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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

DEEPEN: Final 3D PFA Favorability Models and 2D Favorability Maps at Newberry Volcano

Part of the DEEPEN (DE-risking Exploration of geothermal Plays in magmatic ENvironments) project involved developing and testing a methodology for a 3D play fairway analysis (PFA) for multiple play types (conventional hydrothermal, superhot EGS, and supercritical). This was tested using new and existing geoscientific exploration datasets at Newberry Volcano. This GDR submission includes images, data, and models related to the 3D favorability and uncertainty models and the 2D favorability and uncertainty maps. The DEEPEN PFA Methodology, detailed in the journal article below, is based on the method proposed by Poux & O'brien (2020), which uses the Leapfrog Geothermal software with the Edge extension to conduct PFA in 3D. This method uses all available data to build a 3D geodata model which can be broken down into smaller blocks and analyzed with advanced geostatistical methods. Each data set is imported into a 3D model in Leapfrog and divided into smaller blocks. Conditional queries can then be used to assign each block an index value which conditionally ranks each block's favorability, from 0-5 with 5 being most favorable, for each model (e.g., lithologic, seismic, magnetic, structural). The values between 0-5 assigned to each block are referred to as index values. The final step of the process is to combine all the index models to create a favorability index. This involves multiplying each index model by a given weight and then summing the resulting values. The DEEPEN PFA Methodology follows this approach, but split up by the specific geologic components of each play type. These components are defined as follows for each magmatic play type: 1. Conventional hydrothermal plays in magmatic environments: Heat, fluid, and permeability 2. Superhot EGS plays: Heat, thermal insulation, and producibility (the ability to create and sustain fractures suitable for and EGS reservoir) 3. Supercritical plays: Heat, supercritical fluid, pressure seal, and producibility (the proper permeability and pressure conditions to allow production of supercritical fluid) More information on these components and their development can be found in Kolker et al., (2022). For the purposes of subsurface imaging, it is easier to detect a permeable fluid-filled reservoir than it is to detect separate fluid and permeability components. Therefore, in this analysis, we combine fluid and permeability for conventional hydrothermal plays, and supercritical fluid and producibility for supercritical plays. We also project the 3D favorability volumes onto 2D surfaces for simplified joint interpretation, and we incorporate an uncertainty component. Uncertainty was modeled using the best approach for the dataset in question, for the datasets where we had enough information to do so. Identifying which subsurface parameters are the least resolved can help qualify current PFA results and focus future efforts in data collection. Where possible, the resulting uncertainty models/indices were weighted using the same weights applied to the respective datasets, and summed, following the PFA methodology above, but for uncertainty.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Using onset times from frequent seismic surveys to understand fluid flow at the Peace River Field, Canada

With our limited knowledge of the relationship between changes in the state of an aquifer or reservoir and the corresponding changes in the elastic moduli, that is the rock physics model, hampers the effective use of time-lapse seismic observations for estimating flow properties within the Earth. A central problem is the complicated dependence of the magnitude of time-lapse changes on the saturation, pressure, and temperature changes within an aquifer or reservoir. We describe an inversion methodology for reservoir characterization that uses onset times, the calendar time of the change in seismic attributes, rather than the magnitude of the changes. We find that onset times are much less sensitive than magnitudes to the rock physics model used to relate time-lapse observations to changes in saturation, temperature and fluid pressure. We apply the inversion scheme to observations from daily monitoring of enhanced oil recovery at the Peace River field in Canada. An array of 1492 buried hydrophones record seismic signals from 49 buried sources. Time-shifts for elastic waves traversing the reservoir are extracted from the daily time-lapse cubes. In our analysis 175 images of time-shifts are transformed into a single map of onset times, leading to a substantial reduction in the volume of data. These observations are used in conjunction with bottom hole pressure data to infer the initial conditions prior to the injection, and to update the reservoir permeability model. The combination of a global and local inversion scheme produces a collection of reservoir models that are best described by three clusters. The updated model leads to a nearly 70 percent reduction in seismic data misfit. The final set of solutions successfully predict the observed normalized pressure history during the soak and flow-back into the wells between 82 and 175 days into the cyclic steaming operation.

