Machine learning-based prediction of low oil saturation sandstone reservoir parameters: a case study of Lower Karamay Formation in Xia 77 well block of Xiazijie Oilfield, Junggar Basin
-
摘要: 准噶尔盆地夏子街油田夏77井区块下克拉玛依组(简称克下组)特低孔特低渗油藏油水关系复杂、产量低、储层含水高，且具有低含油饱和度、孔渗相关性差、储层参数与测井响应关系不清晰、油水层识别困难等特征，常规储层参数评价及预测方法适用性差。通过对岩性、物性、含油性分析，明确了克下组储层岩性为砂砾岩、砂质砾岩，黏土矿物以伊蒙混层为主；储层为以原生粒间孔和残余粒间孔为主要储集空间的低孔隙度、特低渗透率储集层。通过建立含油饱和度解释模型，确定了本区油藏属于低饱和度油藏，含油饱和度一般为36%~55%。砂砾岩储层物性和含油性优于中细砂岩，储层物性控制含油性，呈现低饱和度特征，电性受含油性和岩性双重影响。通过低含油饱和度油藏形成机理研究，认为储层微观孔隙结构是形成低含油饱和度的主要原因。通过对敏感参数优选，基于自然伽马、电阻率和声波时差测井等资料，引入基于机器学习的BP神经网络技术，对夏子街油田夏77井区块克下组油藏进行了孔隙度、渗透率和含水饱和度的计算及预测，储层参数预测精度均高于80%，相关结论及方法可为低含油饱和度致密砂岩储层的物性参数预测提供依据和参考。Abstract: The Lower Karamay Formation in the Xia 77 well block of the Xiazijie Oilfield in the Junggar Basin features a complex oil and water relationship in its ultra-low porosity and ultra-low permeability reservoirs. These reservoirs are characterized by low production, high water content, low oil saturation, poor correlation between porosity and permeability, unclear relationship between reservoir parameters and logging responses and difficult identification of oil and water layers. Conventional methods for evaluating and predicting reservoir parameters are poorly suited for this block. Through the analysis of lithology, physical properties and oil-bearing characteristics, it was determined that the reservoir lithology of the Lower Karamay Formation is dominated by glutenite and gravelly sandstones, with mixed-layers of illite and smectite as the dominant clay mineral. The reservoirs are characterized by low porosity and ultra-low permeability with primary intergranular and residual intergranular pores as the main storage space. By establishing an oil saturation interpretation model, it was confirmed that the reservoirs in this area are low oil saturation reservoirs, with oil saturation generally ranging between 36%-55%. The physical properties and oil content of glutenite reservoirs are superior to those of medium to fine sandstones, with reservoir physical properties controlling oil content and exhibiting low saturation characteristics. Electrical properties are influenced by both oil content and lithology. Through studying the formation mechanism of low oil saturation oil reservoirs, it was found that the microscopic pore structure of the reservoirs is the main cause of low oil saturation. By selecting sensitive parameters and utilizing data from natural gamma, resistivity, and acoustic time difference logging, BP neural network technology based on machine learning was introduced to calculate and predict porosity, permeability, and water saturation for the Lower Karamay Formation in Xia 77 well block. The prediction accuracy of reservoir parameters exceeded 80%. The conclusions and methods derived from this study can provide a basis and reference for the prediction of physical parameters in low oil saturation tight sandstone reservoirs.
