Pore-Scale Analysis of Calcium Carbonate Precipitation and Dissolution Kinetics in a Microfluidic Device.

In this work, we have characterized the calcium carbonate (CaCO3) precipitates over time caused by reaction-driven precipitation and dissolution in a micromodel. Reactive solutions were continuously injected through two separate inlets, resulting in transverse-mixing induced precipitation during the precipitation phase. Subsequently, a dissolution phase was conducted by injecting clean water (pH = 4). The evolution of precipitates was imaged in two and three dimensions (2-, 3-D) at selected times using optical and confocal microscopy. With estimated reactive surface area, effective precipitation and dissolution rates can be quantitatively compared to results in the previous works. Our comparison indicates that we can evaluate the spatial and temporal variations of effective reactive areas more mechanistically in the microfluidic system only with the knowledge of local hydrodynamics, polymorphs, and comprehensive image analysis. Our analysis clearly highlights the feedback mechanisms between reactions and hydrodynamics. Pore-scale modeling results during the dissolution phase were used to account for experimental observations of dissolved CaCO3 plumes with dissolution of the unstable phase of CaCO3. Mineral precipitation and dissolution induce complex dynamic pore structures, thereby impacting pore-scale fluid dynamics. Pore-scale analysis of the evolution of precipitates can reveal the significance of chemical and pore structural controls on reaction and fluid migration.

Metabolism-Induced CaCO3 Biomineralization During Reactive Transport in a Micromodel: Implications for Porosity Alteration.

The ability of Pseudomonas stutzeri strain DCP-Ps1 to drive CaCO3 biomineralization has been investigated in a microfluidic flowcell (i.e., micromodel) that simulates subsurface porous media. Results indicate that CaCO3 precipitation occurs during NO3(-) reduction with a maximum saturation index (SIcalcite) of ∼1.56, but not when NO3(-) was removed, inactive biomass remained, and pH and alkalinity were adjusted to SIcalcite ∼ 1.56. CaCO3 precipitation was promoted by metabolically active cultures of strain DCP-Ps1, which at similar values of SIcalcite, have a more negative surface charge than inactive strain DCP-Ps1. A two-stage NO3(-) reduction (NO3(-) → NO2(-) → N2) pore-scale reactive transport model was used to evaluate denitrification kinetics, which was observed in the micromodel as upper (NO3(-) reduction) and lower (NO2(-) reduction) horizontal zones of biomass growth with CaCO3 precipitation exclusively in the lower zone. Model results are consistent with two biomass growth regions and indicate that precipitation occurred in the lower zone because the largest increase in pH and alkalinity is associated with NO2(-) reduction. CaCO3 precipitates typically occupied the entire vertical depth of pores and impacted porosity, permeability, and flow. This study provides a framework for incorporating microbial activity in biogeochemistry models, which often base biomineralization only on SI (caused by biotic or abiotic reactions) and, thereby, underpredict the extent of this complex process. These results have wide-ranging implications for understanding reactive transport in relevance to groundwater remediation, CO2 sequestration, and enhanced oil recovery.

Adaptation of Delftia acidovorans for degradation of 2,4-dichlorophenoxyacetate in a microfluidic porous medium.

Delftia acidovorans MC1071 can productively degrade R-2-(2,4-dichlorophenoxy)propionate (R-2,4-DP) but not 2,4-dichlorophenoxyacetate (2,4-D) herbicides. This work demonstrates adaptation of MC1071 to degrade 2,4-D in a model two-dimensional porous medium (referred to here as a micromodel). Adaptation for 2,4-D degradation in the 2 cm-long micromodel occurred within 35 days of exposure to 2,4-D, as documented by substrate removal. The amount of 2,4-D degradation in the adapted cultures in two replicate micromodels (~10 and 20 % over 142 days) was higher than a theoretical maximum (4 %) predicted using published numerical simulation methods, assuming instantaneous biodegradation and a transverse dispersion coefficient obtained for the same pore structure without biomass present. This suggests that the presence of biomass enhances substrate mixing. Additional evidence for adaptation was provided by operation without R-2,4-DP, where degradation of 2,4-D slowly decreased over 20 days, but was restored almost immediately when R-2,4-DP was again provided. Compared to suspended growth systems, the micromodel system retained the ability to degrade 2,4-D longer in the absence of R-2,4-DP, suggesting slower responses and greater resilience to fluctuations in substrates might be expected in the soil environment than in a chemostat.

