• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>SPE Annual Technical Conference and Exhibition >A Framework-Oriented Approach for Determining Attribute Importance When Building Effective Predictive Models for Oil and Gas Data Analytics
【24h】

A Framework-Oriented Approach for Determining Attribute Importance When Building Effective Predictive Models for Oil and Gas Data Analytics

机译：一种面向框架的方法，用于在构建石油和天然气数据分析的有效预测模型时确定属性重要性

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Upstream oil and gas industry services work to deliver success throughout the life cycle of the reservoir. However, conventional sources of oil and gas are declining; hence, operators are increasingly turning their attention to unexplored and underdeveloped regions, such as high-pressure/high-temperature (HP/HT) and deepwater areas, as well as working to increase recoveries in mature fields. As reservoirs become more complex and drilling operations become more expensive, there is a growing need to reduce inefficiencies and costs. Petroleum engineers are increasingly using optimized formation evaluation techniques, software with three-dimensional (3D) visualization, and multidisciplinary data interpretation techniques. In addition, data from new downhole equipment provides reliable, real-time information about downhole conditions. With these improved techniques and better data, operators can model, predict, and control their operations better in real-time, thereby reducing inefficiencies and cost. However, this massive integration of varied data moving in higher volumes and at increased speeds has created an increasing demand on the computation and use of actionable and predictive data-driven analytics. Furthermore, these analytics must work in real-time to quickly discover the important critical data attributes and features for use in forecasting. Attribute importance is a well-known statistical technique used to identify critical attributes and features within a set of attributes that could impact a specific target. The benefits of deriving attribute importance include improved data set comprehension, reduced analysis efforts, and reduced time and computational resources required for actionable predictive analytics. Furthermore, attribute importance aids in understanding the attribute space and interactions, and it enables dimensionality reduction; thereby, leading to comprehendible, accurate, “parsimonious” (i.e., simple) models that lead to actionable predictive analytics. Today, numerous standard and custom techniques—each having their own strengths and weaknesses—are available for performing attribute importance. Each technique applies a different function to evaluate the importance of an attribute and score it to produce a ranked subset of attributes. Hence, it is possible (and is fairly common) to arrive at different subsets of important attributes based on the choice and configurations of the various techniques. To solve this problem, a feasible and effective framework-based approach is proposed that uses multiple attribute importance techniques and then intelligently fuses the results of these methods to arrive at a fused subset of important attributes. This paper describes a framework/“ensemble” (i.e., combined) approach called Segmented Attribute Kerneling (SAK). In addition, it discusses the results obtained from applying this approach on a dataset incorporating real-time drilling surface data logs and drilling parameters. This approach runs multiple attribute importance algorithms simultaneously, finds the intersecting subset of important attributes across the multiple techniques, and then outputs a consolidated ranked set. In addition, the method identifies and presents a ranked subset of the attributes excluded from the union. This paper compares the results of this approach to a single-approach technique based on the output of the predictive models.
机译：上游石油和天然气行业服务致力于在水库的整个生命周期中提供成功。然而，传统的石油和天然气来源正在下降;因此，运营商越来越多地将注意力转向未开发和欠发达的地区，例如高压/高温（HP / HT）和深水地区，以及努力增加成熟领域的回收率。随着水库变得更加复杂，钻井作业变得更加昂贵，越来越需要降低效率低下和成本。石油工程师越来越多地使用优化的形成评估技术，具有三维（3D）可视化的软件和多学科数据解释技术。此外，来自新井下设备的数据提供了有关井下条件的可靠，实时信息。利用这些改进的技术和更好的数据，运营商可以实时模拟，预测和控制其操作，从而降低效率低下和成本。然而，这种在较高卷和增加的速度下移动的各种数据的大规模集成已经为对可操作和预测数据驱动的分析的计算和使用的需求越来越大。此外，这些分析必须实时工作，以便快速发现用于预测的重要关键数据属性和功能。属性重要性是一种众所周知的统计技术，用于识别可能影响特定目标的一组属性中的关键属性和功能。衍生属性重要性的好处包括改进的数据集理化，减少分析工作以及可操作预测分析所需的时间和计算资源。此外，归属于理解属性空间和交互的重要性辅助工具，它能够减少维度;因此，导致理解，准确，“显着的”（即，简单的）模型，导致可操作的预测分析。如今，许多标准和定制技术 - 每个都具有自己的优势和劣势 - 可用于执行属性重要性。每种技术都适用不同的函数来评估属性的重要性并进行评分以生成排名的属性子集。因此，可以基于各种技术的选择和配置来到达重要属性的不同子集的（并且是相同的。为了解决这个问题，提出了一种使用多个属性重要性技术的可行和有效的基于框架的方法，然后智能地使这些方法的结果融合到达融合属性的融合子集。本文介绍了称为分段属性内核（SAK）的框架/“合奏”（即组合）方法。此外，它还讨论了从在包含实时钻井表面数据日志和钻井参数的数据集上应用这种方法获得的结果。此方法同时运行多个属性重要算法，在多种技术中找到重要属性的交叉子集，然后输出统一排名集。此外，该方法识别并呈现从Union中排除的属性的排名子集。本文将这种方法的结果与基于预测模型的输出的单方法技术进行了比较。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号