Leonardo Azevedo

Stochastic Seismic AVO Inversion

One of the main challenge problems in geophysics is getting reliable seismic inverse models while the uncertainty is assessed. Seismic inverse problems may be tackled in a probabilistic framework resulting in a set of equiprobable acoustic and elastic impedance models. Here we show a new geostatistical seismic AVO method from where density, Vp and Vs models are retrieved. With the resulting Earth models we also compute the correspondent synthetic pre-stack data and the zero-reflectivity R(0) and Gradient (G) models. We successfully applied this workflow to a 3D synthetic seismic dataset from where density, Vp and Vs models were known. The final best models achieved a global correlation between the original and the synthetic seismograms of about 0.80.

Geostatistical seismic inversion for non-stationary patterns using direct sequential simulation and co-simulation

Geostatistical seismic inversion methods are routinely used in reservoir characterization studies because of their potential to infer the spatial distribution of the petro-elastic properties of interest (e.g., density, elastic and acoustic impedances) along with the associated spatial uncertainty. Within the geostatistical seismic inversion framework, the retrieved inverse elastic models are conditioned by a global probability distribution function and global spatial continuity model as estimated from the available well-log data for the entire inversion grid. However, the spatial distribution of the real subsurface elastic properties is complex, heterogeneous and in many cases non-stationary since they directly depend on the subsurface geology, i.e., the spatial distribution of the facies of interest. In these complex geological settings, application of a single distribution function and spatial continuity model is not enough to properly model the natural variability of the elastic properties of interest. In this study, we propose a 3D geostatistical inversion technique that is able to incorporate the reservoirs heterogeneities. This method uses a traditional geostatistical seismic inversion conditioned by local multi-distribution functions and spatial continuity models under non-stationary conditions. The procedure of the proposed methodology is based on a zonation criterion along the vertical direction of the reservoir grid. Each zone can be defined by conventional seismic interpretation, with the identification of the main seismic units and significant variations of seismic amplitudes. The proposed method was applied to a highly non-stationary synthetic seismic dataset with different levels of noise. The results of this work clearly show the advantages of the proposed method against conventional geostatistical seismic inversion procedures. It is important to highlight the impact of this technique in terms of a higher convergence between real and inverted reflection seismic data and the more realistic approximation towards the real subsurface geology comparing with traditional techniques. n nThis article is protected by copyright. All rights reserved

Geostatistical seismic inversion for frontier exploration

AbstractNumerical 3D high-resolution models of subsurface petroelastic properties are key tools for exploration and production stages. Stochastic seismic inversion techniques are often used to infer the spatial distribution of the properties of interest by integrating simultaneously seismic reflection and well-log data also allowing accessing the spatial uncertainty of the retrieved models. In frontier exploration areas, the available data set is often composed exclusively of seismic reflection data due to the lack of drilled wells and are therefore of high uncertainty. In these cases, subsurface models are usually retrieved by deterministic seismic inversion methodologies based exclusively on the existing seismic reflection data and an a priori elastic model. The resulting models are smooth representations of the real complex geology and do not allow assessing the uncertainty. To overcome these limitations, we have developed a geostatistical framework that allows inverting seismic reflection data without...

Geostatistical History Matching Conditioned to Seismic Data

History matching is a highly non-linear inverse problem where by perturbing subsurface models (e.g. porosity, permeability models) one tries to match the dynamic responses of this Earth model with the observed production data of a given hydrocarbon reservoir. Geostatistical seismic inversion is a geophysical inverse problem where by creating a set of porosity or facies models one minimizes a mismatch function between the observed and the synthetic seismic data created from simulated acoustic and elastic impedance models. In spite of their different physical principles, both of these inverse problems have the same parameter and solution space. We propose herein a global geostatistical iterative inversion methodology, where the retrieved subsurface models match simultaneously the observed seismic reflection and the reservoir production data.

