Boletín de la Sociedad Geológica Mexicana Volumen 75, núm. 3, A150823, 2023 http://dx.doi.org/10.18268/BSGM2023v75n3a150823

Parametrización petrofísica de secuencias siliciclásticas areno-arcillosas con redes neuronales

Petrophysic parametrization of sand-clay siliciclastic sequences with neural networks

Daniel López-Aguirre^1,*, Silvia Raquel García-Benítez², Rubén Nicolás-López^1,3,Enrique Coconi-Morales³

¹ Posgrado Ingeniería en Exploración y Explotación de Recursos Naturales, Facultad de Ingeniería, Universidad Nacional Autónoma de México, Ciudad Universitaria, Coyoacán, 04510, CDMX, México.

² Instituto de Ingeniería, Universidad Nacional Autónoma de México, Ciudad Universitaria, Coyoacán, 04510, CDMX, México.

³ Instituto Mexicano del Petróleo, Eje Central Lázaro Cárdenas Norte 152, Col. San Bartolo Atepehuacan, Gustavo A. Madero, 88630, CDMX, México.

* Autor para correspondencia: (Daniel López-Aguirre) This email address is being protected from spambots. You need JavaScript enabled to view it.

Cómo citar este artículo:

López-Aguirre, D., García-Benítez, S. R., Nicolás-López, R., Coconi-Morales, E., 2023, Parametrización petrofísica de secuencias siliciclásticas areno-arcillosas con redes neuronales: Boletín de la Sociedad Geológica Mexicana, 75 (3), A150823. http://dx.doi.org/10.18268/BSGM2023v75n3a150823

Manuscrito recibido: 16 de Enero de 2023 ; Manuscrito corregido: 12 de Mayo de 2023 ; Manuscrito aceptado: 12 de Junio de 2023.

ABSTRACT

In this work neural networks are used as an advantageous tool to estimate petrophysical parameters of the stratigraphic column traversed by several wells. The parameters porosity, mineral volumes, and water and hydrocarbon saturation are obtained from basic geophysical well logging (gamma rays, deep resistivity, volumetric density and transit time) and are inferred for other sites, in the same geological area, where they are not measured, so this information matrix is not available. This analysis was performed on sand-clay siliciclastic sequences traversed by several wells drilled to reach a low-permeability hydrocarbon reservoir. Estimates with empirical models are presented to compare them with those obtained with neural networks in order to qualify the performance of the intelligent alternative. The laws that govern the dynamics of the parameters as well as the details of the geological context are immersed in the weights of the network and the phenomenological consistency is defined through the congruence of the inputs to achieve the chosen outputs.The way in which the neural model enables the reliable propagation of property values is shown and becomes an advantageous auxiliary in the study of very complex or poorly parameterized geological contexts in which the conditions for the application of correlations and empirical methods as well as how the time invested in the processes of adjustment and contextualization of records, decreases the quality and quantity of knowledge obtained about the environment.

Keywords: petrophysical mo- dels, well logs, siliciclastic sequences, neural networks.