油气藏评价与开发 >
2024 , Vol. 14 >Issue 6: 990 - 996
DOI: https://doi.org/10.13809/j.cnki.cn32-1825/te.2024.06.022
深层煤层气直丛井产能影响因素确定新方法
收稿日期: 2024-01-23
网络出版日期: 2024-12-10
基金资助
中国石油重大攻关性应用性科技专项课题“深层煤层气开发优化设计关键技术研究”(2023ZZ1804)
A new method for determining factors Influencing productivity of deep coalbed methane vertical cluster wells
Received date: 2024-01-23
Online published: 2024-12-10
深层煤层气直丛井生产解吸规律、游离气与解吸气的转换时机尚不明确,产能差异的主控因素仍不确定,影响产能的提高。为进一步评价产能主控因素,基于36口直丛井的生产动态参数,结合神经网络预测井底流压,形成了以初期米采气指数为核心,综合多种机器学习算法的产能影响因素评价新方法。结果表明:①Beggs & Bill模型和Gray模型对深层煤层气井底流压预测适用性差。单相气体模型随着产水量下降,整体误差变小。采用神经网络方法预测效果较好,与实测相对误差小于10%。②采用Kendall’s tau-b(肯德尔相关系数)分析,离散型主控因素为微构造位置,主要位于抬升区正向构造区;其次为裂缝发育程度,以较发育和发育为主。③联合套索回归-随机森林-决策树逐步剔除非相关影响因素,确定影响产能的连续型主控因素从大到小排序为:灰分含量、平均施工排量、入地总砂量、见气时返排率、垂厚、声波时差、伽马、平均施工压力、百目砂占比、气测平均值,工程因素对气井产能影响不能忽视。该方法结合多种机器学习算法的优点,操作性强,提高煤层气动态预测精确度,有助于优化压裂设计参数,为提高煤层气压后的产能提供科学依据。
关键词: 深层煤层气; 套索回归-随机森林-决策树; 地质-工程因素; 相关性分析; 产能
黄力 , 熊先钺 , 王峰 , 孙雄伟 , 张艺馨 , 赵龙梅 , 石石 , 张稳 , 赵浩阳 , 季亮 , 邓琳 . 深层煤层气直丛井产能影响因素确定新方法[J]. 油气藏评价与开发, 2024 , 14(6) : 990 -996 . DOI: 10.13809/j.cnki.cn32-1825/te.2024.06.022
The desorption production patterns of deep coalbed methane(CBM) vertical cluster wells, as well as the transition timing between free gas and desorbed gas, remain unclear. The dominant factors causing productivity differences are still uncertain, which hinders productivity improvement. To further evaluate the primary factors controlling productivity, a new method for assessing productivity-influencing factors was developed, based on the production dynamic parameters of 36 vertical cluster wells and using neural networks to predict bottom-hole flowing pressure. This method centered on the initial meter gas production index and integrated multiple machine-learning algorithms. The results showed that: 1) The Beggs & Bill model and Gray model exhibited poor applicability for predicting the bottom-hole flowing pressure of deep CBM wells, while the single-phase gas model demonstrated reduced overall error as water production declined. Predictions using the neural network method were more accurate, with a relative error of less than 10% compared to measured values. 2) Using Kendall's tau-b correlation analysis, the discrete dominant factor was identified as the microstructural position, primarily located in uplifted positive structural zones, with the secondary factor being fracture development, categorized mainly as “well-developed” or “developed.” 3) By combining lasso regression-random forest- decision tree algorithm to iteratively eliminate irrelevant factors, the continuous dominant factors influencing productivity were ranked in descending order as: ash content, average construction discharge rate, total sand volume pumped, flowback rate at gas breakthrough, net pay thickness, acoustic travel time, gamma ray log value, average construction pressure, percentage of 100-mesh sand, and average gas measurement value. Engineering factors were found to have a significant impact on gas well productivity and cannot be overlooked. This method leverages the advantages of multiple machine-learning algorithms, demonstrating strong operability and improving the accuracy of CBM dynamic predictions. It aids in optimizing fracturing design parameters and provides a scientific basis for enhancing post-fracturing productivity in CBM wells.
