First EAGE/PESGB Workshop Machine Learning最新文献_第2页

Implementation Of Meta Heuristic Algorithm And Pressure Match Method To Observe Aquifer Constant In Retrograde Gas Condensate Reservoirs 元启发式算法与压力匹配法在逆行凝析气藏含水层常数观测中的实现

First EAGE/PESGB Workshop Machine Learning Pub Date : 2018-11-30 DOI: 10.3997/2214-4609.201803025

M. Ahmadi, Z. Chen

{"title":"Implementation Of Meta Heuristic Algorithm And Pressure Match Method To Observe Aquifer Constant In Retrograde Gas Condensate Reservoirs","authors":"M. Ahmadi, Z. Chen","doi":"10.3997/2214-4609.201803025","DOIUrl":"https://doi.org/10.3997/2214-4609.201803025","url":null,"abstract":"Summary The motivation of doing this research was applying the hybrid of pressure match method and genetic algorithm (GA) to optimize the general material balance equation (GMBE) for a condensate gas reservoir with an almost strong aquifer to Figure out its 3 coefficients which are Nfoi, Gfgi and C. The advantage of implementing genetic algorithm (GA) is that the number of parameters which are supposed to be determined is not a concern. There is no doubt that calculating the aquifer constant without taking the reservoir parameters such as viscosity, porosity, net thickness and absolute permeability through making the observer wells much deeper is the most important, beneficial and technical vantage of the mentioned method. The comparison between obtained results from running the method and acquired outputs from the simulator unmask this fact that the pressure match-GA method has highly been successful of determining the coefficients by generating well matched pressures. As a demerit, the method has some problems with lower pressures based on the nature of general material balance equation (GMBE), being rooted in uncertainty, which defeating this obstacle can be considered as a topic for future studies as well as examining the compatibility of the suggested methodology for the heterogeneous reservoirs.","PeriodicalId":231338,"journal":{"name":"First EAGE/PESGB Workshop Machine Learning","volume":"52 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":"132825113","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

Classification And Suppression Of Blending Noise Using CNN 基于CNN的混合噪声分类与抑制

First EAGE/PESGB Workshop Machine Learning Pub Date : 2018-11-30 DOI: 10.3997/2214-4609.201803017

R. Baardman

引用次数: 2

Analysis Of Gas Production Data Via An Intelligent Model: Application Natural Gas Production 基于智能模型的天然气生产数据分析:应用天然气生产

First EAGE/PESGB Workshop Machine Learning Pub Date : 2018-11-30 DOI: 10.3997/2214-4609.201803024

M. Ahmadi, Zhangxin Chen

{"title":"Analysis Of Gas Production Data Via An Intelligent Model: Application Natural Gas Production","authors":"M. Ahmadi, Zhangxin Chen","doi":"10.3997/2214-4609.201803024","DOIUrl":"https://doi.org/10.3997/2214-4609.201803024","url":null,"abstract":"Predicting the future oil and gas production rate and evaluating oil/gas reserves are very challenging issues. Many engineers have found decline curve analysis a useful approach (Ahmed, 2010; Arps, 1945; Ebrahimi, 2010; Fetkovich, 1980; Gentry, 1972; Li and Horne, 2005; Ling and He, 2012; Oghena, 2012; Shirman, 1999; Zheng and Fei, 2008). The production rate \u0000or cumulative production at a constant bottom-hole pressure declines with time (Ahmed, 2010). Since mechanisms affecting \u0000the production are constant throughout the lifetime of a reservoir, extrapolating decline curves is used to forecast the future production rate. To do so, initial production rate, the decline curvature, and its rate should be considered (Ahmed, 2010). Arps’s equations are fundamental for the most heuristic and conventional decline curve analysis models (Arps, 1945). Arps demonstrated that the hyperbolic family of equations can express mathematically the curvature behaviour of the production rate versus time curve. The Arps (Arps, 1945) equations are divided into three categories, including exponential, hyperbolic, \u0000and harmonic decline curve models. Fetkovich (Fetkovich, 1980) proposed type curves for analysing decline curves. The procedure of type curve matching is summarized by the visual matching with log-log paper that includes pre-plotted curves of \u0000production data. Each of the curves has characteristics which can be shown when plotting them on Cartesian, semi-log and log-log scales as shown in Figure 1.","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":"124689964","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

