Acoustic impedance prediction based on extended seismic attributes using multilayer perceptron, random forest, and extra tree regressor algorithms

Autor: Lutfi Mulyadi Surachman, Abdulazeez Abdulraheem, Abdullatif Al-Shuhail, Sanlinn I. Kaka
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Journal of Petroleum Exploration and Production Technology, Vol 14, Iss 7, Pp 1923-1931 (2024)
Druh dokumentu: article
ISSN: 2190-0558
2190-0566
DOI: 10.1007/s13202-024-01795-7
Popis: Abstract Acoustic impedance is the product of the density of a material and the speed at which an acoustic wave travels through it. Understanding this relationship is essential because low acoustic impedance values are closely associated with high porosity, facilitating the accumulation of more hydrocarbons. In this study, we estimate the acoustic impedance based on nine different inputs of seismic attributes in addition to depth and two-way travel time using three supervised machine learning models, namely extra tree regression (ETR), random forest regression, and a multilayer perceptron regression algorithm using the scikit-learn library. Our results show that the R 2 of multilayer perceptron regression is 0.85, which is close to what has been reported in recent studies. However, the ETR method outperformed those reported in the literature in terms of the mean absolute error, mean squared error, and root-mean-squared error. The novelty of this study lies in achieving more accurate predictions of acoustic impedance for exploration.
Databáze: Directory of Open Access Journals
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