-
图 1 准噶尔盆地夏子街油田夏77井区块地理位置
Figure 1. Geographical location of Xia 77 well block in Xiazijie Oilfield, Junggar Basin
图 2 准噶尔盆地夏子街油田夏35井下克拉玛依组岩心
Figure 2. Core of Lower Karamay Formation of Xia 35 well in Xiazijie Oilfield, Junggar Basin
图 3 准噶尔盆地夏子街油田夏301井下克拉玛依组(1 688.73 m)粒度粒级分布直方图
Figure 3. Grain size distribution histogram of Lower Karamay Formation(1 688.73 m) of Xia 301 well in Xiazijie Oilfield, Junggar Basin
图 4 准噶尔盆地夏子街油田夏77井区下克拉玛依组孔隙度和渗透率直方图
Figure 4. Histogram of porosity and permeability of Lower Karamay Formation in Xia 77 well block in Xiazijie Oilfield, Junggar Basin
图 5 准噶尔盆地夏子街油田夏77井区下克拉玛依组岩心含油级别统计结果
Figure 5. Statistical results of oil-bearing grade of rock cores in Lower Karamay Formation of Xia 77 well block in Xiazijie Oilfield, Junggar Basin
图 6 准噶尔盆地夏子街油田夏77井区下克拉玛依组岩性与物性关系
Figure 6. Relationship between lithology and physical properties of Lower Karamay Formation in Xia 77 well block in Xiazijie Oilfield, Junggar Basin
图 7 准噶尔盆地夏子街油田夏77井区下克拉玛依组岩性与电性关系
Figure 7. Relationship between lithology and electrical properties of Lower Karamay Formation in Xia 77 well block in Xiazijie Oilfield, Junggar Basin
图 8 准噶尔盆地夏子街油田下克拉玛依组三孔隙度测井曲线分布直方图
Figure 8. Histogram of distribution of three porosity logging curves in Lower Karamay Formation in Xiazijie Oilfield, Junggar Basin
图 9 准噶尔盆地夏子街油田夏77井区下克拉玛依组岩性与含油性统计直方图
Figure 9. Statistical histogram of lithology and oil content of Lower Karamay Formation in Xia 77 well block in Xiazijie Oilfield, Junggar Basin
图 10 准噶尔盆地夏子街油田夏77井区下克拉玛依组物性与含油性关系
Figure 10. Relationship between physical properties and oil content of Lower Karamay Formation in Xia 77 well block in Xiazijie Oilfield, Junggar Basin
图 11 准噶尔盆地夏子街油田三层BP神经网络结构
Figure 11. Structure of 3-layer BP neural network in Xiazijie Oilfield, Junggar Basin
图 12 准噶尔盆地夏子街油田BP神经网络对孔隙度训练及预测结果
Figure 12. Training and prediction results of BP neural network for porosity in Xiazijie Oilfield, Junggar Basin
图 13 准噶尔盆地夏子街油田BP神经网络对渗透率的训练及预测结果
Figure 13. Training and prediction results of BP neural network for permeability in Xiazijie Oilfield, Junggar Basin
图 14 准噶尔盆地夏子街油田BP神经网络对含油饱和度的训练及预测结果
Figure 14. Training and prediction results of BP neural network for oil saturation in Xiazijie Oilfield, Junggar Basin
表 1 准噶尔盆地夏子街油田3种机器学习算法评价指标总结
Table 1. Summary of evaluation indicators for three machine learning algorithms in Xiazijie Oilfield, Junggar Basin
评价模型 MSE(均方误差) MAE(平均绝对误差) 孔隙度 渗透率 含油饱和度 孔隙度 渗透 含油饱和度 BP神经网络 0.33 0.45 0.35 0.15 0.14 0.17 随机森林 0.86 0.86 0.84 1.20 1.80 1.10 支持向量机 0.81 0.82 0.79 0.92 0.81 0.99 
下载: 导出CSV
表 2 准噶尔盆地夏子街油田夏038井BP神经网络预测结果
Table 2. Prediction results of BP neural network for Xia 038 well in Xiazijie Oilfield, Junggar Basin
