Machine-learning approach identifies wolfcamp reservoirs

被引：0
|
作者：
Carpenter C.
机构：
来源：
JPT, Journal of Petroleum Technology | 2019年 / 71卷 / 03期
关键词：
3-D image - Classification results - Machine learning approaches - Oil-filled - Packstone - Poststack - Reservoir characterization - Seismic datas;
D O I：
10.2118/0319-0087-JPT
中图分类号：
学科分类号：
摘要：
This paper discusses a project with the objective of leveraging prestack and poststack seismic data in order to reconstruct 3D images of thin, discontinuous, oil-filled packstone pay facies of the Upper and Lower Wolfcamp formation. The classification results were created by neural networks, which can be used as a substitute for traditional amplitude-vs.-offset, inversion, and cross-plotting techniques for seismic reservoir characterization. © 2019 Society of Petroleum Engineers. All rights reserved.
引用
收藏
页码：87 / 89
页数：2
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