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Automatic Seismic First Arrival Picking With Deep-Learning 基于深度学习的地震首点自动拾取
First EAGE/PESGB Workshop Machine Learning Pub Date : 2018-11-30 DOI: 10.3997/2214-4609.201803023
P. Xie, J. Boelle, C. Blais
{"title":"Automatic Seismic First Arrival Picking With Deep-Learning","authors":"P. Xie, J. Boelle, C. Blais","doi":"10.3997/2214-4609.201803023","DOIUrl":"https://doi.org/10.3997/2214-4609.201803023","url":null,"abstract":"Summary This work implements a fully-convolutional neuron network to pick first arrival in difficult field land seismic data. Compared to traditional methods, it greatly improves the productivity. Current work is limited to 2D seismic shot gather and can be extended to 3D without much difficulty. In our test dataset, its picking takes few second per shot and has a credible precision.","PeriodicalId":231338,"journal":{"name":"First EAGE/PESGB Workshop Machine Learning","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123091088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Can Machines Learn To Pick First Breaks As Humans Do? 机器能像人类一样学会选择第一次休息吗?
First EAGE/PESGB Workshop Machine Learning Pub Date : 2018-11-30 DOI: 10.3997/2214-4609.201803026
L. Yalcinoglu, C. Stotter
{"title":"Can Machines Learn To Pick First Breaks As Humans Do?","authors":"L. Yalcinoglu, C. Stotter","doi":"10.3997/2214-4609.201803026","DOIUrl":"https://doi.org/10.3997/2214-4609.201803026","url":null,"abstract":"Machine learning is a well-suited tool for first break picking since the process relies on detecting similar features between the seismic traces and thus is a kind of pattern recognition problem. The method we present in this paper applies support vector machine (SVM) as a machine learning algorithm for first break picking which achieve high accuracy.","PeriodicalId":231338,"journal":{"name":"First EAGE/PESGB Workshop Machine Learning","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127749514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Comparative Study Of Deep Feed Forward Neural Network Application For Seismic Reservoir Characterization 深度前馈神经网络在地震储层表征中的应用对比研究
First EAGE/PESGB Workshop Machine Learning Pub Date : 2018-11-30 DOI: 10.3997/2214-4609.201803009
T. Colwell, Ø. Kjøsnes
{"title":"Comparative Study Of Deep Feed Forward Neural Network Application For Seismic Reservoir Characterization","authors":"T. Colwell, Ø. Kjøsnes","doi":"10.3997/2214-4609.201803009","DOIUrl":"https://doi.org/10.3997/2214-4609.201803009","url":null,"abstract":"Machine learning has been gaining momentum thanks to a new powerful technique called deep learning (Bengio, 2016). These improvements are due to increasing the depth of neural networks to more than one hidden layer. This study uses a Deep Feed-forward Neural Network (DFNN) to predict reservoir properties from seismic attributes similar to Hampson et al. (2001). These are shale, porosity and water saturation volumes, ultimately allowing the estimation of the net pay volume. We compare the results of the DFNN to other forms of machining learning such as multi-linear regression (MLR), Probabilistic Neural Network (PNN).","PeriodicalId":231338,"journal":{"name":"First EAGE/PESGB Workshop Machine Learning","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132071813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Building A Robust, Company-Wide Data Science Pipeline Using Programming Abstraction And Virtualization 使用编程抽象和虚拟化构建健壮的全公司范围的数据科学管道
First EAGE/PESGB Workshop Machine Learning Pub Date : 2018-11-30 DOI: 10.3997/2214-4609.201803030
N. Jones, K. Torbert
{"title":"Building A Robust, Company-Wide Data Science Pipeline Using Programming Abstraction And Virtualization","authors":"N. Jones, K. Torbert","doi":"10.3997/2214-4609.201803030","DOIUrl":"https://doi.org/10.3997/2214-4609.201803030","url":null,"abstract":"The oil and gas industry presents a challenging and exciting environment for data projects due to the size, complexity, and variability in formatting, type, and quality of the data collected. This environment makes delivering and maintaining a data science pipeline from source systems through to the end user an enormous challenge in many companies (Scully et al. 2014). Many projects fail before any analytics can even applied to the data due to difficulties handling legacy systems, data silos, complex dependencies between data sources, and more. In other cases, data science projects can only advance in one area or division of a company because of differences in data handling despite having broad applicability through the company’s assets. This presentation will discuss California Resources Corporation’s new company-wide data analytics effort as a case study of how we have used technologies like data virtualization (Van Der Lans, 2018) and programming architectural principles such as abstraction to tackle difficult data integration and data quality problems to construct a data science pipeline capable of delivering results company-wide. Many of these problems have frustrated multimillion dollar attempts to address them in the recent past.","PeriodicalId":231338,"journal":{"name":"First EAGE/PESGB Workshop Machine Learning","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125664250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Using Machine Learning Techniques To QC Log Data Before A Study 在研究前使用机器学习技术对数据进行QC记录
