Generalization of ML-Based Surrogate Models to Dynamic Injection Schedules using Transfer Learning
For presentation at the FECM/ NETL Carbon Management Project Review Meeting, Pittsburgh, PA, August 28-September 1, 2023.
Riedl, Michael↗
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Deep reinforcement learning to discover multi-fuel injection strategies for compression ignition engines
Over the past several decades, regulation of compression ignition engine emissions has become increasingly stringent as concern about the environmental and health implications of these emissions has grown. These changing constraints have led to a series of new, alternative fuel injection strategies that aim to maintain power output while reducing in-cylinder generated emissions by operating in the low-temperature combustion (LTC) regime. These advanced injection strategies are created and retuned for individual combinations of engine geometry, fuel, and emissions constraints. Deep reinforcement learning has been shown to be an effective alternative to traditional optimization approaches for highly combinatorial control problems, such as discovering the optimal injection schedules for compression ignition engines. In this study, we deploy a previously presented deep reinforcement learning framework to iteratively optimize a series of engine geometries over a range of increasingly strict NO x emissions regulations. We then examine the resulting injection schedules. We discuss the potential for using this deep reinforcement learning framework for fuel selection screening and for discovering unique injection strategies for different engine geometries and future emissions standards.
33 ADVANCED PROPULSION SYSTEMS↗
Impact of coolant temperature on the combustion characteristics and emissions of a stratified-charge direct-injection spark-ignition engine fueled with E30
The direct injection spark ignition (DISI) engine has received considerable attention due to its potential to increase the power density of traditional spark ignition engines while significantly improving fuel economy through lean, unthrottled combustion. However, the market introduction of DISI engines operated in a lean combustion mode is inhibited by their unsatisfactory emissions, especially during cold start conditions that make proper mixture formation more challenging. Ethanol-blended gasoline, now a widely used fuel, makes the cold start of a DISI engine more difficult, leading to higher HC and soot emissions because of the high latent heat of vaporization of ethanol relative to gasoline. This work investigated the impact of coolant temperature on the characteristics of combustion and emissions in a stratified-charge DISI engine fueled with an E30 fuel (i.e. 30% ethanol in gasoline), while the coolant temperature was alternated between four levels (45, 60, 75, and 90 °C) to simulate different conditions throughout the warm-up process. The experiments showed that the coolant temperature affected the post-spark inflammation time, as well as the speed, intensity, and stability of the combustion process in the engine. When the coolant temperature rose, the engine produced more NOX and less CO, PM and HC. In addition, high-speed direct photography was used to obtain crank-angle resolved images of fuel sprays and flames in the cylinder. As the coolant temperature rose, the liquid spray lengths became shorter, reducing the possibility of wall wetting, and reduced irradiance from soot particles also indicated less nonpremixed combustion. The in-cylinder imaging results are consistent with the observed combustion and emission characteristics and shed light on the underlying processes. Finally, some potential solutions to the emissions challenges faced here could be either raising in-cylinder temperatures by using trapped residuals or modifying the injection schedule, for example by increasing the number of injections or to inject later in the cycle into a higher-density environment.
42 ENGINEERING↗
Deep Learning–Assisted Multiobjective Optimization of Geological CO 2 Storage Performance under Geomechanical Risks
In geological CO 2 storage, designing the optimal well control strategy for CO 2 injection to maximize CO 2 storage while minimizing the associated geomechanical risks is not trivial. This challenge arises due to pressure buildup, CO 2 plume migration, the highly nonlinear nature of geomechanical responses to rock-fluid interaction, and the high computational cost associated with coupled flow and geomechanics simulations. In this paper, we introduce a novel optimization framework to address these challenges. The optimization problem is formulated as follows: maximize total CO 2 storage while minimizing geomechanical risks by adjusting the injection schedules within bounded constraints. The geomechanical risks are primarily driven by injection-induced pressure build-up, which is characterized by ground displacement and the induced microseismicity. We used the Fourier neural operator (FNO)-based deep learning model to construct surrogate models, replacing the time-consuming coupled flow and geomechanics simulations for evaluating the aforementioned objective functions. The developed surrogate models have been incorporated into a multiobjective optimization framework through a genetic algorithm to reduce the computational burden. The proposed optimization framework reduces the computational cost from approximately 2,400 hours, when using objective function evaluations based on physics-based simulations, to around 20 minutes. A set of Pareto-optimal solutions of the proposed workflow yields nontrivial optimal decisions, reducing the microseismicity potential and the vertical displacement. This Pareto front highlights the optimal trade-offs between CO 2 storage amount, safety, and ground displacement, emphasizing the need for careful optimization and management of injection strategies to achieve a balanced outcome. The novelty of this work is twofold. First, we demonstrate the importance of incorporating the minimization of the geomechanical risks as objective functions into the CO 2 storage optimization workflow to mitigate the potential risk of induced microseismicity and ground displacement. Second, we leverage the FNO-based surrogate models to optimize a real-field CO 2 storage operation.
