孙歧峰. 选择性集成学习模型在岩性—孔隙度预测中的应用[J]. 科学技术与工程,2020,20（3）：1001-1008.

Duan Youxiang, Wang Yanfei, Sun Qifeng. Application of selective ensemble learning model in lithology-porosity prediction[J]. Science Technology and Engineering, 2020, 20（3）： 1001-1008.
[14] 邓社根,滕新保,华桂钱,等. 砂泥岩储层孔隙度预测的改进模型[J]. 黑龙江科技大学学报,2017,27（6）：621-625.

Deng Shegen, Teng Xinbao, Hua Guiqian, et al. A novel prediction model for porosity in sand shale reservoir[J]. Journal of Heilongjiang University of Science & Technology, 2017, 27（6）： 621-625.
[15] 袁伟,张占松,张泽宇,等. 基于储层分类的支持向量机渗透率预测[J]. 测井技术,2015,39（4）：450-454.

Yuan Wei, Zhang Zhansong, Zhang Zeyu, et al. Permeability prediction using support vector machine based on reservoir classification[J]. Well Logging Technology, 2015, 39（4）： 450-454.
[16] 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.

Yan Xingyu, Gu Hanming, Xiao Yifei, 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.
[17] Pan S W, Zheng Z C, Guo Z, et al. An optimized XGBoost method for predicting reservoir porosity using petrophysical logs[J]. Journal of Petroleum Science and Engineering, 2022, 208： 109520.
[18] Gu Y F, Zhang D Y, Bao Z D. A new data-driven predictor, PSO-XGBoost, used for permeability of tight sandstone reservoirs： A case study of member of chang 4+5, western Jiyuan oilfield, Ordos Basin[J]. Journal of Petroleum Science and Engineering, 2021, 199： 108350.
[19] Zhao X B, Chen X J, Huang Q, et al. Logging-data-driven permeability prediction in low-permeable sandstones based on machine learning with pattern visualization： A case study in Wenchang A Sag, Pearl River Mouth Basin[J]. Journal of Petroleum Science and Engineering, 2022, 214： 110517.
[20] Gu Y F, Zhang D Y, Bao Z D. Lithological classification via an improved extreme gradient boosting： A demonstration of the Chang 4+5 member, Ordos Basin, northern China[J]. Journal of Asian Earth Sciences, 2021, 215： 104798.
[21] Han R Y, Wang Z W, Wang W H, et al. Lithology identification of igneous rocks based on XGboost and conventional logging curves, a case study of the eastern depression of Liaohe Basin[J]. Journal of Applied Geophysics, 2021, 195： 104480.
[22] 谢玉洪. 低油价背景下中国海油油气勘探进展与发展思考[J]. 中国海上油气,2021,33（1）：1-12.

Xie Yuhong. Progress and thinking of CNOOC oil and gas exploration under the background of low oil prices[J]. China Offshore Oil and Gas, 2021, 33（1）： 1-12.
[23] 王金铸,王学忠. 车排子斜坡带岩性油藏的高效勘探[J]. 特种油气藏,2013,20（2）：20-24.

Wang Jinzhu, Wang Xuezhong. Efficient exploration of lithologic reservoirs in the Chepaizi slope zone of western Junggar Basin[J]. Special Oil & Gas Reservoirs, 2013, 20（2）： 20-24.
[24] 吕世超. 车排子东缘沙湾组薄互层油藏分布特征[J]. 科学技术与工程,2020,20（5）：1734-1739.

Shichao Lü. Thin interlayer reservoir distribute character of Shawan Formation in eastern Chepaizi area[J]. Science Technology and Engineering, 2020, 20（5）： 1734-1739.
[25] 张曰静,张奎华,隋风贵,等. 准噶尔盆地车排子凸起白垩系层序—古地貌耦合控砂机制与砂体预测[J]. 东北石油大学学报,2020,44（6）：1-11.

Zhang Yuejing, Zhang Kuihua, Sui Fenggui, et al. Coupling mechanism for sand control and sand body prediction of Cretaceous sequence and paleogeomorphology in the Chepaizi uplift, Junggar Basin[J]. Journal of Northeast Petroleum University, 2020, 44（6）： 1-11.
[26] 赵东娜,朱筱敏,董艳蕾,等. 地震沉积学在湖盆缓坡滩坝砂体预测中的应用：以准噶尔盆地车排子地区下白垩统为例[J]. 石油勘探与开发,2014,41（1）：55-61.

Zhao Dongna, Zhu Xiaomin, Dong Yanlei, et al. Application of seismic sedimentology to prediction of beach and bar sandbodies in gentle slope of lacustrine basin： A case study of the Lower Cretaceous in Chepaizi area, Junggar Basin, NW China[J]. Petroleum Exploration and Development, 2014, 41（1）： 55-61.
[27] 赵东娜,朱筱敏,董艳蕾,等. 准噶尔盆地车排子地区下白垩统层序地层格架及主控因素分析[J]. 沉积学报,2013,31（6）：1070-1080.

