Field-scale Model Research Articles

Enhancing Oil Recovery in Eagle Ford Shale: A Multiscale Simulation Study of Surfactant Huff ’n’ Puff Methodology

In this paper, we present a simulation case study of a surfactant huff ’n’ puff pilot in the black oil window of the Eagle Ford (EF) Shale. The target horizontal well, which had been depleted for nearly 8 years, underwent stimulation via a surfactant huff ’n’ puff treatment. The surfactant was selected through laboratory screening using reservoir rock and fluid samples. After a 17-hour injection and a 1-month shut-in period, the well’s production increased fivefold from the baseline oil rate, sustaining incremental oil production for at least 2 years. The surfactant enhances oil recovery by altering rock wettability toward a more water-wet state and moderating oil/water interfacial tension (IFT). This process is modeled by surfactant adsorption in the simulator, indicating the degree of dynamic changes in relative permeability (krl) and capillary pressure (Pc) curves. We propose a comprehensive workflow comprising three stages: development of core-scale and field-scale models, sequential model calibrations, and multiobjective optimization to integrate laboratory measurements and field data from this pilot into multiscale numerical simulations. By matching oil recoveries from imbibition experiments on the core model and field production histories on the field model, krl and Pc profiles of two extreme states, basic reservoir properties, and additional reservoir properties altered during huff ’n’ puff operations are characterized. The matched core model reproduces a 15.1% incremental oil recovery for surfactant-assisted spontaneous imbibition (SASI) process relative to pure brine imbibition process. The matched reservoir model predicts the surfactant huff ’n’ puff treatment increases the oil production by 21.9% relative to water huff ’n’ puff treatment and by 52.9% relative to primary depletion for a 4-year period. The calibrated reservoir model also serves as a base case for optimizing well operation schedules through the implementation of a multiobjective genetic algorithm. The surfactant injection rate, injection time, and well shut-in time of the base case are varied to achieve higher oil production and reduced surfactant usage. Statistical analysis of eight trade-off cases indicates that optimal well operations, compared with existing practices, frequently involve increased injection rates [16.6–18.9 barrels per minute (bpm)], shorter injection periods (10–11.3 hours), and prolonged shut-indurations (49–65 days). This workflow offers valuable insights into surfactant huff ’n’ puff treatments for unconventional reservoirs, thereby facilitating the optimization of well operations and maximizing tertiary oil recovery.

Integrated wellbore-reservoir modeling based on 3D Navier–Stokes equations with a coupled CFD solver

The occurrence of fluid flow near a wellhead is the major concern of the petroleum industry, as pressure drop, loss of formation, and other variables of interest are mostly affected in this region. The fluid flows from the hydrocarbon reservoir to the wellbore can be characterized as laminar to turbulent; thus, it is important to model this phenomenon with the integrated wellbore-reservoir model. Using 3D Navier–Stokes equations, an integrated wellbore-reservoir model is created in this study, and it incorporates the formation damage zone. For the porous-porous and porous-fluid interfaces, the General Grid Interface (GGI) approach is applied in conjunction with the conservative mass flux interface model. Model equations are solved using a velocity-pressure coupling solver that is pressure-based. For reliable and quick results, the system of equations is solved using an algebraic multigrid approach. The pressure diffusivity equation’s analytical solution under steady-state flow circumstances is used to validate the model. The integrated wellbore-reservoir model is applied to different reservoir scenarios, for example, different production rates, formation zones, and reservoir formation conditions. The results indicate that the present Computational Fluid Dynamics (CFD) model can be extended to simulate the real field scale model. integrated wellbore-reservoir modeling based on 3D Navier–Stokes equations with efficient computational techniques can lead the field of petroleum industries to advance current knowledge.

