Publication

Support Vector Machines for Classification and Mapping of Reservoir Data

2001
Reports

Abstract

The report deals with the novel application of Support Vector Machines (Support Vectore Classification and Support Vector Regression) for the analysis and modelling of reservoir data. 2 problems are considered: classification and mapping of porosity data. Results are compared with geostatistical models - indicator kriging and ordinary kriging. Variography is widely used to control the performance of the machines. Geostatistical explanations for the SVR hyperparameters are discussed. Obtained results demonstrate flexibility and efficiency of SVM application for the reservoir characterisation.

Official source

https://infoscience.epfl.ch/entities/publication/0b664175-2eb4-4edb-84b9-83afee47e85a

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2001
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/0b664175-2eb4-4edb-84b9-83afee47e85a

About this result

Related concepts (32)

Support vector machine

In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories by Vladimir Vapnik with colleagues (Boser et al., 1992, Guyon et al., 1993, Cortes and Vapnik, 1995, Vapnik et al., 1997) SVMs are one of the most robust prediction methods, being based on statistical learning frameworks or VC theory proposed by Vapnik (1982, 1995) and Chervonenkis (1974).

Reservoir modeling

In the oil and gas industry, reservoir modeling involves the construction of a computer model of a petroleum reservoir, for the purposes of improving estimation of reserves and making decisions regarding the development of the field, predicting future production, placing additional wells and evaluating alternative reservoir management scenarios. A reservoir model represents the physical space of the reservoir by an array of discrete cells, delineated by a grid which may be regular or irregular.

Statistical classification

In statistics, classification is the problem of identifying which of a set of categories (sub-populations) an observation (or observations) belongs to. Examples are assigning a given email to the "spam" or "non-spam" class, and assigning a diagnosis to a given patient based on observed characteristics of the patient (sex, blood pressure, presence or absence of certain symptoms, etc.). Often, the individual observations are analyzed into a set of quantifiable properties, known variously as explanatory variables or features.

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Support Vector Machines for Classification and Mapping of Reservoir Data

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