Progressive transfer learning for advancing machine learning-based reduced-order modeling.

To maximize knowledge transfer and improve the data requirement for data-driven machine learning (ML) modeling, a progressive transfer learning for reduced-order modeling (p-ROM) framework is proposed. A key concept of p-ROM is to selectively transfer knowledge from previously trained ML models and effectively develop a new ML model(s) for unseen tasks by optimizing information gates in hidden layers. The p-ROM framework is designed to work with any type of data-driven ROMs. For demonstration purposes, we evaluate the p-ROM with specific Barlow Twins ROMs (p-BT-ROMs) to highlight how progress learning can apply to multiple topological and physical problems with an emphasis on a small training set regime. The proposed p-BT-ROM framework has been tested using multiple examples, including transport, flow, and solid mechanics, to illustrate the importance of progressive knowledge transfer and its impact on model accuracy with reduced training samples. In both similar and different topologies, p-BT-ROM achieves improved model accuracy with much less training data. For instance, p-BT-ROM with four-parent (i.e., pre-trained models) outperforms the no-parent counterpart trained on data nine times larger. The p-ROM framework is poised to significantly enhance the capabilities of ML-based ROM approaches for scientific and engineering applications by mitigating data scarcity through progressively transferring knowledge.

Development and Characterization of a Sustainable Bio-Polymer Concrete with a Low Carbon Footprint.

Polymer concrete (PC) has been used to replace cement concrete when harsh service conditions exist. Polymers have a high carbon footprint when considering their life cycle analysis, and with increased climate change concerns and the need to reduce greenhouse gas emission, bio-based polymers could be used as a sustainable alternative binder to produce PC. This paper examines the development and characterization of a novel bio-polymer concrete (BPC) using bio-based polyurethane used as the binder in lieu of cement, modified with benzoic acid and carboxyl-functionalized multi-walled carbon nanotubes (MWCNTs). The mechanical performance, durability, microstructure, and chemical properties of BPC are investigated. Moreover, the effect of the addition of benzoic acid and MWCNTs on the properties of BPC is studied. The new BPC shows relatively low density, appreciable compressive strength between 20-30 MPa, good tensile strength of 4 MPa, and excellent durability resistance against aggressive environments. The new BPC has a low carbon footprint, 50% lower than ordinary Portland cement concrete, and can provide a sustainable concrete alternative in infrastructural applications.

Permeability-controlled migration of induced seismicity to deeper depths near Venus in North Texas.

Migration of seismic events to deeper depths along basement faults over time has been observed in the wastewater injection sites, which can be correlated spatially and temporally to the propagation or retardation of pressure fronts and corresponding poroelastic response to given operation history. The seismicity rate model has been suggested as a physical indicator for the potential of earthquake nucleation along faults by quantifying poroelastic response to multiple well operations. Our field-scale model indicates that migrating patterns of 2015-2018 seismicity observed near Venus, TX are likely attributed to spatio-temporal evolution of Coulomb stressing rate constrained by the fault permeability. Even after reducing injection volumes since 2015, pore pressure continues to diffuse and steady transfer of elastic energy to the deep fault zone increases stressing rate consistently that can induce more frequent earthquakes at large distance scales. Sensitivity tests with variation in fault permeability show that (1) slow diffusion along a low-permeability fault limits earthquake nucleation near the injection interval or (2) rapid relaxation of pressure buildup within a high-permeability fault, caused by reducing injection volumes, may mitigate the seismic potential promptly.

Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning.

We propose a unified data-driven reduced order model (ROM) that bridges the performance gap between linear and nonlinear manifold approaches. Deep learning ROM (DL-ROM) using deep-convolutional autoencoders (DC-AE) has been shown to capture nonlinear solution manifolds but fails to perform adequately when linear subspace approaches such as proper orthogonal decomposition (POD) would be optimal. Besides, most DL-ROM models rely on convolutional layers, which might limit its application to only a structured mesh. The proposed framework in this study relies on the combination of an autoencoder (AE) and Barlow Twins (BT) self-supervised learning, where BT maximizes the information content of the embedding with the latent space through a joint embedding architecture. Through a series of benchmark problems of natural convection in porous media, BT-AE performs better than the previous DL-ROM framework by providing comparable results to POD-based approaches for problems where the solution lies within a linear subspace as well as DL-ROM autoencoder-based techniques where the solution lies on a nonlinear manifold; consequently, bridges the gap between linear and nonlinear reduced manifolds. We illustrate that a proficient construction of the latent space is key to achieving these results, enabling us to map these latent spaces using regression models. The proposed framework achieves a relative error of 2% on average and 12% in the worst-case scenario (i.e., the training data is small, but the parameter space is large.). We also show that our framework provides a speed-up of [Formula: see text] times, in the best case, and [Formula: see text] times on average compared to a finite element solver. Furthermore, this BT-AE framework can operate on unstructured meshes, which provides flexibility in its application to standard numerical solvers, on-site measurements, experimental data, or a combination of these sources.

