A data-driven approach to predict fracture intensity using machine learning for presalt carbonate reservoirs: A feasibility study in the Mero Field, Santos Basin, Brazil

A data-driven approach to predict fracture intensity using machine learning for presalt carbonate reservoirs: A feasibility study in the Mero Field, Santos Basin, Brazil

Predicting fracture intensity is essential for optimising reservoir production and mitigating drilling risks in the Brazilian pre-salt layer. However, previous studies rely excessively on conceptual models and typically do not integrate multiple types of data to perform such task. Moreover, to date,...

Full description

Saved in:
Bibliographic Details
Main Authors: Eberton Rodrigues de Oliveira Neto, Fábio Júnior Damasceno Fernandes, Tuany Younis Abdul Fatah, Raquel Macedo Dias, Zoraida Roxana Tejada da Piedade, Antonio Fernando Menezes Freire, Wagner Moreira Lupinacci
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2025-06-01
Series:Energy Geoscience
Subjects:
K1 curvature
Naturally fractured reservoirs
P32
Machine learning
Feature importance
Online Access:http://www.sciencedirect.com/science/article/pii/S2666759225000253
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items