Automatic Facies Classification And Horizon Tracking In 3D Seismic Data 三维地震数据的自动相分类与层位跟踪

First EAGE/PESGB Workshop Machine Learning Pub Date : 2018-11-30 DOI: 10.3997/2214-4609.201803010

A. J. Bugge, J. Lie, S. Clark

引用次数: 3

Semi-Supervised Deep-Learning Applied To UK North Sea Well And Seismic Data 半监督深度学习在英国北海油井和地震数据中的应用

First EAGE/PESGB Workshop Machine Learning Pub Date : 2018-11-30 DOI: 10.3997/2214-4609.201803013

Y. Nishitsuji, R. Exley, J. Nasseri

{"title":"Semi-Supervised Deep-Learning Applied To UK North Sea Well And Seismic Data","authors":"Y. Nishitsuji, R. Exley, J. Nasseri","doi":"10.3997/2214-4609.201803013","DOIUrl":"https://doi.org/10.3997/2214-4609.201803013","url":null,"abstract":"Semi-supervised deep-learning architectures provide a multi-layer, pattern recognition, approach that is powerful and ideally suited to the data rich environment that exists at the heart of the oil and gas industry. In this study we apply this technology in order to classify facies using elastic impedances from UK North Sea well and seismic data. The semi-supervised deep-learning method in this study uses a self-training strategy that combines both labelled and unlabelled data during the training phase so that classified data subsequently becomes part of the training dataset in the next iteration. This approach is ideal when the availability of labelled data is limited by practical constraints, which is often the case in subsurface geoscience. The resulting outputs of classified facies were visualised using elastic impedance cross-plots after application to a single training well from a North Sea oil discovery. To validate the result we upscaled the classification model to equivalent seismic data in order to compare the learning from the training well with two blind wells. The results indicate that semi-supervised deep-learning has the potential to accurately determine facies, including hydrocarbon distributions, in subsurface data at a field scale.","PeriodicalId":231338,"journal":{"name":"First EAGE/PESGB Workshop Machine Learning","volume":"58 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":"123841080","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

Input Data Quality Influence On Lithoclass Predictions In Relation To Supervised Machine Learning 与监督机器学习相关的输入数据质量对岩石层预测的影响

First EAGE/PESGB Workshop Machine Learning Pub Date : 2018-11-30 DOI: 10.3997/2214-4609.201803032

H. W. Bøe, K. B. Brandsegg, L. Marello, A. Črne

{"title":"Input Data Quality Influence On Lithoclass Predictions In Relation To Supervised Machine Learning","authors":"H. W. Bøe, K. B. Brandsegg, L. Marello, A. Črne","doi":"10.3997/2214-4609.201803032","DOIUrl":"https://doi.org/10.3997/2214-4609.201803032","url":null,"abstract":"We assess the importance of data availability and consistency prior to applying supervised machine learning for predicting lithoclasses from wireline logs. A dataset is pre-processed and used as training data by three machine learning models in order to investigate the sensitivity of the lithoclasses predictions. The first model uses the quality assured dataset without any modifications. The second model standardizes log signatures, whereas the third model uses the dataset in combination with additional features that dampens extreme outliers. The three models are evaluated against lithofacies interpretations based on CPI’s to show the varying predicting power of the models. The method is applied on a quality-controlled Jurassic interval dataset of ~100 exploration wells within a quadrant of the Norwegian part of the North Sea. The results shows that the number of wireline logs available has a direct influence on the prediction accuracy. For an acceptable prediction accuracy the wells should contain at least the gamma ray, density and neutron log. To distinguish between water-bearing and hydrocarbon-bearing intervals in sandstones the resistivity logs should also be present. When implementing machine learning on a regional scale we should consider varying burial depth and depositional environment in order to gain optimal predicting power.","PeriodicalId":231338,"journal":{"name":"First EAGE/PESGB Workshop Machine Learning","volume":"10 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":"115324916","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

Seismic Data Interpolation With Conditional Generative Adversarial Networks (cGANs) 用条件生成对抗网络(cgan)插值地震数据