Enhancing high-fidelity nonlinear solver with reduced order model.

We propose the use of reduced order modeling (ROM) to reduce the computational cost and improve the convergence rate of nonlinear solvers of full order models (FOM) for solving partial differential equations. In this study, a novel ROM-assisted approach is developed to improve the computational efficiency of FOM nonlinear solvers by using ROM's prediction as an initial guess. We hypothesize that the nonlinear solver will take fewer steps to the converged solutions with an initial guess that is closer to the real solutions. To evaluate our approach, four physical problems with varying degrees of nonlinearity in flow and mechanics have been tested: Richards' equation of water flow in heterogeneous porous media, a contact problem in a hyperelastic material, two-phase flow in layered porous media, and fracture propagation in a homogeneous material. Overall, our approach maintains the FOM's accuracy while speeding up nonlinear solver by 18-73% (through suitable ROM-assisted FOMs). More importantly, the proximity of ROM's prediction to the solution space leads to the improved convergence of FOMs that would have otherwise diverged with default initial guesses. We demonstrate that the ROM's accuracy can impact the computational efficiency with more accurate ROM solutions, resulting in a better cost reduction. We also illustrate that this approach could be used in many FOM discretizations (e.g., finite volume, finite element, or a combination of those). Since our ROMs are data-driven and non-intrusive, the proposed procedure can easily lend itself to any nonlinear physics-based problem.

Fast and scalable earth texture synthesis using spatially assembled generative adversarial neural networks.

The earth texture with complex morphological geometry and compositions such as shale and carbonate rocks, is typically characterized with sparse field samples because of an expensive and time-consuming characterization process. Accordingly, generating arbitrary large size of the geological texture with similar topological structures at a low computation cost has become one of the key tasks for realistic geomaterial reconstruction and subsequent hydro-mechanical evaluation for science and engineering applications. Recently, generative adversarial neural networks (GANs) have demonstrated a potential of synthesizing input textural images and creating equiprobable geomaterial images for stochastic analysis of hydrogeological properties, for example, the feasibility of CO2 storage sites and exploration of unconventional resources. However, the texture synthesis with the GANs framework is often limited by the computational cost and scalability of the output texture size. In this study, we proposed a spatially assembled GANs (SAGANs) that can generate output images of an arbitrary large size regardless of the size of training images with computational efficiency. The performance of the SAGANs was evaluated with two and three dimensional (2D and 3D) rock image samples widely used in geostatistical reconstruction of the earth texture and Lattice-Boltzmann (LB) simulations were performed to compare pore-scale flow patterns and upscaled permeabilities of training and generated geomaterial images. We demonstrate SAGANs can generate the arbitrary large size of statistical realizations with connectivity and structural properties and flow characteristics similar to training images, and also can generate a variety of realizations even on a single training image. In addition, the computational time was significantly improved compared to standard GANs frameworks.

Connectivity-informed drainage network generation using deep convolution generative adversarial networks.

Stochastic network modeling is often limited by high computational costs to generate a large number of networks enough for meaningful statistical evaluation. In this study, Deep Convolutional Generative Adversarial Networks (DCGANs) were applied to quickly reproduce drainage networks from the already generated network samples without repetitive long modeling of the stochastic network model, Gibb's model. In particular, we developed a novel connectivity-informed method that converts the drainage network images to the directional information of flow on each node of the drainage network, and then transforms it into multiple binary layers where the connectivity constraints between nodes in the drainage network are stored. DCGANs trained with three different types of training samples were compared; (1) original drainage network images, (2) their corresponding directional information only, and (3) the connectivity-informed directional information. A comparison of generated images demonstrated that the novel connectivity-informed method outperformed the other two methods by training DCGANs more effectively and better reproducing accurate drainage networks due to its compact representation of the network complexity and connectivity. This work highlights that DCGANs can be applicable for high contrast images common in earth and material sciences where the network, fractures, and other high contrast features are important.