First EAGE/PESGB Workshop Machine Learning Pub Date : 2018-11-30 DOI: 10.3997/2214-4609.201803019
J. Johnston
{"title":"Using Machine Learning Techniques To QC Log Data Before A Study","authors":"J. Johnston","doi":"10.3997/2214-4609.201803019","DOIUrl":"https://doi.org/10.3997/2214-4609.201803019","url":null,"abstract":"Summary A large part of a petrophysics project lies in sorting and tidying up the input data, trying to fix the logs where they are bad or missing. Another step is identifying where the log response is not as expected. Typically this is done by looking at log plots and crossplots and making judgements on the fly, often in individual wells. The answers are often people-dependent. The advent of machine learning techniques has the potential to change this by enabling users to incorporate large quantities of data and view differences in a more holistic way. This project involved a set of wells from the Barents Sea with the objective of calibrating the logs with geological observed depositional facies from cored wells, and then using just the logs to propagate those to uncored wells.","PeriodicalId":231338,"journal":{"name":"First EAGE/PESGB Workshop Machine Learning","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129248325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Pre-Stack Seismic Inversion With Deep Learning 基于深度学习的叠前地震反演
First EAGE/PESGB Workshop Machine Learning Pub Date : 2018-11-30 DOI: 10.3997/2214-4609.201803008
Y. Zheng, Q. Zhang
{"title":"Pre-Stack Seismic Inversion With Deep Learning","authors":"Y. Zheng, Q. Zhang","doi":"10.3997/2214-4609.201803008","DOIUrl":"https://doi.org/10.3997/2214-4609.201803008","url":null,"abstract":"We present a study of seismic inversion using deep learning tools. The purpose is to investigate the feasibility of using neural networks to construct acoustic and elastic earth models directly from pre-stack seismic data. Training and testing of the neural networks are done using thousands of synthetic 1D earth models and seismic gathers. We use 2 different types of neural network architectures in our numerical experiments to investigate seismic inversion in different geological scenarios. In both cases, the quality of the prediction is comparable with that obtained from conventional model building processes as such as travel-time and waveform inversion methods. The predicted earth models contain abundant low and medium wave-number information. In terms of performance, training took only less than 30 minutes on 4 GPUs whilst prediction adds negligible cost.","PeriodicalId":231338,"journal":{"name":"First EAGE/PESGB Workshop Machine Learning","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114045805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Gaussian Mixture Models For Robust Unsupervised Scanning-Electron Microscopy Image Segmentation Of North Sea Chalk 基于高斯混合模型的无监督扫描电镜图像分割
First EAGE/PESGB Workshop Machine Learning Pub Date : 2018-11-30 DOI: 10.3997/2214-4609.201803014
J. Dramsch, F. Amour, M. Lüthje
{"title":"Gaussian Mixture Models For Robust Unsupervised Scanning-Electron Microscopy Image Segmentation Of North Sea Chalk","authors":"J. Dramsch, F. Amour, M. Lüthje","doi":"10.3997/2214-4609.201803014","DOIUrl":"https://doi.org/10.3997/2214-4609.201803014","url":null,"abstract":"Scanning-Electron images from North Sea Chalk are studied for important rock properties. To relieve this manual labor, we investigated several standard image processing methods that underperformed on complicated chalk. Due to the lack of manually labeled data, deep neural networks could not be adequately applied. Gaussian Mixture Models learnt a two-fold representation that separated the background well from the rock. Subsequent morphological filtering cleans up the prediction and enables automatic analysis.","PeriodicalId":231338,"journal":{"name":"First EAGE/PESGB Workshop Machine Learning","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131551761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Machine Learning To Support Technical Document Indexing, How To Measure The Accuracy? 机器学习支持技术文档索引，如何衡量准确性?
