基于随机森林算法的低煤阶煤层气开发选区预测

doi:10.13809/j.cnki.cn32-1825/te.2022.04.007

油气藏评价与开发 ›› 2022, Vol. 12 ›› Issue (4): 596-603.doi: 10.13809/j.cnki.cn32-1825/te.2022.04.007

基于随机森林算法的低煤阶煤层气开发选区预测

陈跃¹(),王丽雅¹(),李国富²,张林³,杨甫⁴,马卓远¹,高正¹

1.西安科技大学地质与环境学院,陕西西安 710054
2.煤与煤层气共采技术国家重点实验室,山西晋城 048000
3.陕西煤层气开发利用有限公司,陕西西安 710065
4.自然资源部煤炭资源勘查与综合利用重点实验室,陕西西安 710021

收稿日期:2022-04-12 发布日期:2022-09-02 出版日期:2022-08-26
通讯作者: 王丽雅 E-mail:cyxust@126.com;1308346025@qq.com
作者简介:陈跃（1988—）,男,博士,副教授,从事非常规天然气地质研究。地址：陕西省西安市雁塔中路58号西安科技大学,邮政编码：710054。E-mail： cyxust@126.com
基金资助:
国家自然科学基金项目“润湿性制约下低阶煤不同煤岩组分甲烷解吸机制”(41902175);山西省揭榜招标项目“基于气藏工程的煤层气井人工智能排采技术与示范”(20201101002);中国博士后科学基金项目“低煤阶镜煤与暗煤润湿性差异及对甲烷解吸的影响”(2019M653873XB)

Prediction of favorable areas for low-rank coalbed methane based on Random Forest algorithm

CHEN Yue¹(),WANG Liya¹(),LI Guofu²,ZHANG Lin³,YANG Fu⁴,MA Zhuoyuan¹,GAO Zheng¹

1. College of Geology and Environment, Xi’an University of Science and Technology, Xi’an, Shaanxi 710054, China
2. Key Laboratory of coal and coalbed methane co-mining technology, Jincheng, Shanxi 048000, China
3. Shaanxi Coalbed Methane Development Corp.Ltd., Xi’an, Shaanxi 710065, China
4. Key Laboratory of Coal Resources Exploration and Comprehensive Utilization, Ministry of Natural Resources, Xi’an, Shaanxi 710021, China

Received:2022-04-12 Online:2022-09-02 Published:2022-08-26
Contact: WANG Liya E-mail:cyxust@126.com;1308346025@qq.com

摘要/Abstract

摘要：

中国低煤阶煤层气资源丰富,且煤层气作为一种清洁能源,其开发和利用可有效地缓解我国天然气资源短缺问题,但是商业化规模开发稍显不足,亟需系统研究。煤层气高效开发的前提是有利区优选,但目前煤层气开发选区评价均涉及一定的主观人为因素,会间接影响或干扰预测效果。将黄陇煤田彬长矿区大佛寺井田低煤阶作为研究对象,以生产实际数据为基础,采用机器学习中的随机森林算法对该区煤层气开发选区做出预测。结果表明：①Pearson关联系数（PCC）分析表明含气量、灰分、煤层净厚度、构造位置、顶板厚度、渗透率、储层压力和埋深这8个煤层气产出相关参数相互独立,可用于模型建立;②随机森林算法将煤层气开发选区划分为5类不同程度的区域,其中Ⅰ类（极高）和Ⅱ类（高有利）区占整个研究区域的13.88 %,主要分布在井田的中部,西部存在Ⅱ类（高有利区）分布,后续开发部署井位也可着重考虑,而井田的东南部不适于后续部署井位;③由接受者操作特征曲线（ROC）可知,一般成功率曲线与预测率曲线下的面积值（AUC）为0.961,表明随机森林模型预测精度较高,结果可靠。通过机器学习算法对煤层气开发选区进行综合预测,可规避传统算法中的人为主观因素,且具有较高的精度,可为后续非常规油气开发选区提供一定的理论参考依据。

关键词: 随机森林, 开发选区预测, 煤层气, 低阶煤, 黄陇煤田

Abstract:

In China, low-rank coal and coalbed methane resources are abundant, meanwhile, as a kind of clean energy, the development and utilization of coalbed methane（CBM） can effectively alleviate the shortage of natural gas resources, but the commercial scale development is slightly insufficient, and systematic research is urgently needed. The premise of efficient CBM development is the selection of favorable areas, but the current CBM development evaluation involves certain subjective human factors, which will indirectly affect or interfere with the prediction effect. Taking the low-rank coal in the Dafosi minefield in the Binchang mining area of Huanglong Coal Field as the research object, based on the actual production data, the random forest algorithm in machine learning is used to predict the favorable area of coalbed methane in the area. The results show that: ① Pearson correlation analysis shows that the gas content, ash content, net thickness of coal seam, structural position, roof thickness, permeability, reservoir pressure and burial depth are eight mutually independent CBM output-related parameters and can be used for model establishment; ② The Random Forest algorithm divides the CBM development area into five types of areas with different degrees, of which type Ⅰ（extremely high） to Ⅱ（highly favorable） areas account for 13.88 % of the entire study area, mainly distributed in the middle of the well field. The southeast is not suitable for subsequent deployment of well locations, and there is a distribution of highly favorable areas in the west, so the well locations for subsequent development and deployment should also be considered. ③ It can be obtained from the receiver operating characteristic（ROC） curve, and the area under the ROC curve （AUC） is 0.961, indicating that the Random Forest model has high prediction accuracy and reliable results. Using machine learning algorithms for comprehensive prediction of CBM favorable areas can avoid human subjective factors in traditional algorithms, and can provide a certain theoretical reference for subsequent unconventional oil and gas development and selection.

Key words: Random Forest, development constituency forecast, coalbed methane（CBM）, low rank coal, Huanglong Coal Field

中图分类号:

P618.11

陈跃,王丽雅,李国富等. 基于随机森林算法的低煤阶煤层气开发选区预测[J]. 油气藏评价与开发, 2022, 12(4): 596-603.

Yue CHEN,Liya WANG,Guofu LI, et al. Prediction of favorable areas for low-rank coalbed methane based on Random Forest algorithm[J]. Petroleum Reservoir Evaluation and Development, 2022, 12(4): 596-603.