42 ENGINEERING↗
Coupled Thermo–Hydro–Mechanical Modeling of Hydro-Shearing Stimulation in an Enhanced Geothermal System in the Raft River Geothermal Field, USA
The Raft River Enhanced Geothermal System (EGS) demonstration project aims to improve the geothermal production by enhancing the reservoir permeability via shear stimulation in well RRG-9. In this paper, we performed a series of 3D thermo– hydro–mechanical (THM) simulations to investigate the influence of hydraulic and thermal effects on the development of this EGS. The model includes synthetic fracture populations based on borehole televiewer images and in-situ stress measurements from well RRG-9. Fracture permeability evolution is determined using an empirical permeability law developed from laboratory experiments. The model was calibrated by comparing the hydraulic response to field observations, including wellhead pressure, injection rate, and well injectivity. Particularly, we analyzed the enhancement of reservoir permeability and the spatial extent of the stimulation zone for the given injection schedule. Our results indicate that the permeability enhancement of fractured geothermal reservoir is caused by the combined effects of injection-induced cooling and fluid pressure increase. The decrease of temperature plays a dominant role in reactivation of natural fractures under the hydro-shearing mechanism, while the higher injection pressure promotes shear failure and enlarges the stimulation zone. Finally, for the specified extensional stress state, the model favors greatest permeability enhancement along the maximum horizontal principal stress, a moderate vertical enhancement, and a smaller gain along the minimum horizontal principal stress.
58 GEOSCIENCES↗
Modeling supercritical CO2 injection induced rupture of a minor fault embedded in a poroelastic layered reservoir-caprock system
CO2 injection for geologic carbon sequestration involves hydromechanical processes that lead to changes in fluid pressure and stresses that can activate existing faults. This paper presents a new method and workflow of modeling fault activation considering more complex three-dimensional geometry of natural faults using the TOUGH-FLAC multiphase fluid flow and geomechanical simulator. In this method and workflow, FLAC3D mechanical interfaces and TOUGH3 finite volume elements are discretized using computer aided design and gridding software along with a tailored mesh translation routine. The method and workflow are demonstrated with a model of a curved minor fault embedded in a poro-elastic layered reservoir-caprock system. The model is used for a comprehensive sensitivity analysis of fault responses to fault length, injection mass rate, injection schedule, well-fault distance, and well locations versus fault location. Four metrics (CO2 plume, shear state of fault, pressure and stress path at fault monitoring points) are selected to assess CO2 migration, pressure change, and the reactivation of faults. The results reveal that CO2 can bypass around the tip of the minor impermeable fault, building up pressure and poro-elastic stress on both sides that tends to impede fault rupture. Our study shows the benefit of carefully designing the injection to achieve the targeted final storage volume, starting at a relatively low rate for considerable time, and then ramping up the injection rate to the full rate of injection. The initial low injection has two distinct benefits: (1) it allows for the formation of an extensive CO2 plume with a much higher mobility through a low viscosity that will result in a lower pressure for a given injection rate, and (2) it allows for gradual build-up of horizontal poro-elastic stress within the reservoir that will tend to impede activation of steeply dipping faults. The injection scenario starting at a low injection rate, denoted here as conservative injection, can significantly reduce the risk of fault activation as high fluid mobility and reservoir strengthening poro-elastic stress has been established long before reaching the peak injection rates. Moreover, simultaneous injection in two injection wells on both sides of fault can provide further reservoir strengthening through poro-elastic stress buildup acting on a fault under normal faulting stress regime. The findings presented in the paper can provide practical and effective guidance on long-term, safe, and reliable geological CO2 storage.
Cao, Meng↗
Fracture Network Prediction Using Physics-based Machine Learning Algorithms
In recent years, systematic CO2 injection into geological reservoirs across the U.S. has gained traction as a strategy to mitigate greenhouse gas emissions. This approach necessitates precise monitoring to ensure secure containment, minimize risks, and optimize storage management. Our study leverages machine learning (ML) techniques to advance the understanding of CO2 injection processes, focusing on the Illinois Basin. Over a three-year injection period, we analyzed microseismic data, identifying 19 temporal intervals with significant bottom-hole pressure changes. By partitioning microseismic events into these intervals and estimating b-values, we revealed over 100 clusters of events related to fracture initiation or reactivation. Advanced spatial analysis highlighted horizontally-oriented fractures along the NNW-SSE axis. This quantification of fracture networks informs dynamic injection scheduling, work-over strategies, and risk assessments, enhancing carbon capture, utilization, and storage (CCUS) operations. Additionally, our methodology offers valuable insights for oil and gas operations and geothermal development, supporting fracture-based monitoring and risk mitigation.
