摘要
产水是导致气井低产低效的主要因素,由于缺少对产水主控因素的准确认识,采用常规排水采气方法复产的效率较低。为了能够全面地研究产水的主控因素,引入大数据剥茧寻优算法,对可能影响产水的地质因素、工程因素、排采因素等资料建立大数据分析的样本库。然后进行数据清洗,淘汰无效数据,在此基础上开展单因素分析,确定各个因素的正负关系,接着开展多因素分析,确定影响致密气井产水的主控因素。最终通过消元法处理后找出39项主控因素,并建立了产水主控方程,明确各个因素对产水量的影响程度。研究表明:对产水量影响较大的主控因素为测井可动孔隙度和总孔隙度、试气和生产套压、泥质含量等。为了避免后期产水影响产气量,压裂段尽量优选可动孔隙度和总孔隙度较高、泥质含量低的储层,且在生产过程中要控制生产制度,防止压差过大加剧地层产水,为后期产水气井采取有效的治理措施提供依据。
It is well known that water production is the main factor leading to low production and low efficiency of gas wells.However,due to the lack of understanding of the main controlling factors of water production,applying the conventional drainage gas production method is always of low efficiency.In order to comprehensively study the main controlling factors of water production,the cocoon stripping optimization algorithm by big data analysis is introduced to firstly establish a big data sample library for factors that may affect water production,including geological,engineering,drainage and production factors;secondly,all the data is cleaned to eliminate invalid data.After that,the single factor analysis is carried out to determine the positive and negative relationship of each factor;then,multifactor analysis is conducted to find out the main controlling factors affecting water production in tight gas wells.Finally,39 main controlling factors are found out by using elimination method and the main control equation of water production is established to define the effect of each factor on water production.The study shows that:the main factors that greatly affect water production are producible porosity and total porosity identified by logging,gas testing and production casing pressure,clay content,etc.In order to avoid the impact of water production on gas production in the later stage,the reservoir sections with larger producible porosity,larger total porosity and lower clay content are preferred for fracturing.Meanwhile,the production scheme shall be implemented in the production process to avoid excessive pressure difference which can aggravate formation water production,so as to provide a basis for taking effective treatment measures for water-producing gas well in the later stage.
作者
李敬松
刘子雄
寇双燕
张秀敏
刘汝敏
王金伟
LI JingSong;LIU Zixiong;KOU Shuangyan;ZHANG Xiuming;LIU Rumin;WANG Jinwei(CNOOC Oilfield Production Research Institute,Tianjin 300459,China)
出处
《新疆石油天然气》
CAS
2022年第4期20-25,共6页
Xinjiang Oil & Gas
基金
中海油国际科技合作项目“中国海油—阿伯丁大学数字岩心Sponsor”(JTKY-GJHZZZ-2019-YF-01)。
关键词
主控因素
大数据
数据清洗
产水
消元法
多因素分析
main controlling factors
big data
data cleaning
water production
elimination method
multi-factor analysis