58 GEOSCIENCES↗
DOE OSTI2022

Experiments and Modeling of Proppant Embedment and Fracture Conductivity for the Caney Shale, Oklahoma, USA

ABSTRACT: The ultimate aim of hydraulic fracturing is to have a long and conductive flow path that extends from the wellbore into the formation. The effective fracture length is part of a hydraulically propped fracture which contributes to production. The difficulty in achieving economical production targets from shale reservoirs is at the forefront in many exploration companies. Fracture conductivity loss is related to; proppant embedment under depletion, proppant crushing, damage as a result of fracturing fluid, fines migration and proppant-pack permeability-damage are some of the factors that contribute to production decline after hydraulic fracturing in shale reservoirs. The Caney Shale is a calcareous organic-rich mudrock. Various studies have investigated the effect that clay on shale well productivity, however, there is currently no literature on the Caney shale in relation to horizontal wells; all available literature exists in vertical wells as well as on formations of the Caney that are shallow in comparison to an emerging play which is twice the depth. In this paper we investigate stress-dependent fracture conductivity of proppant-filled fractures and proppant embedment in Caney shale through laboratory and modeling studies. API fracture conductivity tests were conducted using 2% KCl on five locations within the Caney shale that consisted of selecting three brittle(reservoir) zones and two ductile zones. Confining pressures range from 1,000 psi to 12,000 psi at 210°F. Conductivity, permeability as well as embedment were measured during the test. Our experimental results have confirmed that improved fracture conductivity is attributed to; proppant size, the increase in porosity of the proppant pack, closure pressure changes and the reduction in fracture conductivity are a function of many factors such as fracture closure stress. The findings from this study could help the stimulation design by providing new insights into the critical factors that are to be determined to facilitate the choice of proppants as well as fracturing fluids for long term production and recovery from shale reservoirs. 1 INTRODUCTION The development of low permeability formations, like shales, has been aided by hydraulic fracturing of horizontal wells (Radonjic et al., 2020). Hydraulic fracturing fluid is injected at a high pressure to induce tensile fractures that can link to and stimulate natural fractures (Katende et al., 2021a,b). Preserving adequate conductivity in hydraulic fractures over the life of the wells is required for economic production; nevertheless, conserving such conductivity can be difficult in some circumstances, particularly in soft, clay-rich formations (Wang et al., 2021). Proppant particles help to keep the fractures open when the pumping stops and the fracturing fluid returns to the wellbore, producing one or more propped hydraulic fractures of varying length, breadth, and height (Katende et al., 2021a). The proppant pack within the hydraulic fracture boosts well output by providing a greater permeability flowpath for hydrocarbons (Duenckel et al., 2016). Proppant in the fracture is under complicated stress conditions, and the interplay between the rock formation and the proppant pack has a significant impact on proppant-pack permeability (Karazincir et al., 2019). Proppant may be embedded (Katende et al., 2021a) in the rock or crushed into small pieces if the proppant size and strength characteristics are not specified appropriately, resulting in a loss in proppant-pack permeability and fracture aperture, and consequently a fall in well output.

Katende, A.↗
DOE OSTI2023

Measured CO 2 sorption isotherms with 25 Bakken Petroleum System rock samples from the Lower and Upper Shales, Middle Bakken, and Three Forks formations

Isotherms were measured with 25 Bakken Petroleum System (BPS) rock samples using a magnetic suspension balance at reservoir conditions of 110 °C and pressures up to 345 bar. Samples from four wells represented the Middle Bakken (MB) and Three Forks (TF) production zones, and the Upper Bakken Shale (UBS) and Lower Bakken Shale (LBS) source shales. UBS and LBS CO 2 sorption capacities at 345 bar averaged 12–14 g/kg rock compared to 1.3 to 2.5 g/kg rock for MB and TF. Increasing pressure from 150 to 345 bar resulted in only small increases in CO 2 sorption for all lithofacies. For the UBS and LBS source shales, the higher average total organic carbon (TOC) values (14.7 and 11.4 wt.%) and clay values (25 and 28 wt.%) corresponded to their higher CO 2 sorption values while for the MB and TF samples, lower TOCs (0.3 and 0.5 wt.%) and clay values (8.2 and 18.2 wt.%) corresponded to lower CO 2 sorption values. Here, when all 25 samples (from all lithofacies) were evaluated as a group, sorption capacities were highly correlated with TOC (Spearman's rho values +0.84 to +0.85), with rock density (rho values -0.75 to -0.76), and clay content (rho values +0.66). However, correlations with porosity and thermal maturity (Tmax) were weak. Measured sorption capacities and rock porosities showed that sorption accounted for about 10-times as much CO 2 storage as the available pore space for the UBS and LBS lithofacies. In contrast, the pore space dominated the storage resource of the TF and MB lithofacies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Rheology and stability of nanoparticle-stabilized CO 2 foam under reservoir conditions

