基于改进SSA-BPNN的煤层气直井井底流压预测研究

doi:10.13809/j.cnki.cn32-1825/te.2025.02.009

Abstract

Abstract:

Coalbed methane resources are extensively developed using vertical wells, with controlled-pressure and controlled-water drainage systems. The flowing bottom hole pressure is a crucial parameter in the design of drainage schemes and equipment selection. Therefore, it is of great significance to predict the flowing bottom hole pressure for vertical coalbed methane wells. To conveniently and accurately forecast the flowing bottom hole pressure of vertical coalbed methane and guide their pressure control and drainage, a Backpropagation Neural Network (BPNN) algorithm from the field of machine learning was introduced. Additionally, the Sparrow Search Algorithm (SSA) was improved. These were coupled to establish a forecasting model for flowing bottom hole pressure based on the improved SSA-BPNN approach. Five routinely measured parameters that influence flowing bottom hole pressure were selected as the input parameters for the prediction model, with corresponding bottom hole pressure values as the output parameters. A total of 600 sets of field-measured data were partitioned into training, validation, and testing datasets to develop and validate the forecasting model for vertical coalbed methane wells. The validation set showed that the mean absolute percentage errors for the BPNN model and the Improved SSA-BPNN model on the validation set were 3.10% and 0.53%, respectively. This demonstrated that coupling the Improved SSA and BPNN effectively overcame the propensity of BPNN to converge to local optima, thereby improving the prediction accuracy of flowing bottom hole pressure in a vertical coalbed methane well. Furthermore, the improved SSA-BPNN model was compared with the Genetic Algorithm-Support Vector Regression (GA-SVR) method and the physical model-based analytical method. The results revealed that the mean absolute percentage errors for these three different models were 1.318%, 4.971%, and 18.156%, respectively. The Improved SSA-BPNN model had the lowest error, and its prediction accuracy significantly improved when the flowing bottom hole pressure was low, demonstrating its higher accuracy and strong applicability. The Improved SSA-BPNN model requires only five input parameters, reducing the complexity of input and calculation parameters. It does not require consideration of the fluid distribution within the wellbore and can cover all stages of drainage, maintaining high accuracy across different pressure ranges.

Key words: coalbed methane, sparrow search algorithm, neural network, bottom hole flowing pressure, prediction model

CLC Number:

TE37

YU Yang,DONG Yintao,LI Yunbo, et al. Research on prediction of bottom hole flowing pressure for vertical coalbed methane wells based on improved SSA-BPNN[J]. Petroleum Reservoir Evaluation and Development, 2025, 15(2): 250-256.

Figures/Tables 7

Fig. 1

Fig. 2

Table 1

Fig. 3

Fig. 4

Table 2

Fig. 5

References 30

[1]	邹才能, 杨智, 黄士鹏, 等. 煤系天然气的资源类型、形成分布与发展前景[J]. 石油勘探与开发, 2019, 46(3): 433-442.
	ZOU Caineng, YANG Zhi, HUANG Shipeng, et al. Resource types, formation, distribution and prospects of coal-measure gas[J]. Petroleum Exploration & Development, 2019, 46(3): 433-442.
[2]	娄钰, 潘继平, 王陆新, 等. 中国天然气资源勘探开发现状、问题及对策建议[J]. 国际石油经济, 2018, 26(6): 21-27.
	LOU Yu, PAN Jiping, WANG Luxin, et al. Problems and countermeasures in the exploration and development of natural gas resources in China[J]. International Petroleum Economics, 2018, 26(6): 21-27.
[3]	赵贤正, 朱庆忠, 孙粉锦, 等. 沁水盆地高阶煤层气勘探开发实践与思考[J]. 煤炭学报, 2015, 40(9): 2131-2136.
	ZHAO Xianzheng, ZHU Qingzhong, SUN Fenjin, et al. Practice of coalbed methane exploration and development in Qinshui Basin[J]. Journal of China Coal Society, 2015, 40(9): 2131-2136.
[4]	张遂安, 曹立虎, 杜彩霞. 煤层气井产气机理及排采控压控粉研究[J]. 煤炭学报, 2014, 39(9): 1927-1931.
	ZHANG Suian, CAO Lihu, DU Caixia, et al. Study on CBM production mechanism and control theory of bottom-hole pressure and coal fines during CBM well production[J]. Journal of China Coal Society, 2014, 39(9): 1927-1931.
[5]	刘斌, 杜海为, 崔金榜, 等. 煤层气井排采控制技术发展现状与展望[J]. 石油钻采工艺, 2019, 41(4): 489-493.
	LIU Bin, DU Haiwei, CUI Jinbang, et al. Development status and prospect of CBM well production control technologies[J]. Oil Drilling & Production Technology, 2019, 41(4): 489-493.
[6]	杜新锋, 郭盛强, 张群, 等. 多煤层煤层气井分层控压合层排采技术及装备[J]. 煤炭科学技术, 2018, 46(6): 114-118.
	DU Xinfeng, GUO Shengqiang, ZHANG Qun, et al. Separate－layer pressure control and multi－layer drainage technology and device for coalbed methane wells with multiple seams[J]. Coal Science and Technology, 2018, 46(6): 114-118.
[7]	刘新福, 綦耀光, 李延祥, 等. 单相流煤层气井井底流压预测方法研究[J]. 中国矿业大学学报, 2010, 39(6): 876-880.
	LIU Xinfu, QI Yaoguang, LI Yanxiang, et al. Prediction of flowing bottomhole pressures for single-phase coalbed methane wells[J]. Journal of China University of Mining & Technology, 2010, 39(6): 876-880.
[8]	孙仁远, 宣英龙, 任晓霞, 等. 煤层气井井底流压计算方法[J]. 石油钻采工艺, 2012, 34(4): 100-103.
	SUN Renyuan, XUAN Yinglong, REN Xiaoxia, et al. Methods of bottom-hole flow pressure calculations for coalbed methane wells[J]. Oil Drilling & Production Technology, 2012, 34(4): 100-103.
[9]	杨焦生, 王一兵, 王宪花. 煤层气井井底流压分析及计算[J]. 天然气工业, 2010, 30(2): 66-68.
	YANG Jiaosheng, WANG Yibing, WANG Xianhua. Analysis and computation of flowing bottom hole pressure in coalbed methane wells[J]. Natural Gas Industry, 2010, 30(2): 66-68.
[10]	张永平, 孟召平, 刘贺, 等. 煤层气井排采初期井底流压动态模型及应用分析[J]. 煤田地质与勘探, 2016, 44(2): 29-33.
	ZHANG Yongping, MENG Zhaoping, LIU He, et al. Dynamic model for bottomhole flowing pressure in initial stage of CBM wells drainage and its application[J]. Coal Geology & Exploration, 2016, 44(2): 29-33.
[11]	刘新福, 綦耀光, 刘春花, 等. 气水两相煤层气井井底流压预测方法[J]. 石油学报, 2010, 31(6): 998-1003.
	LIU Xinfu, QI Yaoguang, LIU Chunhua, et al. Prediction of flowing bottomhole pressures for two-phase coalbed methane wells[J]. Acta Petrolei Sinica, 2010, 31(6): 998-1003.
[12]	赵金, 张遂安. 煤层气井底流压生产动态研究[J]. 煤田地质与勘探, 2013, 41(2): 21-24.
	ZHAO Jin, ZHANG Suian. Production dynamics of CBM bottom hole pressure[J]. Coal Geology & Exploration, 2013, 41(2): 21-24.
[13]	毛慧, 韩国庆, 吴晓东, 等. 确定煤层气井合理生产压差的新思路[J]. 天然气工业, 2011, 31(3): 52-55.
	MAO Hui, HAN Guoqing, WU Xiaodong, et al. A new discussion on the determination methods of reasonable drawdown pressure for a coalbed methane gas well[J]. Natural Gas Industry, 2011, 31(3): 52-55.
[14]	董银涛, 鞠斌山, 张遂安. 煤层气直井井筒环空压力模型[J]. 煤炭学报, 2018, 43(9): 2534-2542.
	DONG Yintao, JU Binshan, ZHANG Suian. Wellbore annulus pressure model of the vertical coalbed methane well[J]. Journal of China Coal Society, 2018, 43(9): 2534-2542.
