地层原油物性参数大数据处理方法研究

宋文广; 李振智; 陈汉林; 郭海敏

doi:10.13568/j.cnki.651094.2016.03.004

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地层原油物性参数大数据处理方法研究

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- 地层原油物性参数大数据处理方法研究
- Journal of Xinjiang University (Natural Science Edition in Chinese and English) Vol. 33, Issue 3, Pages: 270-274(2016)
- 作者机构：
  
  1. 中石化中原石油工程有限公司地球物理测井公司
  2. 油气资源与勘探技术教育部重点实验室(长江大学)
- 作者简介：
- 基金信息：
- DOI：10.13568/j.cnki.651094.2016.03.004
  CLC： TP311.13
- Published：2016
- 稿件说明：
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[1]宋文广,李振智,陈汉林,等.地层原油物性参数大数据处理方法研究[J].新疆大学学报(自然科学版),2016,33(03):270-274.
[1]宋文广,李振智,陈汉林,等.地层原油物性参数大数据处理方法研究[J].新疆大学学报(自然科学版),2016,33(03):270-274. DOI： 10.13568/j.cnki.651094.2016.03.004.

DOI：10.13568/j.cnki.651094.2016.03.004.

摘要

生产测井数据解释过程中遇到测量数据不完整的生产井时

解释人员往往会主观加上人为因素

凭借经验进行解释

其结论是否合理需要进一步研究.文章提出基于生产测井大数据平台

提出一种流体物性参数深度学习处理方法

很好的解决了前述问题

科学合理的完善了生产测井数据中不足之处.由于生产测井解释中关键参数流体物性值计算不准确

直接导致生产测井解释精度偏低

不能解决生产实践问题.传统的计算方法

是利用在特定环境中分析出的公式来计算流体物性值

这些公式多有一定局限性、不能通用.研究流体物性参数深度学习大数据处理方法

设计新的计算模型

能综合考虑不同地区的井况特色

结合深度学习算法自适应调整模式

形成一套科学合理的算法系统弥补测量中的不足

达到准确计算地层原油物性参数值的目的.通过对生产测井数据的验证

其计算精度达到95%

满足测井解释的生产需求

能提高产液剖面解释精度

为提高油气增产提供有力的科学依据.

Abstract

When the production logging data interpretation process is encountered in the production wells with incomplete measurement data

it is often subjective and human factors

and the conclusion is reasonable and need to be further studied. Based on the data platform of production logging

this paper puts forward a kind of fluid physical parameters deep learning processing method

which is a good solution to the above problems. Because of the inaccurate calculation of the key parameters in the production logging interpretation

the accuracy of production logging interpretation is low

which cannot solve the problem of production practice. The traditional calculation method is to use the formula to calculate the physical property of the fluid in a particular environment

which has some limitations and cannot be used in general.Study on fluid physical parameters and depth study of large data processing method

a new model to calculate the design

can consider different regions of the well condition characteristics

combined with the depth of learning algorithm based on adaptive adjustment mode

formed a set of scientific and reasonable algorithm system for measurement

the accurate calculation of formation crude oil physical property parameter value is reached. Through the verification of production logging data

the calculation precision can reach 95%

which can improve the interpretation accuracy of production fluid profile

and provide a scientific basis for the improvement of oil and gas production.

关键词

Keywords

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