元素录井在煤层随钻判识过程中的应用  

Application of Element Mud-logging to Identifying Coal Seam While Drilling

作　　者：龚才喜[1] 梁海波[2] 张娜[2] 陈家晓[3] 

机构地区：[1]中国石化华北分公司工程技术处,河南郑州450006 [2]西南石油大学,四川成都610500 [3]中国石油西南油气田公司采气工程研究院,四川广汉618300

出　　处：《天然气技术与经济》2012年第4期31-33,78,共3页Natural Gas Technology and Economy

基　　金：国家油气重大专项多分支水平井综合地层判识技术(编号:国专项087)

摘　　要：目前,煤层气水平井钻井采用PDC钻头+螺杆钻具的复合钻井方式,造成岩屑细碎、传统录井方式不能有效识别产层、钻头钻穿煤层或丢失煤层,给煤层气的勘探开发带来巨大的损失。为此,提出一种以元素录井技术为主体的煤层随钻判识方法,通过对元素录井原理以及煤层气水平井钻井的特殊工艺需求的分析,建立了一套煤层气水平井煤层随钻判识的RBF神经网络模型,能够保证煤层地质导向钻井有效实施。During an exploration and development of CBM,two technologies of both horizontal well and multi-branch horizontal well are the effective tools for increasing single-well production.So,it ’s necessary to carry out a geo-steering drilling in order to not only guarantee an effective extension of drilling bit but also improve an effective drilling ratio,finally to increase the production.To identify coal seam while drilling is the key technology during geo-steering drilling.At present,a compound drilling mode,adopting PDC drilling bit and screw drilling tool,is used for drilling CBM ’s horizontal well.However,this mode brings about a fine crushing for cuttings in order that payzone can not be effectively identified by traditional mud-logging; moreover drilling bit may easily drill through coal seam,resulted in its missing; those will bring a great loss.Therefore,a new method to identify coal seam while drilling,with element mud-logging technique as the soul,is presented.And its principle is analyzed; and then,some technological requirements special for CBM ’s horizontal-well drilling are also studied; a set of RBF neural network model is established to identify coal seam while drilling to further ensure an effective geo-steering drilling.

关 键 词：煤层气 岩屑 元素录井 X射线荧光 煤层随钻判识 RBF神经网络 

分 类 号：TE21[石油与天然气工程—油气井工程]