图/表 7

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参考文献 30

[1]	李勇, 许卫凯, 高计县, 等. “源-储-输导系统”联控煤系气富集成藏机制——以鄂尔多斯盆地东缘为例[J]. 煤炭学报, 2021, 46(8):2440-2453.
	LI Yong, XU Weikai, GAO Jixian, et al. Mechanism of coal measure gas accumulation under integrated control of “source reservoir-transport system”: A case study from east margin of Ordos Basin[J]. Journal of China Coal Society, 2021, 46(8): 2440-2453.
[2]	李勇, 王延斌, 孟尚志, 等. 煤系非常规天然气合采地质基础理论进展及展望[J]. 煤炭学报, 2020, 45(4):1406-1418.
	LI Yong, WANG Yanbin, MENG Shangzhi, et al. Theoretical basis and prospect of coal measure unconventional natural gas co-production[J]. Journal of China Coal Society, 2020, 45(4): 1406-1418.
[3]	陈晓智, 汤达祯, 许浩, 等. 低、中煤阶煤层气地质选区评价体系[J]. 吉林大学学报(地球科学版), 2012, 42(S2):115-120.
	CHEN Xiaozhi, TANG Dazhen, XU Hao, et al. Geological evaluation system of potential coalbed methane exploration and development blocks with Low and Medium coal ranks[J]. Journal of Jilin University( Earth Science Edition), 2012, 42(S2): 115-120.
[4]	张莉娜, 刘欣, 张耀祖. 基于正交试验设计的页岩气藏压裂敏感性分析[J]. 非常规油气, 2021, 8(5):77-86.
	ZHANG Lina, LIU Xin, ZHANG Yaozu. Sensitivity analysis of shale gas reservoir based on orthogonal experimental design[J]. Unconventional Oil & Gas, 2021, 8(5): 77-86.
[5]	胡凯. 川西南威远地区五峰—龙马溪组页岩储层特征及甜点分布规律研究[J]. 非常规油气, 2021, 8(5):34-44.
	HU Kai. Reservoir and sweet pot distribution characteristics of shale gas in Wufeng and Longmaxi Formation, southwest of Sichuan Basin, China[J]. Unconventional Oil & Gas, 2021, 8(5): 34-44.
[6]	王金, 康永尚, 姜杉钰, 等. 沁水盆地寿阳区块煤层气井产水差异性原因分析及有利区预测[J]. 天然气工业, 2016, 36(8):52-59.
	WANG Jin, KANG Yongshang, JIANG Shanyu, et al. Reasons for water production difference of CBM wells in Shouyang Block, Qinshui Basin, and prediction on favorable areas[J]. Natural Gas Industry, 2016, 36(8): 52-59.
[7]	XU H, TANG D Z, LIU D M, et al. Study on coalbed methane accumulation characteristics and favorable areas in the Binchang area, southwestern Ordos Basin, China[J]. International Journal of Coal Geology, 2012, 95: 1-11. doi: 10.1016/j.coal.2012.02.001
[8]	姚艳斌, 刘大锰, 汤达祯, 等. 平顶山煤田煤储层物性特征与煤层气有利区预测[J]. 地球科学(中国地质大学学报), 2007, 32(2):285-290.
	YAO Yanbin, LIU Dameng, TANG Dazhen, et al. Coal reservoir physical characteristics and prospective areas for CBM exploitation in Pingdingshan coalfield[J]. Earth Science(Journal of China University of Geoscience), 2007, 32(2): 285-290.
[9]	SHAO L Y, HOU H H, TANG Y, et al. Selection of strategic replacement areas for CBM exploration and development in China[J]. Natural Gas Industry B, 2015, 2(2): 211-221. doi: 10.1016/j.ngib.2015.07.013
[10]	邵龙义, 王学天, 张家强, 等. 滇东北地区煤层气富集特征及勘探目标优选[J]. 天然气工业, 2018, 38(9):17-27.
	SHAO Longyi, WANG Xuetian, ZHANG Jiaqiang, et al. CBM accumulation characteristics and exploration target selection in northeastern Yunnan, China[J]. Natural Gas Industry, 2018, 38(9): 17-27.
[11]	邵龙义, 文怀军, 李永红, 等. 青海省天峻县木里煤田煤层气有利区块的多层次模糊数学评判[J]. 地质通报, 2011, 30(12):1896-1903.
	SHAO Longyi, WEN Huaijun, LI Yonghong, et al. Assessment of favorable areas for coalbed methane resources exploration in the Muli coalfield of Qin hai Province based on multi-layered fuzzy mathematics[J]. Geological Bulletin of China, 2011, 30(12): 1896-1903.
[12]	FU H, TANG D, XU H, et al. Geological characteristics and CBM exploration potential evaluation: A case study in the middle of the southern Junggar Basin, NW China[J]. Journal of Natural Gas Science and Engineering, 2016, 30: 557-570. doi: 10.1016/j.jngse.2016.02.024
[13]	刘人和, 刘飞, 周文, 等. 沁水盆地煤岩储层特征及有利区预测[J]. 油气地质与采收率, 2008, 73(4):16-19.
	LIU Renhe, LIU Fei, ZHOU Wen, et al. Characteristics and favorable area prediction of coal reservoirs in Qinshui Basin[J]. Petroleum Geology and Recovery Efficiency, 2008, 73(4): 16-19.
[14]	张小东, 张硕, 许亚坤, 等. 基于模糊数学的豫东煤系气资源勘探有利区预测[J]. 煤炭科学技术, 2018, 46(11):172-181.
	ZHANG Xiaodong, ZHANG Shuo, XU Yakun, et al. Favorable block prediction of coal measure gas resource exploration in eastern Henan area based on fuzzy mathematics[J]. Coal Science and Technology, 2018, 46(11): 172-181.
[15]	王鹏, 李图南. 基于MapGIS的大佛寺井田煤层气资源有利区预测[J]. 煤炭科学技术, 2019, 47(5):193-197.