Kumar, Abhash↗
Computational Analysis of the Combustion Processes in an Axisymmetric, RBCC Flowpath
Computational fluid dynamic simulations have been used to study the combustion processes within an axisymmetric, RBCC flowpath. Two distinct operating modes have been analyzed to date, including the independent ramjet stream (IRS) cycle and the supersonic combustion ramjet (scramjet) cycle. The IRS cycle investigation examined the influence of fuel-air ratio, fuel distribution, and rocket chamber pressure upon the combustion physics and thermal choke characteristics. Results indicate that adjustment of the amount and radial distribution of fuel can control the thermal choke point The secondary mass flow rate was very sensitive to the fuel-air ratio and the rocket chamber pressure. The scramjet investigation examined the influence of fuel-air ratio and fuel injection schedule upon combustion performance estimates An analysis of the mesh-dependence of these calculations was presented. Jet penetration data was extracted from the three-dimensional simulations and compared favorably with experimental correlations of similar flows. Results indicate that combustion efficiency was very sensitive to the fuel schedule.
Steffen, Christopher J., Jr.↗
Computational Analysis of the Combustion Processes in an Axisymmetric, RBCC Flowpath
Computational fluid dynamic simulations have been used to study the combustion processes within an axisymmetric, RBCC flowpath. Two distinct operating modes have been analyzed to date, including the independent ramjet stream (IRS) cycle and the supersonic combustion ramjet (scramJet) cycle. The IRS cycle investigation examined the influence of fuel-air ratio, fuel distribution, and rocket chamber pressure upon the combustion physics and thermal choke characteristics. Results indicate that adjustment of the amount and radial distribution of fuel can control the thermal choke point. The secondary massflow rate was very sensitive to the fuel-air ratio and the rocket chamber pressure. The scramjet investigation examined the influence of fuel-air ratio and fuel injection schedule upon combustion performance estimates. An analysis of the mesh-dependence of these calculations was presented. Jet penetration data was extracted from the three-dimensional simulations and compared favorably with experimental correlations of similar flows. Results indicate that combustion efficiency was very sensitive to the fuel schedule.
Steffen, Christopher J., Jr.↗
Accelerated CO2 Storage Optimization Using Multi-Resolution Fourier Neural Operator at the Illinois Basin Decatur Project (IBDP)
This paper presents a deep learning-based approach for optimizing CO2 injection in carbon capture and storage (CCS) operations. We developed a multi-resolution machine learning model to significantly reduce data generation costs. Utilizing this proxy model, we implemented a multi-objective genetic algorithm to optimize well control during the CO2 injection process. The proposed approach was applied to the Illinois Basin Decatur Project (IBDP), successfully optimizing the CO2 injection schedule based on three key objectives: maximizing the amount of CO2 stored, maximizing sweep efficiency, and minimizing pressure increase. The use of the proxy model accelerated the optimization workflow by two orders of magnitude, while the cost of data generation for the proxy model was reduced by 90% by utilizing a coarse-scale model.
accelerated CO2 storage optimization↗
Effect of water injection and off scheduling of variable inlet guide vanes, gas generator speed and power turbine nozzle angle on the performance of an automotive gas turbine engine
The Chrysler/ERDA baseline automotive gas turbine engine was used to experimentally determine the power augmentation and emissions reductions achieved by the effect of variable compressor and power engine geometry, water injection downstream of the compressor, and increases in gas generator speed. Results were dependent on the mode of variable geometry utilization. Over 20 percent increase in power was accompanied by over 5 percent reduction in SFC. A fuel economy improvement of at least 6 percent was estimated for a vehicle with a 75 kW (100 hp) engine which could be augmented to 89 kW (120 hp) relative to an 89 Kw (120 hp) unaugmented engine.
