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
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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.
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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 
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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
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