Subsurface Applications Research Articles

Splitting tensile strength of shale cores: intact versus fractured and sealed with ureolysis-induced calcium carbonate precipitation (UICP)

AbstractUreolysis-induced calcium carbonate precipitation (UICP) is a biomineral solution where the urease enzyme converts urea and calcium into calcium carbonate. The resulting biomineral can bridge gaps in fractured shale, reduce undesired fluid flow, limit fracture propagation, better store carbon dioxide, and potentially enhance well efficiency. The mechanical properties of shale cores were investigated using a modified Brazilian indirect tensile strength test. An investigation of intact shale using Eagle Ford and Wolfcamp cores was conducted at varying temperatures. Results show no significant difference between shale types (average tensile strength = 6.19 MPa). Eagle Ford displayed higher strength at elevated temperature, but temperature did not influence Wolfcamp. Comparatively, cores with a single, lengthwise heterogeneous fracture were sealed with UICP and further tested for tensile strength. UICP was delivered via a flow-through method which injected 20–30 sequential patterns of ureolytic microorganisms and UICP-promoting fluids into the fracture until permeability reduced by three orders of magnitude or with an immersion method which placed cores treated with guar gum and UICP-promoting fluids into a batch reactor, demonstrating that guar gum is a suitable inclusion and may reduce the number of flow-through injections required. Tensile results for both delivery methods were variable (0.15–8 MPa), and in some cores the biomineralized fracture split apart, possibly due to insufficient sealing and/or heterogeneity in the composite UICP-shale cores. Notably in other cores the biomineralized fracture remained intact, demonstrating more cohesion than the surrounding shale, indicating that UICP may produce a strong seal for subsurface application.

Multiscale preconditioning of Stokes flow in complex porous geometries

Fluid flow through porous media is central to many subsurface (e.g., CO2 storage) and industrial (e.g., fuel cell) applications. The optimization of design and operational protocols, and quantifying the associated uncertainties, requires fluid-dynamics simulations inside the microscale void space of porous samples. This often results in large and ill-conditioned linear(ized) systems that require iterative solvers, for which preconditioning is key to ensure rapid convergence. We present robust and efficient preconditioners for the accelerated solution of saddle-point systems arising from the discretization of the Stokes equation on geometrically complex porous microstructures. They are based on the recently proposed pore-level multiscale method (PLMM) and the more established reduced-order method called the pore network model (PNM). The four preconditioners presented are the monolithic PLMM, monolithic PNM, block PLMM, and block PNM. Compared to existing block preconditioners, accelerated by the algebraic multigrid method, we show our preconditioners are far more robust and efficient. The monolithic PLMM is an algebraic reformulation of the original PLMM, which renders it portable and amenable to non-intrusive implementation in existing software. Similarly, the monolithic PNM is an algebraization of PNM, allowing it to be used as an accelerator of direct numerical simulations (DNS). This bestows PNM with the, heretofore absent, ability to estimate and control prediction errors. The monolithic PLMM/PNM can also be used as approximate solvers that yield globally flux-conservative solutions, usable in many practical settings. We systematically test all preconditioners on 2D/3D geometries and show the monolithic PLMM outperforms all others. All preconditioners can be built and applied on parallel machines.

GEOS: A performance portable multi-physics simulation framework for subsurface applications

GEOS: A performance portable multi-physics simulation framework for subsurface applications

Pipe experiment elucidates biochar application depth affects nitrogen leaching under crop present condition