Zhao Dongna, Zhu Xiaomin, Dong Yanlei, et al. Sequence stratigraphic framework of Lower Cretaceous and its main controlling factors in Chepaizi area, Junggar Basin[J]. Acta Sedimentologica Sinica, 2013, 31（6）： 1070-1080.
[28] 杨勇,陈世悦,向奎,等. 准噶尔盆地西北缘车排子地区下白垩统层序地层与沉积演化[J]. 中国石油大学学报（自然科学版）,2011,35（5）：20-26.

Yang Yong, Chen Shiyue, Xiang Kui, et al. Sequence stratigraphy and sedimentary evolution of Lower Cretaceous series in Chepaizi area, northweastern margin of Junggar Basin[J]. Journal of China University of Petroleum, 2011, 35（5）： 20-26.
[29] 董大伟,李理,王晓蕾,等. 准噶尔盆地西缘车排子凸起构造演化及断层形成机制[J]. 吉林大学学报（地球科学版）,2015,45（4）：1132-1141.

Dong Dawei, Li Li, Wang Xiaolei, et al. Structural evolution and dislocation mechanism of western margin Chepaizi uplift of Junggar Basin[J]. Journal of Jilin University （Earth Science Edition）, 2015, 45（4）： 1132-1141.
[30] 胡秋媛,董大伟,赵利,等. 准噶尔盆地车排子凸起构造演化特征及其成因[J]. 石油与天然气地质,2016,37（4）：556-564.

Hu Qiuyuan, Dong Dawei, Zhao Li, et al. Tectonic evolutionary characteristics and their causes of Chepaizi uplift in Junggar Basin[J]. Oil & Gas Geology, 2016, 37（4）： 556-564.
[31] 陈石,郭召杰,漆家福,等. 准噶尔盆地西北缘三期走滑构造及其油气意义[J]. 石油与天然气地质,2016,37（3）：322-331.

Chen Shi, Guo Zhaojie, Qi Jiafu, et al. Three-stage strike-slip fault systems at northwestern margin of Junggar Basin and their implications for hydrocarbon exploration[J]. Oil & Gas Geology, 2016, 37（3）： 322-331.
[32] 张枝焕,向奎,秦黎明,等. 准噶尔盆地四棵树凹陷烃源岩地球化学特征及其对车排子凸起油气聚集的贡献[J]. 中国地质,2012,39（2）：326-337.

Zhang Zhihuan, Xiang Kui, Qin Liming, et al. Geochemical characteristics of source rocks and their contribution to petroleum accumulation of Chepaizi area in Sikeshu Depression, Junggar Basin[J]. Geology in China, 2012, 39（2）： 326-337.
[33] 李二庭,靳军,米巨磊,等. 准噶尔盆地车排子地区原油油源分析[J]. 地球化学,2021,50（5）：492-502.

Li Erting, Jin Jun, Mi Julei, et al. Analysis of crude oil sources in the Chepaizi area, Junggar Basin[J]. Geochimica, 2021, 50（5）： 492-502.
[34] 曹剑,胡文瑄,张义杰,等. 准噶尔盆地红山嘴—车排子断裂带含油气流体活动特点地球化学研究[J]. 地质论评,2005,51（5）：591-599.

Cao Jian, Hu Wenxuan, Zhang Yijie, et al. Geochemical analysis on petroleum fluid activity in the Hongshanzui-Chepaizi fault zone, the Junggar Basin[J]. Geological Review, 2005, 51（5）： 591-599.
[35] 孟凡超,操应长,崔岩,等. 准噶尔盆地西缘车排子凸起石炭系火山岩储层成因[J]. 中国石油大学学报（自然科学版）,2016,40（5）：22-31.

Meng Fanchao, Cao Yingchang, Cui Yan, et al. Genesis of Carboniferous volcanic reservoirs in Chepaizi salient in western margin of Junggar Basin[J]. Journal of China University of Petroleum, 2016, 40（5）： 22-31.
[36] 何登发,陈新发,况军,等. 准噶尔盆地石炭系烃源岩分布与含油气系统[J]. 石油勘探与开发,2010,37（4）：397-408.

He Dengfa, Chen Xinfa, Kuang Jun, et al. Distribution of Carboniferous source rocks and petroleum systems in the Junggar Basin[J]. Petroleum Exploration and Development, 2010, 37（4）： 397-408.
[37] 宋明水,吕明久,赵乐强,等. 准噶尔盆地车排子凸起油气资源潜力与成藏模式[J]. 中国石油勘探,2016,21（3）：83-91.

Song Mingshui, Mingjiu Lü, Zhao Leqiang, et al. Hydrocarbon potential and accumulation model in Chepaizi uplift, Junggar Basin[J]. China Petroleum Exploration, 2016, 21（3）： 83-91.
[38] 中华人民共和国国家质量监督检验检疫总局,中国国家标准化管理委员会. GB/T 29172—2012 岩心分析方法 [S]. 北京：中国标准出版社,2013：32-34.

General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China, Standardization Administration of the People's Republic of China. GB/T 29172-2012 practices for core analysis [S]. Beijing： Standards Press of China, 2013： 32-34.
[39] Chen T Q, Guestrin C. XGBoost： A scalable tree boosting system[C]//Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. San Francisco： ACM, 2016： 785-794.