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We propose a novel framework for optimizing injection strategies in large-scale CO$_2$ storage combining multi-agent models with multi-objective optimization, and reservoir simulation. We investigate whether agents should form coalitions for collaboration to maximize the outcome of their storage activities. In multi-agent systems, it is typically assumed that the optimal strategy for any given coalition structure is already known, and it remains to identify which coalition structure is optimal according to some predefined criterion. For any coalition structure in this work, the optimal CO$_2$ injection strategy is not a priori known, and needs to be found by a combination of reservoir simulation and a multi-objective optimization problem. The multi-objective optimization problems all come with the numerical challenges of repeated evaluations of complex-physics models. We use versatile evolutionary algorithms to solve the multi-objective optimization problems, where the solution is a set of values, e.g., a Pareto front. The Pareto fronts are first computed using the so-called weighted sum method that transforms the multi-objective optimization problem into a set of single-objective optimization problems. Results based on two different Pareto front selection criteria are presented. Then a truly multi-objective optimization method is used to obtain the Pareto fronts, and compared to the previous weighted sum method. We demonstrate the proposed framework on the Bjarmeland formation, a pressure-limited prospective storage site in the Barents Sea. The problem is constrained by the maximum sustainable pressure buildup and a supply of CO$_2$ that can vary over time. In addition to identifying the optimal coalitions, the methodology shows how distinct suboptimal coalitions perform in comparison to the optimum.

Subsurface storage of CO$_2$ is an important means to mitigate climate change, and to investigate the fate of CO$_2$ over several decades in vast reservoirs, numerical simulation based on realistic models is essential. Faults and other complex geological structures introduce modeling challenges as their effects on storage operations are uncertain due to limited data. In this work, we present a computational framework for forward propagation of uncertainty, including stochastic upscaling and copula representation of flow functions for a CO$_2$ storage site using the Vette fault zone in the Smeaheia formation in the North Sea as a test case. The upscaling method leads to a reduction of the number of stochastic dimensions and the cost of evaluating the reservoir model. A viable model that represents the upscaled data needs to capture dependencies between variables, and allow sampling. Copulas provide representation of dependent multidimensional random variables and a good fit to data, allow fast sampling, and coupling to the forward propagation method via independent uniform random variables. The non-stationary correlation within some of the upscaled flow function are accurately captured by a data-driven transformation model. The uncertainty in upscaled flow functions and other parameters are propagated to uncertain leakage estimates using numerical reservoir simulation of a two-phase system. The expectations of leakage are estimated by an adaptive stratified sampling technique, where samples are sequentially concentrated to regions of the parameter space to greedily maximize variance reduction. We demonstrate cost reduction compared to standard Monte Carlo of one or two orders of magnitude for simpler test cases with only fault and reservoir layer permeabilities assumed uncertain, and factors 2--8 cost reduction for stochastic multi-phase flow properties and more complex stochastic models.

Given two probability densities on related data spaces, we seek a map pushing one density to the other while satisfying application-dependent constraints. For maps to have utility in a broad application space (including domain translation, domain adaptation, and generative modeling), the map must be available to apply on out-of-sample data points and should correspond to a probabilistic model over the two spaces. Unfortunately, existing approaches, which are primarily based on optimal transport, do not address these needs. In this paper, we introduce a novel pushforward map learning algorithm that utilizes normalizing flows to parameterize the map. We first re-formulate the classical optimal transport problem to be map-focused and propose a learning algorithm to select from all possible maps under the constraint that the map minimizes a probability distance and application-specific regularizers; thus, our method can be seen as solving a modified optimal transport problem. Once the map is learned, it can be used to map samples from a source domain to a target domain. In addition, because the map is parameterized as a composition of normalizing flows, it models the empirical distributions over the two data spaces and allows both sampling and likelihood evaluation for both data sets. We compare our method (parOT) to related optimal transport approaches in the context of domain adaptation and domain translation on benchmark data sets. Finally, to illustrate the impact of our work on applied problems, we apply parOT to a real scientific application: spectral calibration for high-dimensional measurements from two vastly different environments, Comment: 19 pages, 7 figures