Nitrogen leaching, resulting from the inefficient use of fertilizers, pollutes the environment, such as groundwater. Biochar can be applied to farmlands to mitigate nitrogen leaching. The effect depends on the application depth. However, the effect has not been examined under crop-farming conditions. Evaluating the interactions between biochar application depth and crop growth is indispensable for considering depth in the actual field. To address this, we conducted a pipe experiment with four treatments, no biochar (control), surface (0–5 cm), plow layer (0–30 cm), and subsurface (25–30 cm) applications, and compared the results with no-crop conditions from a previous study. Biochar application depth affected soil NO3−−N and NH4+−N absorption ability and also influenced soil-water stress conditions, affecting crop growth. Surface biochar application improved nitrogen absorption and reduced soil-water stress, improving crop growth. The NO3−−N leaching was reduced to 87.7%. Plow layer application worsened nitrogen absorption and resulted in frequent dry stress in the shallow-soil layer, preventing root growth in this layer. The NO3−−N and NH4+−N leaching increased 106.4% and 264.1%, respectively. The effects of subsurface application were similar to those in the control. Selecting an appropriate application depth can simultaneously improve crop growth and reduce nitrogen leaching.

Percolation without trapping: How Ostwald ripening during two-phase displacement in porous media alters capillary pressure and relative permeability.

Conventional measurements of two-phase flow in porous media often use completely immiscible fluids, or are performed over timescales of days to weeks. If applied to the study of gas storage and recovery, these measurements do not properly account for Ostwald ripening, significantly overestimating the amount of trapping and hysteresis. When there is transport of dissolved species in the aqueous phase, local capillary equilibrium is achieved: this may take weeks to months on the centimeter-sized samples on which measurements are performed. However, in most subsurface applications where the two phases reside for many years, equilibrium can be achieved. We demonstrate that in this case, two-phase displacement in porous media needs to be modeled as percolation without trapping. A pore network model is used to quantify how to convert measurements of trapped saturation, capillary pressure and relative permeability made ignoring Ostwald ripening to account for this effect. We show that conventional measurements overestimate the amount of capillary trapping by 20-25%.

Impact of hydrolysis on the quantification of sulfonated acrylamide copolymers by size exclusion chromatography with UV detection

AbstractSize exclusion chromatography with UV detection (SEC‐UV) is a precise method for quantifying polyacrylamides in water. However, its accuracy can be compromised in complex matrices due to chemical degradation of the polymer. This study focuses on the impact of hydrolysis on quantifying sulfonated polyacrylamide copolymers (PAM‐AMPS) via SEC‐UV, analyzing key influencing factors. Using controlled hydrolysis, hydrolyzed copolymer (HPAM‐AMPS) standards with varying hydrolysis degree (HD) (HD = 7%–33%) were prepared without significant changes in polymer chain size. SEC‐UV analysis revealed that quantification accuracy is highly dependent on hydrolysis extent. Errors are minimal for HD up to 10%, but significantly higher for HDs of 22%–33%. Furthermore, this effect is modulated by polymer concentration. Our findings indicate that there are two compensating factors for the decrease in molar absorptivity due to amide‐to‐carboxylate substitution: the sulfonate group acting as an additional chromophore, and a redshift in absorption spectra due to hydrogen bonding at higher concentrations. This interplay between HD and concentration in this type of sulfonated polyacrylamide can help reduce quantification errors in complex aqueous samples prone to chemical degradation, like those found in environmental and subsurface applications.

Full‐waveform inversion as a tool to predict fault zone acoustic properties

AbstractUnderstanding the physical properties of fault zones is essential for various subsurface applications, including carbon capture and geologic storage, geothermal energy and seismic hazard assessment. Although three‐dimensional seismic reflection data can image the geometries of faults in the sub‐surface, it does not provide any direct information on the physical properties of fault zones. We currently cannot use seismic reflection data to infer directly which faults may be leaking or sealing and are reliant instead on shale‐gauge ratio type calculations, which are fraught with uncertainties. In this paper, we propose that full‐waveform inversion P‐wave velocity models can be used to extract information on fault zone acoustic properties directly, which may be a proxy for subsurface fault transmissibility. In this study, we use high‐quality post‐stack depth–migrated seismic reflection and full‐waveform inversion velocity data to investigate the characteristics of fault zones in the Samson Dome in the SW Barents Sea. We analyse the variance attribute of the post‐stack depth migrated and full‐waveform inversion volumes, revealing linear features that consistently appear in both datasets. These features correspond to locations of rapid velocity changes and seismic trace distortions, which we interpret as faults. These observations demonstrate the capability of full‐waveform inversion to recover fault zone velocity structures. Our findings also reveal the natural heterogeneity and complexity of fault zones, with varying P‐wave velocity anomalies within the studied fault network and along individual faults. Our results indicate a correlation between P‐wave velocity anomalies within fault zones and the modern‐day stress orientation. Faults with high P‐wave velocity are the ones that are perpendicular to the present‐day maximum horizontal stress orientation and are likely under compression. Faults with lower P‐wave velocity are the ones more parallel to the present‐day maximum horizontal stress orientation and are likely in extension. We propose that these P‐wave velocity anomalies may indicate differences in how ‘open’ and fluid filled the fault zones are (i.e. faults in extension are more open, more fluid filled and have lower VP) and therefore may provide a promising proxy for fault transmissibility.

