A Method for Predicting Productivity of Sandstone Reservoirs Based on Permeability Synthesis Technology
-
摘要: 利用常规测井资料准确计算钻杆地层测试（DST）渗透率，能够大幅提高海上非均质砂岩油藏产能预测精度。为此，综合考虑惠州凹陷宏观沉积成岩作用和微观孔隙结构对储层渗透率的影响，建立了不同类型储层绝对渗透率的测井解释模型。正演分析表明，射孔层段不同渗透率级差的储层对产能的贡献明显不同；对不同级别储层渗透率进行加权求和得到合成测井渗透率，并对权系数大小进行约束，突出优势储层对产能的贡献，建立了DST渗透率的回归拟合方程；采用差分进化算法进行迭代，得到DST渗透率计算方程的最优解。采用该方法对惠州凹陷72个油层产能进行预测，48个油层的产能大于100 m3/d，预测相对误差小于30%的油层占比90%；24个油层的产能为10～100 m3/d，相对误差小于50%的油层占比79%。研究结果表明，基于渗透率合成技术的砂岩油藏产能预测方法，能够为海上油田测试作业决策提供指导，降低勘探作业成本。Abstract: Using conventional logging data to accurately calculate the permeability of drill-stem test (DST) can greatly improve the accuracy of productivity predictions of offshore heterogeneous sandstone reservoirs. Based on this, the influence of sedimentary diagenesis and pore structure of Huizhou Sag on reservoir permeability were comprehensively considered from macroscopic and microscopic perspectives, respectively. In this work, a logging interpretation model of absolute permeability was built for different reservoir types. Forward analysis results show that reservoirs with different permeability contrast in perforated intervals have significantly different contributions to productivity.The synthetic logging permeability was calculated by weighted summation of permeability at different levels of the reservoir, and the weight coefficient was constrained to highlight the contribution of favorable reservoirs to productivity. An iterative analysis was performed with a differential evolution algorithm to yield the optimal solution of the equation. This method has been applied in 72 oil layers in Huizhou Sag for productivity prediction. The productivity from 48 layers was found greater than 100 m3/d, and the proportion of layers whose relative prediction errors within 30% was 90%. In addition, 24 layers had the productivity of 10–100 m3/d, among which the layers whose relative error was less than 50% accounted for 79% of the oil layers. This study indicates that the productivity prediction method based on permeability synthesis technology can guide the decision-making of offshore field tests and operations to reduce the exploration cost.
-
Key words:
- logging /
- permeability /
- synthesis technology /
- productivity prediction /
- DST permeability /
- Huizhou Sag
-
图 1 惠州凹陷米采油指数与DST渗透率和测井平均渗透率交会图
Figure 1. Cross plot of productivity index per meter with DST permeability and average logging permeability of Huizhou Sag
图 2 惠州凹陷射孔方式与表皮系数分布直方图
Figure 2. Histogram of perforation method and skin coefficient distribution in Huizhou Sag
图 3 不同渗透率级差下的储层产能正演模拟成果
Figure 3. Forward modeling results of reservoir productivity with different permeability contrast
图 4 惠州凹陷测井平均渗透率和合成测井渗透率与DST渗透率交会图
Figure 4. Cross plot of average and synthetic logging permeability with DST permeability of Huizhou Sag
图 5 惠州凹陷油藏产能预测结果误差分析
Figure 5. Error analysis of reservoir productivity prediction results for Huizhou Sag
图 6 惠州凹陷A井珠海组M层产能预测结果
Figure 6. Productivity prediction results of Layer M of Zhuhai Formation in Well A in Huizhou Sag
表 1 惠州凹陷不同油藏类型的供油半径计算模型
Table 1. Calculation models of oil supply radius for different reservoir types in Huizhou Sag