Assessing SWAT+ Performance in Simulating Drainage Water Management and Parameter Transferability for Watershed-Scale Applications

Drainage Water Management (DWM) is widely implemented to mitigate nutrient outflow from tile-drained croplands. However, the performance of process-based hydrological models in simulating DWM remains untested. This study aimed to assess the Soil and Water Assessment Tool (SWAT+) in simulating daily tile flow for a paired DWM field site in eastern South Dakota. Additionally, it sought to develop a method for determining the transferability of field-scale model parameterization to the surrounding watershed. Field-scale SWAT+ yielded satisfactory results during the calibration period (2016–2017) (East: NSE = 0.82, PBIAS = -26.8, RSR = 0.42; West: NSE = 0.75, PBIAS = -30.5, RSR = 0.5) and validation period (2018) (East: NSE = 0.71, PBIAS = -17, RSR = 0.55; West: NSE = 0.81, PBIAS = -4.6, RSR = 0.44) for each half of the experimental site. Further, ten-year SWAT+ simulations for the calibrated site showed an average annual reduction of 6.5 ± 1.7 mm in tile flow across the field due to DWM. Additionally, the study utilized remotely sensed Evapotranspiration to demonstrate that the calibrated site’s model parametrization could be transferred to only 28 % to 46 % of surrounding croplands each year. Overall, the implications of the study extend beyond South Dakota, making the proposed research applicable to other geographical regions with tiled croplands as well.

From Streamline to Pathline: Visualizing Particle Trajectories Under Changing Velocity Fields

Summary Monitoring of subsurface fluid motion is critical for optimizing hydrocarbon production and CO2 sequestration. Streamlines are frequently used to visualize fluid flow; however, they provide only an instantaneous snapshot of the velocity field and do not offer an exact representation of fluid movement under varying field conditions. In contrast, pathlines are constructed by tracking individual particles within the fluid, enabling us to trace the movement of these particles as they traverse through changing velocity fields. Pathline is the trajectory that an individual fluid particle follows in the reservoir. It can be thought of as “recording” the path of a fluid element in the flow field for a given time interval. Pathlines are distinct from streamlines which represent a snapshot of the velocity field at a given instant. The direction the path takes is determined by the streamlines at a specific instant. To start with, streamlines are traced based on the grid face fluxes of finite-difference simulation. Streamline tracing continues till the time of flight equals the current time. The endpoints of the current streamlines become the starting points for the next tracing period. Thus, our formulation incorporates changing flow fields, and the process is repeated for each time interval until the end. The proposed injection monitoring method is tested using a 3D field-scale model with complex geologic features to demonstrate its power and utility. The pathlines were compared with streamlines, as well as the water saturation distribution. Three scenarios are tested: a constant well schedule, a changing well schedule with partial shut-in, and a changing well schedule with a whole field cessation. Results indicate that the pathline provides a more accurate swept volume, consistent with saturation distribution. The robustness of our algorithm and implementation is demonstrated with a complex embedded discrete fracture model (EDFM) to visualize flow patterns in discrete facture network. Pathlines display the fluid flow across fractures and are subsequently used to explore the sweep efficiency and the well connectivity.

Physics-informed neural networks in groundwater flow modeling: Advantages and future directions

In recent years, there has been enormous development in soft computing, especially artificial intelligence (AI), which has developed robust methods for solving complex engineering problems. Researchers in the field of water resources engineering have applied these AI methods to solve a variety of hydrological problems. Despite their widespread use in the surface and atmospheric hydrology fields, groundwater hydrologists have not widely used AI methods in their routine field-scale modeling efforts. This is because AI models have been primarily considered black box models that lack physical meaning. Furthermore, using AI models to generate the space-time distribution of transient groundwater level variations is challenging and requires further flux balance and mass transport analyses. More recently, a new type of physics-informed neural network (PINN) model has been developed to address several limitations by integrating governing physics (groundwater flow equations) into the AI tools. This study presents the systematic advantages of the PINN algorithm for solving groundwater problems using a set of classic test problems. As discussed in detail in the article, these advantages and potentials are associated with the meshless nature of PINN, its continuous time and space dimensions, its independence from time-stepping and incremental marching in space, and its efficiency in running time. However, despite PINN's promising attributes, it is important to acknowledge its nascent stage of development and the inherent limitations of all neural network models, such as training challenges and hyperparameter selection. Thus, collaborative efforts between groundwater modelers and computer scientists are imperative to explore and exploit the full potential of PINN in tackling increasingly complex groundwater problems and nurturing PINN into a dependable modeling tool in industry and academia.