Coupling Upscaled Discrete Fracture Matrix and Apparent Permeability Modelling in DFNWORKS for Shale Reservoir Simulation

Modelling non-Darcy flow behaviour in shale rocks, composed of nanometer-sized pores and multi-scale fracture networks, is crucial for various subsurface energy applications. However, incorporating multiple physical mechanisms across numerous scales is not trivial. This work proposes an improved and practical upscaling workflow for coupling an Upscaled Discrete Fracture Matrix (UDFM) model and a pressure-dependent apparent permeability (Kapp) model to capture the effects of non-Darcy flow in multi-scale fractured shale reservoirs.First, a 3D DFN is upscaled into octree-refined continuum meshes, where equivalent rock parameters and rock-fluid functions are defined using the UDFM approach. Then, the flow simulation is coupled with a pressure-dependent Kapp updating scheme using an existing Kapp model and a multiple-restart technique. The effects of non-Darcy flow mechanisms (e.g., slip flow, transitional flow, Knudsen diffusion) are captured. The constructed models are then used to study the impacts of fracture network connectivity and pressure interference on production. The results of this new approach are compared against those obtained from another commercial package while preserving the advantages of DFNWORKS. Neglecting non-Darcy flow behaviours could significantly underestimate gas production and water recovery. It is illustrated that the nanoscale flow mechanisms help to enhance matrix-matrix and matrix-fracture flow. The constructed models are also utilized to study the effects of disconnected or isolated fractures, pressure interference, water retention, and shut-in durations on well performance. The proposed flexible strategies can be adopted in other commercial/open-source fractured-porous-media subsurface-flow simulation frameworks.

Mapping hydrodynamic structure with sparse or no well data

Hydrodynamic traps are usually mapped using well pressure data to transform structural depth maps, but if well data are sparse then hydrodynamic maps produced this way may have large uncertainties. An alternative approach that does not rely on well data is described here, utilizing simplified, locally planar representation of the potentiometric surface. Ranges of hydraulic gradient magnitudes and azimuths representing different potentiometric surface orientations, together with a range of possible density contrasts between the flowing and trapped fluids, define a three-dimensional array, termed here ‘hydrodynamic space’. This array can be constrained and simplified by reasonable assumptions and the introduction of an additional new concept that combines the hydraulic gradient magnitude and fluid density contrast into a single parameter termed ‘potentiometric transform’. Ranges of these parameters yield a set of hydrodynamic structural maps. The fineness of sampling the hydrodynamic space parameters is limited only by the resources available to support the workflow. The array can be automatically assessed in terms of hydrodynamic trap volumes by applying a structural closure algorithm that isolates and characterizes dip-closed structure. Closure volumes across the map set are ranked by spatial distribution analysis that informs exploration programs relevant to any subsurface fluid management application. The method is described for the first time here and illustrated by application to a real structural dataset.