In this work, we propose a novel generative model for mapping inputs to structured, high-dimensional outputs using structured conditional normalizing flows and Gaussian process regression. The model is motivated by the need to characterize uncertainty in the input/output relationship when making inferences on new data. In particular, in the physical sciences, limited training data may not adequately characterize future observed data; it is critical that models adequately indicate uncertainty, particularly when they may be asked to extrapolate. In our proposed model, structured conditional normalizing flows provide parsimonious latent representations that relate to the inputs through a Gaussian process, providing exact likelihood calculations and uncertainty that naturally increases away from the training data inputs. We demonstrate the methodology on laser-induced breakdown spectroscopy data from the ChemCam instrument onboard the Mars rover Curiosity. ChemCam was designed to recover the chemical composition of rock and soil samples by measuring the spectral properties of plasma atomic emissions induced by a laser pulse. We show that our model can generate realistic spectra conditional on a given chemical composition and that we can use the model to perform uncertainty quantification of chemical compositions for new observed spectra. Based on our results, we anticipate that our proposed modeling approach may be useful in other scientific domains with high-dimensional, complex structure where it is important to quantify predictive uncertainty., Comment: Best paper award, 1st Annual AAAI Workshop on AI to Accelerate Science and Engineering (AI2ASE), February 2022

We examine the observed properties of the Nili Fossae olivine-clay-carbonate lithology from orbital data and in situ by the Mars 2020 rover at the S\'e\'itah unit in Jezero crater, including: 1) composition (Liu, 2022) 2) grain size (Tice, 2022) 3) inferred viscosity (calculated based on geochemistry collected by SuperCam (Wiens, 2022)). Based on the low viscosity and distribution of the unit we postulate a flood lava origin for the olivine-clay-carbonate at S\'e\'itah. We include a new CRISM map of the clay 2.38 {\mu}m band and use in situ data to show that the clay in the olivine cumulate in the S\'e\'itah formation is consistent with talc or serpentine from Mars 2020 SuperCam LIBS and VISIR and MastCam-Z observations. We discuss two intertwining aspects of the history of the lithology: 1) the emplacement and properties of the cumulate layer within a lava lake, based on terrestrial analogs in the Pilbara, Western Australia, and using previously published models of flood lavas and lava lakes, and 2) the limited extent of post emplacement alteration, including clay and carbonate alteration (Clave, 2022; Mandon, 2022)., Comment: 34 pages, 15 figures

Subsurface storage of CO2 is predicted to rise exponentially in response to the increasing levels of CO2 in the atmosphere. Large-scale CO2 injections into the subsurface require understanding of the potential for fluid flow through faults to mitigate risk of leakage. Here, we study how to obtain effective permeability of deformation bands in the damage zone of faults. Deformation bands are relatively small, low permeability features that can have a significant effect on flow dynamics, however, the discrepancy of scales is a challenge for field-scale simulation. A new analytical upscaling model is proposed in order to overcome some of the shortcomings of conventional upscaling approaches for heterogeneous porous media. The new model captures the fine-scale impact of deformation bands on fluid flow in the near-fault region, and can be derived from knowledge of large-scale fault properties. To test the accuracy of the model it is compared to fine-scale numerical simulations that explicitly include individual deformation bands. For a wide range of different stochastically generated deformation bands networks, the upscaling model shows improved estimate of effective permeability compared to conventional upscaling approaches. By applying the upscaling model to a full-field simulation of the Smeaheia storage site in the North Sea, we show that deformation bands with a permeability contrast higher than three orders of magnitude may act as an extra layer of protection from fluid flow through faults.

The wettability of the rock surface in porous media has an effect on the constitutive saturation functions that govern capillary pressure and relative permeability. The term wettability alteration refers to the change of this property over time by processes such as CO$_2$ interactions with the rock. In this work, we perform numerical simulations considering a two-phase two-component flow model including time-dependent wettability alteration in a two-dimensional aquifer-caprock system using the open porous media framework. Particularly, we study the spatial distribution over time of injected CO$_2$ into the aquifer neglecting and including wettability alteration effects. The numerical simulations show that wettability alteration on the caprock results in a loss of containment; however, the CO$_2$ front into the caprock advances very slow since the unexposed caprock along the vertical migration path also needs to be changed by the slow wettability alteration process. The simulations also show that wettability alteration on the aquifer results in an enhancement of storage efficiency; this since the CO$_2$ front migrates more slowly and the capillary entry pressure decreases after wettability alteration., Comment: 2nd Geoscience & Engineering in Energy Transition Conference (2021)