A general purpose parallel Fortran code for grid projected concentration reconstruction from multidimensional particle distributions

Particle-based transport models are widely used for simulating the movement of pollutants in environmental systems. Unlike grid-based methods, particles naturally address advective transport without numerical dispersion. However, these models require concentration reconstruction from the discrete particle information. This is computationally demanding in multidimensional problems, posing a challenge for field-scale models requiring frequent reconstruction. Grid Projected Kernel Density Estimation (GPKDE) is a cell-averaged reconstruction with improvements in computational performance compared to classical KDE. Currently, no programs implementing this method are readily integrated into particle simulators, compatible with three-dimensional domains, and particles with unequal weights. This article introduces a Fortran code for general-purpose GPKDE, with modular functionalities facilitating the integration into external software. The program implements locally adaptive kernel bandwidth selection and alternatives for the reconstruction from particles with non-uniform weights. The code is parallelized with the OpenMP library. Numerical test cases demonstrate the program’s applicability and scalability of the parallel implementation.

BARA: cellular automata simulation of multidimensional smouldering in peat with horizontally varying moisture contents

Background Smouldering peatland wildfires can last for months and create a positive feedback for climate change. These flameless, slow-burning fires spread horizontally and vertically and are strongly influenced by peat moisture content. Most models neglect the non-uniform nature of peat moisture. Aims We conducted a computational study into the spread behaviour of smouldering peat with horizontally varying moisture contents. Methods We developed a discrete cellular automaton model called BARA, and calibrated it against laboratory experiments. Key results BARA demonstrated high accuracy in predicting fire spread under non-uniform moisture conditions, with >80% similarity between observed and predicted shapes, and captured complex phenomena. BARA simulated 1 h of peat smouldering in 3 min, showing its potential for field-scale modelling. Conclusion Our findings demonstrate: (i) the critical role of moisture distribution in determining smouldering behaviour; (ii) incorporating peat moisture distribution into BARA’s simple rules achieved reliable predictions of smouldering spread; (iii) given its high accuracy and low computational requirement, BARA can be upscaled to field applications. Implications BARA contributes to our understanding of peatland wildfires and their underlying drivers. BARA could form part of an early fire warning system for peatland.

A numerical study of complex fractures propagation path in naturally fractured gas shale: Reorientation, deflection and convergence

Shale gas reservoir is characterized as the naturally fractured formation, which is benefit for complex hydraulic fracture network generation. The propagation path is crucial to determine the reservoir stimulation result, and evaluate the quality of complex hydraulic fracture network. To investigate the propagation path of complex hydraulic fracture in gas shale, the hybrid combined discrete fracture network-finite element method (DFN-FEM) model is involved. Combining a linear hardening and a non-linear softening process, a non-linear traction-separation law is proposed to describe the fracture damage evolution. Validated with open-reported laboratory experiments, the hydraulic-natural fracture (HF–NF) interaction criterion is depicted under four geomechanical and injection factors. Based on a field-scale model of Sichuan basin shale gas reservoir, the complex hydraulic fractures propagation process is simulated with rock deformation and stress shadow. Several propagation path indexes are defined with fracture morphology to discuss the effect of representative geomechanical, injection and multi-clustering parameters. The results show that the tension fractures are predominant to hydraulic fractures propagation path and shear the natural fractures, due to the combined influence of natural fractures network and interference between clusters. The horizontal stress contrast and the injection fluid viscosity is governing to the propagation path and morphology, rather than the friction coefficient and injection rate. Multi-clustered hydraulic fractures converge into a dominant fracture due to the attraction with a short clusters-spacing, while the exclusion is observed along the propagation path with the increase of clusters number.

BCH modelling studies on biocementation process in mitigating leaks from a CO2 sequestrated aquifer

BCH modelling studies on biocementation process in mitigating leaks from a CO2 sequestrated aquifer

Integrated Formation Evaluation for Site-Specific Evaluation, Optimization, and Permitting of Carbon Storage Projects

