Can I read SPE Russian Petroleum Technology Conference - Machine Learning for Fracture Parameter Estimation in Fractured Reservoirs from Seismic Data on EtoBox?

SPE Russian Petroleum Technology Conference - Machine Learning for Fracture Parameter Estimation in Fractured Reservoirs from Seismic Data by Sabinin, Grigory; Chichinina, Tatiana; Tulchinsky, Vadim; Romero-Salcedo, Manuel is a scholarly article available to read on EtoBox.

What is SPE Russian Petroleum Technology Conference - Machine Learning for Fracture Parameter Estimation in Fractured Reservoirs from Seismic Data about?

## Abstract Nowadays, Machine Learning (ML) is actively used in geophysical prospecting including seismic exploration. This study focuses on the applicability and feasibility of Deep Learning for the inverse problem in seismic exploration that is the estimation of the rock-physics parameters for a fractured reservoir, from seismic data. The main goal of this paper is to prove the efficiency of a neural network in estimating fractured medium parameters, represented as anisotropy parameters of HTI model. (HTI is "Horizontal Transverse Isotropy".) As such fracture parameters, we consider the normal and tangential weaknesses of fractures ΔN and ΔT, Thomsen anisotropy parameters ε, δ, γ, as well as the crack density e and the crack aspect ratio α (fracture opening). In addition, we consider a fractured medium, in which there are two fracture networks, characterized by two pairs of weaknesses (ΔN1, ΔT1) and (ΔN2, ΔT2); this is the so-called orthorhombic model. We validate the accuracy of our neural network by comparing the predicted parameter values with the a priori given. We use mathematic formulae, which relate the considered parameters estimation to different effective-medium anisotr

Author

Sabinin, Grigory; Chichinina, Tatiana; Tulchinsky, Vadim; Romero-Salcedo, Manuel

Publisher

Society of Petroleum Engineers

Published

2020

Language

EN