	WANG Peng, LI Tunan. Prediction on favorable areas of CBM resources based on MapGIS in Dafosi Minefield[J]. Coal Science and Technology, 2019, 47(5): 193-197.
[16]	LIU H, SANG S X, WANG G X, et al. Evaluation of the synergetic gas-enrichment and higher-permeability regions for coalbed methane recovery with a fuzzy model[J]. Energy, 2012, 39: 426-439. doi: 10.1016/j.energy.2011.12.027
[17]	刘灵童, 王文升, 尹彦君, 等. 灰色关联分析在中阶煤层气有利井区快速优选中的应用[J]. 长江大学学报(自然科学版), 2016, 13(10):17-21.
	LIU Lingtong, WANG Wensheng, YIN Yanjun, et al. Application of grey correlational analysis in fast optimization of favorable well block of medium-rank coalbed methane[J]. Journal of Yangtze University(Natural Science Edition), 2016, 13(10): 17-21.
[18]	白利娜, 曾家瑶, 高为. 基于灰色关联分析的盘关向斜煤层气有利井区优选[J]. 煤炭科学技术, 2019, 47(4):169-173.
	BAI Li'na, ZENG Jiayao, GAO Wei. Optimization of favorable well for CBM based on grey correlation analysis in Panguan Syncline[J]. Coal Science and Technology, 2019, 47(4): 169-173.
[19]	张嘉睿, 夏玉成, 李涛, 等. Entropy-Kmeans方法在煤层气开发前景评价中的应用[J]. 煤矿安全, 2020, 51(8):158-163.
	ZHANG Jiarui, XIA Yucheng, LI Tao, et al. Application of Entropy-Kmeans in evaluation of coalbed methane development prospect[J]. Coal Mine Safety, 2020, 51(8): 158-163.
[20]	罗金辉, 杨永国, 秦勇, 等. 基于组合权重的煤层气有利区块模糊优选[J]. 煤炭学报, 2012, 37(2):242-246.
	LUO Jinhui, YANG Yongguo, QIN Yong, et al. Fuzzy optimization for CBM favorable targets based on combined weights[J]. Journal of China Coal Society, 2012, 37(2): 242-246.
[21]	张吉军. 模糊层次分析法(FAHP)[J]. 模糊系统与数学, 2000(2):80-88.
	ZHANG Jijun. Fuzzy analytical hierarchy process[J]. Fuzzy Systems and Mathematics, 2000(2): 80-88.
[22]	邓雪, 李家铭, 曾浩健, 等. 层次分析法权重计算方法分析及其应用研究[J]. 数学的实践与认识, 2012, 42(7):93-100.
	DENG Xue, LI Jiaming, ZENG Haojian, et al. Research on computation methods of AHP wight vector and its applications[J]. Mathematics in Practice and Knowledge, 2012, 42(7): 93-100.
[23]	常建娥, 蒋太立. 层次分析法确定权重的研究[J]. 武汉理工大学学报(信息与管理工程版), 2007, 134(1):153-156.
	CHANG Jian'e, JIANG Taili. Research on the weight of coefficient through analytic hierarchy process[J]. Journal of Wuhan University of Technology (Information and Management Engineering Edition), 2007, 134(1): 153-156.
[24]	康保平, 姜帆. 四川盆地威远地区下志留统龙马溪组页岩储层有利区评价[J]. 天然气勘探与开发, 2021, 44(3):87-95.
	KANG Baoping, JIANG Fan. Evaluation on favorable shale reservoirs of Lower Silurian Longmaxi Formation, Weiyuan area, Sichuan Basin[J]. Natural Gas Exploration and Development, 2021, 44(3): 87-95.
[25]	来鹏, 杜世涛, 杨曙光, 等. 博乐盆地石炭系阿克沙克组沉积演化及页岩气有利区预测[J]. 非常规油气, 2020, 7(5):32-40.
	LAI Peng, DU Shitao, YANG Shuguang, et al. Sedimentary evolution of Carboniferous Akshak Formation and prediction of favorable shale gas areas in Bole Basin[J]. Unconventional Oil & Gas, 2020, 7(5): 32-40.
[26]	朱庆忠, 胡秋嘉, 杜海为, 等. 基于随机森林算法的煤层气直井产气量模型[J]. 煤炭学报, 2020, 45(8):2846-2855.
	ZHU Qingzhong, HU Qiujia, DU Haiwei, et al. A gas production model of vertical coalbed methane well based on random forest algorithm[J]. Journal of China Coal Society, 2020, 45(8): 2846-2855.
[27]	蔺亚兵, 宋一民, 蒋同昌, 等. 黄陇煤田永陇矿区煤层气成藏条件及主控因素研究[J]. 煤炭科学技术, 2018, 46(3):168-175.
	LIN Yabing, SONG Yimin, JIANG Tongchang, et al. Study on forming conditions and main controlling factors of CBM reservoirs in Yonglong Mining Area of Huanglong Coalfield[J]. Coal Science and Technology, 2018, 46(3): 168-175.
[28]	高正, 马东民, 陈跃, 等. 含水率对不同宏观煤岩类型甲烷吸附/解吸特征的影响[J]. 煤炭科学技术, 2020, 48(8):97-105.
	GAO Zheng, MA Dongmin, CHEN Yue, et al. Effect of water content on adsorption/desorption of methane of different macroscopic lithotypes[J]. Coal Science and Technology, 2020, 48(8): 97-105.
[29]	马东民, 王传涛, 夏玉成, 等. 大佛寺井田煤层气井压裂参数优化方案[J]. 西安科技大学学报, 2019, 39(2):263-269.
	MA Dongmin, WANG Chuantao, XIA Yucheng, et al. Optimization program of fracturing parameters for coalbed methane wells in Dafosi Minefield[J]. Journal of Xi'an University of Science and Technology, 2019, 39(2): 263-269.
[30]	彭文利, 薛冽, 马效杰, 等. 准噶尔盆地南缘齐古地区煤层气地质特征[J]. 非常规油气, 2021, 8(1):8-14.
	PENG Wenli, XUE Lie, MA Xiaojie, et al. Geological characteristics of coalbed methane in Qigu area, southern Margin of Junggar Basin[J]. Unconventional Oil & Gas, 2021, 8(1): 8-14.