Participation in over 80 carbon capture and sequestration (CCS) projects spanning 25 years has led to the evolution of a recommended well-based appraisal workflow for CO2 sequestration in saline aquifers. Interpretation methods are expressly adapted for CCS applications to resolve key reservoir parameters, constrain field-scale modeling, provide answers required for the permitting process, and de-risk unique CCS evaluation challenges, such as Storage capacity Injectivity Containment. A challenge complicating all of the above is the eventual impact of three-way interaction among rock matrix, brine, and (impure) CO2 streams. Most logging, sampling, and laboratory techniques are adapted from established domains such as enhanced oil recovery, underground gas storage, and unconventional reservoir evaluation, though some CCS-specific innovation is also needed. Storage evaluation begins with established methods for lithology, porosity, permeability, and pressure, while special core analysis (SCAL) determines CO2 storage efficiency and relative permeability. Containment evaluation spans multiple disciplines and methods: the petrophysicist’s task to quantify seal capacity relies heavily on laboratory analysis, while geologists leverage downhole imaging tools to verify caprock structural/tectonic integrity. Geomechanics engineers define safe injection pressure via mechanical earth models (MEMs) built on advanced acoustic logs calibrated by core geomechanics, wellbore failure observations, and in-situ stress tests. The impact of rock-brine-CO2 interactions is studied via custom SCAL experiments and/or pore-scale digital rock simulations that rigorously represent chemical and thermal processes. Wireline formation tester samples provide representative formation brine as feedstock for SCAL. Water samples also enable operators to prove injection within regulatory limits while establishing baselines for future monitoring programs. Examples applied to recent CCS projects in North America are presented. All of the above data need to be integrated into a CCS model predicting the CO2 plume behavior across the area of interest and within multiple horizons.

Downscaling the APSIM crop model for simulation at the within-field scale

CONTEXTMost crop models are designed for point-based modeling and to simulate agronomic variables on their native spatial footprint, i.e. typically as a uniform field-scale value. Precision agriculture needs crop model simulations at sub-field scales to support differential management application. Spatialization processes are used to change the simulation scale of crop models. OBJECTIVEThe objective of this study is to investigate the spatialization of a complex crop model by using a spatial calibration approach to modify its native spatial footprint and to evaluate if it is relevant to use this kind of crop model at the within-field scale. METHODSAPSIM was spatialized to simulate durum wheat yield at different spatial scales (field, within-field and site-scale) on an experimental field under Mediterranean conditions in southern Italy. Ancillary soil data were used to derive potential management (modeling) zones at different scales, which were then used to spatially calibrate soil and biomass parameters in APSIM to spatially predict yield in two different production years (one year was used for calibration and the other for evaluation). Spatialized crop model performances were evaluated using the spatial balanced accuracy (SBA) score, a metric to evaluate the global preservation of patterns between maps. RESULTS AND CONCLUSIONSThe spatial structure of the yield data influenced the effectiveness of the spatial calibration process. When the agronomic variable (durum wheat yield) was spatially structured, a spatialized APSIM approached performed best (5-zone modeling scale, SBA = 0.17) and outperformed the field-scale (native footprint) model (SBA = 0.19). In contrast, when the target agronomic variable was more random (less spatially structured), the uniform field-scale modeling performed best and spatial calibration had no benefit. The spatialized APSIM performances were mainly based on the reliability of the delineated zones that undeniably affected the quality of the spatialized model outputs. Thus, more research is needed on how best to model scale-dependent processes to have more reliable modeling at the within-field scale. SIGNIFICANCEBased on the example of a complex crop model like APSIM, this study showed that spatial calibration can be effective and has a role to play in the spatialization of complex crop models.

A Systemic Comparison of Physical Models for Simulating Surfactant–Polymer Flooding

Three different reservoir simulators that utilize both two-phase and three-phase microemulsion phase behavior models are used to model surfactant–polymer flooding to determine and compare their results. Different models are used in each simulator to describe the physical behavior of injected chemicals into the reservoir, which raises the need to benchmark their results. The physical behavior models of polymer and surfactant were constructed and verified on a 1D scale reservoir model and further verified in a 3D model. Finally, simulations were conducted in a field-scale reservoir containing 680,400 grids, where results were compared and analyzed. The 1D and 3D model results suggest an excellent match between the different simulators in modeling surfactant–polymer floods. In the case of the field-scale model, the simulators matched in terms of oil recovery and total volumes produced and injected, while having similar reservoir pressure profiles but with significant discrepancies in terms of injected and produced chemicals. These results indicate that despite the differences in the calculated injected and produced chemicals due to the different models in the simulators, the effect of surfactant–polymer floods on oil recovery, total injected and produced fluids, and average pressure profiles can be comparably modeled in all of the three simulators.

