鄂尔多斯盆地神府区块二叠系太原组煤层气产能预测方法

doi:10.12108/yxyqc.20260518

Lithologic Reservoirs ›› 2026, Vol. 38 ›› Issue (5): 191-200.doi: 10.12108/yxyqc.20260518

• PETROLEUM ENGINEERING AND OIL & GAS FIELD DEVELOPMENT • Previous Articles

Prediction method for coalbed methane deliverability of Permian Taiyuan Formation in Shenfu block, Ordos Basin

LI Xinze¹(), WU Hao¹, CHAO Jiahao², LI Na¹(), WANG Suran¹, BAI Yuhu¹, FENG Ruyong¹, CHENG Jialu³

¹ Exploration and Development Research Institute， Beijing Research Center of CNOOC （China） Ltd., Beijing 100028, China
² Research Institute of Exploration and Development， China United Coalbed Methane Co., Ltd., Beijing 100016, China
³ College of Petroleum Engineering， China University of Petroleum （Beijing）, Beijing 102249, China

Received:2025-09-12 Revised:2025-11-25 Online:2026-09-01 Published:2026-09-04
Contact: LI Na E-mail:lixz@cnooc.com.cn;lina28@cnooc.com.cn

Abstract

Abstract:

During the development of deep coalbed methane, significant variability exists in single-well production, which makes it difficult to predict the deliverability. Based on actual production data of deep coalbed methane from Permian Taiyuan Formation in Shenfu block of Ordos Basin, a research path of “data purification-pattern mining-model construction” was adopted to conduct deliverability evaluation and prediction methods. The results show that: （1） Three typical deliverability patterns are identified in Shenfu block of Ordos Basin: “high production-decline”, “production ramp-up-stable production-decline”, and “dewatering-production rampup-stable production-decline”, with “dewatering-production rampup-stable production-decline” being the dominant type for deep coalbed methane wells in this area. （2） Utilizing the dynamic screening method of deliverability controlling factors and integrating the monotonicity constraint mechanism of parameters on deliverability in seepage theory, a deliverability prediction model that considers both data patterns and seepage mechanisms was constructed. （3） The main controlling factors of deliverability exhibit dynamic evolution characteristics. Initial productivity is primarily controlled by geological factors, while productivity during the stable production period is significantly affected by engineering factors. The physical-constrained Random Forest prediction model achieves a prediction accuracy of 86%, which is 64.5% higher than that of conventional multivariate linear empirical formulas. Engineering optimization strategies proposed based on the model results have effectively increased the average single-well production in the block by nearly two times.

Key words: coalbed methane, machine learning, data mining, random forest prediction model, deliverability prediction, Taiyuan Formation, Permian, Shenfu block, Ordos Basin

CLC Number:

TE319

LI Xinze, WU Hao, CHAO Jiahao, LI Na, WANG Suran, BAI Yuhu, FENG Ruyong, CHENG Jialu. Prediction method for coalbed methane deliverability of Permian Taiyuan Formation in Shenfu block, Ordos Basin[J].Lithologic Reservoirs, 2026, 38(5): 191-200.

