地下压力模型的区域评价价值:在墨西哥近海的应用

T. Sheehy, S. Green, E. Hoskin, A. Edwards
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引用次数: 0

摘要

无论盆地成熟度如何(如前沿到成熟),全球数据集都是石油工业的有用工具。随着最近机器学习在石油和天然气行业的普及和出现,人们重新认识到全球和区域数据集的重要性。这部分是由于在实施各种机器学习代码时,很明显,训练数据的可用性和结果的质量控制(感觉检查)对算法的有效性起着重要作用(Naeini和Prindle, 2018)。在本文中,我们介绍了一个完全专注于地下压力评估的区域研究的概念,我们试图强调这些类型的研究如何在整个勘探、开发和生产周期中具有高价值的重要性(Edwards和O'Connor, 2015)。我们特别关注被视为类似信息的区域数据集的价值,以帮助降低新区域的风险,例如,在大部分未开发的深水区域,墨西哥近海区域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Value of Regional Assessments of Subsurface Pressure Models: An Application to Offshore Mexico
Summary Global datasets are useful tools in the petroleum industry regardless of the maturity of a basin (e.g. Frontier to Mature). With the recent popularity and emergence of machine learning in the oil and gas industry there has been a renewed appreciation of the importance of global and regional datasets. This is in part due to the fact that when implementing various machine learning codes, it is apparent that the availability of training data and the quality control (sense-checking) of the outcomes play an important role on the validity of the algorithms ( Naeini and Prindle, 2018 ). In this paper we introduce one concept of a regional study that focuses entirely on subsurface pressure assessment and we seek to highlight how these types of studies can be of high value importance throughout the entire exploration, development and production cycle ( Edwards and O'Connor, 2015 ). We focus particularly on the value of regional datasets viewed as analogous information to help de-risk new acreage for example, in the largely un-explored deep-water, Offshore Mexico acreages.
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