EAGE Subsurface Intelligence Workshop最新文献

Automatic fault interpretation from seismic data via convolutional neural networks 基于卷积神经网络的地震数据自动断层解释

EAGE Subsurface Intelligence Workshop Pub Date : 2019-12-09 DOI: 10.3997/2214-4609.2019X610105

D. Egorov

{"title":"Automatic fault interpretation from seismic data via convolutional neural networks","authors":"D. Egorov","doi":"10.3997/2214-4609.2019X610105","DOIUrl":"https://doi.org/10.3997/2214-4609.2019X610105","url":null,"abstract":"Summary These days understanding of fault geometry distribution across a particular oil or gas reservoir becomes very important task. It arises from the fact that fluid flow of present unconventional deposits is mostly driven by natural fractures instead of sedimentary porosity and corresponding permeability. On the other, complex compartmentalized reservoir separated into small discontinuous deposits by tectonic activity could lead to economic risks during field development. Most of conventional tools for fault interpretation from seismic data are highly affected by noise from data and deterministic so cannot produce probabilistic output. In the presented research application of convolutional neural networks for fault interpretation from seismic data was considered. Proposed architecture and training process were described. It was shown by metrics and visual analysis that developed method is able to delineate faults from seismic data in different geological and geophysical conditions. Additional advantage of suggested approach is its ability to produce probabilistic output allowing robust work with geological uncertainties and economic risks related to them due to consideration of many probable cases.","PeriodicalId":369295,"journal":{"name":"EAGE Subsurface Intelligence Workshop","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114477669","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

AI-assisted Core Description - Unsupervised Facies Classification and Manifold Learning of Fluvio-Deltaic Shaly Sands 人工智能辅助岩心描述-河流三角洲泥质砂的无监督相分类和流形学习

EAGE Subsurface Intelligence Workshop Pub Date : 2019-12-09 DOI: 10.3997/2214-4609.2019X6106

N. Leseur, P. Ragettli

引用次数: 0

Rock-physics based Augmented Machine Learning for Reservoir Characterization 基于岩石物理的增强机器学习油藏表征

EAGE Subsurface Intelligence Workshop Pub Date : 2019-12-09 DOI: 10.3997/2214-4609.2019x610102

J. Downton, O. Collet, T. Colwell

引用次数: 1

Aspects of automated seismic interpretation using supervised and unsupervised machine learning 使用监督和无监督机器学习的自动地震解释方面

EAGE Subsurface Intelligence Workshop Pub Date : 2019-12-09 DOI: 10.3997/2214-4609.2019X610101

A. J. Bugge, J. Lie

{"title":"Aspects of automated seismic interpretation using supervised and unsupervised machine learning","authors":"A. J. Bugge, J. Lie","doi":"10.3997/2214-4609.2019X610101","DOIUrl":"https://doi.org/10.3997/2214-4609.2019X610101","url":null,"abstract":"Summary The state-of-the-art seismic interpretation workflow is based on extraction of information from seismic images, which typically involves manual mapping of seed points along targeted geological structures. This process requires expert geophysical knowledge, interpretive experience, intuition and creativity. With increasing computational power, data science is continuously evolving and provide new digital tools applicable to various disciplines, including geoscience. Most of these tools are based on open-source signal processing, image processing and machine learning algorithms. By utilizing these digital tools and automate the extraction of information from seismic images, we can accumulate knowledge and build a subsurface understanding faster and better. Here, we introduce data-driven methods based on both supervised and unsupervised machine learning to address key aspects of an automated seismic interpretation workflow. We automatically identify and extract faults using a pre-trained conditional generative adaptive network together with image processing such as morphological operations. Further, we address stratigraphic units with a new 3D texture descriptor for seismic data, and compute and cluster feature vectors that describe seismic stratigraphy for given seismic sub-volumes. Finally, we correlate dislocated and truncated seismic horizons we introduce a non-local trace matching approach.","PeriodicalId":369295,"journal":{"name":"EAGE Subsurface Intelligence Workshop","volume":"269 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132182670","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

