A random forest regressor based uncertainty quantification of porosity estimation from multiple seismic attributes

The accurate inference of reservoir properties such as porosity and permeability is crucial in reservoir characterization for oil and gas exploration and production as well as for other geologic applications. In most cases, direct measurements of those properties are done in wells that provide high vertical resolution but limited lateral coverage. To fill this gap, geophysical methods can often offer data with dense 3D coverage that can serve as proxy for the variable of interest. All the information available can then be integrated using multivariate geostatistical methods to provide stochastic or deterministic estimate of the reservoir properties. Our objective is to generate multiple scenarios of porosity at different scales, considering four formations of the Fort Worth Basin altogether and then restricting the process to the Marble Falls limestones. Under the hypothesis that a statistical relation between 3D seismic attributes and porosity can be inferred from well logs, a Bayesian sequential simulation (BSS) framework proved to be an efficient approach to infer reservoir porosity from an acoustic impedance cube. However, previous BBS approaches only took two variables upscaled at the resolution of the seismic data, which is not suitable for thin-bed reservoirs. We have developed three modified BSS algorithms that better adapt the BSS approach for unconventional reservoir petrophysical properties estimation from deterministic prestack seismic inversion. A methodology that includes a stochastic downscaling procedure is built and one that integrates two secondary downscaled constraints to the porosity estimation process. Results suggest that when working at resolution higher than surface seismic, it is better to execute the workflow for each geologic formation separately.

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Random Forest for Toxicity of Chemical Emissions: Features Selection and Uncertainty Quantification

Journal of Environmental Accounting and Management ◽

10.5890/jeam.2015.09.003 ◽

2015 ◽

Vol 3 (3) ◽

pp. 229-241 ◽

Cited By ~ 2

Author(s):

Antonino Marvuglia ◽

Michael Leuenberger ◽

Mikhail Kanevski ◽

Enrico Benetto

Keyword(s):

Random Forest ◽

Uncertainty Quantification ◽

Features Selection

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Implementation of data mining as a support of business application strategy

Journal of Applied Information, Communication and Technology ◽

10.33555/ejaict.v5i1.49 ◽

2018 ◽

Vol 5 (1) ◽

pp. 47-55

Author(s):

Florensia Unggul Damayanti

Keyword(s):

Data Mining ◽

Random Forest ◽

Business Strategy ◽

Input Parameter ◽

Data Mining Algorithm ◽

Complex Data ◽

Business Decision ◽

Marketing Department ◽

Business Application ◽

Complex Data Sets

Data mining help industries create intelligent decision on complex problems. Data mining algorithm can be applied to the data in order to forecasting, identity pattern, make rules and recommendations, analyze the sequence in complex data sets and retrieve fresh insights. Yet, increasing of technology and various techniques among data mining availability data give opportunity to industries to explore and gain valuable information from their data and use the information to support business decision making. This paper implement classification data mining in order to retrieve knowledge in customer databases to support marketing department while planning strategy for predict plan premium. The dataset decompose into conceptual analytic to identify characteristic data that can be used as input parameter of data mining model. Business decision and application is characterized by processing step, processing characteristic and processing outcome (Seng, J.L., Chen T.C. 2010). This paper set up experimental of data mining based on J48 and Random Forest classifiers and put a light on performance evaluation between J48 and random forest in the context of dataset in insurance industries. The experiment result are about classification accuracy and efficiency of J48 and Random Forest , also find out the most attribute that can be used to predict plan premium in context of strategic planning to support business strategy.

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