[1] | 熊先钺, 闫霞, 徐凤银, 等. 深部煤层气多要素耦合控制机理、解吸规律与开发效果剖析[J]. 石油学报, 2023, 44(11): 1812-1826. |
XIONG Xianyue, YAN Xia, XU Fengyin, et al. Analysis of multi-factor coupling control mechanism,desorption law and development effect of deep coalbed methane[J]. Acta Petrolei Sinica, 2023, 44(11): 1812-1826. | |
[2] | 闫霞, 徐凤银, 聂志宏, 等. 深部微构造特征及其对煤层气高产“甜点区”的控制: 以鄂尔多斯盆地东缘大吉地区为例[J]. 煤炭学报, 2021, 46(8): 2426-2439. |
YAN Xia, XU Fengyin, NIE Zhihong, et al. Microstructure characteristics of Daji area in east Ordos Basin and its control over the high yield dessert of CBM[J]. Journal of China Coal Society, 2021, 46(8): 2426-2439. | |
[3] | 徐凤银, 闫霞, 林振盘, 等. 我国煤层气高效开发关键技术研究进展与发展方向[J]. 煤田地质与勘探, 2022, 50(3): 1-14. |
XU Fengyin, YAN Xia, LIN Zhenpan, et al. Research progress and development direction of key technologies for efficient coalbed methane development in China[J]. Coal Geology & Exploration, 2022, 50(3): 1-14. | |
[4] | 徐凤银, 闫霞, 李曙光, 等. 鄂尔多斯盆地东缘深部(层)煤层气勘探开发理论技术难点与对策[J]. 煤田地质与勘探, 2023, 51(1): 115-130. |
XU Fengyin, YAN Xia, LI Shuguang, et al. Theoretical and technological difficulties and countermeasures of deep CBM exploration and development in the eastern edge of Ordos Basin[J]. Coal Geology & Exploration, 2023, 51(1): 115-130. | |
[5] | 秦勇, 申建. 论深部煤层气基本地质问题[J]. 石油学报, 2016, 37(1): 125-136. |
QIN Yong, SHEN Jian. On the fundamental issues of deep coalbed methane geology[J]. Acta Petrolei Sinica, 2016, 37(1): 125-136. | |
[6] | 闫霞, 徐凤银, 张雷, 等. 微构造对煤层气的控藏机理与控产模式[J]. 煤炭学报, 2022, 47(2): 893-905. |
YAN Xia, XU Fengyin, ZHANG Lei, et al. Reservoir-controlling mechanism and production-controlling patterns of microstructure to coalbed methane[J]. Journal of China Coal Society, 2022, 47(2): 893-905. | |
[7] | 张遂安, 刘欣佳, 温庆志, 等. 煤层气增产改造技术发展现状与趋势[J]. 石油学报, 2021, 42(1): 105-118. |
ZHANG Sui’an, LIU Xinjia, WEN Qingzhi, et al. Development situation and trend of stimulation and reforming technology of coalbed methane[J]. Acta Petrolei Sinica, 2021, 42(1): 105-118. | |
[8] | 马新仿, 李忠城, 孔鹏, 等. 基于聚类分析的煤层气二次压裂选井选层方法研究[J]. 中国矿业, 2022, 31(1): 79-87. |
MA Xinfang, LI Zhongcheng, KONG Peng, et al. Study on secondary fracturing candidate selection of coalbed methane based on clustering analysis[J]. China Mining Industry, 2022, 31(1): 79-87. | |
[9] | 倪小明, 王延斌, 接铭训, 等. 煤层气开采模式探讨[J]. 煤矿安全, 2007, 38(3): 45-48. |
NI Xiaoming, WANG Yanbin, JIE Mingxun, et al. Coalbed methane mining mode discussion[J]. Coal Mine Safety, 2007, 38(3): 45-48. | |
[10] | 杨兆中, 杨晨曦, 李小刚, 等. 基于灰色关联的逼近理想解排序法的煤层气井重复压裂选井: 以沁水盆地柿庄南区块为例[J]. 科学技术与工程, 2020, 20(12): 4680-4686. |