The Digital Underground: Integrating petroleum geoscience with data science principles to create an intelligent subsurface platform 数字地下:将石油地球科学与数据科学原理相结合，创建智能地下平台

EAGE Subsurface Intelligence Workshop Pub Date : 2019-12-09 DOI: 10.3997/2214-4609.2019X610110

B. Alaei, S. Purves, E. Larsen, D. Economou, D. Austin

{"title":"The Digital Underground: Integrating petroleum geoscience with data science principles to create an intelligent subsurface platform","authors":"B. Alaei, S. Purves, E. Larsen, D. Economou, D. Austin","doi":"10.3997/2214-4609.2019X610110","DOIUrl":"https://doi.org/10.3997/2214-4609.2019X610110","url":null,"abstract":"Summary The history of hydrocarbon exploration consistently indicates the advantages of integrating knowledge and data derived from different disciplines such as basin modelling, structural geology and geophysics. We have designed a complete subsurface workflow or platform, that we call Digital Underground. It combines semi-automated data wrangling, highly accessible structured analytics ready data in large databases, direct integration of data analytics and machine learning technology, tracking of data provenance, enabling reproducible scientific workflows, and the practical use of ML methods by the geoscientist in making their decisions. The workflow uses ML approaches at different scales, from core to seismic, and basin to prospect scale; while providing dynamic access to large amounts of data throughout. The workflow includes four main stages starting with well data analysis and ending up in integration of data-driven distributions of different properties required for risk and volumetric estimations together with corresponding uncertainties. We have shown the advantage of the platform by testing it on several examples. ML technology paired with solid data science practice; facilitates the integration of data and disciplines, enables geoscientists to exceed current best practice with the ML tools, and paves the way to the \"new\" best practice which is integrated data science and geoscience.","PeriodicalId":369295,"journal":{"name":"EAGE Subsurface Intelligence Workshop","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126882596","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

Data Driven Approach to Image, Classify and extract Seismic Discontinuities in Complex Geological Settings 复杂地质背景下地震不连续面成像、分类和提取的数据驱动方法

EAGE Subsurface Intelligence Workshop Pub Date : 1900-01-01 DOI: 10.3997/2214-4609.2019x610115

S. A. Syed, T. Turkistani, M. Khan

引用次数: 0

Data-driven well placement strategy based on variational simulations 基于变分模拟的数据驱动的井眼布置策略

EAGE Subsurface Intelligence Workshop Pub Date : 1900-01-01 DOI: 10.3997/2214-4609.2019x610114

N. Bukhanov, A. Orlov, M. Ozhgibesov, E. Grishnyaev, T. Dogadova, B. Belozerov

引用次数: 0

WellNet: improvement of machine learning models robustness via comprehensive multi oilfield dataset WellNet:通过综合多油田数据集提高机器学习模型的鲁棒性

EAGE Subsurface Intelligence Workshop Pub Date : 1900-01-01 DOI: 10.3997/2214-4609.2019x610116

A. A. Reshytko, D. Egorov, A. Klenitskiy, A. Shchepetnov

引用次数: 0

Quantification of errors in well-trace positions and uncertain measurements for improvement of subsurface imaging 井迹位置误差和不确定测量的量化改进地下成像

EAGE Subsurface Intelligence Workshop Pub Date : 1900-01-01 DOI: 10.3997/2214-4609.2019x610113

I. Fernandes, K. Mosegaard

引用次数: 0

Pseudo-Wells based HitCube ‘trace-matching’ and Machine Learning Inversions: Seismic Reservoir Characterization in a Challenging Environment 基于HitCube“轨迹匹配”和机器学习反演的伪井:具有挑战性环境下的地震储层表征

EAGE Subsurface Intelligence Workshop Pub Date : 1900-01-01 DOI: 10.3997/2214-4609.2019x610104

G. Kocsis, H. Jaglan

引用次数: 0