[15]	黄家宸, 张金川. 机器学习预测油气产量现状[J]. 油气藏评价与开发, 2021, 11(4): 613-620.
	HUANG Jiachen, ZHANG Jinchuan. Overview of oil and gas production forecasting by machine learning[J]. Petroleum Reservoir Evaluation and Development, 2021, 11(4): 613-620.
[16]	韩力群, 施彦. 人工神经网络理论、设计及应用[M]. 北京: 化学工业出版社, 2002.
	HAN Liqun, SHI Yan. Artificial neural network theory, design and application[M]. Beijing: Chemical Industry Press, 2002.
[17]	沙林秀, 胥陈卓. 基于主成分分析的NCPSO-BP机械钻速预测[J]. 石油钻采工艺, 2022, 44(4): 515-521.
	SHA Linxiu, XU Chenzhuo. Prediction of NCPSO-BP ROP based on principal component ana[J]. Oil Drilling & Production Technology, 2022, 44(4): 515-521.
[18]	王虹, 尤秀松, 李首滨, 等. 基于遗传算法与BP神经网络的支架跟机自动化研究[J]. 煤炭科学技术, 2021, 49(1): 272-277.
	WANG Hong, YOU Xiusong, LI Shoubin, et al. Research on automation of support based on genetic algorithm and BP neural network[J]. Coal Science and Technology, 2021, 49(1): 272-277.
[19]	张金梦, 刘慧君. 遗传算法优化BP神经网络的泊车位数量预测[J]. 重庆大学学报, 2018, 41(3): 80-85.
	ZHANG Jinmeng, LIU Huijun. Prediction of spare parking spaces based on BP neural network optimized by genetic algorithm[J]. Journal of Chongqing University, 2018, 41(3): 80-85.
[20]	臧子婧, 吴海波, 张平松, 等. 基于ABC-BP模型的煤层含气量预测[J]. 煤田地质与勘探, 2021, 49(2): 152-158.
	ZANG Zijing, WU Haibo, ZHANG Pingsong, et al. Prediction of coal seam gas content based on ABC-BP model[J]. Coal Geology & Exploration, 2021, 49(2): 152-158.
[21]	XUE J K, SHEN B. A novel swarm intelligence optimization approach: sparrow search algorithm[J]. Systems Science & Control Engineering An Open Access Journal, 2020, 8(1): 22-34.
[22]	赵涛, 于师建. 基于GA-BP神经网络算法的高密度电法非线性反演[J]. 煤田地质与勘探, 2017, 45(2): 147-151.
	ZHAO Tao, YU Shijian. GA-BP neural network algorithm-based nonlinear inversion for high density resistivity method[J]. Coal Geology & Exploration, 2017, 45(2): 147-151.
[23]	LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.
[24]	罗发强, 刘景涛, 陈修平, 等. 基于BP和LSTM神经网络的顺北油田5号断裂带地层孔隙压力智能预测方法[J]. 石油钻采工艺, 2022, 44(4): 506-514.
	LUO Faqiang, LIU Jingtao, CHEN Xiuping. Intelligent method for predicting formation pore pressure in No. 5 fault zone in Shunbei oilfield based on BP and LSTM neural network [J]. Oil Drilling & Production Technology, 2022, 44(4): 506-514.
[25]	刘建军, 石定元, 武国宁. 基于Kent映射的混合混沌优化算法[J]. 计算机工程与设计, 2015, 36(6): 1498-1503.
	LIU Jianjun, SHI Dingyuan, WU Guoning. Hybrid chaotic optimization algorithm based on Kent map[J]. Computer Engineering And Design, 2015, 36(6): 1498-1503.
[26]	周刘喜, 张兴华, 李纬. 一种改进的多目标粒子群优化算法[J]. 计算机工程与应用, 2009, 45(33): 38-41.
	ZHOU Liuxi, ZHANG Xinghua, LI Wei. Improved multi-objective particle swarm optimization algorithm[J]. Computer Engineering and Applications, 2009, 45(33): 38-41.
[27]	李永恒, 赵志刚. 基于越界重置和高斯变异的蝙蝠优化算法[J]. 计算机工程与科学, 2019, 41(1): 144-152.
	LI Yongheng, ZHAO Zhigang. An improved bat algorithm based on cross-border relocation and Gaussian mutation[J]. Computer Engineering & Science, 2019, 41(1): 144-152.
[28]	吕鑫, 慕晓冬, 张钧, 等. 混沌麻雀搜索优化算法[J]. 北京航空航天大学学报, 2021, 47(8): 1712-1720.
	LV Xin, MU Xiaodong, ZHANG Jun, et al. Chaos sparrow search optimization algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(8): 1712-1720.
[29]	LUO Z, HASANIPANAH M, AMNIEH H B. GA-SVR: a novel hybrid data-driven model to simulate vertical load capacity of driven piles[J]. Engineering with Computers, 2021, 37(12): 823-831.
[30]	孙雷, 罗强, 潘毅, 等. 基于GA-SVR的CO₂驱原油最小混相压力预测模型[J]. 大庆石油地质与开发, 2017, 36(3): 123-129.
	SUN Lei, LUO Qiang, PAN Yi, et al. Predicting model of the oil minimal miscible pressure for the CO₂ flooding based on GA-SVR[J]. Petroleum Geology & Oilfield Development in Daqing, 2017, 36(3): 123-129.

