Leandro Passos de Figueiredo

• PhD
• Researcher at LTrace Geosciences

We propose a Bayesian approach for seismic inversion to estimate acoustic impedance, porosity and lithofacies within the reservoir conditioned to post-stack seismic and well data. The link between elastic and petrophysical properties is given by a joint prior distribution for the logarithm of impedance and porosity, based on a rock-physics model. T...

(CODE AVAIBILITY: https://github.com/leandrofgr/GaussianMixtureMCMC) We have developed a Markov chain Monte Carlo (MCMC) method for joint inversion of seismic data for the prediction of facies and elastic properties. The solution of the inverse problem is defined by the Bayesian posterior distribution of the properties of interest. The prior distr...

Several methodologies can be found in the literature for inversion of partial angle stack seismic data, including deterministic and stochastic frameworks. However, most methods require a low frequency model (LFM) of each property as an input. These models are usually obtained by the interpolation of the wells along a stratigraphic grid based on the...

The physics that describes the seismic response of an interval of saturated porous rocks with known petrophysical properties is relatively well understood and includes rock physics, petrophysics and wave propagation models. The main goal of seismic reservoir characterization is to predict the rock and fluid properties given a set of seismic measure...

Seismic reservoir characterization is a subfield of geophysics that combines seismic and rock physics modeling with mathematical inverse theory to predict the reservoir variables from the measured seismic data. An open-source comprehensive modeling library that includes the main concepts and tools is still missing. We present a Python library named...

Facies classification based on amplitude-versus-offset (AVO) attributes is one of the key elements of seismic reservoir characterization, since facies models are often used to describe the spatial variability of petrophysical properties in the reservoir. At the seismic scale, facies are generally defined in the joint domain of elastic attributes, f...

Complex pore structures with multiple inclusions challenge the predictive accuracy of rock physics models. This study introduces a novel method for estimating a single equivalent pore aspect ratio that optimizes rock physics model predictions by minimizing discrepancies with experimental measurements in porous rocks with multiple inclusions with va...

Currently, the Oil and Gas industry invests considerable resources in the acquisition of data of different natures and scales to improve the accuracy of geological models and optimize production. However, integrating multiscale data remains a challenge, especially in pre-salt reservoirs, which predominantly consist of heterogeneous carbonate rocks....

Petrophysical inversion of seismic data is one of the key components of seismic reservoir characterization. The goal of petrophysical inversion is to estimate petrophysical properties of reservoir rocks, such as porosity, volumes of minerals or lithologies, and water and hydrocarbon saturations from seismic data. This process can be performed by co...

Seismic facies inversion is an important process in the oil and gas industry to estimate subsurface geological facies or rock types based on seismic data. Recently, the Ensemble Smoother with Multi Data Assimilation (ES-MDA) has shown great success in solving complex inverse problems by generating an ensemble of solutions of the model variables for...

A Bayesian approach is proposed to estimate litho-fluid facies and other rock properties conditioned on seismic and electromagnetic data for reservoir characterization. Prior distributions are assumed to be facies-related Gaussian modes of geophysical rock properties directly acquired or converted from petrophysical properties by calibrated rock ph...

Assimilation of time-lapse (4D) seismic data with ensemble-based methods is challenging because of the massive number of data points. This situation requires excessive computational time and memory usage during the model updating step. We addressed this problem using a deep convolutional autoencoder to extract the relevant features of 4D images and...

Pre-salt carbonate rocks are highly heterogeneous due to several factors, both from the deposition environment and from post-depositional processes. Therefore, rock properties estimated by lab experiments based on plug samples are oftenly not representative of the reservoir scale. Recently published works in literature propose to build 3D models of...

History matching is applied to update reservoir parameters, such as the porosity and permeability of the subsurface rocks, according to new indirect observations. Local fluid production and pressure measurements in the drilled wells are the commonly dynamic observations used in the process. Another dynamic reservoir observation is the time-lapse se...

The assimilation of time-lapse (4D) seismic data is challenging with ensemble-based methods because of the massive number of data points. This situation requires an excessive computational time and memory usage during the model updating step. We addressed this problem using a deep convolutional autoencoder to extract the relevant features of 4D ima...

Seismic facies classification aims to predict a facies model, or a set of facies models, from measured seismic data. We focus on stochastic classification methods to estimate the probability distribution of facies conditioned on seismic data. Bayesian classification methods based on analytical solutions are generally applied to seismically inverted...

Estimating rock and fluid properties in the subsurface from geophysical measurements is a computationally and memory intensive inverse problem. For nonlinear problems with non-Gaussian variables, analytical solutions are generally not available, and the solutions of those inverse problems must be approximated using sampling and optimization methods...