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References 29

[1]	秦勇. 中国深部煤层气地质研究进展[J]. 石油学报, 2023, 44(11):1791-1811.
	QIN Yong. Progress on geological research of deep coalbed methane in China[J]. Acta Petrolei Sinica, 2023, 44(11):1791-1811.
[2]	申有义, 王凯峰, 唐书恒, 等. 沁水盆地榆社—武乡区块二叠系煤系页岩储层地质建模及“甜点”预测[J]. 岩性油气藏, 2024, 36(4):98-108.
	SHEN Youyi, WANG Kaifeng, TANG Shuheng, et al. Geological modeling and “sweet spot” prediction of Permian coal measures shale reservoirs in Yushe-Wuxiang block,Qinshui Basin[J]. Lithologic Reservoirs, 2024, 36(4):98-108.
[3]	徐长贵, 季洪泉, 王存武, 等. 鄂尔多斯盆地东缘临兴-神府区块深部煤层气富集规律与勘探对策[J]. 煤田地质与勘探, 2024, 52(8):1-11.
	XU Changgui, JI Hongquan, WANG Cunwu, et al. Enrichment patterns and exploration countermeasures of deep coalbed methane in the Linxing-Shenfu block on the eastern margin of the Ordos Basin[J]. Coal Geology & Exploration, 2024, 52(8):1-11.
[4]	戴小河, 雷肖霄.我国发现千亿方深煤层气田, 储量或超1100亿立方米[N]. 新华每日电讯,2023-10-24(3).
	DAI Xiaohe, LEI Xiaoxiao. China discovers hundred-billion-cubic-meter deep coalbed methane field with reserves exceeding 110 billion cubic meters[N]. Xinhua Daily Telegraph,2023-10-24(3).
[5]	安琦, 杨帆, 杨睿月, 等. 鄂尔多斯盆地神府区块深部煤层气体积压裂实践与认识[J]. 煤炭学报, 2024, 49(5):2376-2393.
	AN Qi, YANG Fan, YANG Ruiyue, et al. Practice and understanding of deep coalbed methane massive hydraulic fracturing in Shenfu Block,Ordos Basin[J]. Journal of China Coal Society, 2024, 49(5):2376-2393.
[6]	朱光辉, 季洪泉, 米洪刚, 等. 神府深部煤层气大气田的发现与启示[J]. 煤田地质与勘探, 2024, 52(8):12-21.
	ZHU Guanghui, JI Hongquan, MI Honggang, et al. Discovery of a large gas field of deep coalbed methane in the Shenfu block and its implications[J]. Coal Geology & Exploration, 2024, 52(8):12-21.
[7]	周德华, 陈刚, 陈贞龙, 等. 中国深层煤层气勘探开发进展、关键评价参数与前景展望[J]. 天然气工业, 2022, 42(6):43-51.
	ZHOU Dehua, CHEN Gang, CHEN Zhenlong, et al. Exploration and development progress,key evaluation parameters and prospect of deep CBM in China[J]. Natural Gas Industry, 2022, 42(6):43-51.
[8]	刘建忠, 朱光辉, 刘彦成, 等. 鄂尔多斯盆地东缘深部煤层气勘探突破及未来面临的挑战与对策:以临兴—神府区块为例[J]. 石油学报, 2023, 44(11):1827-1839.
	LIU Jianzhong, ZHU Guanghui, L1U Yancheng, et al. Breakthrough,future challenges and countermeasures of deep coalbed methane in the eastern margin of Ordos Basin:A case study of Linxing-Shenfu block[J]. Acta Petrolei Sinica, 2023, 44(11):1827-1839.
[9]	邓英尔, 黄润秋, 郭大浩, 等. 煤层气产量的影响因素及不稳定渗流产量预测[J]. 天然气工业, 2005, 25(1):117-119.
	DENG Ying’er, HUANG Runqiu, GUO Dahao, et al. Affecting factors of coal-bed gas production and production prediction of unstable percolation[J]. Natural Gas Industry, 2005, 25(1):117-119.
[10]	张海茹, 李昊. 煤层气峰值产量拟合及产量动态预测方法研究[J]. 岩性油气藏, 2013, 25(4):116-118.
	ZHANG Hairu, LI Hao. Study on coalbed methane peak production fitting and production forecast by different dynamic analysis methods[J]. Lithologic Reservoirs, 2013, 25(4):116-118.
[11]	吕玉民, 汤达祯, 李治平, 等. 煤层气井动态产能拟合与预测模型[J]. 煤炭学报, 2011, 36(9):1481-1485.
	LYU Yumin, TANG Dazhen, LI Zhiping, et al. Fitting and predicting models for coalbed methane wells dynamic productivity[J]. Journal of China Coal Society, 2011, 36(9):1481-1485.
[12]	吴晓东, 王国强, 李安启, 等. 煤层气井产能预测研究[J]. 天然气工业, 2004, 24(8):82-84.
	WU Xiaodong, WANG Guoqiang, LI Anqi, et al. Productivity prediction of coal-bed gas wells[J]. Natural Gas Industry, 2004, 24(8):82-84.
[13]	未志杰, 康晓东, 刘玉洋, 等. 煤层气藏全流固耦合数学模型[J]. 岩性油气藏, 2019, 31(2):151-158.
	WEI Zhijie, KANG Xiaodong, LIU Yuyang, et al. A fully coupled fluid flow and geomechanics model for coalbed methane reservoir[J]. Lithologic Reservoirs, 2019, 31(2):151-158.
[14]	未志杰, 康晓东. 煤层气藏强化采收全流固耦合模型[J]. 岩性油气藏, 2021, 33(5):181-188.
	WEI Zhijie, KANG Xiaodong. A fully coupled fluid flow and geomechanics model for enhanced coalbed methane recovery[J]. Lithologic Reservoirs, 2021, 33(5):181-188.
