Facies‐constrained transdimensional amplitude versus angle inversion using machine learning assisted priors

Abstract

We present a methodology for seismic inversion that generates high-resolution models of facies and elastic properties from pre-stack data. Our inversion algorithm uses a transdimensional approach where, in addition to the layer properties, the number of layers is treated as unknown. In other words, the data itself determine the correct model parameterization, that is, the number of layers. The reversible jump Markov Chain Monte Carlo method is an effective tool to solve such transdimensional problems as it generates models of reservoir properties along with uncertainty estimates. However, current implementations of the reversible jump Markov Chain Monte Carlo algorithms do not account for the non-Gaussian and multimodal nature of model parameters. The target elastic reservoir properties generally have multimodal and non-parametric distribution at each location of the model. The number of modes is equal to the number of facies.Taking these factors into account, we extend the reversible jump Markov Chain Monte Carlo algorithm to simultaneously invert for discrete facies and continuous elastic reservoir properties. The proposed extension to the algorithm iteratively samples the facies, by moving from one mode to another, and elastic properties by sampling within the same mode. The integration of facies classification within the inversion reduces nonuniqueness, improves convergence speed and produces geologically consistent results.The workflow uses machine learning to generate probabilistic priors for the model parameters. We validate our approach by applying it to a synthetic dataset generated from a well log with two facies and then to a complex synthetic two-dimensional model involving three facies having overlapping elastic property distribution. Finally, we apply our algorithm to a field dataset acquired over an unconventional reservoir. Our algorithm demonstrates the usefulness of incorporating facies information in seismic inversion and also the feasibility of inverting for facies from seismic data.

A facies‐constrained geostatistical seismic inversion method based on multi‐scale sparse representation

“…In addition, prior information about structural features can hardly be integrated into the inversion process (e.g. Dhara et al., 2023). Herein, the inability of representing the reservoir structural features accurately is still the main issue for the current geostatistical inversion methods (Azevedo & Soares, 2017; Bosch et al., 2010; Dash et al., 2020; Saussus & Sams, 2012).…”

Building initial model for seismic inversion based on semi‐supervised learning

“…Deep learning has been widely used in geophysical problems such as seismic facies analysis (Liu et al., 2021; Nishitsuji & Exley, 2019), first‐break picking (Wang et al., 2019; Yuan et al., 2018; Zwartjes & Yoo, 2022), fault identification (Huang et al., 2017; Wu et al., 2019; Zhou et al., 2021) and model building (Araya‐Polo et al., 2017; Fabien‐Ouellet & Sarkar, 2019; Ovcharenko et al., 2022). Recently, the application of deep neural networks in reservoir characterization has also been investigated (Chen & Saygin, 2021; Dhara et al., 2023; Di & Abubakar, 2021; Sun et al., 2021; Wu et al., 2021). Based on different training data, seismic inversion methods using deep learning can be divided into three categories: unsupervised learning, supervised learning and semi‐supervised learning.…”