井口套压/MPa	动液面高度/m	动液面深度/m	日产气量/m³	日产水量/m³	井底流压/MPa
0.09	45.29	563.37	12 772.69	24.21	0.54
0.08	65.08	543.58	7 452.36	4.20	0.71
0.10	52.63	556.03	10 896.58	8.50	0.62
0.15	16.72	410.45	4 151.44	18.00	0.31
0.09	62.99	364.18	2 026.69	10.83	0.71
0.13	9.12	424.43	235.92	5.37	0.21
0.20	66.94	364.22	260.09	1.89	0.86
0.14	302.00	361.10	0	4.00	3.18
1.25	17.90	845.00	501.00	1.30	1.40
0.05	64.47	544.19	9 152.99	23.86	0.68
0.15	11.67	421.88	232.83	5.34	0.26
1.39	11.10	300.00	3 650.00	0.90	1.39
2.62	3.20	659.90	158.00	3.90	2.73
0.08	185.40	600.30	0	7.40	1.92

预测模型	均方误差	平均绝对误差	平均绝对百分比误差/%
改进SSA-BPNN模型	2.52×10^-4	0.013	1.318
GA-SVR模型	5.54×10^-4	0.027	4.971
文献[14]中解析模型	1.55×10^-3	0.087	18.156

Research on prediction of bottom hole flowing pressure for vertical coalbed methane wells based on improved SSA-BPNN

