数据驱动的页岩油水平井压裂施工参数智能优化研究

曾凡辉; 胡大淦; 张宇; 郭建春; 田福春; 郑彬涛

doi:10.11911/syztjs.2023087

数据驱动的页岩油水平井压裂施工参数智能优化研究

1.
油气藏地质及开发工程全国重点实验室（西南石油大学），四川成都 610500
2.
中国石油大港油田分公司石油工程研究院，天津 300280
3.
中国石化胜利油田分公司石油工程技术研究院，山东东营 257000

基金项目: 国家自然科学基金面上项目“大数据驱动的深层页岩压裂参数协同优化与实时调控研究” （编号：52374045）、四川省自然科学基金面上项目“深层页岩储层多簇射孔压裂竞争扩展多目标协同智能优化与调控”（编号：2023NSFSC0424）联合资助

详细信息

作者简介:
曾凡辉（1980—），男，四川达州人，2004年毕业于西南石油学院石油工程专业，2009年获西南石油大学油气田开发工程专业博士学位，教授，主要从事油气藏增产理论与技术研究工作。E-mail：zengfanhui023024@126.com。

中图分类号: TE357.1⁺1
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出版历程
- 收稿日期: 2023-05-16
- 修回日期: 2023-08-06
- 网络出版日期: 2023-08-24
- 刊出日期: 2023-10-30

Research on Data-Driven Intelligent Optimization of Fracturing Treatment Parameters for Shale Oil Horizontal Wells

1.
National Key Laboratory of Oil and Gas Reservoir Geology and Exploitation(Southwest Petroleum University), Chengdu, Sichuan, 610500, China
2.
Petroleum Engineering Research Institute, PetroChina Dagang Oilfield Company, Tianjin, 300280, China
3.
Petroleum Engineering Technology Research Institute, Sinopec Shengli Oilfield Company, Dongying, Shandong, 257000, China

摘要

摘要:
针对目前数智化压裂施工参数设计针对性不足、流程不畅通等问题，建立了基于数据驱动的压裂施工参数智能优化方法。以CD区块32口页岩油井为研究对象，采用主成分分析法处理代表储层地质特征、工程品质和施工参数的15项产量影响因素，使之降低维度，引入高斯隶属函数和熵权法进行储层压裂非均质性模糊综合评价，结合支持向量回归和粒子群优化算法，以产量最高为目标，推荐射孔位置、段长、簇间距、单位长度液量、单位长度砂量和排量。研究结果表明，渗透率、孔隙度、热解游离烃含量、单位长度液量和单位长度砂量为研究区块的产量主控因素。应用实例井采用优化的参数施工后，第一压裂段8簇均成功起裂，裂缝半长59.50～154.80 m，产量预测符合率为94.86%。研究表明，该方法可实现有效储层质量评价、产量预测和匹配储层地质条件施工参数的快速优化，推动页岩油等非常规储层高效开发。
- 页岩油 /
- 水平井 /
- 数据驱动 /
- 储层质量综合评价 /
- 产量预测 /
- 水力压裂 /
- 压裂参数优化
Abstract:
A data-driven intelligent optimization method for fracturing treatment parameters was proposed to address the issues of insufficient pertinence and incomplete process design in digital fracturing treatment parameters. With 32 shale oil wells in the CD block as the research object, principal component analysis was used to reduce the 15 production-influencing factor dimensions representing geological attributes, engineering quality, and construction parameters of the reservoir. A Gaussian membership function and entropy weight method were introduced for a fuzzy comprehensive evaluation of reservoir fracturing heterogeneity. Combined with support vector regression and particle swarm optimization algorithms, the perforation location, segment length, cluster spacing, fracturing fluid intensity, sanding intensity, and discharge capacity were recommended with the highest production as the goal. The research results indicated that permeability, porosity, free hydrocarbon content by pyrolysis, fracturing fluid intensity, and sanding intensity were the main control factors for the production of the target block. All eight clusters of the first fracturing section of the application well have successfully initiated fractures during treatment with optimized parameters, with a half-length of 59.50–154.80 m and a production prediction accuracy of 94.86%. The method proposed can achieve effective reservoir quality evaluation, production prediction, and rapid optimization of treatment parameters that match reservoir geological conditions, promoting efficient shale oil development in unconventional reservoirs.
- shale oil /
- horizontal well /
- data driven /
- comprehensive evaluation of reservoir quality /
- production prediction /
- hydraulic fracturing /
- optimization of fracturing parameters