Operational and geological controls of coupled poroelastic stressing and pore-pressure accumulation along faults: Induced earthquakes in Pohang, South Korea.

Coupled poroelastic stressing and pore-pressure accumulation along pre-existing faults in deep basement contribute to recent occurrence of seismic events at subsurface energy exploration sites. Our coupled fluid-flow and geomechanical model describes the physical processes inducing seismicity corresponding to the sequential stimulation operations in Pohang, South Korea. Simulation results show that prolonged accumulation of poroelastic energy and pore pressure along a fault can nucleate seismic events larger than Mw3 even after terminating well operations. In particular the possibility of large seismic events can be increased by multiple-well operations with alternate injection and extraction that can enhance the degree of pore-pressure diffusion and subsequent stress transfer through a rigid and low-permeability rock to the fault. This study demonstrates that the proper mechanistic model and optimal well operations need to be accounted for to mitigate unexpected seismic hazards in the presence of the site-specific uncertainty such as hidden/undetected faults and stress regime.

Mineral Fabric as a Hidden Variable in Fracture Formation in Layered Media.

Two longstanding goals in subsurface science are to induce fractures with a desired geometry and to adaptively control the interstitial geometry of existing fractures in response to changing subsurface conditions. Here, we demonstrate that microscopic mineral fabric and structure interact with macroscopic strain fields to generate emergent meso-scale geometries of induced fractures. These geometries define preferential directions of flow. Using additively manufactured rock, we demonstrate that highly conductive flow paths can be formed in tensile fractures by creating corrugated surfaces. Generation, suppression and enhancement of corrugations depend on the relative orientation between mineral fabric and layering. These insights into the role of micro-scale structure on macro-scale flow provide a new method for designing subsurface strategies to maximize potential production or to inhibit flow.

Impact of nonaqueous phase liquid (NAPL) source zone architecture on mass removal mechanisms in strongly layered heterogeneous porous media during soil vapor extraction.

An existing multiphase flow simulator was modified in order to determine the effects of four mechanisms on NAPL mass removal in a strongly layered heterogeneous vadose zone during soil vapor extraction (SVE): a) NAPL flow, b) diffusion and dispersion from low permeability zones, c) slow desorption from sediment grains, and d) rate-limited dissolution of trapped NAPL. The impacts of water and NAPL saturation distribution, NAPL-type (i.e., free, residual, or trapped) distribution, and spatial heterogeneity of the permeability field on these mechanisms were evaluated. Two different initial source zone architectures (one with and one without trapped NAPL) were considered and these architectures were used to evaluate seven different SVE scenarios. For all runs, slow diffusion from low permeability zones that gas flow bypassed was a dominant factor for diminished SVE effectiveness at later times. This effect was more significant at high water saturation due to the decrease of gas-phase relative permeability. Transverse dispersion contributed to fast NAPL mass removal from the low permeability layer in both source zone architectures, but longitudinal dispersion did not affect overall mass removal time. Both slow desorption from sediment grains and rate-limited mass transfer from trapped NAPL only marginally affected removal times. However, mass transfer from trapped NAPL did affect mass removal at later time, as well as the NAPL distribution. NAPL flow from low to high permeability zones contributed to faster mass removal from the low permeability layer, and this effect increased when water infiltration was eliminated. These simulations indicate that if trapped NAPL exists in heterogeneous porous media, mass transfer can be improved by delivering gas directly to zones with trapped NAPL and by lowering the water content, which increases the gas relative permeability and changes trapped NAPL to free NAPL.

Evaluation of simplified mass transfer models to simulate the impacts of source zone architecture on nonaqueous phase liquid dissolution in heterogeneous porous media.

