Keynote Talks Abstracts
Dr Santiago Drexler
Relative Permeability from Core Analysis Experiments – Advances in Numerical Interpretation
This presentation will discuss recent advancements in numerical methods for interpreting relative permeability from core analysis experiments. We will focus on techniques that enhance uncertainty quantification and experimental design to reduce uncertainty, thereby improving data reliability in core analysis.
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Dr Rodrigo Surmas
Advancing Reservoir Analysis: Applications of Digital Rock Physics in Relative Permeability Curve Challenges
The application of relative permeability curves in reservoir analysis often poses a critical challenge for engineers: which curve is the most representative for accurate production forecasting in the specific field being studied? This question emerges throughout the reservoir’s lifecycle, starting with early production decisions that involve selecting suitable analogs and defining criteria, and extending to data acquisition, where engineers must navigate issues such as sample selection, determining the necessary number of samples, managing high costs, and addressing significant uncertainties. Field-specific upscaling processes further complicate the ability to perform retrospective analyses and generalize best practices. As reservoirs mature, discrepancies frequently surface between laboratory-derived relative permeability (krel) curves and those obtained through history matching, raising fundamental questions about which aspects of laboratory data should be prioritized and preserved in production forecasting simulations. To address these challenges, Digital Rock Physics (DRP) has evolved significantly, expanding its scope to include cutting-edge applications such as numerical simulations, Artificial Intelligence (AI)-driven image analysis, and more. At Petrobras, DRP has become a cornerstone of reservoir analysis, with key applications in three major areas: sample characterization, determination of petrophysical properties through numerical simulations, and advanced image analysis using AI. This presentation will provide an in-depth exploration of these innovative DRP applications, highlighting their strengths and limitations. Special attention will be given to their current use in the oil and gas industry, laying the groundwork for their potential application in unconventional scenarios beyond traditional contexts, offering new insights into how these tools can shape the future of reservoir analysis.
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Dr Paulo Cesar Philippi
Lattice-Boltzmann models for immiscible fluids
The numerical simulation of multiphase flows in porous media based on the spatial and temporal discretization of the Fourier-Navier-Stokes equations has proven to be quite difficult and uncertain in the future. Indeed, in addition to the geometric complexity of the porous structure, the triple contact line between the phases constitutes a singularity that requires, in this approach, the support of models based on lubricating films of questionable validity to understand the phenomena arising from the interaction between the fluids and the porous surface. The same difficulties are encountered when trying to understand the dynamic phenomena due to the rupture, formation, and displacement of the interface between them: coalescence, bubbling of the invading phase within the resident phase, segregation, and nucleation. Lattice Boltzmann models (LBMs) for the study of immiscible flows were proposed in the early 1990s. These lmodels recover the Navier-Stokes equations with third-order errors in the macroscopic velocity and, for low Re and Ca flow problems, are quite interesting from a practical point of view given the hyperbolic basis of the computational algorithms, which does not have the drawbacks of simulation, e.g., by finite volumes of the Navier-Stokes equations, which are elliptic. The recognition of LB models as finite Hermite polynomial expansions of the kinetic equations has made it possible to relate the order of approximation of the LB equation to the set of velocities in discrete space and to separate the discretization problem from the problem of constructing the kinetic model. Indeed, the aim is to understand the influence of the underlying molecular physics on macroscopic phenomena that are apparent at the pore scale, and this requires fairly detailed models. The purpose of this work is to present the state of the art of kinetic-based models for the study of immiscible flows that take the phenomena related to the interface formation and dynamics into account. Particular attention will be paid to the phenomena of coalescence, segregation, and phase transition, as well as to detailed liquid-solid interaction models for the study of wettability.
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Dr Eduardo Gildin
Fast and Robust Data-driven surface and subsurface simulation for leveraging decarbonization efforts in the Oil & Gas Industry
Sustainable hydrocarbon production considering a decarbonization paradigm demands complex decision-support strategies involving fast risk assessment and optimal injection-production scheduling. At the core of these decisions is the prediction of reservoir performance, usually done by running computationally demanding complex simulators. As a fast substitute, physics-aware machine learning (ML) techniques have been used to endow data-driven proxy models with features closely related to the ones encountered in nature, especially conservation laws. They can lead to fast, reliable, and interpretable simulations used in many reservoir management workflows. In this talk, I will build upon our recently developed deep-learning-based reduced-order modeling framework for fast and reliable proxy for reservoir simulation, especially for C02 sequestration and storage. I will show advances in data-driven model reduction using Physics Informed Neural Nets (PINNs) to heterogeneous stratified saline aquifers, where CO2 migration is governed by highly discontinuous, multi-valued flux functions. I will also show examples of Dynamic Mode Decomposition (DMD) and Operator Inference (OpInf).