Efficacy of fertilizer nitrogen source, stabilizer, and application timing for corn nitrogen nutrition

AbstractEnhancing fertilizer nitrogen use efficiency (NUE) in corn (Zea mays L.) production is critical for closing yield gaps, increasing producer profitability, and promoting environmental stewardship. In 2014 and 2015, a field experiment was conducted to determine the potential for fertilizer N stabilizer products to improve NUE of granular urea and urea ammonium nitrate (UAN) solution applied to strip‐till corn. A urease inhibitor (UI) or nitrification inhibitor (NI) or both were added at labeled rates to urea or UAN solution for a target rate of 180 kg N ha−1. At V3, a single application of broadcast granular urea and subsurface banded UAN solution with and without fertilizer N stabilizers was made. A split application (50% at V3; 50% at V6) of subsurface banded UAN solution served as a control representing a standard grower practice. Fertilizer N stabilizers improved components of NUE, such as grain N recovery efficiency (GNRE) and partial factor productivity (PFP). A single full rate UAN application did not differ in terms of grain yield each year but did result in less PFP and GNRE in 2015 as compared to the grower standard practice. A timely one‐time full season N rate subsurface banded application of UAN treated with UI and NI to improve NUE could be a viable substitute for the practice of multiple fertilizations. Untreated broadcast urea was inferior to UAN as a N source for corn, but when treated with both a UI and NI, NUE was improved.

New constraints from in-situ U-Pb ages and fluid inclusions of calcite cement and structural analysis on multiple stages of strike-slip fault activities in the northern Tarim Basin, NW China

Dating the activity of complex faults and fracture systems is crucial for creating reliable geological models for various tectonics and subsurface engineering applications. This study presents a comprehensive study integrating U–Pb fracture cement dating, trace elements, and fluid inclusion temperature analysis with seismic analysis of faults, fault diagenesis, and burial history studies to better constrain faulting and fracture activities in an intra-cratonic strike-slip fault system in the northern Tarim Basin. Seismic profiles indicate at least three distinct phases of fault activity corresponding to the Middle Ordovician, Permian, and Paleogene periods. Fracture cementation and crosscutting relationships corroborate the identification of three fracturing stages. U–Pb dating of fractured cement has widely detected Middle Ordovician and Early Permian age intervals. Fluid inclusion homogenization temperatures from the fractured cements, ranging across < 50 °C, 70–130 °C, and 150–180 °C, correspond to three episodes of rapid subsidence during the Ordovician, Permian, and Neogene, respectively. These results suggest three phases of fault/fracture reactivation in the Middle Ordovician (prior to 470 Ma), Early Permian (prior to 295 Ma), and Cenozoic. The fault/fracture reactivation in the Ordovician is closely related to the regional tectonic transition from extension to compression, while fault and fracture reactivation in the Early Permian may be related to hydrothermal activity associated with large-scale igneous province and oil emplacement. Fault/fracture activity in the Cenozoic may be related to a reduction in subsidence, gradual reduction of geothermal gradients, and massive oil emplacement. This research underscores the significance of integrating geochemical and subsurface datasets for accurately determining the timing of faulting and fracturing in sedimentary basins.

A pore-network modeling perspective on the dynamics of residual trapping in geological carbon storage

This study presents the application of an efficient and robust Dynamic Pore-Network Modeling (DPNM) framework for accurate prediction of carbon dioxide (CO2) trapping behavior under the dynamic flow conditions prevalent in subsurface applications. Residual trapping of CO2 is crucial in the context of geological sequestration, serving as a constitutive relationship that controls relative permeability hysteresis and thereby determining the magnitude of CO2 trapping within formations over varying timescales. Utilizing our DPNM framework, we study the complexities of residual trapping in miniature-core-sized digital replicate of a sandstone. We systematically conduct series of two-phase flow simulations to examine the relationships between total residual CO2 amount, trapping efficiency, and initial saturations across a spectrum of capillary numbers and wettability states. Dynamic simulation results lead to a simple power-law scaling equation correlating CO2 trapping efficiency with initial saturation across various capillary numbers. Further analysis explores the morphological and topological characteristics of fluids during primary drainage and various imbibition displacements within the pore network. This work contributes an essential tool and deepens our understanding of CO2 trapping dynamics, driving progress towards more effective and safer strategies for carbon capture and storage.