序号 深度/m 自然伽马/API 深电阻率/(Ω·m) 声波时差/(μs/ft) 岩性密度/(g/cm3) 孔隙度/% 预测孔隙度/% 渗透率/10-3 μm2 预测渗透率/10-3 μm2 含油饱和度/% 预测含油饱和度/% 1 2 338.4 87.55 34.11 71.98 2.49 10.51 10.84 0.14 0.15 39.69 39.63 2 2 347.5 73.52 43.26 70.03 2.52 9.29 10.69 0.07 0.06 41.11 42.67 3 2 352.2 78.63 39.07 70.69 2.49 9.70 9.08 0.09 0.06 40.10 42.10 4 2 447.0 88.72 54.13 67.30 2.55 8.88 9.01 0.06 0.07 45.71 44.00 5 2 451.9 93.78 48.80 67.21 2.51 8.79 8.57 0.06 0.03 42.07 41.28 6 2 455.4 98.74 65.70 66.73 2.54 8.48 8.84 0.04 0.04 49.21 48.06 7 2 462.6 89.87 46.50 68.67 2.56 9.87 9.62 0.10 0.09 45.80 42.75 8 2 466.4 86.04 44.38 67.45 2.56 8.95 8.88 0.06 0.05 40.17 43.11 9 2 467.2 83.85 35.55 70.54 2.58 11.18 11.48 0.21 0.27 43.30 41.32 10 2 472.1 89.05 36.64 69.14 2.56 10.16 10.52 0.11 0.11 40.24 41.58 11 2 473.9 80.42 47.44 69.16 2.53 10.23 9.98 1.32 0.06 47.24 44.95 12 2 483.4 84.94 53.35 65.28 2.59 7.36 7.86 0.02 0.03 44.49 43.41 13 2 484.0 87.07 51.17 67.28 2.54 8.85 8.85 0.06 0.06 51.89 51.55 14 2 491.8 91.87 57.07 66.71 2.55 8.44 8.53 0.05 0.08 52.68 54.37 15 2 496.2 81.68 51.65 67.08 2.56 8.70 8.75 0.05 0.05 51.67 51.08 16 2 501.5 81.29 49.38 67.13 2.56 8.73 8.81 0.05 0.05 50.75 50.49 17 2 506.2 83.05 43.70 67.76 2.56 9.18 9.57 0.07 0.09 49.91 46.11 18 2 514.2 88.60 48.23 66.56 2.55 8.31 8.82 0.04 0.05 47.81 46.71 19 2 515.6 86.09 54.25 66.70 2.55 8.43 8.88 0.05 0.06 51.17 51.72 20 2 522.4 94.75 50.73 67.20 2.53 8.79 8.65 0.06 0.05 51.57 51.30 21 2 524.6 91.54 48.69 66.70 2.56 8.41 8.53 0.04 0.05 48.51 47.14 22 2 527.1 76.79 40.23 68.16 2.56 9.46 9.98 0.08 0.06 49.33 45.05 23 2 530.0 80.97 37.49 69.81 2.55 10.65 10.79 0.16 0.14 52.80 53.18 24 2 532.9 87.18 40.81 66.63 2.56 8.32 8.75 0.04 0.05 43.28 46.89 25 2 533.5 84.83 37.38 68.12 2.57 9.41 9.45 0.08 0.08 47.18 47.21 26 2 535.4 81.69 38.29 68.02 2.61 9.34 9.53 0.07 0.07 47.51 45.63 27 2 541.0 80.20 42.77 67.73 2.55 9.15 9.57 0.07 0.07 49.13 51.47
下载: 导出CSV
-
[1] 王林生, 赵晓东, 刘文锋, 等. 准噶尔盆地玛西斜坡低饱和度油藏微观赋存状态及高含量胶质和沥青质成因[J]. 地质科学, 2023, 58(4): 1325-1339.WANG Linsheng, ZHAO Xiaodong, LIU Wenfeng, et al. Microcosmic occurrence state of low saturation reservoir and origin of high content of resin and asphaltene in Maxi slope, Junggar Basin[J]. Chinese Journal of Geology (Scientia Geologica Sinica), 2023, 58(4): 1325-1339. [2] 王爱霞, 徐怀宝, 耿梅, 等. 玛湖凹陷YB4井区百口泉组低饱和度油藏成因及勘探方向分析[J]. 特种油气藏, 2023, 30(2): 44-50. doi: 10.3969/j.issn.1006-6535.2023.02.006WANG Aixia, XU Huaibao, GENG Mei, et al. Analysis of the genesis and exploration direction of low-saturation reservoirs in the Baikouquan Formation, wellblock YB4, Mahu Sag[J]. Special Oil & Gas Reservoirs, 2023, 30(2): 44-50. doi: 10.3969/j.issn.1006-6535.2023.02.006 [3] 杨朝洪, 司马立强, 王亮, 等. 准噶尔盆地莫索湾凸起八道湾组低饱和度油藏成因分析[J]. 