基于主成分分析法和熵值法的煤层气开采剩余资源量评价
Evaluation of Residual Resources of Coalbed Methane Exploitation Based on Principal Component Analysis and Entropy Method
-
摘要: 为了对晋城寺河矿西二盘区煤层气井开采剩余资源量进行评价,以14口井为研究对象,选取原始含气量、煤厚、埋深、抽采时间、累计产水量和累计产气量6个评价指标,利用主成分分析法,在6个评价指标中提取2个主成分(煤层气资源因素、煤层气工程排采因素)并得到其分值,再通过熵值法赋权和主成分分析法特征值赋权方法求取14口井综合得分,印证了2种赋权方法的可靠性。研究表明:单井累计抽采量、原始含气量与综合评分值分别呈正相关和负相关,即综合评分值越高单井影响范围内的剩余煤层气资源量越小,表明预测结论与原始数据存在较好的拟合,证明主成分分析法和熵权值结合法在煤层气开采剩余资源量评价中具有可靠性和适用性。Abstract: This paper evaluated the remaining resources of CBM wells in the west 2nd block of Jincheng Sihe Coal Mine. Taking 14 wells as the research objects, six evaluation indexes including original gas content, coal thickness, buried depth, extraction time, cumulative water production and cumulative gas production were selected. Two principal components (coalbed methane resource factor, coalbed methane engineering drainage factor) were extracted from six evaluation indexes by principal component analysis and their scores were obtained. Then, the comprehensive scores of 14 wells are obtained by entropy method and principal component analysis method, and the reliability of the two methods is verified. The study shows that the cumulative production and original gas content of a single well are positively and negatively correlated with the comprehensive scoring value respectively. In other words, the higher the comprehensive score value is, the smaller the remaining coalbed methane resources within the single well influence range. It indicates that the prediction conclusion has a good fit with the original data. It proves that the principal component analysis method and the entropy weight combination method are reliability and applicability in the evaluation of remaining resources of coalbed methane mining.
-
-
[1] 李国福,李贵红,刘刚.晋城矿区典型区煤层气地面抽采效果分析[J].煤炭学报,2014,39(9):1932-1937. [2] 降文萍.地面煤层气抽采效果评价指标初探[J].资源与产业,2010,12(5):61-67. [3] 范金城,梅长林.数据分析[M].北京:科学出版社,2002:27-42. [4] 何晓群.现代统计分析方法与应用[M].北京:中国人民大学出版社,2016. [5] 张春朋,吴财芳,李腾,等.主成分分析法在煤层气选区评价中的应用[J].煤炭科学技术,2016,44(8):137-142. [6] 宫凤强,鲁金涛.基于主成分分析与距离判别分析的突水水源识别方法[J].采矿与安全工程学报,2014, 31(2):236-242. [7] 汪东华.多元统计分析与SPSS应用[M].上海:华东理工大学出版社,2010. [8] 孙东升.基于主成分分析法和熵值法结合的开发区土地集约利用评价[J].绵阳师范学院学报(自然科学版),2015,34(8):105-110. [9] 黄聿铭,郑文龙.基于主成分分析法的煤层气井压裂造缝效果评价[J].煤炭工程,2018,50(2):137-141. [10] 陈仲堂,赵德平,李彦平,等.应用数理统计[M].北京:国防工业出版社,2011:178-184. [11] 吕开宇,李肖,张崇尚.基于主成分分析法和熵值法的地区农业保险发展水平分析[J].农业技术经济, 2016 (3):4-15. [12] 王富喜,毛爱华,李赫龙,等.基于熵值法的山东省城镇化质量测度及空间差异分析[J].地理科学,2013, 33(11):1323-1329. [13] 赵丽,朱永明,付梅臣,等.主成分分析法和熵值法在农村居民点集约利用评价中的比较[J].农业工程学报,2012,28(7):235-242. [14] 陆添超,康凯.熵值法和层次分析法在权重确定中的应用[J].软件开发与设计,2009,22(11):19-20. [15] 史学飞,孙钰,崔寅.基于熵值主成分分析法的天津市低碳经济发展水平评价[J].科技管理研究,2018, 37(3):247-252. [16] 韩小孩,张耀辉,孙福军,等.基于主成分分析的指标权重确定方法[J].四川兵工学报,2012,33(10):124.
计量
- 文章访问数: 172
- HTML全文浏览量: 0
- PDF下载量: 0
下载:
