Incremental correlation of multiple well logs following geologically optimal neighbors

相关性 路径(计算) 稳健性(进化) 动态时间归整 测井 地质学 算法 欧几里德距离 数据挖掘 计算机科学 数学 人工智能 几何学 地球物理学 基因 化学 程序设计语言 生物化学
作者
Xinming Wu,Yunzhi Shi,Sergey Fomel,Fangyu Li
出处
期刊:Interpretation [Society of Exploration Geophysicists]
卷期号:6 (3): T713-T722 被引量:8
标识
DOI:10.1190/int-2018-0020.1
摘要

Well-log correlation is a crucial step to construct cross sections in estimating structures between wells and building subsurface models. Manually correlating multiple logs can be highly subjective and labor intensive. We have developed a weighted incremental correlation method to efficiently correlate multiple well logs following a geologically optimal path. In this method, we first automatically compute an optimal path that starts with longer logs and follows geologically continuous structures. Then, we use the dynamic warping technique to sequentially correlate the logs following the path. To avoid potential error propagation with the path, we modify the dynamic warping algorithm to use all the previously correlated logs as references to correlate the current log in the path. During the sequential correlations, we compute the geologic distances between the current log and all of the reference logs. Such distances are proportional to Euclidean distances, but they increase dramatically across discontinuous structures such as faults and unconformities that separate the current log from the reference logs. We also compute correlation confidences to provide quantitative quality control of the correlation results. We use the geologic distances and correlation confidences to weight the references in correlating the current log. By using this weighted incremental correlation method, each log is optimally correlated with all the logs that are geologically closer and are ordered with higher priorities in the path. Hundreds of well logs from the Teapot Dome survey demonstrate the efficiency and robustness of the method.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
JamesPei应助专注的冰菱采纳,获得10
刚刚
1秒前
2秒前
2秒前
量子星尘发布了新的文献求助10
2秒前
zxt发布了新的文献求助10
3秒前
元66666完成签到 ,获得积分10
4秒前
高高爱读文献完成签到,获得积分10
4秒前
押尾完成签到,获得积分20
4秒前
7秒前
ChemPhys完成签到 ,获得积分10
8秒前
8秒前
深情安青应助林海采纳,获得10
10秒前
炙热的书本关注了科研通微信公众号
10秒前
富贵龙完成签到,获得积分10
10秒前
10秒前
11秒前
yu发布了新的文献求助10
11秒前
内向人生完成签到,获得积分10
11秒前
猛龙FC20发布了新的文献求助10
11秒前
烟花发布了新的文献求助10
11秒前
体贴绮露完成签到,获得积分10
12秒前
Godzilla完成签到,获得积分10
12秒前
12秒前
13秒前
13秒前
十驾医学僧完成签到,获得积分10
14秒前
xh发布了新的文献求助10
14秒前
14秒前
15秒前
伶俐冥王星应助内向人生采纳,获得10
15秒前
Pandies发布了新的文献求助10
15秒前
xh发布了新的文献求助10
16秒前
17秒前
xh发布了新的文献求助10
17秒前
111发布了新的文献求助10
17秒前
xh发布了新的文献求助10
17秒前
18秒前
18秒前
赶紧毕业完成签到,获得积分10
18秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Handbook of pharmaceutical excipients, Ninth edition 5000
Aerospace Standards Index - 2026 ASIN2026 3000
Signals, Systems, and Signal Processing 610
Discrete-Time Signals and Systems 610
Principles of town planning : translating concepts to applications 500
Short-Wavelength Infrared Windows for Biomedical Applications 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 纳米技术 有机化学 物理 生物化学 化学工程 计算机科学 复合材料 内科学 催化作用 光电子学 物理化学 电极 冶金 遗传学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 6061195
求助须知:如何正确求助?哪些是违规求助? 7893547
关于积分的说明 16305686
捐赠科研通 5205059
什么是DOI,文献DOI怎么找? 2784642
邀请新用户注册赠送积分活动 1767244
关于科研通互助平台的介绍 1647359