Warren, E. L.↗
Storage Field Development Plan: One Earth Energy
This Storage Field Development plan presents the Storage Complex characterization results, construction, monitoring, and operational plans, and costs associated with the proposed One Earth Sequestration Carbon Capture and Storage (OES-CCS) site in McLean County, Illinois, near Gibson City. The proposed storage complex, known as the Mt. Simon Storage Complex, comprises the Cambrian Mt. Simon Sandstone reservoir and the primary seal, the Cambrian Eau Claire Formation. The lowermost Underground Source of Drinking Water (USDW) identified for the site is the Ordovician St. Peter Sandstone. Geologic characterization of the Mt. Simon Storage Complex at the OES-CCS site was performed by the Illinois Storage Corridor CarbonSAFE Phase III project, which also prepared and submitted three UIC Class VI applications to construct three injection wells; the permit applications were submitted and are in the federal EPA review process. A characterization well, OEE #1, was drilled to collect site-specific data. These data were analyzed and used to develop the UIC Class VI applications. The OEE #1 well will be converted to an in-zone monitoring (IZM) well for the injection phase. The proposed buildout for the OES-CCS site includes (1) three injection wells (OES #1, OES #2, and OES #3), (2) two IZM wells, (3) two above confining zone (ACZ) monitoring wells, one of which will be used to monitor the lowermost USDW, (4) capture and compression facilities, and (5) transportation facilities, i.e., pipelines. A pre-operational testing program was proposed in the Class VI permit application and will be employed at the site pending approval. Additional pre-injection (baseline), syn-injection, and post-injection monitoring and site care procedures will be followed by OES to ensure that injection activities are protective of human health and the environment. Injection is scheduled to begin in 2025, distributed across the three injection wells in accordance with the permit operating conditions. One Earth Sequestration intends to inject up to 90 million tonnes of CO 2 over a period of approximately 20 years. Injection will begin at approximately 0.5 million tonnes of CO 2 annually and ramp up to a maximum of 4.5 million tonnes annually. Daily injection rates are expected to range from 1,400 to 1,500 tonnes per day initially and reach a maximum of approximately 4,225 tonnes per day, depending on site geology and injectivity at each injection well location, and CO 2 availability. The costs associated with the OES-CCS project include pre-operational costs (e. g. additional seismic data acquisition and well drilling), capture and transportation facility and equipment costs, predicted field operating expenditures (OpEx), and decommissioning and post-injection site care (PISC) costs. The risks associated with project activities, such as site construction, injection operations, and verification of secure storage were evaluated, and mitigation strategies proposed to alleviate those risks.
09 BIOMASS FUELS↗
Deep reinforcement learning for dynamic control of fuel injection timing in multi-pulse compression ignition engines
Conventional compression-ignition (CI) engines have long offered high thermal efficiencies and torque across a wide range of loads, but often require extensive exhaust gas treatment that decreases efficiency to meet ever-increasing emissions regulations. One strategy to decrease emissions is to split the fuel injection into a series of smaller injections. In this paper, we explore a new way of discovering optimal control strategies for the next generation of CI engines using deep reinforcement learning (DRL). We outline a DRL procedure to maximize the weighted reward of engine work while minimizing end-of-cycle NO x emissions. Through the procedure outlined in this paper, we show that the DRL agent is able to reduce NO x emissions threefold while only decreasing network by 2%. We demonstrate the use of transfer learning (TL) across hierarchies of physical models to accelerate the learning process, making this approach feasible for a range of control problems within this space. This paper presents a framework and demonstration for using DRL to design control systems in technology areas such as multi-pulse engine control where a hierarchy of models combined with multi-objective rewards are used for optimal operation.
33 ADVANCED PROPULSION SYSTEMS↗
Impact of Air Injection on Jet Noise
The objective of this viewgraph presentation is to review the program to determine impact of core fluidic chevrons on noise produced by dual stream jets (i.e., broadband shock noise - supersonic, and mixing noise - subsonic and supersonic). The presentation reviews the sources of jet noise. It shows designs of Generation II Fluidic Chevrons. The injection impacts shock structure and stream disturbances through enhanced mixing. This may impact constructive interference between acoustic sources. The high fan pressures may inhibit mixing produced by core injectors. A fan stream injection may be required for better noise reduction. In future the modification of Gen II nozzles to allow for some azimuthal control: will allow for higher mass flow rates and will allow for shallower injection angles A Flow field study is scheduled for spring, 2008 The conclusions are that injection can reduce well-defined shock noise and injection reduces mixing noise near peak jet noise angle
Henderson, Brenda↗
Cyclic Injection Leads to Larger and More Frequent Induced Earthquakes under Volume-Controlled Conditions
As carbon storage technologies advance globally, methods to understand and mitigate induced earthquakes become increasingly important. Although the physical processes that relate increased subsurface pore pressure changes to induced earthquakes have long been known, reliable methods to forecast and control induced seismic sequences remain elusive. Suggested reservoir engineering scenarios for mitigating induced earthquakes typically involve modulation of the injection rate. Some operators have implemented periodic shutdowns (i.e., effective cycling of injection rates) to allow reservoir pressures to equilibrate (e.g., Paradox Valley) or shut-in wells after the occurrence of an event of concern (e.g., Basel, Switzerland). Other proposed scenarios include altering injection rates, actively managing pressures through coproduction of fluids, and preinjection brine extraction. Here, in this work, we use 3D physics-based earthquake simulations to understand the effects of different injection scenarios on induced earthquake rates, maximum event magnitudes, and postinjection seismicity. For comparability, the modeled injection considers the same cumulative volume over the project’s operational life but varies the schedule and rates of fluid injected. Simulation results show that cyclic injection leads to more frequent and larger events than constant injection. Furthermore, with intermittent injection scenario, a significant number of events are shown to occur during pauses in injection, and the seismicity rate remains elevated for longer into the postinjection phase compared to the constant injection scenario.