A Practical and Innovative Workflow to Support the Numerical Simulation of CO2 Storage in Large Field-Scale Models

Summary Numerical simulation of the CO2 storage process in porous media, such as in hydrocarbon (gas or oil) depleted reservoirs and in saline aquifers, has been the most indicated tool due to its ability to represent CO2 capacity and the different trapping mechanisms that retain CO2 in the subsurface. Given the complexity of the physicochemical phenomena involved, the modeling needs to incorporate multiphase flow, complex representation of fluids, rock, and rock-fluid interaction properties. These include CO2 reactions with aqueous species and with reservoir rock minerals, in addition to the structural and stratigraphic aspects of the reservoir heterogeneity. These phenomena need to be represented on suitable temporal and spatial scales for accurate predictions of their impacts. Currently, many studies are focused on simulating submodels or sectors of the reservoir, where using finer grids is still practical. This level of grid refinement can be prohibitive, in terms of simulation times, for modeling the entire reservoir. To address this challenge, we propose a new and practical workflow to simulate CO2 storage projects in large field-scale models. When the proposed workflow is applied in both synthetic and real field cases, simulation time is reduced by up to 96% compared to that of the fine-grid model, preserving the same results in representing the aforementioned mechanisms. The workflow is based on classical and standard approaches to handle the high simulation time, but in this study, they are structured and sequenced in three steps. The first one considers the most relevant mechanisms for CO2 storage, ranked from a high-resolution sector model. With the mechanisms prioritized in the previous step, a single-phase upscaling of petrophysical properties can be applied in the field-scale model, followed by adopting a grid with dynamic sizing. The proposed methodology is applied to saline aquifer models in this study, but it can be extended for storage in depleted hydrocarbon reservoirs.

The Influence of Interlayer on the Development of Steam Chamber in Steam Stimulation during Heavy Oil Recovery

Cyclic steam stimulation is an effective thermal recovery method for heavy oil recovery. The key potential mechanism is the growth of the steam chamber after steam injection. Taking the LD5X heavy oil reservoir as an example, besides the interlayer developed in this area, the top water and bottom water distribute above and below the interlayer. These factors may have adverse effects on the development of the steam chamber, thus affecting the final heavy oil exploitation. In this work, our goal is to study the effects of interlayer permeability and well–interlayer distance on CSS performance (in the presence of top and bottom water). We developed a high-temperature-resistant interlayer. Based on the simulated interlayer, the field scale model was converted into a laboratory element model through the similarity criterion. In order to quantitatively evaluate the performance of steam stimulation, a thermal detector was used to measure the dynamic growth of the steam chamber and record the production data. The experimental results show that the self-made interlayer has high-temperature resistance, adjustable permeability, and little difference between the physical parameters and the target interlayer. During the cyclic steam stimulation process, the steam chamber presents two different stages in the presence of the top water area, namely the normal production stage and the top water discharge stage. The bottom water has little effect on the growth of the steam chamber. The small interlayer permeability, the increase in horizontal well–interlayer distance, and the existence of the interlayer will delay the top water leakage during steam stimulation. This study has reference significance for us to develop heavy oil resources with a top water barrier when implementing steam stimulation technology.

Inference of relative permeability curves in reservoir rocks with ensemble Kalman method.

Multiphase flows through reservoir rocks are a universal and complex phenomenon. Relative permeability is one of the primary determinants in reservoir performance calculations. Accurate estimation of the relative permeability is crucial for reservoir management and future production. In this paper, we propose inferring relative permeability curves from sparse saturation data with an ensemble Kalman method. We represent these curves through a series of positive increments of relative permeability at specified saturation values, which guarantees monotonicity within, and boundedness between 0 and 1. The proposed method is validated by the inference performances in two synthetic benchmarks designed by SPE and a field-scale model developed by Equinor that includes certain real field features. The results indicate that the relative permeability curves can be accurately estimated within the saturation intervals having available observations and appropriately extrapolated to the remaining saturations by virtue of the embedded constraints. The predicted well responses are comparable to the ground truths, even though they are not included as the observation. The study demonstrates the feasibility of using ensemble Kalman method to infer relative permeability curves from saturation data, which can aid in the predictions of multiphase flow and reservoir production.