[15]	龚斌, 王虹雅, 王红娜, 等. 基于大数据分析算法的深部煤层气地质—工程一体化智能决策技术[J]. 石油学报, 2023, 44(11):1949-1958.
	GONG Bin, WANG Hongya, WANG Hongna, et al. Integrated intelligent decision-making technology for deep coalbed methane geology and engineering based on big data analysis algorithms[J]. Acta Petrolei Sinica, 2023, 44(11):1949-1958.
[16]	董维强, 孟召平, 沈振, 等. 基于循环神经网络的煤层气井产气量预测方法研究[J]. 煤炭科学技术, 2021, 49(9):176-183.
	DONG Weiqiang, MENG Zhaoping, SHEN Zhen, et al. Research on coalbed methane well gas production forecast method based on cyclic neural network[J]. Coal Science and Techno-logy, 2021, 49(9):176-183.
[17]	孔鹏, 李忠城, 杨晨曦, 等. 基于PCA-NB方法的煤层气井压后产量预测研究[J]. 特种油气藏, 2019, 26(6):108-112.
	KONG Peng, LI Zhongcheng, YANG Chenxi, et al. Production forecast of fractured coalbed methane wells based on PCA-NB Method[J]. Special Oil & Gas Reservoirs, 2019, 26(6):108-112.
[18]	李媛, 郭大立, 康芸玮. 融合注意力机制的煤层气产量动态预测[J]. 科学技术与工程, 2023, 23(2):550-557.
	LI Yuan, GUO Dali, KANG Yunwei. Dynamic prediction of coalbed methane production with attention mechanisms[J]. Science Technology and Engineering, 2023, 23(2):550-557.
[19]	冯玉龙, 周林元, 王乾, 等. 基于多层次模糊数学法的煤层气井产能综合评价模型[J]. 煤田地质与勘探, 2021, 49(2):125-132.
	FENG Yulong, ZHOU Linyuan, WANG Qian, et al. Multi-level fuzzy mathematics-based comprehensive evaluation model of CBM well productivity[J]. Coal Geology & Exploration, 2021, 49(2):125-132.
[20]	GUO Zixi, ZHAO Jinzhou, YOU Zhenjing, et al. Prediction of coalbed methane production based on deep learning[J]. Energy, 2021, 230:120847.
[21]	宋宣毅, 刘月田, 马晶, 等. 基于灰狼算法优化的支持向量机产能预测[J]. 岩性油气藏, 2020, 32(2):134-140.
	SONG Xuanyi, LIU Yuetian, MA Jing, et al. Productivity forecast based on support vector machine optimized by grey wolf optimizer[J]. Lithologic Reservoirs, 2020, 32(2):134-140.
[22]	DU Shuyi, WANG Meizhu, YANG Jiaosheng, et al. An enhanced prediction framework for coalbed methane production incorporating deep learning and transfer learning[J]. Energy, 2023, 282:128877.
[23]	YANG Ruiyue, QIN Xiaozhou, LIU Wei, et al. A physics-constrained data-driven workflow for predicting coalbed methane well production using artificial neural network[J]. SPE Journal, 2022, 27(3):1531-1552.
[24]	孙立春, 刘佳, 李娜, 等. 鄂尔多斯盆地神府区块深部煤层气井产量主控因素及合理压裂规模优化[J]. 石油实验地质, 2025, 47(1):43-53.
	SUN Lichun, LIU Jia, LI Na, et al. Main controlling factors of production and reasonable fracturing scale optimization of deep coalbed methane wells in Shenfu block,Ordos Basin[J]. Petroleum Geology & Experiment, 2025, 47(1):43-53.
[25]	李启晖, 任大忠, 甯波, 等. 鄂尔多斯盆地神木地区侏罗系延安组煤层微观孔隙结构特征[J]. 岩性油气藏, 2024, 36(2):76-88.
	LI Qihui, REN Dazhong, NING Bo, et al. Micro-pore structure characteristics of coal seams of Jurassic Yan’an Formation in Shenmu area,Ordos Basin[J]. Lithologic Reservoirs, 2024, 36(2):76-88.
[26]	ZHU Jie, ZHAO Yuhao, HU Qiujia, et al. Coalbed methane production model based on random forests optimized by a genetic algorithm[J]. ACS Omega, 2022, 7(15):13083-13094.
[27]	LIU Feitony, TING Kaiming, ZHOU Zhihua. Isolation forest[R]. Pisa,2008 Eighth IEEE International Conference on Data Mining (ICDM), 2008.
[28]	闵超, 文国权, 李小刚, 等. 可解释机器学习在油气领域人工智能中的研究进展与应用展望[J]. 天然气工业, 2024, 44(9):114-126.
	MIN Chao, WEN Guoquan, LI Xiaogang, et al. Research pro-gress and application prospect of interpretable machine learning in artificial intelligence of oil and gas industry[J]. Natural Gas Industry, 2024, 44(9):114-126.
[29]	吴辰文, 梁靖涵, 王伟, 等. 基于递归特征消除方法的随机森林算法[J]. 统计与决策, 2017, 33(21):60-63.
	WU Chenwen, LIANG Jinghan, WANG Wei, et al. Random forest algorithm based on recursive feature elimination[J]. Statistics & Decision, 2017, 33(21):60-63.