Stochastic petrophysical inversion is a method to predict reservoir properties from seismic data. Recent advances in stochastic optimization allow generating multiple realizations of rock and fluid properties conditioned on seismic data. To match the measured data and represent the uncertainty of the model variables, a large number of realizations...

Stochastic methods for seismic inversion problems for the estimation of rock and fluid properties are commonly adopted in reservoir characterization studies. Among the numerous algorithms, Markov chain Monte Carlo (McMC) methods represent a family of algorithms for the estimation of the posterior distribution of the variables of interest. In seismi...

Generally, in inverse modeling in geoscience, we aim to predict the values of a group of model variables from a set of observed data, based on physical relations between model parameters and data. Specifically, in seismic inversion, the goal is to predict rock and fluid properties in the subsurface from seismic and well-log data. The relation betwe...

We present a case study of geophysical reservoir characterization where we use elastic inversion and probabilistic prediction to predict 9 carbonate lithofacies and the associated porosity distribution. The study focuses on an isolated carbonate platform of middle Miocene age, offshore Sarawak in Malaysia, which has been partly dolomitized — a proc...

Several applications in geoscience require the generation of multiple realizations of random fields of physical properties to mimic their spatial distribution and quantify the model uncertainty. Some modeling problems present complex multivariate distributions with heteroscedasticity and non-linear relations among the variables. We propose a new al...

Accurate subsurface modeling and characterization requires the prediction of facies and rock properties within the reservoir model. This is commonly achieved by inverting geophysical data, such as seismic reflection data, using a two-step approach either in the discrete or the continuous domain. We propose an iterative simultaneous method, namely,...

In seismic reservoir characterization, facies prediction from seismic data is often formulated as an inverse problem. However, the uncertainty in the parameters that control their spatial distributions is usually not investigated. In a probabilistic setting the vertical distribution of facies is often described by statistical models, such as Markov...

The fast Fourier transform-moving average (FFT-MA) is an efficient method for the generation of geostatistical simulations. The method relies on the calculation of a filter operator based on the covariance function of interest and the convolution of the filter with a white noise, to generate multiple realizations of spatially correlated variables....

The prediction of rock properties in the subsurface from geophysical data generally requires the solution of a mathematical inverse problem. Because of the large size of geophysical (seismic) datasets and subsurface models, it is common to reduce the dimension of the problem by applying dimension reduction methods and considering a reparameterizati...

Proposal of a 3D convolutional neural network to characterize the grain size distribution of porous rocks based on the 3D segmented micro tomography images. The training is based on synthetic simulated random packing of spheres with diverse grain sizes. The methodology was applied at segmented 3D images of the Berea sandstone and a sintered spheres...

One of the main objectives in the reservoir characterization is estimating the rock properties based on seismic measurements. We propose a stochastic sampling method for the joint prediction of facies and petrophysical properties, assuming a non-parametric mixture prior distribution and a non-linear forward model.The proposed methodology is based o...

In this paper, we propose a deep convolutional gen-erative adversarial network model to reconstruct the petroleum reservoir connectivity patterns. In the petroleum exploration industry, the critical issue is determining the internal reservoir structure and connectivity, aiming to find a flow channel for placing the injection and the production well...

The joint inversion of seismic data for elastic and petrophysical properties is an inverse problem with a non-unique solution. There are several factors that impact the accuracy of the results, such as the statistical rock-physics relations and observation errors. We present a general methodology to incorporate a linearized rock-physics model in a...

Seismic inversion is an important technique for reservoir modeling and characterization due to its potential in inferring the spatial distribution of the subsurface elastic properties of interest. Two of the most common seismic inversion methodologies within the oil and gas industry are iterative geostatistical seismic inversion and Bayesian linear...

Geostatistical simulations are widely used to generate random field realizations that mimic the subsurface heterogeneities, in order to reproduce the expected spatial variability related to geological stratigraphy and deposition. There are several techniques that can be used to generate multiple realizations of the stochastic models, the most popul...

Modeling uncertainty in seismic inversion problems is a topic of interest for both the oil and gas industry and the academia. Although recent advances in methodologies for sampling the posterior space of the petro-elastic properties of interest, integrating the a priori knowledge, they still have high computational cost. Global Stochastic Inversion...

In this letter, we show how a seismic inversion method based on a Bayesian framework can be applied on poststack seismic data to estimate the wavelet, the seismic noise level, and the subsurface acoustic impedance. We propose a different linearized forward model and discuss in detail how some stochastic quantities are defined in a geophysical inter...