First EAGE/PESGB Workshop Machine Learning Pub Date : 2018-11-30 DOI: 10.3997/2214-4609.201803012
H. Blondelle, J. Micaelli
{"title":"Machine Learning To Support Technical Document Indexing, How To Measure The Accuracy?","authors":"H. Blondelle, J. Micaelli","doi":"10.3997/2214-4609.201803012","DOIUrl":"https://doi.org/10.3997/2214-4609.201803012","url":null,"abstract":"Using a machine learning systems, a set of seismic documents has been automatically indexed on 25 metadata. The hold-out methodology has been used to evaluate the accuracy of the models. Results and lessons learnt are discussed.","PeriodicalId":231338,"journal":{"name":"First EAGE/PESGB Workshop Machine Learning","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116672281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Functional Estimator For Reservoir Proxy Models Made Scalable Through A Big Data Platform 基于大数据平台的油藏代理模型函数估计
First EAGE/PESGB Workshop Machine Learning Pub Date : 2018-11-30 DOI: 10.3997/2214-4609.201803028
M. Piantanida, A. Amendola, G. Formato
{"title":"Functional Estimator For Reservoir Proxy Models Made Scalable Through A Big Data Platform","authors":"M. Piantanida, A. Amendola, G. Formato","doi":"10.3997/2214-4609.201803028","DOIUrl":"https://doi.org/10.3997/2214-4609.201803028","url":null,"abstract":"Summary The abstract documents how a Big Data Analytics platform allowed to implement a complex functional estimator of a reservoir proxy model, involving complex machine learning operations on dynamic reservoir models, so that it can scale up to the size of realistic reservoir models.","PeriodicalId":231338,"journal":{"name":"First EAGE/PESGB Workshop Machine Learning","volume":"37 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132851102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
DNN Application For Pseudo-Spectral FWI DNN在伪频谱wi中的应用
First EAGE/PESGB Workshop Machine Learning Pub Date : 2018-11-30 DOI: 10.3997/2214-4609.201803015
C. Zerafa
{"title":"DNN Application For Pseudo-Spectral FWI","authors":"C. Zerafa","doi":"10.3997/2214-4609.201803015","DOIUrl":"https://doi.org/10.3997/2214-4609.201803015","url":null,"abstract":"Full-waveform inversion (FWI) is a widely used technique in seismic processing to produce high resolution earth models iteratively improved an earth model using a sequence of linearized local inversions to solve a fully non-linear problem. Deep Neural Networks (DNN) are a subset of machine learning algorithms that are efficient in learning non-linear functionals between input and output pairs. The learning process within DNN involves iteratively updating network neuron weights to best approximate input-to-output mapping. There is clearly a similarity between FWI and DNN for optimization applications. I propose casting FWI as a DNN problem and implement a novel approach which learns pseudo-spectral data-driven FWI. I test this methodology by training a DNN on 1D data and then apply this to previously unseen data. Initial results achieved promising levels of accuracy, although not fully reconstructing the model. Future work will investigate deeper DNNs for better generalization and the application to real data.","PeriodicalId":231338,"journal":{"name":"First EAGE/PESGB Workshop Machine Learning","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132205774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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