油藏类型 供油半径计算模型 相关系数 油藏内部发育断层 $ {r_{\text{e}}}{\text{ = 29}}{\text{.883}} \left(\dfrac{{{K_{{\text{DST}}}}}}{\mu }\right){^{0.499\;2}} $ 0.975 9 边水驱动油藏 ${r_{\text{e}}}{\text{ = 41}}{\text{.321}} \left(\dfrac{{{K_{{\text{DST}}}}}}{\mu }\right){^{0.443\;8}} $ 0.927 7 底水驱动油藏 $ {r_{\text{e}}}{\text{ = 4}}{{.629\;9}}\left(\dfrac{{{K_{{\text{DST}}}}}}{\mu }\right){^{0.706\;3}} $ 0.853 9 
下载: 导出CSV
表 2 惠州凹陷不同储层类型的孔、渗模型和Fisher识别结果
Table 2. Porosity and permeability models and Fisher identification results of different reservoir types in Huizhou Sag
储层类型 岩性 沉积微相 渗透率计算模型 相关系数 Fisher识别结果 符合 不符合 PF1 中、粗砂岩，含砾砂岩 辫状分流河道、滩砂水道和沿岸坝 $K = 2.473\;6{{\rm{e}}^{0.309\;6\phi }}$ 0.82 60 5 PF2 中—细砂岩 分流河道、河口坝和风暴席状砂 $K = 0.411{{\rm{e}}^{0.335\;1\phi }}$ 0.91 210 11 PF3 钙质中—细砂岩 潮汐水道、远砂坝 $K = 0.000\;06{{\rm{e}}^{0.636\;2\phi }}$ 0.83 28 0 PF4 细砂岩、粉砂岩 远砂坝 $K = {10^{ - 9.045}}{\phi ^{8.402}}$ 0.84 39 8 PF5 泥质粉砂岩 分流河道间湾、远砂坝 $K = 0.004\;7{{\rm{e}}^{0.403\;7\phi }}$ 0.79 16 4
下载: 导出CSV
表 3 惠州凹陷储层分级标准
Table 3. Reservoir classification standard of Huizhou Sag
储层
级别孔隙度，% 渗透率/
mD米采油指数/
（m3·d−1·MPa−1·m−1）产量分类 Ⅰ级 ≥30.0 ≥2 000 10.50～163.90 高产 Ⅱ级 25.0～30.0 500～2 000 6.80～62.50 高产 Ⅲ级 20.0～25.0 200～500 4.10～21.60 中—高产 Ⅳ级 17.5～20.0 50～200 0.92～11.25 中—低产 Ⅴ级 15.0～17.5 20～50 0.87～3.65 中—低产 Ⅵ级 12.0～15.0 5～20 0.47～1.78 低产—少产 Ⅶ级 ＜12.0 ＜5 0.02～0.54 少产—无产
下载: 导出CSV
-
[1] 刘彦成,罗宪波,康凯,等. 陆相多层砂岩油藏渗透率表征与定向井初期产能预测:以蓬莱19-3油田为例[J]. 石油勘探与开发,2017,44(1):97–103. doi: 10.1016/S1876-3804(17)30012-5LIU Yancheng, LUO Xianbo, KANG Kai, et al. Permeability characterization and directional wells initial productivity prediction in the continental multilayer sandstone reservoirs: a case from Penglai 19-3 Oil Field, Bohai Bay Basin[J]. Petroleum Exploration and Development, 2017, 44(1): 97–103. doi: 10.1016/S1876-3804(17)30012-5 [2] 田亚鹏,鞠斌山,胡杰. 考虑蒸汽超覆的稠油蒸汽吞吐产能预测模型[J]. 石油钻探技术,2018,46(1):100–116.TIAN Yapeng, JU Binshan, HU Jie. A productivity prediction model for heavy oil steam huff and puff considering steam override[J]. Petroleum Drilling Techniques, 2018, 46(1): 100–116. [3] 吴春新,刘学,刘英宪,等. 黄河口凹陷比采油指数预测方法及应用[J]. 断块油气田,2018,25(2):218–221.WU Chunxin, LIU Xue, LIU Yingxian, et al. Method of specific productivity index prediction of Huanghekou Sag and its application[J]. Fault-Block Oil & Gas Field, 2018, 25(2): 218–221. [4] 时新磊,崔云江,许万坤,等. 基于随钻测压流度的地层渗透率评价方法及产能预测[J]. 石油勘探与开发,2020,47(1):140–147.SHI Xinlei, CUI Yunjiang, XU Wankun, et al. Formation permeability evaluation and productivity prediction based on mobility from pressure measurement while drilling[J]. Petroleum Exploration and Development, 2020, 47(1): 140–147. [5] 谭忠健,胡云,张国强,等. 渤中19-6构造复杂储层流体评价及产能预测[J]. 石油钻采工艺,2018,40(6):764–774.TAN Zhongjian, HU Yun, ZHANG Guoqiang, et al. Fluid evaluation and productivity prediction on complex reservoirs in Bozhong 19-6 Structure[J]. Oil Drilling & Production Technology, 2018, 40(6): 764–774. [6] 蒋兴才. 辽河葵东地区低电阻率油层产能影响因素分析及预测[J]. 特种油气藏,2019,26(4):70–75. doi: 10.3969/j.issn.1006-6535.2019.04.012JIANG Xingcai. Low-resistivity reservoir productivity analysis and forecast in Kuidong of Liaohe[J]. Special Oil & Gas Reservoirs, 2019, 26(4): 70–75. doi: 10.3969/j.issn.1006-6535.2019.04.012 [7] 张龙海,刘国强,周灿灿,等. 基于阵列感应测井资料的油气层产能预测[J]. 