Constitutive functions that govern macroscale capillary pressure and relative permeability are central in constraining both storage efficiency and sealing properties of CO$_2$ storage systems. Constitutive functions for porous systems are in part determined by wettability, which is a pore-scale phenomenon that influences macroscale displacement. While wettability of saline aquifers and caprocks are assumed to remain water-wet when CO$_2$ is injected, there is recent evidence of contact angle change due to long-term CO$_2$ exposure. Weakening of capillary forces alters the saturation functions dynamically over time. Recently, new dynamic models were developed for saturation functions that capture the impact of wettability alteration (WA) due to long-term CO$_2$ exposure. In this paper, these functions are implemented into a two-phase two-component simulator to study long-term WA dynamics for field-scale CO$_2$ storage. We simulate WA effects on horizontal migration patterns under injection and buoyancy-driven migration in the caprock. We characterize the behavior of each scenario for different flow regimes. Our results show the impact on storage efficiency can be described by the capillary number, while vertical leakage can be scaled by caprock sealing parameters. Scaling models for CO$_2$ migration into the caprock show that long-term WA poses little risk to CO$_2$ containment over relevant timescales.

Microbially induced calcite precipitation (MICP) is a technology for sealing leakage paths to ensure the safe storage of CO$_2$ in geological formations. In this work we introduce a numerical simulator of MICP for field-scale studies. This simulator is implemented in the open porous media (OPM) framework. We compare the numerical results to simulations using an upgraded implementation of the mathematical model in the MATLAB reservoir simulation toolbox (MRST). Finally, we consider a 3D system consisting of two aquifers separated by caprock with a leakage path across the width of the reservoir. We study a strategy where microbial solution is injected only at the beginning of the treatment and subsequently either growth solution or cementation solution is injected for biofilm development or calcite precipitation. By applying this strategy, the numerical results show that the MICP technology could be used to seal these leakage paths.

The Curiosity rover investigated a topographic structure known as Vera Rubin ridge, associated with a hematite signature in orbital spectra. There, Curiosity encountered mudstones interpreted as lacustrine deposits, in continuity with the 300 m-thick underlying sedimentary rocks of the Murray formation at the base of Mount Sharp. While the presence of hematite ($\alpha$-Fe2O3) was confirmed insitu by both Mastcam and ChemCam spectral observations and by the CheMin instrument, neither ChemCam nor APXS observed any significant increase in FeO$_T$ (total iron oxide) abundances compared to the Murray formation. Instead, Curiosity discovered dark-toned diagenetic features displaying anomalously high FeO$_T$ abundances, commonly observed in association with light-toned Ca-sulfate veins but also as crystal pseudomorphs in the host rock. These iron-rich diagenetic features are predominantly observed in "grey" outcrops on the upper part of the ridge, which lack the telltale ferric signature of other Vera Rubin ridge outcrops. Their composition is consistent with anhydrous Fe-oxide, as the enrichment in iron is not associated with enrichment in any other elements, nor with detections of volatiles. The lack of ferric absorption features in the ChemCam reflectance spectra and the hexagonal crystalline structure associated with dark-toned crystals points toward coarse "grey" hematite. In addition, the host rock adjacent to these features appears bleached and show low-FeO$_T$ content as well as depletion in Mn, indicating mobilization of these redox-sensitive elements during diagenesis. Thus, groundwater fluid circulations could account for the remobilization of iron and recrystallization as crystalline hematite during diagenesis as well as color variations observed in the Vera Rubin ridge outcrops.

Microbially induced calcite precipitation (MICP) is a new and sustainable technology which utilizes biochemical processes to create barriers by calcium carbonate cementation; therefore, this technology has a potential to be used for sealing leakage zones in geological formations. The complexity of current MICP models and present computer power limit the size of numerical simulations. We describe a mathematical model for MICP suitable for field-scale studies. The main mechanisms in the conceptual model are as follow: suspended microbes attach themselves to the pore walls to form biofilm, growth solution is added to stimulate the biofilm development, the biofilm uses cementation solution for production of calcite, and the calcite reduces the pore space which in turn decreases the rock permeability. We apply the model to study the MICP technology in two sets of reservoir properties including a well-established field-scale benchmark system for CO$_2$ leakage. A two-phase flow model for CO$_2$ and water is used to assess the leakage prior to and with MICP treatment. Based on the numerical results, this study confirms the potential for this technology to seal leakage paths in reservoir-caprock systems.