A novel immersed boundary approach for irregular topography with acoustic wave equations

Irregular terrain has a pronounced effect on the propagation of seismic and acoustic wavefields but is not straightforwardly reconciled with structured finite-difference (FD) methods used to model such phenomena. Accurate wavefield simulation is paramount in subsurface imaging applications such as reverse time migration and full-waveform inversion, requiring suitable topography handling. Methods currently detailed in the literature generally are limited in scope application-wise or nontrivial to apply to real-world geometries. A general immersed boundary treatment capable of imposing a range of boundary conditions in a relatively equation-agnostic manner has been developed, alongside a framework implementing this approach to complement emerging code-generation paradigms. The approach is distinguished by the use of N-dimensional Taylor-series extrapolants constrained by boundary conditions imposed at some suitably distributed set of surface points. The extrapolation process is encapsulated in modified derivative stencils applied in the vicinity of the boundary, using hyperspherical support regions. This method ensures boundary representation is consistent with the FD discretization. Furthermore, high-dimensional and vector boundary conditions can be applied without approximation prior to discretization. A consistent methodology can thus be applied across free and rigid surfaces with first- and second-order acoustic wave equation formulations. Application to both equations is demonstrated, and numerical examples based on analytic and real-world topography implementing free and rigid surfaces in 2D and 3D are presented. Numerical examples and convergence tests demonstrate the accuracy of boundary treatments devised by the prescribed approach, their suitability to practical applications, and the feasibility of automatically generating treatments to suit each case.

Accurate measurement techniques and prediction approaches for the in-situ rock stress

The precise calculation and evaluation of the in-situ rock stress tensor is a crucial factor in addressing the major challenges related to subsurface engineering applications and earth science research. To improve the accuracy of in-situ stress measurement and prediction, an improved overcoring technique involving a measurement circuit, temperature compensation, and calculation method is presented for accurately measuring the in-situ rock stress tensor. Furthermore, an embedded grey BP neural network (GM–BPNN) model is established for predicting in-situ rock stress values. The results indicate that the improved overcoring technique has significantly improved the stress measurement accuracy, and a large number of valuable stress data obtained from many mines have proved the testing performance of this technique. Moreover, the mean relative errors of the prediction results of GM(0, 1) for the three principal stresses all reach 6–30%, and the accuracy of the model fails to meet the requirements. The average relative errors of the prediction results of the BPNN model are all less than 10%, and the model accuracy meets the requirements and has sufficient credibility. Compared with the GM and BPNN models, the embedded GM–BPNN model produces the best results, with mean relative errors of 0.0001–4.8338%. The embedded GM–BPNN model fully utilizes the characteristics of grey theory and BP neural network, which require a small sample size, weaken the randomness of the original data, and gradually approach the accuracy of the model, making it particularly suitable for situations with limited stress data.

The fate of 17β-estradiol in snowmelt from a field with a history of manure application: A laboratory simulation and field study

17β-estradiol is a naturally occurring estrogen, and livestock manure applied to agricultural fields is a major source to the environment. Liquid swine manure is widely applied to agricultural fields in the Canadian Prairies, a region where the majority of the annual runoff occurs during a brief snowmelt period over frozen soil. Transport of estrogens from manure amendments to soil during this important hydrological period is not well understood but is critical to mitigating the snowmelt-driven offsite transport of estrogens. This study quantified the concentration and load of 17β-estradiol in snowmelt from an agricultural field with a history of manure application under manure application methods: no manure applied, manure applied on the sub-surface, and on the surface, using a laboratory simulation study with flooded intact soil cores and a field study during snowmelt. A higher concentration of 17β-estradiol was in the laboratory simulation than in the field (mean laboratory pore water = 1.65 ± 1.2 μg/L; mean laboratory flood water = 0.488 ± 0.58 μg/L; and mean field snowmelt = 0.0619 ± 0.048 μg/L). There were no significant differences among manure application methods for 17β-estradiol concentration. Laboratory pore water concentrations significantly increased over time, corresponding with changes in pH. In contrast, there was no significant change in the field snowmelt concentrations of 17β-estradiol over time. However, for both laboratory simulation experiments and field-based snowmelt experiments, mean concentrations of 17β-estradiol were higher with subsurface than surface-applied manure, and the cumulative load of 17β-estradiol was significantly higher in the sub-surface than in surface applied. The mean cumulative load from the field study across all treatments (6.91 ± 3.7 ng/m2) approximates the magnitude of 17β-estradiol that could be mobilized from manured fields. The sub-surface application of manure seems to increase the persistence of 17β-estradiol in soil, thus enhancing the potential loss to snowmelt runoff.

