ESTIMACIÓN DE LA POROSIDAD A PARTIR DE MODELOS PROBABILÍSTICOS Y TÉCNICAS DE MACHINE LEARNING EN LA FORMACIÓN VACA MUERTA (Record no. 196082)

MARC details
000 -LEADER
fixed length control field 03147nmc a2200229 4500
008 - CÓDIGOS DE INFORMACIÓN DE LONGITUD FIJA - INFORMACIÓN GENERAL
Campo de control de longitud fija 260224s xxu
082 ## - CLASIFICACIÓN DEWEY
Número de clasificación 068.82 553.28 C62 15730
210 ## - TITULO ABREVIADO
Título abreviado Evaluación de formaciones
245 00 - TITULO
Título ESTIMACIÓN DE LA POROSIDAD A PARTIR DE MODELOS PROBABILÍSTICOS Y TÉCNICAS DE MACHINE LEARNING EN LA FORMACIÓN VACA MUERTA
270 ## - FECHA DE CARGA
Fecha de carga 29/08/2023 ; 29/08/2023
300 ## - DESCRIPCION FISICA
Otra extensión 20 p. ; 197-216
520 ## - RESUMEN, ETC
Resumen In shale reservoirs, quantification of total porosity and water saturation are considered key parameters for calculating the volume of hydrocarbon in place. However, the petrophysical interpretation necessary for the definition of these parameters represents a challenge due to mineralogical complexity of the reservoir as Well as the presence of kerogen and different fluids depending on their thermal maturity. Therefore, for a good petrophysical characterization, it is necessary to drill pilot vertical Wells to acquire a wide variety of data, both from logging tools and core samples. As a result of this, there is a rise in the budget associated with the acquisition of data in the shale characterization projects. On the other hand, there are many old Wells that have been drilled and logged in the Vaca Muerta-Quintuco formation section, due to their position in the lithological column overlying deeper conventional targets. The purpose of this work is to present a workflow that allows the use of data from these legacy Wells with a limited set of logs applying calibrations carried out using machine learning techniques based on probabilistic petrophysical interpretation carried out in Wells with a complete set of logs and laboratory data. Once the target log has been defined, an exploratory analysis of the logs available in the aforementioned set of Wells is carried out. The logs are studied separately and their relations as a whole, obtaining as a result a set of data with validated quality and with a format suitable for feeding machine learning models. Data is divided into training and validation sets. Then, different models are trained and validated, adjusting them to finally obtain the best result. The implementation of this technique not only provides relevant information for improving property maps and volumetric calculations, taking into account the uncertainty associated with the results obtained, but also allows to analyze variability of porosity related to other geological characteristics, such as the depositional system and the thermal maturity of the rock. Finally, a better use of the available data allows to reduce costs allocated to the acquisition of new data in areas where the information is available from previously drilled Wells.
773 ## - CORRECCIÓN
Partes relacionadas
942 ## - DESC. DE MATERIAL
Tipo de item KOHA Congresos (trabajos presentados)
100 1# - RESPONSABLE PERSONAL
Apellido, Nombre Bernhardt, Carolina
9 (RLIN) 857
100 1# - RESPONSABLE PERSONAL
Apellido, Nombre Gallart, Diego
9 (RLIN) 858
100 1# - RESPONSABLE PERSONAL
Apellido, Nombre Ortiz, Alberto
9 (RLIN) 668
111 2# - AUTORIDAD DE CONGRESO
Nombre de congreso Congreso de Exploración y Desarrollo de Hidrocarburos (11er : 2022 nov. 8 - 11 : Mendoza)
9 (RLIN) 61064
856 ## - LINK
Link <a href="https://biblioteca.iapg.org.ar/ArchivosAdjuntos/Conexplor2022/EvaluacionFormaciones/forma14.pdf">https://biblioteca.iapg.org.ar/ArchivosAdjuntos/Conexplor2022/EvaluacionFormaciones/forma14.pdf</a>
Holdings
Biblioteca propietaria Biblioteca actual Ubicación Fecha de adquisición Inventario Total de préstamos Signatura topográfica Inventario Fecha de carga Tipo de item KOHA
Biblioteca virtual Biblioteca virtual 068.82 553.28 C62 15730 06/03/2026 200068500   068.82 553.28 C62 15730 200068500 06/03/2026 Archivo electrónico


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