First EAGE/PESGB Workshop Machine Learning Pub Date : 2018-11-30 DOI: 10.3997/2214-4609.201803021

Dimas Estrasulas de Oliveira, R. Ferreira, Rui F. Silva, E. V. Brazil

引用次数: 1

An Automated Information Retrieval Platform For Unstructured Well Data Utilizing Smart Machine Learning Algorithms Within A Hybrid Cloud Container 在混合云容器中使用智能机器学习算法的非结构化井数据自动信息检索平台

First EAGE/PESGB Workshop Machine Learning Pub Date : 2018-11-30 DOI: 10.3997/2214-4609.201803031

N. M. Hernandez, P. Lucañas, J. C. Graciosa, C. Mamador, L. Caezar, I. Panganiban, Cong Yu, K. Maver, M. Maver

{"title":"An Automated Information Retrieval Platform For Unstructured Well Data Utilizing Smart Machine Learning Algorithms Within A Hybrid Cloud Container","authors":"N. M. Hernandez, P. Lucañas, J. C. Graciosa, C. Mamador, L. Caezar, I. Panganiban, Cong Yu, K. Maver, M. Maver","doi":"10.3997/2214-4609.201803031","DOIUrl":"https://doi.org/10.3997/2214-4609.201803031","url":null,"abstract":"There is a large amount of historic and valuable well information available stored either on paper and more recently as digital documents and reports in the oil and gas industry especially by national data management systems and oil companies. These technical documents contain valuable information from disciplines like geoscience and engineering and are in general stored in a unstructured format. To extract and utilize all this well data, a machine learning-enabled platform, consisting of a carefully selected sequence of algorithms, has been developed as a hybrid cloud container that automatically reads and understands the technical documents with little human supervision. The user can upload raw data to the platform, which are stored on a private local server. The machine learning algorithms are activated and implement the necessary processing and workflows. Structured data is generated as output, which are pushed through to a search engine that is accessible to the user in the cloud. The aim of the platform is to ease the identification of important parts of the technical documents, automatically extract relevant information and visualize it for the user, so they can easily do further analysis, share it with colleagues or agnostically port it to other platforms as input.","PeriodicalId":231338,"journal":{"name":"First EAGE/PESGB Workshop Machine Learning","volume":"15 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":"129303440","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

Streamlining Petrophysical Workflows With Machine Learning 利用机器学习简化岩石物理工作流程

First EAGE/PESGB Workshop Machine Learning Pub Date : 2018-11-30 DOI: 10.3997/2214-4609.201803027

L. MacGregor, N. Brown, A. Roubícková, I. Lampaki, J. Berrizbeitia, Michelle Ellis

{"title":"Streamlining Petrophysical Workflows With Machine Learning","authors":"L. MacGregor, N. Brown, A. Roubícková, I. Lampaki, J. Berrizbeitia, Michelle Ellis","doi":"10.3997/2214-4609.201803027","DOIUrl":"https://doi.org/10.3997/2214-4609.201803027","url":null,"abstract":"The oil and gas industry is not short of data, in the form of wells, seismic and other geophysical information. However, often because of the complexity of workflows and the time taken to execute them, only a fraction of this information is utilized. Making better use of information, using modern data analytics techniques, and presenting this information in a way that is immediately useful to geologists and decision makers has the potential to dramatically reduce time to decision and the quality of the decision that is made. Here we concentrate on using machine learning approaches to streamline petrophysical workflows. However, to do this requires a rich and diverse training dataset of wells that have been consistently processed for geophysical analysis. The work discussed in this paper has focused on the estimation of clay volume, determination of mineral volumes and determination of porosity and water saturation. A variety of machine learning techniques and algorithms have been tested to find the one most suited to this application. Initial analysis is regionally focused, but we plan to investigate whether the approaches and models developed can be generalized across regions, basins and geological settings.","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":"121076348","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

Deep Learning Approach For Automatic Detection Of Oil Slicks 基于深度学习的浮油自动检测方法

First EAGE/PESGB Workshop Machine Learning Pub Date : 2018-11-30 DOI: 10.3997/2214-4609.201803022

Z. Huang, P. Xie, V. Miegebielle

引用次数: 1