Controlled ion transport in the subsurface: A coupled advection–diffusion–electromigration system

Ion transport within saturated porous media is an intricate process in which efficient ion delivery is desired in many engineering problems. However, controlling the behavior of ion transport proves challenging, as ion transport is influenced by a variety of driving mechanisms, which requires a systematic understanding. Herein, we study a coupled advection–diffusion–electromigration system for controlled ion transport within porous media using the scaling analysis. Using the Lattice–Boltzmann–Poisson method, we establish a transport regime classification based on an Advection Diffusion Index (ADI) and a novel Electrodiffusivity Index (EDI) for a two-dimensional (2D) microchannel model under various electric potentials, pressure gradients, and concentration conditions. The resulting transport regimes can be well controlled by changing the applied electric potential, the pressure field, and the injected ions concentration. Furthermore, we conduct numerical simulations in a synthetic 2D porous media and an x-ray microcomputed tomography sandstone image to validate the prevailing transport regime. The simulation results highlight that the defined transport regime observed in our simple micromodel domain is also observed in the synthetic two- and three-dimensional domains, but the boundary between each transport regime differs depending on the variation of the pore size within a given domain. Consequently, the proposed ADI and EDI emerge as dimensionless indicators for controlled ion transport. Overall, our proof-of-concept for ion transport control in porous media is demonstrated under advection–diffusion–electromigration transport, demonstrating the richness of transport regimes that can develop and provide future research directions for subsurface engineering applications.

Steady-state two-phase relative permeability measurements in proppant-packed rough-walled fractures

Understanding multiphase flow in fractures filled with minerals and proppants is vital in various subsurface applications. Limited experimental data have led to reliance on correlations lacking physical basis. We conducted experiments to characterize relative permeability in rough-walled fractures packed with unconsolidated porous media. We tested fractures packed with water-wet 40/70 sand (silica) and surface-coated ceramic proppants. Brine and mineral oil were used as the wetting and non-wetting fluid phases, respectively. Steady-state (SS) drainage (non-wetting-phase displacing wetting-phase) and imbibition (wetting-phase displacing non-wetting-phase) tests were performed under a wide range of saturation histories (full-cycle and scanning-curves) to study relative permeability hysteresis of the propped fractures. Every SS drainage or imbibition test consisted of several discrete points at which fluid saturations and the corresponding relative permeability were measured by varying the fractional flow rates of fluids whilst maintaining a constant total flow rate. We analyzed residual non-wetting phase saturations and relative permeability trends to understand two-phase flow behavior in each proppant pack. High-resolution x-ray microtomography was used to understand the pore-scale topology, wettability, and to provide insights about the pore-scale displacement mechanisms involved in this study. The results showed that commonly used models to estimate relative permeabilities of fractures significantly overestimated the SS brine and oil relative permeabilities (denoted as krw and kro) measured in this study. Further analysis unveiled that the kro values during imbibition exceeded their drainage counterparts in both proppants, the ceramic proppant exhibited a lower initial water saturation and a higher end-point kro permeability at the end of the drainage displacement, as well as higher krw across all flooding processes. Updated fitting parameters for a Brooks-Corey-type relative permeability correlation are introduced. This study presents improved insights, extensive experimentally generated relative permeability data, and an updated relative permeability correlation, which can be collectively utilized to reduce uncertainties associated with continuum-scale forecasts of multiphase flow behavior in fractured subsurface formations.