HTML全文

图 1 产量影响因素之间相关性分析热图

Figure 1. Heat map for correlation analysis of production influencing factors

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图 2 主成分特征选择

Figure 2. Principal component feature selection

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图 3 主成分特征向量

Figure 3. Eigenvectors of principal components

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图 4 不同等级的高斯隶属函数曲线

Figure 4. Gauss's membership function curve of different grades

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图 5 主成分权重

Figure 5. Weights of principal components

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图 6 1年千米累计产油量与得分的拟合关系

Figure 6. Fitting relationship between accumulated oil production per kilometer in 12 months and scores

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图 7 T1井基础参数

Figure 7. The underlying parameters of Well T1

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图 8 T1井储层非均质性评价结果

Figure 8. Evaluation results of reservoir heterogeneity in Well T1

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图 9 训练集产量预测值与真实值对比

Figure 9. Comparison between predicted and actual production of the training set

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图 10 测试集产量预测值与真实值对比

Figure 10. Comparison between predicted and actual production of the test set

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图 11 第1段不同施工时间点的裂缝监测剖面

Figure 11. Fracture monitoring profile of the first fracturing section at different treatment time

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表 1 样本数据库

Table 1 Sample database

井号	C_to， %	q_API/ API	S₁/ （mg·g⁻¹）	ϕ	K/ mD	I_B	ν	E/ GPa	σ_h/ MPa	σ_dif	L/ m	δ/ m	η_w/ （m³·m⁻¹）	η_s/ （m³·m⁻¹）	Q_m/ （m³·min⁻¹·m⁻¹）	N_p/ （t·km⁻¹）
Y1	3.1	93.6	0.5	6.5	6.11	0.51	0.230	30.77	56.7	0.250	48.0	6.9	23.9	2.6	0.25	8 302.9
Y2	1.8	52.5	1.1	5.5	1.24	0.62	0.228	36.61	79.8	0.279	29.8	5.2	47.9	3.2	0.43	2 682.9
Y3	3.5	89.4	0.9	5.6	0.42	0.56	0.231	27.10	89.7	0.286	65.1	7.6	26.7	2.1	0.17	491.6
Y4	4.5	91.1	2.0	7.3	0.96	0.48	0.231	27.20	56.8	0.235	51.7	6.7	36.2	3.5	0.28	2 466.0
Y5	1.9	95.5	0.5	6.4	0.87	0.30	0.230	30.59	82.8	0.271	66.0	7.9	22.7	2.5	0.13	1 575.9
Y6	4.0	91.8	5.0	8.5	5.80	0.52	0.228	35.25	83.3	0.267	59.7	9.0	30.7	2.6	0.20	8 094.0
Y7	2.2	91.6	0.5	5.4	0.56	0.30	0.230	30.59	73.8	0.251	63.5	7.9	20.9	2.4	0.17	1 566.0
Y8	2.2	98.4	3.1	4.9	0.75	0.57	0.228	37.38	83.0	0.28	43.1	6.4	27.0	2.2	0.31	2 920.6
Y9	2.9	99.2	2.0	5.5	1.06	0.62	0.228	37.67	83.9	0.279	43.9	5.9	23.7	2.0	0.28	1 674.0
Y10	1.9	107.6	2.7	5.9	1.89	0.62	0.228	37.67	83.9	0.279	47.8	5.9	21.5	1.6	0.25	3 167.9
Y11	4.0	93.8	1.9	7.6	1.59	0.51	0.230	32.26	74.2	0.255	46.3	6.0	32.3	3.7	0.24	3 212.2
Y12	2.9	112.8	0.7	7.8	3.85	0.41	0.230	30.57	58.6	0.244	36.2	5.9	26.9	2.6	0.32	5 956.8
Y13	3.2	82.2	3.4	9.9	8.73	0.62	0.228	36.61	79.8	0.279	45.9	6.6	35.0	3.2	0.29	10 581.3
Y14	3.7	103.0	4.7	4.5	0.45	0.57	0.227	36.91	88.4	0.275	55.4	7.9	28.9	2.5	0.23	2 873.9
Y15	4.0	85.3	4.4	4.4	0.51	0.42	0.228	37.14	85.2	0.278	56.7	7.4	29.2	2.8	0.22	2 449.7
Y16	3.1	102.2	4.6	7.7	1.87	0.60	0.228	39.67	89.6	0.288	53.4	7.7	31.7	2.9	0.24	4 371.5
Y17	3.8	92.1	4.5	7.5	4.35	0.62	0.228	36.61	79.8	0.279	42.9	6.2	36.8	3.1	0.31	7 145.3
Y18	2.6	110.3	2.3	4.8	0.88	0.62	0.228	37.67	83.9	0.279	41.1	5.6	26.8	2.2	0.30	2 759.9
Y19	3.4	92.6	2.0	5.3	1.16	0.62	0.228	36.61	79.8	0.279	32.7	5.5	41.4	2.8	0.38	2 300.7
Y20	2.2	91.0	0.4	6.5	1.45	0.48	0.228	33.51	68.2	0.259	56.6	7.1	37.4	2.8	0.31	3 533.4
Y21	3.7	110.0	2.7	7.3	2.73	0.57	0.228	37.38	83.0	0.28	63.1	16.5	36.0	2.4	0.20	5 011.7
Y22	3.5	91.1	3.0	5.6	0.68	0.50	0.228	34.37	84.5	0.264	64.0	8.7	27.1	2.6	0.19	2 943.1
Y23	3.6	101.6	3.9	9.1	6.23	0.62	0.228	36.61	79.8	0.249	43.5	6.3	39.2	3.4	0.30	8 105.3
Y24	4.1	84.9	6.1	6.4	0.82	0.60	0.228	39.67	89.6	0.288	56.9	8.4	28.2	2.5	0.22	4 144.5
Y25	4.2	109.0	3.2	4.7	0.48	0.50	0.229	32.78	82.0	0.255	56.9	6.9	30.8	2.7	0.22	3 109.4
Y26	3.7	102.3	1.1	4.8	1.13	0.57	0.229	33.67	70.9	0.264	53.9	7.3	25.4	2.8	0.21	2 018.5
Y27	3.2	68.2	5.4	6.9	1.46	0.60	0.228	39.67	89.6	0.288	35.2	6.9	42.5	3.1	0.38	3 314.0
Y28	2.5	86.0	0.6	8.2	4.21	0.51	0.230	30.99	60.4	0.249	49.3	7.0	36.7	3.6	0.29	7 863.2
Y29	2.7	104.9	2.3	7.0	2.23	0.62	0.228	37.67	83.9	0.279	61.4	15.8	33.1	2.3	0.17	4 449.0
Y30	2.7	89.0	5.0	10.8	9.36	0.54	0.229	36.91	76.8	0.264	53.8	8.0	29.7	3.5	0.20	10 144.7
Y31	3.0	103.7	4.8	5.2	1.25	0.57	0.228	36.48	86.4	0.271	30.4	5.1	42.0	3.3	0.45	3384.3
Y32	2.1	107.9	2.8	5.8	0.97	0.62	0.228	37.67	83.9	0.279	57.7	6.9	20.6	1.5	0.20	2192.4

