基于渗透率合成技术的砂岩油藏产能预测方法

王清辉; 朱明; 冯进; 管耀; 侯博恒

doi:10.11911/syztjs.2021122

基于渗透率合成技术的砂岩油藏产能预测方法

doi: 10.11911/syztjs.2021122

中海石油（中国）有限公司深圳分公司研究院，广东深圳 518067

基金项目: 国家科技重大专项“南海东部海域勘探新领域及关键技术”（编号：2016ZX05024-004）资助

详细信息

作者简介:
王清辉（1986—），男，湖北咸宁人，2010年毕业于长江大学地球探测与信息技术专业，2013年获中国石油大学（北京）地球探测与信息技术专业硕士学位，测井工程师，主要从事珠江口盆地（东部）地区测井储层评价研究。E-mail：wangqh24@cnooc.com.cn

中图分类号: TE32⁺8
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出版历程
- 收稿日期: 2020-12-29
- 修回日期: 2021-08-22
- 网络出版日期: 2021-09-17
- 刊出日期: 2021-11-25

A Method for Predicting Productivity of Sandstone Reservoirs Based on Permeability Synthesis Technology

Research Institute of Shenzhen Branch, CNOOC China Limited, Shenzhen, Guangdong, 518067, China

摘要

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。
- 测井 /
- 渗透率 /
- 合成技术 /
- 产能预测 /
- DST渗透率 /
- 惠州凹陷
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.
- logging /
- permeability /
- synthesis technology /
- productivity prediction /
- DST permeability /
- Huizhou Sag

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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

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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

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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

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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

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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

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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

下载: 全尺寸图片幻灯片

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

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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
储层类型	岩性	沉积微相	渗透率计算模型	相关系数	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

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