YANG Zhaozhong, YANG Chenxi, LI Xiaogang, et al. Multiple fracturing well selection of coalbed methane wells based on technique for order preference by similarity to ideal solution method of gray correlation: Taking the case of Qinshui Basin Shizhuang South Block as an examples[J]. Science and Technology and Engineering, 2020, 20(12): 4680-4686. | |
[11] | 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 Technology, 2021, 49(9): 176-183. | |
[12] | 陈娟, 黄浩勇, 刘俊辰, 等. 基于GA-BP神经网络的长宁地区页岩气水平井产能预测技术[J]. 科学技术与工程, 2020, 20(5): 1851-1858. |
CHEN Juan, HUANG Haoyong, LIU Junchen, et al. Production predicting technology of shale gas fracturing horizontal well in Changning Area based on the GA-BP neural network model[J]. Science Technology and Engineering, 2020, 20(5): 1851-1858. | |
[13] | 李勇, 汤达祯, 孟尚志, 等. 鄂尔多斯盆地东缘煤储层地应力状态及其对煤层气勘探开发的影响[J]. 矿业科学学报, 2017, 2(5): 416-424. |
LI Yong, TANG Dazhen, MENG Shangzhi, et al. The in-situ stress of coal reservoirs in east margin of Ordos Basin and its influence on coalbed methane development[J]. Journal of Mining Science and Technology, 2017, 2(5): 416-424. | |
[14] | 孟艳军, 汤达祯, 许浩, 等. 煤层气解吸阶段划分方法及其意义[J]. 石油勘探与开发, 2014, 41(5): 612-617. |
MENG Yanjun, TANG Dazhen, XU Hao, et al. Division of coalbed methane desorption stages and its significance[J]. Petroleum Exploration and Development, 2014, 41(5): 612-617. | |
[15] | 毛恒博, 刘毅, 李丽, 等. 新型氮气泡沫压裂液体系研究及其在煤层气储层中的应用[J]. 钻采工艺, 2022, 45(5): 139-143. |
MAO Hengbo, LIU Yi, LI Li, et al. Study on new nitrogen foam fracturing fluid system and its application in coalbed methane reservoir[J]. Drilling & Production Technology, 2022, 45(5): 139-143. | |
[16] | 朱庆忠, 杨延辉, 左银卿, 等. 对于高煤阶煤层气资源科学开发的思考[J]. 天然气工业, 2020, 40(1): 55-60. |
ZHU Qingzhong, YANG Yanhui, ZUO Yinqing, et al. On the scientific exploitation of high-rank CBM resources[J]. Natural Gas Industry, 2020, 40(1): 55-60. | |
[17] | 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 and thought of coalbed methane exploration and development in Qinshui Basin[J]. Journal of China Coal Society, 2015, 40(9): 2131-2136. | |
[18] | ZHANG Z, QIN Y, WANG G X, et al. Numerical description of coalbed methane desorption stages based on isothermal adsorption experiment[J]. Science China Earth Sciences, 2013, 56(6): 1029-1036. |
[19] | 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. | |
[20] | 孔祥伟, 谢昕, 王存武, 等. 基于灰色关联方法的深层煤层气井压后产能影响地质工程因素评价[J]. 油气藏评价与开发, 2023, 13(4): 433-440. |
KONG Xiangwei, XIE Xin, WANG Cunwu, et al. Evaluation of geological engineering factors for productivity of deep CBM well after fracturing based on grey correlation method[J]. Petroleum Reservoir Evaluation and Development, 2023, 13(4): 433-440. | |
[21] | BOLAND J, HOWLETT P, PIANTADOSI J. Matching the grade correlation coefficient using a copula with maximum disorder[J]. Journal of Industrial & Management Optimization, 2017, 3(2): 305-312. |
