Pub Date : 2018-11-30DOI: 10.3997/2214-4609.201803023
P. Xie, J. Boelle, C. Blais
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.
{"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}
Pub Date : 2018-11-30DOI: 10.3997/2214-4609.201803026
L. Yalcinoglu, C. Stotter
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.
{"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}
Pub Date : 2018-11-30DOI: 10.3997/2214-4609.201803009
T. Colwell, Ø. Kjøsnes
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).
{"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}
Pub Date : 2018-11-30DOI: 10.3997/2214-4609.201803030
N. Jones, K. Torbert
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.
由于所收集数据的规模、复杂性、格式、类型和质量的可变性，石油和天然气行业的数据项目具有挑战性和令人兴奋的环境。这种环境使得交付和维护从源系统到最终用户的数据科学管道对许多公司来说是一个巨大的挑战(Scully et al. 2014)。由于难以处理遗留系统、数据孤岛、数据源之间复杂的依赖关系等原因，许多项目甚至在对数据进行分析之前就失败了。在其他情况下，数据科学项目只能在公司的一个领域或部门推进，因为数据处理的差异，尽管在公司的资产中具有广泛的适用性。本演讲将讨论加州资源公司新的全公司范围的数据分析工作，作为我们如何使用数据虚拟化等技术(Van Der Lans, 2018)和编程架构原则(如抽象)来解决困难的数据集成和数据质量问题，以构建能够在全公司范围内交付结果的数据科学管道的案例研究。在最近的一段时间里，这些问题中的许多都使数百万美元的努力付诸于失败。
{"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}
Pub Date : 2018-11-30DOI: 10.3997/2214-4609.201803019
J. Johnston
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.
{"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}
Pub Date : 2018-11-30DOI: 10.3997/2214-4609.201803008
Y. Zheng, Q. Zhang
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.
{"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}
Pub Date : 2018-11-30DOI: 10.3997/2214-4609.201803014
J. Dramsch, F. Amour, M. Lüthje
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.
{"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}
Pub Date : 2018-11-30DOI: 10.3997/2214-4609.201803012
H. Blondelle, J. Micaelli
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.
{"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}
Pub Date : 2018-11-30DOI: 10.3997/2214-4609.201803028
M. Piantanida, A. Amendola, G. Formato
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.
{"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}
Pub Date : 2018-11-30DOI: 10.3997/2214-4609.201803015
C. Zerafa
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.
{"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}
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