特种油气藏, 2022, 29(5): 42-48. doi: 10.3969/j.issn.1006-6535.2022.05.006YANG Chaohong, SIMA Liqiang, WANG Liang, et al. Genesis analysis of low saturation oil reservoirs in Badaowan Formation of Mosuowan Bulge in Junggar Basin[J]. Special Oil & Gas Reservoirs, 2022, 29(5): 42-48. doi: 10.3969/j.issn.1006-6535.2022.05.006 [4] 郑佳奎, 王波, 徐传艳, 等. 丘陵油田西山窑组低饱和度油藏成因分析[J]. 新疆石油天然气, 2017, 13(3): 11-14. doi: 10.3969/j.issn.1673-2677.2017.03.004ZHENG Jiakui, WANG Bo, XU Chuanyan, et al. Genetic analysis of low oil saturation reservoir of Xishanyao Formation in Qiuling Oilfield[J]. Xinjiang Oil & Gas, 2017, 13(3): 11-14. doi: 10.3969/j.issn.1673-2677.2017.03.004 [5] 许秀才. 古龙凹陷葡萄花油层低饱和度油藏成因[J]. 断块油气田, 2017, 24(3): 320-323.XU Xiucai. Genesis of low oil-saturated reservoir of Putaohua oil layer in Gulong Sag[J]. Fault-Block Oil & Gas Field, 2017, 24(3): 320-323. [6] 左卿伶. 克拉玛依油田七中区克下组砾岩储层特征及三维地质建模[D]. 北京: 中国石油大学(北京), 2018.ZUO Qingling. The reservoir characteristics and 3D geological modeling of the Lower Karamay Group in the 7th central region of Karamay Oilfield[D]. Beijing: China University of Petroleum (Beijing), 2018. [7] 尚映润. 准噶尔盆地WEH油田克下组储层孔隙结构及控制因素研究[J]. 科技创新与应用, 2018(35): 61-62. doi: 10.3969/j.issn.2095-2945.2018.35.025SHANG Yingrun. Research on pore structure and control factors of Kexia Formation reservoir in WEH oilfield of the Junggar Basin[J]. Technology Innovation and Application, 2018(35): 61-62. doi: 10.3969/j.issn.2095-2945.2018.35.025 [8] 许小龙. 新疆H6井区克下组油藏综合地质研究[D]. 西安: 西北大学, 2017.XU Xiaolong. Comprehensive geological research on the Kexia Formation oil reservoir in the H6 well area of Xinjiang[D]. Xi'an: Northwest University, 2017. [9] 马小林. 青西油田砂砾岩储层测井评价方法研究[D]. 成都: 西南石油大学, 2020.MA Xiaolin. Research on logging evaluation method for sandstone and conglomerate reservoirs in Qingxi Oilfield[D]. Chengdu: Southwest Petroleum University, 2020. [10] 张川, 欧阳云丽, 王旭, 等. 九2区清水河组砂砾岩储层测井精细评价[J]. 中外企业家, 2020(17): 251.ZHANG Chuan, OUYANG Yunli, WANG Xu, et al. Fine logging evaluation of the Qingshuihe Formation sand and gravel reservoir in the Jiu2 area[J]. Chinese & Foreign Entrepreneurs, 2020(17): 251. [11] 徐倩茹, 谢义林, 杨星星, 等. 强非均质性低饱和度油层成因机理与油气层发现测井技术支持[C]//2022油气田勘探与开发国际会议论文集Ⅰ. 西安: 中国石油新疆油田分公司(新疆砾岩油藏实验室), 西安石油大学, 2022.XU Qianru, XIE Yilin, YANG Xingxing, et al. Formation mechanism of highly heterogeneous low saturation reservoir and support of reservoir discovery logging technology[C]//Proceedings of the 2022 International Conference on Oil and Gas Field Exploration and Development I. Xi'an: PetroChina Xinjiang Oilfield Branch (Xinjiang Conglomerate Reservoir Laboratory), Xi'an Shiyou University, 2022. [12] 万乔升. 低饱和度油藏自然产能预测方法研究[D]. 大庆: 东北石油大学, 2021.WAN Qiaosheng. Study on prediction method of natural productivity in low saturation reservoirs[D]. Daqing: Northeast University of Petroleum, 2021. [13] 冯利娟, 钱川川, 李勇, 等. 