基于贝叶斯推论的致密储层水力单元预测表征技术

doi:10.3969/j.issn.1006-6535.2020.06.011

特种油气藏 ›› 2020, Vol. 27 ›› Issue (6): 81-87.DOI: 10.3969/j.issn.1006-6535.2020.06.011

基于贝叶斯推论的致密储层水力单元预测表征技术

喻鹏¹, 杨付林¹, Kegang Ling², Oyinkepreye David Orodu³, 杨兴业⁴

1.北部湾大学,广西钦州 535000;
2.University of North Dakota, North Dakota Grand Forks 58202-8264;
3.Covenant University,Ogun Ota 999062;
4.中海石油(中国)有限公司深圳分公司,广东深圳 518000

收稿日期:2020-02-05 修回日期:2020-09-01 出版日期:2020-12-25 发布日期:2022-02-18
作者简介:喻鹏(1987—),男,副教授,2008年毕业于黄山学院数学与应用数学专业,2014年毕业于中国地质大学(武汉)石油与天然气工程专业,获博士学位,现主要从事油气田开发地质方面的研究。
基金资助:
广西自然科学基金“北部湾离岸油田多孔介质内CO₂-原油体系相态特征及渗流机理”(2016GXNSFBA380180)、“北部湾边际油田群CO₂-EOR进程中的多相多组分转化、迁移及其数值模拟”(2017GXNSFAA198105)、“利用SAFT状态方程预测CO₂驱油与地质埋存中MMP及相平衡行为”(2018JJA120001);北部湾大学科研项目“高凝油CO₂-EOR进程中的多相多组分迁移转化”(2016PY-GJ09)

Prediction and Characterization Technology of Hydraulic Unit in Tight Reservoirs Based on Bayesian Inference

Yu Peng¹, Yang Fulin¹, Kegang Ling², Oyinkepreye David Orodu³, Yang Xingye⁴

1. Beibu Gulf University, Qinzhou, Guangxi 535000, China;
2. University of North Dakota, Grand Forks, North Dakota 58202-8264, USA;
3. University of Covenant, Ota Town, Ogun State 999062, Nigeria;
4. Shenzhen Branch of CNOOC (China) Co., Ltd., Shenzhen, Guangdong 518000, China

Received:2020-02-05 Revised:2020-09-01 Online:2020-12-25 Published:2022-02-18

摘要/Abstract

摘要： 针对小壕兔-大16井区储层结构复杂、有利区预测困难的问题,运用贝叶斯挖掘算法,利用计算机辅助模拟手段,构建研究区精细水力单元模型,探明储层砂体发育规律。研究结果表明,基于贝叶斯推论的表征技术能高效识别储层水力单元类型,为气藏描述提供准确的地质信息;指数函数递增法在测井曲线划段过程中表现更加出色,R_t测井段间样点的聚簇规律归因于储层的高含水特性;3类水力单元具有较大的原始油气储量和潜力,亦是提高气藏采收率过程中亟待突破的对象。该研究成果对预测储层有利区具有重要参考价值。

关键词: 贝叶斯推论, 致密储层, 水力单元, 流动层带指标, 测井交会, 单元预测, 致密气

Abstract: Aiming at the problem of complex reservoir structure and difficult prediction in Xiaohaotu-Da16 well area, Bayesian mining algorithm and computer-aided simulation method were used to build the fine hydraulic unit model and correlate the development law of reservoir sand bodies in the target area. The results show that the characterization technology based on Bayesian inference can effectively identify the types of reservoir hydraulic units and provide accurate geological information for gas reservoir description. The exponential function increment method performs better in the process of well logging curve segmentation. The clustering rule of the samples in R_t logging interval is attributed to the high water-cut characteristics of the reservoir. The three types of hydraulic units have large original oil and gas reserves and potential, and they are also urgent to be broken through in EGR process. The research results provide reference for predicting favorable reservoir areas.

Key words: Bayesian inference, tight reservoir, hydraulic unit, flow zone index, well logging intersection, unit forecast, dense gas

中图分类号:

TE122.23

喻鹏, 杨付林, Kegang Ling, Oyinkepreye David Orodu, 杨兴业. 基于贝叶斯推论的致密储层水力单元预测表征技术[J]. 特种油气藏, 2020, 27(6): 81-87.

Yu Peng, Yang Fulin, Kegang Ling, Oyinkepreye David Orodu, Yang Xingye. Prediction and Characterization Technology of Hydraulic Unit in Tight Reservoirs Based on Bayesian Inference[J]. Special Oil & Gas Reservoirs, 2020, 27(6): 81-87.

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基于贝叶斯推论的致密储层水力单元预测表征技术

Prediction and Characterization Technology of Hydraulic Unit in Tight Reservoirs Based on Bayesian Inference

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