Physics-informed neural network simulation of two-phase flow in heterogeneous and fractured porous media

Physics-informed neural networks (PINNs) have received great attention as a promising paradigm for forward, inverse, and surrogate modeling of various physical processes with limited or no labeled data. However, PINNs are rarely used to predict two-phase flow in heterogeneous and fractured porous media, which is critical to lots of subsurface applications, due to the significant challenges in their training. In this work, we present an Enriched Physics-Informed Neural Network (E-PINN) to overcome these barriers and realize the simulation of such flow. Specifically, the Embedded Discrete Fracture Model (EDFM) is adopted to explicitly represent fractures, and then the finite volume method (FVM) instead of the Automatic Differentiation (AD) is used to evaluate spatial derivatives and construct the physics-informed loss function, so that the flux continuity between neighboring elements with different properties (e.g. matrix and fracture) can be defined rigorously. Besides, we develop a novel physics-informed neural network (NN) architecture adopting the adjacency-location anchoring, adaptive activation function, skip connection and gated updating to enrich the pressure information and enhance the learning ability of NN. Additionally, the initial and boundary conditions are constrained through a hard approach, which encodes them into network design, to improve the accuracy and efficiency of network training. In order to further reduce the difficulty of training, the Implicit-Pressure Explicit-Saturation (IMPES) scheme is used to calculate pressure and saturation, in which only the pressure needs to be solved by training NN. Finally, the superiority and applicability of E-PINN to complex practical problems is demonstrated through the simulations of immiscible displacement in 2D/3D heterogeneous and fractured reservoirs.

Agronomic and economic productivity of summer annual forage systems under different poultry litter application methods

AbstractPoultry litter (litter) is a nutrient dense fertilizer that increases nutritive value and yield in pastures in the mid‐southern US. Nutrient losses due to runoff and nitrogen volatilization are common when broadcasting litter. As such, incorporating litter below the soil surface (subsurface) was evaluated in comparison to broadcasting in 2021 and 2022 by quantifying yield and nutritive value of annual forages. The study was a randomized complete block design with three forage treatments—sorghum‐sudangrass only (Sorghum bicolor L.), cowpea only (Vigna unguiculata L.), and their mixture, and three litter application methods (broadcast, subsurface, and a no litter control). Litter was applied in 2021 only as biennial application is common to save on application cost. Nutritive analyses included neutral detergent fiber and crude protein (CP). Partial budgeting led to relative profitability estimates by accounting for yield and cost differences across treatments. In comparison to the second‐highest yielding forage mixture, sorghum‐sudangrass yielded 4.5%–18.4% more regardless of litter application method. The forage mixture did not improve forage nutritive value, as cowpea were vastly outcompeted and did not average more than 5% of the total forage harvested in mixtures. Cowpea yields did not benefit from litter application. Subsurface application resulted in 8%–10% greater CP content compared to no litter and broadcast litter, respectively, across all forage species. Sorghum‐sudangrass with subsurface applied litter earned nearly $70/acre more than sorghum‐sudangrass with broadcast litter, the next highest treatment combination, and, with lesser nutrient loss.

De-risking fault leakage risk and containment integrity for subsurface storage applications

The subsurface is pivotal in the energy transition, for the sequestration of CO2 and energy storage. It is crucial to understand to what extent geological faults may form leakage pathways that threaten the containment integrity of these projects. Fault flow behavior has been studied in the context of hydrocarbon development, supported by observations from wells drilled through faults, but such observations are rare in geoenergy projects. Focusing on mechanical behavior as early indicator of potential leakage risks, a probabilistic Coulomb Failure Stress workflow is developed and demonstrated using data from the Decatur CO2 sequestration project to rank faults based on their containment risk. The analysis emphasizes the importance of fault throw relative to reservoir thickness and pore pressure change in assessing reactivation risks. Integrating this mechanical assessment with geological and dynamic fault analyses contributes to derisking fault containment for geoenergy applications, providing valuable insights for the successful development of subsurface storage projects.