[22] | 郑勋烨. 数学建模实验[M]. 西安: 西安交通大学出版社, 2018. |
ZHENG Xunye. Mathematical modeling experiment[M]. Xi'an: Xi'an Jiaotong University Press, 2018. | |
[23] | 王梅, 孙莺萁, 宋考平, 等. 基于Lasso-Lars的密井网水驱注水量影响因素分析[J]. 数学的实践与认识, 2016, 46(8): 124-131. |
WANG Mei, SUN Yingqi, SONG Kaoping, et al. Research on water injection rate of dense well pattern using Lasso-Lars[J]. Practice and understanding of mathematics, 2016, 46(8): 124-131. | |
[24] | 柯郑林. Lasso及其相关方法在多元线性回归模型中的应用[D]. 北京: 北京交通大学, 2011. |
KE Zhenglin. Lasso and related methods in the application of multiple linear regression model[D]. Beijing: Beijing Jiaotong University, 2011. | |
[25] | 朱苏阳, 孟尚志, 彭小龙, 等. 煤岩润湿性对煤层气赋存的影响机理[J]. 油气藏评价与开发, 2022, 12(4): 580-588. |
ZHU Suyang, MENG Shangzhi, PENG Xiaolong, et al. Mechanism of coal wettability on storage state of undersaturated CBM reservoirs[J]. Petroleum Reservoir Evaluation and Development, 2022, 12(4): 580-588. | |
[26] | 秦勇, 申建, 王宝文, 等. 深部煤层气成藏效应及其耦合关系[J]. 石油学报, 2012, 33(1): 48-54. |
QIN Yong, SHEN Jian, WANG Baowen, et al. Accumulation effects and coupling relationship of deep coalbed methane[J]. Acta Petrolei Sinica, 2012, 33(1): 48-54. | |
[27] | 秦勇. 煤系气聚集系统与开发地质研究战略思考[J]. 煤炭学报, 2021, 46(8): 2387-2399. |
QIN Yong. Strategic thinking on research of coal measure gas accumulation system and development geology[J]. Journal of China Coal Society, 2021, 46(8): 2387-2399. | |
[28] | 何发岐, 董昭雄. 深部煤层气资源开发潜力: 以鄂尔多斯盆地大牛地气田为例[J]. 石油与天然气地质, 2022, 43(2): 277-285. |
HE Faqi, DONG Zhaoxiong. Development potential of deep coalbed methane: A case study in the Daniudi gas field, Ordos Basin[J]. Oil & Gas Geology, 2022, 43(2): 277-285. | |
[29] | 李建忠. 第四次油气资源评价[M]. 北京: 石油工业出版社, 2019: 256-298. |
LI Jianzhong. Fourth assessment for oil and gas resource[M]. Beijing: Petroleum Industry Press, 2019: 256-298. | |
[30] | 康永尚, 皇甫玉慧, 张兵, 等. 含煤盆地深层“超饱和”煤层气形成条件[J]. 石油学报, 2019, 40(12): 1426-1438. |
KANG Yongshang, HUANGFU Yuhui, ZHANG Bing, et al. Formation conditions for deep over saturated coalbed methane in coal-bearing basins[J]. Acta Petrolei Sinica, 2019, 40(12): 1426-1438. | |
[31] | 聂志宏, 时小松, 孙伟, 等. 大宁—吉县区块深层煤层气生产特征与开发技术对策[J]. 煤田地质与勘探, 2022, 50(3): 193-200. |
NIE Zhihong, SHI Xiaosong, SUN Wei, et al. Production characteristics of deep coalbed methane gas reservoirs in Daning-Jixian Block and its development technology countermeasures[J]. Coal Geology & Exploration, 2022, 50(3): 193-200. | |
[32] | 杨秀春, 宋柏荣, 陈国辉, 等. 大宁—吉县区块深层煤岩多尺度孔缝结构特征[J]. 特种油气藏, 2022, 29(5): 94-100. |
YANG Xiuchun, SONG Borong, CHEN Guohui, et al. Characteristics of multi-scale pore-fracture structure of deep coal rocks in the Daning-Jixian Block[J]. Special Oil & Gas Reservoirs, 2022, 29(5): 94-100. |
/
〈 | 〉 |