石油勘探与开发,2005,32(3):84–87. doi: 10.3321/j.issn:1000-0747.2005.03.021ZHANG Longhai, LIU Guoqiang, ZHOU Cancan, et al. Reservoir productivity prediction by array induction logging data[J]. Petroleum Exploration and Development, 2005, 32(3): 84–87. doi: 10.3321/j.issn:1000-0747.2005.03.021 [8] 张利军,田冀,朱国金. 海上断块油田定向井初期产能评价方法分析[J]. 石油钻探技术,2015,43(1):111–116.ZHANG Lijun, TIAN Ji, ZHU Guojin. Evaluation methods for initial productivity of directional wells in offshore fault block oilfields[J]. Petroleum Drilling Techniques, 2015, 43(1): 111–116. [9] 谭成仟,马娜蕊,苏超. 储层油气产能的预测模型和方法[J]. 地球科学与环境学报,2004,26(2):42–46. doi: 10.3969/j.issn.1672-6561.2004.02.010TAN Chenqian, MA Narui, SU Chao. Model and method for oil and gas productivity prediction of reservoir[J]. Journal of Earth Sciences and Environment, 2004, 26(2): 42–46. doi: 10.3969/j.issn.1672-6561.2004.02.010 [10] 马文礼,李治平,孙玉平,等. 基于机器学习的页岩气产能非确定性预测方法研究[J]. 特种油气藏,2019,26(2):101–105. doi: 10.3969/j.issn.1006-6535.2019.02.018MA Wenli, LI Zhiping, SUN Yuping, et al. Non-deterministic shale gas productivity forecast based on machine learning[J]. Special Oil & Gas Reservoirs, 2019, 26(2): 101–105. doi: 10.3969/j.issn.1006-6535.2019.02.018 [11] 安小平,李相方,程时清,等. 不同方法获取渗透率的对比分析[J]. 油气井测试,2005,14(5):14–17. doi: 10.3969/j.issn.1004-4388.2005.05.006AN Xiaoping, LI Xiangfang, CHENG Shiqing, et al. Comparative analysis for permeability acquired from different methods[J]. Well Testing, 2005, 14(5): 14–17. doi: 10.3969/j.issn.1004-4388.2005.05.006 [12] 陈长民, 施和生, 许仕策, 等. 珠江口盆地（东部）第三系油气藏形成条件[M]. 北京: 科学出版社, 2003: 147-153．CHEN Changmin, SHI Hesheng, XU Shice, et al. The conditions of hydrocarbon accumulation of the tertiary petroleum system in the Pearl River Mouth Basin[M]. Beijing: Science Press, 2003: 147-153. [13] 张振城. 储层损害比与产能预测[D]. 北京: 中国石油大学（北京）, 2006.ZHANG Zhencheng. Formation damage and prediction of productivity[D]. Beijing: China University of Petroleum(Beijing), 2006. [14] 王清辉, 冯进, 管耀, 等. 基于动态资料的低孔低渗砂岩储层渗透率测井评价方法: 以陆丰凹陷古近系为例[J]. 石油学报, 2019, 40（增刊1）: 206-216.WANG Qinghui, FENG Jin, GUAN Yao, et al. Permeability logging evaluation method of low-porosity low-permeability sandstone reservoirs based on dynamic data: a case study of Paleogene Strata in Lufeng Sag[J]. Acta Petrolei Sinica, 2019, 40(supplement 1): 206-216. [15] 熊万林,朱俊章,施洋,等. 珠江口盆地珠一坳陷原油密度分布及其成因[J]. 海洋地质前沿,2019,35(1):43–52.XIONG Wanlin, ZHU Junzhang, SHI Yang, et al. Density distribution of crude oil in the Zhuyi Depression of Pearl River Mouth Basin and control factors[J]. Marine Geology Frontiers, 2019, 35(1): 43–52. [16] 石玉江,张海涛,侯雨庭,等. 基于岩石物理相分类的测井储层参数精细解释建模[J]. 测井技术,2005,29(4):328–332. doi: 10.3969/j.issn.1004-1338.2005.04.014SHI Yujiang, ZHANG Haitao, HOU Yuting, et al. The fine logging interpretation method based on petrophysical faces[J]. Well Logging Technology, 2005, 29(4): 328–332. doi: 10.3969/j.issn.1004-1338.2005.04.014 [17] 孙利国, 王玉梅, 何石. 利用平面径向流公式预测油层自然产能的方法[J]. 测井技术, 2000, 24（增刊1）: 527–530SUN Liguo, WANG Yumei, HE Shi. A method to predict natural productivity in oil zones with the plan radial flow formula[J]. Well Logging Technology, 2000, 24(supplement 1): 527–530. -
点击查看大图
计量
- 文章访问数: 192
- HTML全文浏览量: 106
- PDF下载量: 18
- 被引次数: 0
京公网安备 11010502036328号 