克拉玛依油田七西区三叠系下克拉玛依组储层微观非均质性定量评价[J]. 科学技术与工程, 2023, 23(16): 6792-6801. doi: 10.12404/j.issn.1671-1815.2023.23.16.06792FENG Lijuan, QIAN Chuanchuan, LI Yong, et al. Quantity evaluation on micro-heterogeneity of reservoirs in Triassic Lower Karamay Formation of Qixi region in Karamay Oilfield[J]. Science Technology and Engineering, 2023, 23(16): 6792-6801. doi: 10.12404/j.issn.1671-1815.2023.23.16.06792 [14] 董磊, 李庆峰, 许淑梅, 等. A盆地C组低饱和度油层解释方法[C]//2022油气田勘探与开发国际会议论文集Ⅱ. 西安: 中国石油新疆油田分公司(新疆砾岩油藏实验室), 西安石油大学, 2022: 8.DONG Lei, LI Qingfeng, XU Shumei, et al. Interpretation method of low saturation oil layers in C Formation, A Basin[C]//Proceedings of the 2022 International Conference on Oil and Gas Field Exploration and Development Ⅱ. Xi'an: Petro China Xinjiang Oilfield Branch (Xinjiang Conglomerate Reservoir Laboratory), Xi'an Shiyou University, 2022: 8. [15] 陈少云, 杨勇强, 邱隆伟, 等. 致密砂岩孔喉结构分析与渗透率预测方法: 以川中地区侏罗系沙溪庙组为例[J]. 石油实验地质, 2024, 46(1): 202-214. doi: 10.11781/sysydz202401202CHEN Shaoyun, YANG Yongqiang, QIU Longwei, et al. Analysis of pore throat structure and permeability prediction method in tight sandstone: a case study of the Jurassic Shaximiao Formation in central Sichuan[J]. Petroleum Geology & Experiment, 2024, 46(1): 202-214 doi: 10.11781/sysydz202401202 [16] 李雪英, 万乔升, 王福霖, 等. 低饱和度油藏油水层解释方法: 以新肇油田古628区块葡萄花油层为例[J]. 地球物理学进展, 2021, 36(3): 1088-1094.LI Xueying, WAN Qiaosheng, WANG Fulin, et al. Interpretation method of oil-water layer in low oil-saturation reservoirs: a case study in Putaohua reservoirs in Gu628 block of Xinzhao Oilfield[J]. Progress in Geophysics, 2021, 36(3): 1088-1094. [17] 何澳庭, 闫建平, 李志鹏, 等. 低饱和度砂岩储层含油非均质性与岩性、物性、电性的关系[C]//第十七届全国古地理学及沉积学学术会议摘要集: 专题6测井地质学在非常规油气沉积储层评价中的应用. 青岛: 中国矿物岩石地球化学学会岩相古地理专业委员会, 2023: 1.HE Aoting, YAN Jianping, LI Zhipeng, et al. The relationship between oil bearing heterogeneity and lithology, physical properties, and electrical properties of low saturated sandstone reservoirs[C]//Summary of the 17th National Conference on Paleogeography and Sedimentology - Topic 6: Application of well logging geology in unconventional oil and gas sedimentary reservoir evaluation. Qingdao: Lithofacies Paleogeography Professional Committee of the Chinese Society of Mineral, Rock and Geochemistry, 2023: 1. [18] 肖晓, 闫建平, 郭伟, 等. 基于LightGBM算法的页岩气储层甜点参数预测方法[J]. 中国煤炭地质, 2023, 35(10): 28-37.XIAO Xiao, YAN Jianping, GUO Wei, et al. A prediction method for sweet spot parameters of shale gas reservoirs based on LightGBM Algorithm[J]. Coal Geology of China, 2023, 35(10): 28-37. [19] 曲端刚. 松辽盆地B井区青山口组页岩储层参数预测方法研究[D]. 