Nonaqueous phase liquid (NAPL) dissolution was studied in three-dimensional (3D) heterogeneous experimental aquifers (25.5 cm x 9 cm x 8.5 cm) with two different longitudinal correlation lengths (2.1 cm and 1.1 cm) and initial spill volumes (22.5 ml and 10.5 ml). Spatial and temporal distributions of NAPL during dissolution were measured using magnetic resonance imaging (MRI). At high NAPL spill volume, average effluent concentrations initially increased during dissolution, as NAPL pools transitioned to NAPL ganglia, and then decreased as the total NAPL-water interfacial area decreased over time. Experimental results were used to test six dissolution models: (i and ii) a one-dimensional (1D) model using either specific NAPL-water interfacial area values estimated from MR images at each time step (i.e., 1D quasi-steady state model), or an empirical mass transfer (Sh') correlation (i.e., 1D transient model), (iii and iv) a multiple analytical source superposition technique (MASST) using either the NAPL distribution determined from MR images at each time step (i.e., MASST steady state model), or the NAPL distribution determined from mass balance calculations (i.e., MASST transient model), (v) an equilibrium streamtube model, and (vi) a 3D grid-scale pool dissolution model (PDM) with a dispersive mass flux term. The 1D quasi-steady state model and 3D PDM captured effluent concentration values most closely, including some concentration fluctuations due to changes in the extent of flow reduction. The 1D transient, MASST steady state and transient, and streamtube models all showed a monotonic decrease in effluent concentration values over time, and the streamtube model was the most computationally efficient. Changes during dissolution of the effective NAPL-water interfacial area estimated from imaging data are similar to changes in effluent concentration values. The 1D steady state model incorporates estimates of the effective NAPL-water interfacial area directly at each time point; the 3D PDM does so indirectly through mass balance and a relative permeability function, which causes reduced water flow through high saturation NAPL regions. Hence, when model accuracy is required, the results indicate that a surrogate of this effective interfacial area is required. Approaches to include this surrogate in the MASST and streamtube models are recommended.

Effect of soil moisture dynamics on dense nonaqueous phase liquid (DNAPL) spill zone architecture in heterogeneous porous media.

The amount, location, and form of NAPL in contaminated vadose zones are controlled by the spatial distribution of water saturation and soil permeability, the NAPL spill scenario, water infiltration events, and vapor transport. To evaluate the effects of these processes, we used the three-phase flow simulator STOMP, which includes a new permeability-liquid saturation-capillary pressure (k-S-P) constitutive model. This new constitutive model considers three NAPL forms: free, residual, and trapped. A 2-D vertical cross-section with five stratigraphic layers was assumed, and simulations were performed for seven cases. The conceptual model of the soil heterogeneity was based upon the stratigraphy at the Hanford carbon tetrachloride (CT) spill site. Some cases considered co-disposal of NAPL with large volumes of wastewater, as also occurred at the Hanford CT site. In these cases, the form and location of NAPL were most strongly influenced by high water discharge rates and NAPL evaporation to the atmosphere. In order to investigate the impact of heterogeneity, the hydraulic conductivity within the lower permeability layer was modeled as a realization of a random field having three different classes. For six extreme cases of 100 realizations, the CT mass that reached the water table varied by a factor of two, and was primarily controlled by the degree of lateral connectivity of the low conductivity class within the lowest permeability layer. The grid size at the top boundary had a dramatic impact on NAPL diffusive flux just after the spill event when the NAPL was present near the ground surface. NAPL evaporation with a fine grid spacing at the top boundary decreased CT mass that reached the water table by 74%, compared to the case with a coarse grid spacing, while barometric pumping had a marginal effect for the case of a continuous NAPL spill scenario considered in this work. For low water infiltration rate scenarios, the distribution of water content prior to a NAPL spill event decreased CT mass that reached the water table by 98% and had a significant impact on the formation of trapped NAPL. For all cases simulated, use of the new constitutive model that allows the formation of residual NAPL increased the amount of NAPL retained in the vadose zone. Density-driven advective gas flow from the ground surface controlled vapor migration in strongly anisotropic layers, causing NAPL mass flux to the lower layer to be reduced. These simulations indicate that consideration of the formation of residual and trapped NAPLs and dynamic boundary conditions (e.g., areas, rates, and periods of different NAPL and water discharge and fluctuations of atmospheric pressure) in the context of full three-phase flow are needed, especially for NAPL spill events at the ground surface. In addition, NAPL evaporation, density-driven gas advection, and NAPL vertical movement enhanced by water flow must be considered in order to predict NAPL distribution and migration in the vadose zone.

Effect of water content on transient nonequilibrium NAPL-gas mass transfer during soil vapor extraction.