井号	分类	产气量（m³/d）	含气量（m³/t）	埋深（m）	煤厚（m）	渗透率（10^-3 μm²）	储层压力（MPa）	灰分（%）	构造部位	顶板厚度（m）
D68	高产井	10 246.09	4.3	610	11.0	7.7	2.8	14.0	0.8	2.2
D02		10 181.94	2.8	590	16.8	10.0	3.3	11.0	0.8	2.1
D05		8 316.52	3.6	589	14.0	9.2	3.1	12.0	0.7	2.0
D124		4 205.09	8.1	615	11.8	6.6	3.2	15.5	1.0	2.1
D85		4 025.73	1.8	535	8.3	7.3	3.8	14.9	0.7	1.2
D09		3 998.08	6.8	465	12.6	15.0	1.2	15.2	0.7	6.0
D143		3 633.23	8.0	490	13.6	7.8	1.4	13.9	0.7	0.7
D04		3 236.77	3.3	500	10.3	3.8	2.9	10.5	0.8	0.7
D133		2 590.39	8.3	510	12.2	11.0	5.5	15.9	1.0	3.0
D01		2 032.40	3.8	505	17.6	7.8	0.8	12.0	0.8	1.2
D134		1 961.95	8.0	490	10.2	6.8	4.3	17.0	0.7	0.6
D131		1 876.32	10.0	435	13.9	12.3	3.6	14.4	1.0	1.8
D45		1 696.16	2.2	435	14.3	11.8	2.4	15.2	0.7	1.8
D148		1 629.34	7.7	500	8.3	7.4	5.5	15.8	0.7	1.4
D06		1 339.94	3.2	540	11.8	7.3	1.3	11.9	0.7	1.4
D03		1 248.76	2.8	570	10.2	4.8	2.5	19.0	0.8	1.5
D128	低产井	1 177.44	9.1	448	10.8	12.5	4.6	15.2	1.0	3.6
D132		836.56	8.3	480	11.0	18.8	5.6	15.1	1.0	6.8
DU53		643.54	3.5	540	12.3	6.0	6.0	16.0	1.0	0.8
D152		554.33	8.2	505	8.3	7.4	6.0	15.2	0.7	1.4
D59		460.25	8.6	585	20.3	6.5	3.3	17.0	1.0	2.6
D73		458.52	9.3	630	13.5	8.0	3.8	16.8	0.7	3.0
D135		450.94	8.1	510	12.3	8.1	4.8	16.3	0.7	1.0
D150		434.58	7.3	510	10.3	7.9	4.6	14.5	0.7	1.4
D105		356.06	10.0	440	14.2	11.1	3.6	14.3	0.7	0.9
D69		344.81	8.5	628	14.0	7.0	3.4	16.2	0.7	2.6
D71		340.54	8.6	600	11.0	8.1	3.7	16.2	0.7	3.0
D122		304.95	9.3	445	12.0	11.1	2.6	15.9	0.7	3.8
D84		256.95	8.2	630	14.7	7.5	4.0	17.2	0.7	2.6
D114		244.23	6.5	565	17.8	14.0	3.0	13.0	0.7	3.6
D86		239.53	8.1	650	13.0	6.8	3.9	17.8	0.8	2.3
D120		212.47	8.1	500	12.2	11.0	2.5	15.8	1.0	3.7

	含气量	埋深	煤厚	渗透率	储层压力	构造部位	灰分	顶板厚度
含气量	1.000	-0.130	0.044	0.284	0.312	0.417	0.194	0.318
埋深	-0.130	1.000	0.186	-0.475	0.011	0.199	-0.127	-0.047
煤厚	0.044	0.186	1.000	0.166	-0.396	-0.198	0.141	0.106
渗透率	0.284	-0.475	0.166	1.000	0.021	-0.122	0.218	0.768
储层压力	0.312	0.011	-0.396	0.021	1.000	0.350	0.231	-0.012
构造部位	0.417	0.199	-0.198	-0.122	0.350	1.000	0.075	0.146
灰分	0.194	-0.127	0.141	0.218	0.231	0.075	1.000	0.271
顶板厚度	0.318	-0.047	0.106	0.768	-0.012	0.146	0.271	1.000

基于随机森林算法的低煤阶煤层气开发选区预测

Prediction of favorable areas for low-rank coalbed methane based on Random Forest algorithm

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