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 7

References 30

Related Articles 15

Metrics

Comments

Recommended 0

[1]	WU Xi. Technology and practice for efficient development of coalbed methane horizontal wells in high-rank coal of Qinshui Basin [J]. Petroleum Reservoir Evaluation and Development, 2025, 15(2): 167-174.
[2]	WANG Liangjun, WANG Yong, ZHANG Xinwen, JIN Yunyun, ZHU Yan, ZHANG Gaoyuan, LI Hui, LI Wangju. Coal accumulation control on gas and coalbed methane exploration potential in southern Ordos Basin: A case study of Carboniferous Taiyuan Formation in Xunyi exploration area [J]. Petroleum Reservoir Evaluation and Development, 2025, 15(2): 175-184.
[3]	ZHU Suyang, LIU Wei, WANG Yunfeng, JIA Chunsheng, CHEN Chaogang, PENG Xiaolong. Current situation and prospects of coalbed methane exploration and development in Sichuan Basin [J]. Petroleum Reservoir Evaluation and Development, 2025, 15(2): 185-193.
[4]	YANG Xue, TIAN Chong, YANG Yuran, ZHANG Jingyuan, WANG Qing, WU Wei, LUO Chao. Accumulation characteristics and exploration potential of deep coalbed methane in Changning area of Sichuan Basin [J]. Petroleum Reservoir Evaluation and Development, 2025, 15(2): 194-204.
[5]	WANG Pengxiang, ZHANG Zhou, YU Wanying, ZOU Qiang, YANG Zhengtao. Characteristics of pore-fracture structure and three-dimensional spatial distribution differences in deep and shallow coal reservoirs: A case study of Junggar Basin [J]. Petroleum Reservoir Evaluation and Development, 2025, 15(2): 227-236.
[6]	ZHANG Wen, HUANG Hongxing, LIU Ying, FENG Yanqing, SUN Wei, LI Ziling, WANG Jing, ZHAO Zengping. Research on recoverable reserves and gas production characteristics of coalbed methane wells in Baode block of Ordos Basin [J]. Petroleum Reservoir Evaluation and Development, 2025, 15(2): 257-265.
[7]	HU Qiujia, LIU Chunchun, ZHANG Jianguo, CUI Xinrui, WANG Qian, WANG Qi, LI Jun, HE Shan. Machine learning-based coalbed methane well production prediction and fracturing parameter optimization [J]. Petroleum Reservoir Evaluation and Development, 2025, 15(2): 266-273.
[8]	ZHAO Chongsheng, WANG Bo, GOU Bo, LUO Pengfei, CHEN Guojun, WU Guoquan. Equipment configuration and process technology of hybrid oil-electric fracturing for deep coalbed methane [J]. Petroleum Reservoir Evaluation and Development, 2025, 15(2): 292-299.
[9]	LIN Weiqiang, CONG Peng, WANG Hong, WEI Zichen, YANG Yuntian, YAO Zhiqiang, QU Lili, MA Limin, WANG Fanglu. Application and discussion of geological guidance technology for deep coalbed methane horizontal wells: A case study of block X in Shenmu gas field, Ordos Basin [J]. Petroleum Reservoir Evaluation and Development, 2025, 15(2): 300-309.
[10]	ZHAO Haifeng, WANG Chengwang, XI Yue, WANG Chaowei. Study on dynamic stress field of fracturing in horizontal wells of deep coal seams: A case study of Daning-Jixian block in Ordos Basin [J]. Petroleum Reservoir Evaluation and Development, 2025, 15(2): 310-323.
[11]	CHEN Weiming, JIANG Lin, LUO Tongtong, LI Yue, WANG Jianhua. Research on deep learning-based fracture network inversion method for shale gas reservoirs [J]. Petroleum Reservoir Evaluation and Development, 2025, 15(1): 142-151.
[12]	KONG Xiangwei, XIE Xin, WANG Cunwu. Optimization of segmented fracturing parameters for coalbed methane horizontal wells based on comprehensive fracability index [J]. Petroleum Reservoir Evaluation and Development, 2024, 14(6): 925-932.
[13]	ZHANG Bing, DU Fengfeng, ZHANG Haifeng, WEI Chao. Selection of favorable areas for coalbed methane development based on economic benefit evaluation: A case study of Yangjiapo block in eastern margin of Ordos Basin [J]. Petroleum Reservoir Evaluation and Development, 2024, 14(6): 933-941.
[14]	QIU Wenci, SANG Shuxun, GUO Zhijun, HAN Sijie, ZHOU Xiaozhi, ZHOU Peiming, WU Zhangli, SANG Guoyun, ZHANG Binbin, GAO Wei. Characteristics of stratified coal reservoirs in Liupanshui coalfield of Guizhou Province and exploration and development direction of coalbed methane [J]. Petroleum Reservoir Evaluation and Development, 2024, 14(6): 959-966.
[15]	WANG Zhijian. Mechanism study on effect of CO₂ phase transition fracturing on methane adsorption in coal [J]. Petroleum Reservoir Evaluation and Development, 2024, 14(6): 967-974.