Feasibility of a TDR-based technique for fluid hydrocarbon leak detection

Undetected leakage of fluid hydrocarbons from pipelines or underground storage tanks into the subsurface environment are environmental and public health hazards. It is evident that there is a need for efficient, effective and accurate technology to detect these leaks rapidly and non-destructively, so as to make the process safe, to make quantitative risk assessment, and to provide effective preventive measures. There are unsurmountable limitations in many existing leak detection techniques. Moreover, early detection of leakage would allow rapid remediation at source to significantly reduce the remediation costs, and more importantly the environmental impact. The results of an experimental investigation for the technical feasibility and practicality of a technique for detection and location of fluid hydrocarbon leaks in underground pipelines and storage tanks based on the principles of time-domain reflectometry (TDR) are presented in this paper. The measurement sensitivity of the technique was evaluated for 3 different types of cables, 2 cable configurations (parallel-wires or coaxial), 2 leaking fluids (tap water or petrol), a single leak or double leaks, and 2 backfill materials (fine sand or gravel) by a series of field-scale model tests. The testing parameters were varied systematically. The experimental results indicate that TDR is a technically feasible technique for the detection of fluid hydrocarbon leaks from pipelines and underground storage tanks when the setup configuration is appropriate.

Assessing nutrient and sediment load reduction potential of vegetation by utilizing the nutrient tracking tool at the field and watershed scale in a Great Lakes priority watershed

PurposeNutrient and sediment pollution of surface waters remains a critical challenge for improving water quality. This study takes a user-friendly field-scale tool and assesses its ability to model at both the field and watershed scale within the Fox River Watershed (FRW), Wisconsin, USA, along with assessing how targeted vegetation implementation could attenuate nutrient and sediment exports.MethodsTo assess potential load reductions, the nutrient tracking tool (NTT) was used with a scoring system to identify areas where vegetation mitigation could be implemented within three selected FRW sub-watersheds. A corn soybean rotation, an implementation of a 10-m-vegetated buffer, a full forest conversion, and tiling were modeled and assessed. The corn–soybean results were aggregated and compared to watershed level gauge data in two sub-watersheds. Edge-of-field data was compared to modeled results using multiple parameterization schemes.ResultsThe agricultural areas that scored higher and were untiled showed greater potential nutrient and sediment export reduction (up to 80 to 95%) when vegetation mitigation was implemented in the model. Field-scale results aggregated to the watershed scale showed disparities between modeled and measured phosphorus exports but modeled sediment exports fell within observed gauge data ranges. Field-specific parameter adjustments resulted in more accurate modeled results compared to measured edge-of-field export data but needed further refinement.ConclusionTargeted mitigation using a vegetation-based scoring system with the NTT model was shown to be a helpful tool for predicting nutrient and sediment reductions. Using a field-scale model aggregated to the watershed scale presents tradeoffs regarding processes found beyond the edge of field.

Cluster-based learning and evolution algorithm for optimization

In this work, we present a cluster-based learning and evolution optimizer (CLEO) for solving optimization problems. CLEO is a metaheuristic algorithm that uses cluster-based manipulation of the problem space during the exploration phase, followed by fine-tuning solutions in the exploitation phase using updated knowledge of the problem space. We propose two approaches based on this new algorithm: one using only Latin hypercube sampling (LHS) and the other using LHS in combination with reservoir engineering insights. In addition to ensuring realistic simulation scenarios, we employed intuitive engineering insights to reveal how empirical knowledge enhances efficiency. Also, we propose simulating the partial life instead of the complete lifespan in the second approach. Technical results obtained at the end of this period are processed and used to find the optimized field development plan (FDP). We conducted both deterministic and probabilistic studies to assess the performance of the proposed approaches for various decision variables, both numerous and restricted. We validated the algorithm by optimizing the FDP for a simple numerical simulation model and a giant field-scale model, and compared our approaches to four well-established optimizers (particle swarm optimization (PSO), differential evolution (DE), designed exploration controlled evolution (DECE), and iterative discrete Latin hypercube sampling method (IDLHC)) in terms of simulation time and objective function results. Overall, the comparison demonstrates the advantages of the newly proposed algorithm. The results indicate that our first approach performs as well as any well-established optimizer, notably when working with large scale optimization problems. The second approach has slightly lower objective function results than the first one, but it is the most efficient among the compared algorithms, as the best FDP can be obtained by covering as little as 40% of the field's life. This attribute makes it an excellent alternative for developing oil and gas fields, which are fraught with uncertainty and errors in time-consuming simulation models. As a feasibility study on a complex and expensive compositional model, using second approach resulted in a 75% increase in efficiency while saving 120 days.

