Application of Class-Based Machine Learning for Potential Hydrocarbon Zones Identification: A Case Study

S. Wiyoga, Jhonny Xu, Aulia Desiani Carolina, Ratna Dewanda
{"title":"Application of Class-Based Machine Learning for Potential Hydrocarbon Zones Identification: A Case Study","authors":"S. Wiyoga, Jhonny Xu, Aulia Desiani Carolina, Ratna Dewanda","doi":"10.2118/205617-ms","DOIUrl":null,"url":null,"abstract":"\n At times, petrophysicists are expected to evaluate potential of the well in time-constraint situations while maintaining consistency of the parameters and interpretation. Other than that, some challenges may also occur when working with older wells where the dataset are not as complete as current wells and processing parameters are not transferable. In this case study, class-based machine learning (CBML) approach is used to perform petrophysical evaluation to identify potential hydrocarbon zones in the target wells. The objective is to find solution to improve efficiency and consistency in those challenging situations.\n A class-based machine learning (CBML) workflow uses cross-entropy clustering (CEC)-Gaussian mixture model (GMM)- hidden Markov model (HMM) workflow that identifies locally stationary zones sharing similar statistical properties in logs, and then propagates zonation information from training wells to other wells (Jain, et al., 2019). The workflow is divided into two (2) main steps: training and prediction. Key wells which best represent the formation in the field are used to train the model. This approach automatically generates the number of cluster (class) using unsupervised or supervised depending on the input data. The model from key wells data is then used to reconstruct inputs and outputs along with uncertainty and outlier flags. This allows expert to QC and validate the generated class which is the most crucial part of the workflow. Once the model from the key wells has been built, it is applied to predict the same set of zones in the new wells that require interpretation and predict output curves.\n The result matched well over the good data interval with the petrophysical interpretation result from conventional approach. While in the bad interval, some discrepancies can be observed. The discrepancy was identified easily from the uncertainty and outlier flags which helps petrophysicists to identify which interval to fix or re-evaluate. Some requirements to condition the input were observed (no missing value over the input and outlier) to get the best result. A number of inputs used in the model need to be consistent over the set of wells used in the training and prediction target. This machine learning workflow speeds-up the petrophysical analysis process, reduce analyst bias and improve consistency result between one well to another within the same field. This machine learning application can also generate auto log QC, zonation class for rock typing also reconstructed logs which enrich the petrophysical interpretation even for wells with limited logs availability.\n This paper offers practical examples and lessons learned of CBML approach application to perform petrophysical evaluation and identify potential zones while being in time-constrained and limited resource situations.","PeriodicalId":11017,"journal":{"name":"Day 2 Wed, October 13, 2021","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Day 2 Wed, October 13, 2021","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2118/205617-ms","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

At times, petrophysicists are expected to evaluate potential of the well in time-constraint situations while maintaining consistency of the parameters and interpretation. Other than that, some challenges may also occur when working with older wells where the dataset are not as complete as current wells and processing parameters are not transferable. In this case study, class-based machine learning (CBML) approach is used to perform petrophysical evaluation to identify potential hydrocarbon zones in the target wells. The objective is to find solution to improve efficiency and consistency in those challenging situations. A class-based machine learning (CBML) workflow uses cross-entropy clustering (CEC)-Gaussian mixture model (GMM)- hidden Markov model (HMM) workflow that identifies locally stationary zones sharing similar statistical properties in logs, and then propagates zonation information from training wells to other wells (Jain, et al., 2019). The workflow is divided into two (2) main steps: training and prediction. Key wells which best represent the formation in the field are used to train the model. This approach automatically generates the number of cluster (class) using unsupervised or supervised depending on the input data. The model from key wells data is then used to reconstruct inputs and outputs along with uncertainty and outlier flags. This allows expert to QC and validate the generated class which is the most crucial part of the workflow. Once the model from the key wells has been built, it is applied to predict the same set of zones in the new wells that require interpretation and predict output curves. The result matched well over the good data interval with the petrophysical interpretation result from conventional approach. While in the bad interval, some discrepancies can be observed. The discrepancy was identified easily from the uncertainty and outlier flags which helps petrophysicists to identify which interval to fix or re-evaluate. Some requirements to condition the input were observed (no missing value over the input and outlier) to get the best result. A number of inputs used in the model need to be consistent over the set of wells used in the training and prediction target. This machine learning workflow speeds-up the petrophysical analysis process, reduce analyst bias and improve consistency result between one well to another within the same field. This machine learning application can also generate auto log QC, zonation class for rock typing also reconstructed logs which enrich the petrophysical interpretation even for wells with limited logs availability. This paper offers practical examples and lessons learned of CBML approach application to perform petrophysical evaluation and identify potential zones while being in time-constrained and limited resource situations.
基于类的机器学习在潜在油气带识别中的应用:一个案例研究
有时,岩石物理学家需要在时间限制的情况下评估井的潜力,同时保持参数和解释的一致性。除此之外,在数据集不如现有井完整且处理参数不可转移的老井中,也可能会遇到一些挑战。在本案例研究中,采用基于类的机器学习(CBML)方法进行岩石物理评价,以识别目标井中的潜在油气层。目标是在这些具有挑战性的情况下找到提高效率和一致性的解决办法。基于类的机器学习(CBML)工作流使用交叉熵聚类(CEC)-高斯混合模型(GMM)-隐马尔可夫模型(HMM)工作流,该工作流识别在日志中具有相似统计属性的局部静止区域,然后将分区信息从训练井传播到其他井(Jain等,2019)。该工作流程分为两(2)个主要步骤:训练和预测。利用最能代表油田地层的关键井对模型进行训练。该方法根据输入数据使用无监督或有监督自动生成簇(类)的数量。然后使用关键井数据的模型来重建输入和输出,以及不确定性和异常标记。这允许专家QC和验证生成的类,这是工作流中最关键的部分。一旦建立了关键井的模型,就可以应用它来预测需要解释和预测产量曲线的新井的同一组区域。在良好的数据区间内,结果与常规方法的岩石物理解释结果吻合良好。而在不良区间,可以观察到一些差异。这种差异很容易从不确定性和异常标记中识别出来,这有助于岩石物理学家确定需要修复或重新评估的层段。为了获得最佳结果,观察了对输入条件的一些要求(输入和离群值之间没有缺失值)。模型中使用的许多输入需要在训练和预测目标中使用的井集上保持一致。这种机器学习工作流程加快了岩石物理分析过程,减少了分析师的偏见,提高了同一油田内井间结果的一致性。该机器学习应用程序还可以生成自动测井QC,用于岩石类型的分带类,还可以重建测井数据,即使对于测井数据有限的井,也可以丰富岩石物理解释。本文给出了在时间有限、资源有限的情况下,CBML方法应用于岩石物理评价和识别潜在层的实例和经验教训。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信