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TRENDMD:

地质参数	范围/平均值	工程参数	范围/平均值	排采参数	范围/平均值
煤层深度/m	1 533.00 ~ 2 638.50 2 129.40	射孔厚度/m	0.6 ~ 16.4 4.9	泵径/（10^-3 m）	32.00 ~ 70.00 44.60
煤层垂厚/m	4.50 ~ 27.90 13.40	射开程度/%	7.2 ~ 104.2 37.2	下泵深度/m	1 296.90 ~ 2 552.50 1 989.90
夹层垂厚/m	0 ~ 33.80 3.20	实际净液量/m³	1 193.1 ~ 6 556.9 2 471.6	冲程/m	2.40 ~ 3.95 3.20
含气量/（m³·t^-1）	7.80 ~ 22.12 14.14	实际砂量/m³	119.8 ~ 901.2 317.5	冲次	0.60 ~ 4.56 1.79
煤岩密度/(10^-3 kg·m^-3）	1.27 ~ 1.65 1.42	净液强度(m³·m^-1）	15.5 ~ 560.0 164.8	见气时井底流压/MPa	10.66 ~ 25.87 19.93
孔隙度/%	2.91 ~ 5.54 4.23	加砂强度(m³·m^-1）	1.8 ~ 78.9 20.9	见气时间/d	0 ~ 132.00 26.70
渗透率/mD	0.01 ~ 0.38 0.10	返排率/%	11.3 ~ 120.2 27.7	见气前排采速度/ (10^-3 MPa·d^-1)	11.00 ~ 331.00 120.00
储层压力/MPa	10.10 ~ 26.40 19.10	砂比/%	4.1 ~ 22.9 11.7
地质强度指标GSI	43.85 ~ 77.52 63.40	破裂压力/MPa	26.1 ~ 36.0 30.4
储隔层应力差/MPa	0.60 ~ 7.60 2.90	混砂液量/m³	0 ~ 4 927.6 1 624.9
水平最小主应力/MPa	13.40 ~ 47.70 32.40	施工排量/m³	7.5 ~ 22.0 17.5
抗压强度/MPa	3.00 ~ 145.40 39.00	前置液量/m³	210.5 ~ 869.5 435.6
弹性模量/（10³ MPa）	0 ~ 19.90 6.52	携砂液量/m³	961.3 ~ 3 864.3 1 787.1
泊松比	0.14 ~ 0.44 0.33	顶替液量/m³	20.1 ~ 132.6 43.4

井名	加液量/m³	加砂量/m³	排量/ (m³·min^-1)	90 d峰值产气量(m³·d^-1)
SF-82-1D	3 156	300.4	15	2 500
SF-82-2D	2 589	263.0	15	2 300
SF-82-3D	3 786	504.0	20	4 600
SF-82-4D	3 668	500.0	20	5 400

Prediction method for coalbed methane deliverability of Permian Taiyuan Formation in Shenfu block, Ordos Basin

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