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表 2 不同段施工参数优化与产量预测结果

Table 2 Optimization of treatment parameters and production prediction results for segments

井段/m	平均得分	压裂段长/m	簇间距/m	加砂强度/ （m³·m⁻¹）	用液强度/ （m³·m⁻¹）	排量/ （m³·min⁻¹）	预测产量/ （t·km⁻¹）
3 850～3 931	59.2	60	6.50	3.7	31.0	12.5	4 113.5
3 932～4 056	55.1	45	5.80	2.8	26.0	12.5	3 354.3
4 057～4 232	55.8	50	6.30	2.6	28.0	12.0	3 412.9
4 233～4 306	52.9	52	5.80	3.1	29.0	12.5	2 985.9
4 307～4 450	58.4	45	5.30	3.6	34.5	12.5	3 896.4

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表 3 第1段射孔位置优化结果

Table 3 Optimization results of perforation position in the first fracturing section

簇号	顶深/m	底深/m	簇间距/m	簇号	顶深/m	底深/m	簇间距/m
①	4 407.50	4 408.00	5.50	⑤	4 431.00	4 431.50	5.00
②	4 413.50	4 414.00	5.50	⑥	4 436.50	4 437.00	5.00
③	4 419.50	4 420.00	5.20	⑦	4 442.00	4 442.50	5.00
④	4 425.20	4 425.70	5.30	⑧	4 447.50	4 448.00

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