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Dr Tannaz Pak
Design considerations for dynamic fluid flow in porous media experiments using X-ray computed micro tomography
Over the past decade, X-ray micro computed tomography (µCT) has emerged as a powerful, non-destructive technique for visualising and quantifying fluid flow in porous media at the micrometer scale. With improvements in both spatial and temporal resolution (driven by laboratory µCT systems and synchrotron facilities) µCT is now widely used to capture dynamic, multiphase flow processes in situ. A key enabler of these studies is the design of X-ray transparent flow cells that must balance experimental requirements with the constraints of imaging platforms. In this talk, I will review the evolution of flow cell design, highlighting trade-offs, technical challenges, and application-specific innovations. Drawing from our recent review and experimental insights, I will discuss how design choices impact imaging quality and data interpretation, and how µCT continues to push the frontiers of porous media research.
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Dr Veronica I. Marconi
Soils-on-a-chip: innovative tools for studying microconfined flagellar motility of soil bacteria used as biofertilizers.
Bradyrhizobium diazoefficiens is a nitrogen-fixing symbiont of soybean, worldwide used as biofertilizer and 96% of farmers in our South America countries, says to use it. This soil bacterium possesses two flagellar systems enabling it to swim in water-saturated natural or artificial porous media. The motility in dark soil pores, which may be crucial for competitiveness for root nodulation, is difficult to be studied and predicted. To address this gap, we fabricated microfluidic devices with networks of connected microchannels surrounding grains. In them, we directly visualise bacterial behaviour i.e. in transparent soils-on-a-chip (SOCs). We measured the population motility for two strains: the wild-type and a mutant with only a subpolar flagellum. A detailed statistical analysis revealed that both strains exhibited reduced speeds and increased changes of direction of 180°, in channels of decreasing cross sectional area, down to a few microns in width. Interestingly, while the wild-type strain displayed faster swimming in unconfined spaces, this advantage was negated in the SOCs with the narrowest microchannels. We employed the measured motility parameters to propose a realistic model and simulate B. diazoefficiens confined dynamics being able to reproduce their behaviour, which additionally can be extended enabling further predictions for long time and macro scales. This multidisciplinary work (CommsBio2025), combining design, microfabrication, microbiology and modelling, offers useful methods to study soil bacteria and may be readily extended to other beneficial/harmful soil species. We hope that this interpore community can be inspired and can use this tool offered by microfluidics, generating spin-offs (PoFluids2025), collaborations among disciplines and applications.
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Dr Denis Voskov
Modeling and Experimental Insights into CO2 Sequestration in Depleted Fields
Subsurface reservoirs are used for various applications driving the energy transition towards zero-carbon energy. Making optimal use of subsurface reservoirs is a great challenge for society these days. CO2 capture, utilization and sequestration (CCUS) can play a significant role in reducing anthropogenic CO2 emissions while allowing society to phase out traditional energy sources. Depleted hydrocarbon fields are particularly well-suited for CO2 sequestration due to their well-documented storage capacity and integrity. Physics-based modeling is essential for effective planning and successful execution of CCUS operations. However, accurately modeling CCUS processes in depleted fields requires addressing complex physical phenomena at various scales.
The models used to represent these phenomena are computationally expensive and rely on uncertain reservoir parameters and imprecise input data, which complicates their accuracy. In this presentation, I will discuss our approach to study CCUS applications at different scales through lab experiments and simulation. I will introduce a unified modeling framework with a multiphase thermal-compositional formulation, designed to address a broad range of challenges associated with CO2 sequestration. Key experiments and modeling efforts related to CO2 sequestration in depleted fields, including the impact of Joule-Thompson cooling, salt precipitation, and hydrate formation, will also be presented to demonstrate the effectiveness and versatility of our approach in real-world applications.
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Dr Dachamir Hotza
Advanced porous systems from waste-derived geopolymers: processing, functionality and environmental impact
The increasing demand for sustainable materials in environmental and industrial applications has driven significant interest in geopolymers synthesized from industrial residues. These aluminosilicate-based materials not only present an opportunity to reduce CO₂ emissions but also enable the design of porous systems with tailored functionalities. In this lecture, I will present recent advances in the processing of waste-derived geopolymers for the development of highly porous structures, including monoliths and membranes, engineered through extrusion, additive manufacturing, and foaming techniques. Special emphasis will be placed on their physicochemical and textural properties, as well as on their performance in environmental applications such as acid mine drainage (AMD) remediation, gas filtration, and moisture buffering. I will also discuss the circular economy perspective of using mining, metallurgical, and construction wastes as precursors, evaluating the environmental impacts and benefits of such valorization routes. This integrative approach not only contributes to cleaner production but also opens new pathways for functional materials engineering based on sustainable principles.
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Dr Sebastião Lucena
Molecular simulation methods for H2 storage at multiple pore scale
Abstract soon…