58 GEOSCIENCES↗
Specific immunization of mice against Leishmania mexicana amazonensis using solubilized promastigotes
In this work, it was demonstrated that mice (BALB/c strain) highly susceptible to Leishmania mexicana amazonensis can be protected against infection by this parasite by being preimmunized with whole solubilized (in a buffer that contained EDTA, NP-40, and SDS) promastigotes; the use of adjuvant or intact inactivated parasite cells is shown to be not necessary. The best immunization schedule consisted of three intravenous injections of 5 x 10 to the 7th parasite equivalents, administered one to eight weeks before infection. Immunized mice exhibited a marked inhibition of primary lesion development, reduced numbers of parasites in the spleen, and reduced death rate.
Barral-Netto, M.↗
Base Support Building Solid Waste Management Unit (SWMU) 014 PlumeStop Injection Pilot Study Work Plan
This document is a Pilot Study Work Plan (PSWP) for a PlumeStop® permeable reactive barrier (PRB) at the Base Support Building (BSB; the Site) located at the John F. Kennedy Space Center (KSC), Florida. The Site has been designated as Solid Waste Management Unit (SWMU) 014 under KSC’s Resource Conservation and Recovery Act (RCRA) Corrective Action program. This document was prepared by AECOM Technical Services, Inc. (AECOM) for the National Aeronautics and Space Administration (NASA) under Indefinite Delivery Indefinite Quantity Contract 80KSC019D0010, Task Order 80KSC021F0096. This PSWP details the permeable reactive barrier (PRB) being installed at the BSB to decrease concentrations of per- and polyfluoroalkyl substances (PFAS) in downgradient groundwater using PlumeStop®. PlumeStop® is a colloidal activated carbon that PFAS compounds readily adsorb to. This PSWP outlines the design criteria, injection plan, and performance monitoring schedule for the proposed PlumeStop® PRB study that will guide the possible selection for use and future design of future full-scale PlumeStop® PRBs at KSC.
PFAS↗
Influence of sequential stimulation practices on geochemical alteration of shale
Water-based hydraulic fracturing fluids (HFFs) can chemically interact with formation shale, resulting in altered porosity and permeability of the host rock. Experimental investigations of spatial and temporal shale-HFF interactions are helpful in interpreting chemical compositions of the injectate, as well as predicting alteration of hydraulic properties in the reservoir due to mineral dissolution and precipitation. Most bench-top experiments designed to study shale-HFF chemical interactions, either using batch reactors or flow-through setups, are carried out assuming that the acid spearhead has already become mixed with neutral HFFs. During operations, however, HFFs are typically injected according to a sequenced pumping schedule, starting with a concentrated acid spearhead, followed by multiple additions of near-neutral pH HFFs containing chemical amendments and proppant. In this study, we use geochemical modeling to consider whether this pre-mixed experimental protocol provides results directly comparable to a sequential discrete fluid-shale interaction protocol. Our results show that for the batch system, the transient evolution in major ion concentrations is faster with the sequential procedure. After 2 h of reaction time, the two protocols converge to the same aqueous concentrations. In a flow-through geometry, the pre-mixed model predicts extensive chemical alteration close to the injection point but negligible alteration downstream. In contrast, the sequential model predicts mineral reactions over hundreds of meters along the flow path. The extent of shale alteration in the sequential model at a given location depends on shale mineralogy and where the acid spearhead resides during the shut-in period. The predictive model developed in this study can help experimentalists to design bench-top tests and operators to better translate the results of laboratory experiments into practical applications.
Li, Qingyun↗