Geostatistics for Seismic Characterization of Oil Reservoirs

In the oil industry, exploratory targets tend to be increasingly complex and located deeper and deeper offshore. The usual absence of well data and the increase in the quality of the geophysical data, verified in the last decades, make these data unavoidable for the practice of oil reservoir modeling and characterization. In fact the integration of geophysical data in the characterization of the subsurface petrophysical variables has been a priority target for geoscientists. Geostatistics has been a key discipline to provide a theoretical framework and corresponding practical tools to incorporate as much as possible different types of data for reservoir modeling and characterization, in particular the integration of well-log and seismic reflection data. Geostatistical seismic inversion techniques have been shown to be quite important and efficient tools to integrate simultaneously seismic reflection and well-log data for predicting and characterizing the subsurface lithofacies, and its petro-elastic properties, in hydrocarbon reservoirs. The first part of this chapter presents the state of the art and the most recent advances of geostatistical seismic inversion methods, to evaluate the reservoir properties through the acoustic, elastic and AVA seismic inversion methods with real case applications examples. In the second part we present a methodology based on seismic inversion to assess uncertainty and risk at early stages of exploration, characterized by the absence of well data for the entire region of interest. The concept of analog data is used to generate scenarios about the morphology of the geological units, distribution of acoustic properties and their spatial continuity. A real case study illustrates the this approach.

Introduction—Geostatistical Methods for Integrating Seismic Reflection Data into Subsurface Earth Models

This chapter introduces the main goals of this book defining the main questions addressed along the text. It also refers the value of geostatistical techniques in subsurface modeling and characterization, and their ability for uncertainty assessment. These concepts are introduced focusing its application in real problems of the oil and gas industry.

Integration of Geophysical Data for Reservoir Modeling and Characterization

This chapter deals with the integration of geophysical data into the geo-modeling workflow. It starts by introducing the seismic inversion problem and its natural characteristics. Then, we review the most well-known seismic inversion methodologies used among the industry and finally present in detail different geostatistical seismic inversion methodologies. The different methodologies are presented along with its main advantages and the environments where they are most suitable. It describes geostatistical seismic inversion for acoustic and elastic impedances, and density, P-wave and S-wave velocities from pre-stack seismic data. Each methodology presented in this chapter is accompanied along with examples from real case studies.

Fundamental Geostatistical Tools for Data Integration

This chapter deals with the fundamentals concepts which are the basis for the geostatistical inference algorithms described in Chap. 3. It presents the concepts related with Random Fields and Random Variables and how a bi-point statistics framework (i.e. the variogram model) can be used to characterize the spatial continuity pattern of a given property of interest. After which the different linear estimation models based on Kriging are presented. This chapter includes the necessary and basic theoretical background on geostatistics used in the following chapters. The methodologies described in this part of the book are illustrated with real application examples and best-practices.

Simulation Models of Physical Phenomena in Earth Sciences

The estimation techniques introduced in the previous chapter are the necessary background to describe the stochastic sequential simulation methodologies used to infer the probability distribution of a given property of interest at an unknown location, allowing at the same time the assessment of the spatial uncertainty of that property. This chapter includes a description of the most known stochastic sequential simulation used in the oil industry and discusses its pros and cons. Besides the maturity of these algorithms, there is still a lack of documentation in placing these algorithms within a single framework and as part of the modeling workflow for the oil and gas industry. The stochastic simulation algorithms presented herein are used as model perturbation techniques in the geostatistical seismic inversion methodologies presented in the following chapters.

Data Integration with Geostatistical Seismic Inversion Methodologies

The chapter of this book is dedicated to methods for integrating data from different sources within the geo-modelling workflow. It introduces two ideas to integrate CSEM (electromagnetic) data into seismic reflection data and historic production data into seismic inversion. With this, we intend to open the door for new frameworks and tools to properly integrate all the available data from different nature into reservoir modelling and characterization.