大庆: 东北石油大学, 2023.QU Duangang. Research on the method of predicting shale reservoir parameters in the Qingshankou Formation of well B area in the Songliao Basin[D]. Daqing: Northeast Petroleum University, 2023. [20] 唐俊方, 熊健, 刘向君, 等. 玛湖凹陷风城组岩石力学参数自适应权重组合预测[J]. 石油地球物理勘探, 2024, 59(1): 1-11.TANG Junfang, XIONG Jian, LIU Xiangjun, et al. Adaptive weight combination forecast of rock mechanical parameters in the Fengcheng Formation of Mahu Sag[J]. Oil Geophysical Prospecting, 2024, 59(1): 1-11. [21] 刘仕友, 曲福良, 周凡, 等. 基于地震属性约简的深度学习储层物性参数预测: 以莺歌海盆地乐东区为例[J]. CT理论与应用研究, 2022, 31(5): 577-586.LIU Shiyou, QU Fuliang, ZHOU Fan, et al. Deep learning reservoir parameter prediction based on seismic attribute reduction: take Ledong area of Yinggehai Basin as an example[J]. Computerized Tomography Theory and Applications, 2022, 31(5): 577-586. [22] 罗博峰. 基于机器学习的核磁共振测井数据预测地层渗透率方法研究[D]. 北京: 中国石油大学(北京), 2022.LUO Bofeng. The reservoir permeability prediction based on machine learning and NMR logging data[D]. Beijing: China University of Petroleum (Beijing), 2022. [23] 宫博识. 准噶尔盆地乌尔禾地区二叠系风城组湖相云质岩形成机理[D]. 成都: 成都理工大学, 2014.GONG Bozhi. Forming mechanism of Fengcheng Formation of lacustrine hydrothermal dolomite in the Junggar Basin in Urho[D]. Chengdu: Chengdu University of Technology, 2014. [24] 孙浩. 玛北斜坡区复杂油气藏储层预测方法研究[D]. 北京: 中国石油大学(北京), 2016.SUN Hao. Prediction methods of complex reservoirs in Mabei slope[D]. Beijing: China University of Petroleum (Beijing), 2016. [25] 冯亚琴, 刘俊田, 李楠, 等. 马朗凹陷三叠系砂砾岩油藏特征及主控因素[J]. 石油地质与工程, 2021, 35(3): 25-29.FENG Yaqin, LIU Juntian, LI Nan, et al. Geological characteristics and main controlling factors of the Triassic glutenite reservoirs in Malang Sag[J]. Petroleum Geology and Engineering, 2021, 35(3): 25-29. [26] 王志豪, 周明顺, 魏新路, 等. BP神经网络算法在页岩气饱和度评价的应用[J]. 工程地球物理学报, 2022, 19(2): 216-222. doi: 10.3969/j.issn.1672-7940.2022.02.011WANG Zhihao, ZHOU Mingshun, WEI Xinlu, et al. Application of BP neural network algorithm in shale gas saturation evaluation[J]. Chinese Journal of Engineering Geophysics, 2022, 19(2): 216-222. doi: 10.3969/j.issn.1672-7940.2022.02.011 [27] 王祥, 马劲风, 王飞龙, 等. 渤海湾盆地渤中凹陷西南部深层烃源岩有机相预测[J]. 石油实验地质, 2022, 44(6): 1070-1080. doi: 10.11781/sysydz2022061070WANG Xiang, MA Jinfeng, WANG Feilong, et al. Prediction of organic facies in deep source rocks in the southwest of Bozhong Depression, Bohai Bay Basin[J]. Petroleum Geology & Experiment, 2022, 44 (6): 1070-1080 doi: 10.11781/sysydz2022061070 [28] 王猛, 董宇, 蔡军, 等. 基于BP神经网络的储层渗透率预测及质量评价方法[J]. 地球物理学进展, 2023, 38(1): 321-327.WANG Meng, DONG Yu, CAI Jun, et al. Method for reservoir permeability prediction and quality evaluation based on BP neural network[J]. Progress in Geophysics, 2023, 38(1): 321-327. -
计量
- 文章访问数: 223
- HTML全文浏览量: 69
- PDF下载量: 28
- 被引次数: 0
苏公网安备32021102000780号