Wettability is a pore-scale property that impacts the relative movement and distribution of fluids in a porous medium. There are reservoir fluids that provoke the surface within pores to undergo a wettability change. This wettability change, in turn, alters the dynamics of relative permeabilities at the Darcy scale. Thus, modeling the impact of wettability change in relative permeabilities is essential to understand fluids interaction in porous media. In this study, we include time-dependent wettability change into the relative permeability--saturation relation by modifying the existing relative permeability function. To do so, we assume the wettability change is represented by the sorption-based model that is exposure time and chemistry dependent. This pore-scale model is then coupled with a triangular bundle-of-tubes model to simulate exposure time-dependent relative permeabilities data. The simulated data is used to characterize and quantify the wettability dynamics in the relative permeability--saturation curves. This study further shows the importance of accurate prediction of the relative permeability in a dynamically altering porous medium.

Nuclear spectroscopy is the only instrumentation that provides bulk geochemical constraints at depth (up to one meter in the surface). These instruments identify and quantify water and other key elements relevant to planetary exploration, including assessing planetary processes, context in the search for life, and in-situ resource utilization., Comment: White paper submitted to the Planetary Science Decadal Survey 2023-2032

In this paper, we consider a non-local (in time) two-phase flow model. The non-locality is introduced through the wettability alteration induced dynamic capillary pressure function. We present a monotone fixed-point iterative linearization scheme for the resulting non-standard model. The scheme treats the dynamic capillary pressure functions semi-implicitly and introduces an $L$-scheme type \cite{List2016, Radu2015} stabilization term in the pressure as well as the transport equations. We prove the convergence of the proposed scheme theoretically under physically acceptable assumptions and verify the theoretical analysis with numerical simulations. The scheme is implemented and tested for a variety of reservoir heterogeneity in addition to the dynamic change of the capillary pressure function. The proposed scheme satisfies the predefined stopping criterion within a few numbers of iterations. We also compared the performance of the proposed scheme against the iterative IMplicit Pressure Explicit Saturation scheme

Wettability is a pore-scale property that has an important impact on capillarity, residual trapping, and hysteresis in porous media systems. In many applications, the wettability of the rock surface is assumed to be constant in time and uniform in space. However, many fluids are capable of altering the wettability of rock surfaces permanently and dynamically in time. Experiments have shown wettability alteration can significantly decrease capillarity in CO$_2$ storage applications. For these systems, the standard capillary-pressure model that assumes static wettability is insufficient to describe the physics. In this paper, we develop a new dynamic capillary-pressure model that takes into account changes in wettability at the pore-level by adding a dynamic term to the standard capillary pressure function. We simulate the dynamic system using a bundle-of-tubes (BoT) approach, where a mechanistic model for time-dependent contact angle change is introduced at the pore scale. The resulting capillary pressure curves are then used to quantify the dynamic component of the capillary pressure function. This study shows the importance of time-dependent wettability for determining capillary pressure over timescales of months to years. The impact of wettability has implications for experimental methodology as well as macroscale simulation of wettability-altering fluids.

When injecting CO2 or other fluids into a geological formation, pressure plays an important role both as a driver of flow and as a risk factor for mechanical integrity. The full effect of geomechanics on aquifer flow can only be captured using a coupled flow-geomechanics model. In order to solve this computationally expensive system, various strategies have been put forwards over the years, with some of the best current methods based on sequential splitting. In the present work, we seek to approximate the full geomechanical effect on flow without the need of coupling with a geomechanics solver during simulation, and at a computational cost comparable to that of an uncoupled model. We do this by means of precomputed pressure response functions. At grid model generation time, a geomechanics solver is used to compute the mechanical response of the aquifer for a set of pressure fields. The relevant information from these responses is then stored in a compact form and embedded with the grid model. We test the accuracy and computational performance of our approach on a simple 2D and a more complex 3D model, and compare the results with those produced by a fully coupled approach as well as from a simple decoupled method based on Geertsma's uniaxial expansion coefficient., Comment: Preprint, ECMOR XV