The effect of water content on the volatilization of nonaqueous phase liquid (NAPL) in unsaturated soils was characterized by one-dimensional venting experiments conducted to evaluate the lumped mass transfer coefficient. An empirical correlation based upon the modified Sherwood number, Peclet number, and normalized mean grain size was used to estimate initial lumped mass transfer coefficients over a range of water content. The effects of water content on the soil vapor extraction SVE process have been investigated through experimentation and mathematical modeling. The experimental results indicated that a rate-limited NAPL-gas mass transfer occurred in water-wet soils. A severe mass transfer limitation was observed at 61.0% water saturation where the normalized effluent gas concentrations fell below 1.0 almost immediately, declined exponentially from the initiation of venting, and showed long tailing. This result was attributed to the reduction of interfacial area between the NAPL and mobile gas phases due to the increased water content. A transient mathematical model describing the change of the lumped mass transfer coefficient was used. Simulations showed that the nonequilibrium mass transfer process could be characterized by the exponent beta, a parameter which described the reduction of the specific area available for NAPL volatilization. The nonequilibrium mass transfer limitations were controlled by the soil mean grain size and pore gas velocity, were well described by beta values below 1.0 at low water saturation, and were well predicted with beta values greater than 1.0 at high water saturation.

Magnetic resonance imaging of nonaqueous phase liquid during soil vapor extraction in heterogeneous porous media.

Soil vapor extraction (SVE) is commonly used to remediate nonaqueous phase liquids (NAPLs) from the vadose zone. This paper aims to determine the effect of grain size heterogeneity on the removal of NAPL in porous media during SVE. Magnetic resonance imaging (MRI) was used to observe and quantify the amount and location of NAPL in flow-through columns filled with silica gel grains. MRI is unique because it is nondestructive, allowing three-dimensional images to be taken of the phases as a function of space and time. Columns were packed with silica gel in three ways: coarse grains (250-550 microm) only, fine grains (32-63 microm) only, and a core of fine grains surrounded by a shell of coarse grains. Columns saturated with water were drained under a constant suction head, contaminated with decane, and then drained to different decane saturations. Each column was then continuously purged with water-saturated nitrogen gas and images were taken intermittently. Results showed that at residual saturation, a sharp volatilization front moved through the columns filled with either coarse-grain or fine-grain silica gel. In the heterogeneous columns, the volatilization front in the core lagged just behind the shell because gas flow was greater through the shell and decane in the core diffused outward to the shell. When decane saturation in the core was above residual saturation, decane volatilization occurred near the inlet, the relative decane saturation throughout the core dropped uniformly, and decane in the core flowed in the liquid phase to the shell to replenish volatilized decane. These results indicate that NAPL trapped in low-permeability zones can flow to replenish areas where NAPL is lost due to SVE. However, when residual NAPL saturation is reached, NAPL flow no longer occurs and diffusion limits removal from low-permeability zones.

Environmental risk analysis of hazardous material rail transportation.

An important aspect of railroad environmental risk management involves tank car transportation of hazardous materials. This paper describes a quantitative, environmental risk analysis of rail transportation of a group of light, non-aqueous-phase liquid (LNAPL) chemicals commonly transported by rail in North America. The Hazardous Materials Transportation Environmental Consequence Model (HMTECM) was used in conjunction with a geographic information system (GIS) analysis of environmental characteristics to develop probabilistic estimates of exposure to different spill scenarios along the North American rail network. The risk analysis incorporated the estimated clean-up cost developed using the HMTECM, route-specific probability distributions of soil type and depth to groundwater, annual traffic volume, railcar accident rate, and tank car safety features, to estimate the nationwide annual risk of transporting each product. The annual risk per car-mile (car-km) and per ton-mile (ton-km) was also calculated to enable comparison between chemicals and to provide information on the risk cost associated with shipments of these products. The analysis and the methodology provide a quantitative approach that will enable more effective management of the environmental risk of transporting hazardous materials.

NAPL source zone depletion model and its application to railroad-tank-car spills.

We developed a new semi-analytical source zone depletion model (SZDM) for multicomponent light nonaqueous phase liquids (LNAPLs) and incorporated this into an existing screening model for estimating cleanup times for chemical spills from railroad tank cars that previously considered only single-component LNAPLs. Results from the SZDM compare favorably to those from a three-dimensional numerical model, and from another semi-analytical model that does not consider source zone depletion. The model was used to evaluate groundwater contamination and cleanup times for four complex mixtures of concern in the railroad industry. Among the petroleum hydrocarbon mixtures considered, the cleanup time of diesel fuel was much longer than E95, gasoline, and crude oil. This is mainly due to the high fraction of low solubility components in diesel fuel. The results demonstrate that the updated screening model with the newly developed SZDM is computationally efficient, and provides valuable comparisons of cleanup times that can be used in assessing the health and financial risk associated with chemical mixture spills from railroad-tank-car accidents.