Numerical investigation of the effect of fluid pressurization rate on laboratory-scale injection-induced fault slip

The effect of normal stress variations on fault frictional strength has been extensively characterized in laboratory experiments and modelling studies based on a rate-and-state-dependent fault friction formalism. However, the role of pore pressure changes during injection-induced fault reactivation and associated frictional phenomena is still not well understood. We apply rate-and-state friction (RSF) theory in finite element models to investigate the effect of fluid pressurization rate on fault (re)activation and on the resulting frictional slip characteristics at the laboratory scale. We consider a stepwise injection scenario where each fluid injection cycle consists of a fluid pressurization phase followed by a constant fluid pressure phase. We first calibrate our model formulation to recently published laboratory results of injection-driven shear slip experiments. In a second stage, we perform a parametric study by varying fluid pressurization rates to cover a higher dimensional parameter space. We demonstrate that, for high permeability laboratory samples, the energy release rate associated with fault reactivation can be effectively controlled by a stepwise fluid injection scheme, i.e. by the applied fluid pressurization rate and the duration of the constant pressure phase between each successive fluid pressurization phase. We observe a gradual transition from fault creep to slow stick–slip as the fluid pressurization rate increases. Furthermore, computed peak velocities for an extended range of fluid pressurization rate scenarios (0.5 MPa/min to 10 MPa/min) indicate a non-linear (power-law) relationship between the imposed fluid pressurization rate and the peak slip velocities, and consequently with the energy release rate, for scenarios with a fluid pressurization rate higher than a critical value of 4 MPa/min. We also observe that higher pressurization rates cause a delay in the stress release by the fault. We therefore argue that by adopting a stepwise fluid injection scheme with lower fluid pressurization rates may provide the operator with a better control over potential induced seismicity. The implications for field-scale applications that we can derive from our study are limited by the high matrix and fault permeability of the selected sample and the direct hydraulic connection between the injection well and the fault, which may not necessarily represent the conditions typical for fracture dominated deep geothermal reservoirs. Nevertheless, our results can serve as a basis for further laboratory experiments and field-scale modelling studies focused on better understanding the impact of stepwise injection protocols on fluid injection-induced seismicity.

Atmospheric deposition of reactive nitrogen to a deciduous forest in the southern Appalachian Mountains.

Assessing nutrient critical load exceedances requires complete and accurate atmospheric deposition budgets for reactive nitrogen (Nr). The exceedance is the total amount of Nr deposited to the ecosystem in excess of the critical load, which is the amount of Nr input below which harmful effects do not occur. Total deposition includes all forms of Nr (i.e., organic and inorganic) deposited to the ecosystem by wet and dry pathways. Here we present results from the Southern Appalachian Nitrogen Deposition Study (SANDS), in which a combination of measurements and field-scale modeling was used to develop a complete annual Nr deposition budget for a deciduous forest at the Coweeta Hydrologic Laboratory. Wet deposition of ammonium, nitrate, nitrite, and bulk organic N were measured directly. The dry deposited Nr fraction was estimated using a bidirectional resistance-based model driven with speciated measurements of Nr air concentrations (e.g., ammonia, ammonium aerosol, nitric acid, nitrate aerosol, bulk organic N in aerosol, total alkyl nitrates, and total peroxy nitrates), micrometeorology, canopy structure, and biogeochemistry. Total annual deposition was ~6.7 kg N ha-1 yr-1, which is on the upper end of Nr critical load estimates recently developed for similar ecosystems in the nearby Great Smoky Mountains National Park. Of the total (wet + dry) budget, 51.1% was contributed by reduced forms of Nr , with oxidized and organic forms contributing ~41.3% and 7.6%, respectively. Our results indicate that reductions in deposition would be needed to achieve the lowest estimates (~3.0 kg N ha-1 yr-1) of Nr critical loads in southern Appalachian forests.