This work investigates the rheology and stability of nanoparticle-stabilized CO 2 foams under reservoir conditions (high temperature and high pressure) for fracturing applications. The effects of different parameters on foam apparent viscosity and foam stability were experimentally investigated including the effects of nanoparticle concentration, salinity, foam quality (Γ), shear rate and temperature. The power law model was applied to calculate foam apparent viscosity owing to its pseudo-plastic behavior, and the changes of foam heights over time were used to evaluate foam stability. Results showed that the CO 2 foam apparent viscosity featured a mountain-shaped curve versus Γ, with the peak apparent viscosity obtained at 70% foam quality. The increase of salinity (up to 11%) in the continuous phase improved both foam stability and rheology. Higher nanoparticle concentration could contribute to better foam stability, but there was a threshold concentration, above which the foam apparent viscosity remained stabilized. The CO 2 foam stabilized by nanoparticles displayed a shear-thickening behavior as the experiment flow rate increased from 6 mL/min to 18 mL/min. Further studies showed that as the total flow rate increased, the CO 2 foam became finer-textured with better stability. Elevated temperatures could undermine foam apparent viscosity and long-term stability. The results of this study could provide guidelines as to the design of foam fracturing systems for field applications.

03 NATURAL GAS↗
DOE OSTI2020

Utah FORGE: Data for 3-D Model Development - Lithology, Temperature, Pressure, and Stress

This submission comprises two downloadable .zip archives. Each archive contains spreadsheets with 3-D data and a .txt document describing the data. The Mesh Files .zip download contains data regarding the lithologic contacts of the granitoid and overlying sedimentary basin fill. The Initial Conditions .zip download contains data regarding temperature, pressure, and stress. These data were generated during Utah FORGE Phase 2 and exported from Seequent Leapfrog Geothermal. They are intended for use in 3-D earth modeling.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Fracture Description of the HFTS-2 Slant Core, Delaware Basin, West Texas

Previous work in the Midland Basin Hydraulic Fracture Test Site project (HFTS1) has shown the value of core recovered from a stimulated volume in providing information on the geometry and extent of hydraulic fractures. This project (HFTS2) is in the Wolfcamp Formation of the Delaware Basin. A slant core through the stimulated volume was acquired above and below a stimulated well and the results of the fracture description are presented here. Here, nine, 3-inch-diameter whole cores from the slant well were examined for fractures prior to slabbing, together with a CT scan of the core. We used criteria developed during the HFTS1 project to identify hydraulic, natural, drilling-induced and core-handling fractures. Material collected from a few fracture faces was cleaned, sieved, and examined under a microscope to look for proppant. A total of 1261 fractures, including 500 hydraulic fractures, were described in 948 feet of core. Hydraulic fractures are not evenly distributed through the cores; they tend to occur in clusters. Many are remarkably planar, smooth, and featureless, while others have twist hackles, steps, plumose, and kinks. Natural, subvertical fractures, mostly less than 1 mm in width, sealed with blocky calcite, are oriented NE-SW (Set 1) and WNW-ESE (Set 2). Two Set 1 fractures have drusy cement on parted faces and were possibly open prior to drilling. A few subvertical sealed fractures with different orientations are also present. Bed-parallel natural fractures, most having fibrous cement (beef), and some showing top-to-north bed-parallel shear are present. Faults with normal/oblique movement were found in one part of the core. Drilling induced and core-handling breaks were identified in all cores, and were also noted to have reactivated some natural fractures. The results from this work will allow hydraulic fracture distribution relative to the stimulated well to be assessed. The number of fractures is generally greater than the number of perforations, but these can be compared for different stages and perforation clusters. Surface features provide information about the direction of hydraulic fracture propagation and about segmentation and bifurcation that might increase the number of fracture strands. Reactivation of natural fractures, including bedding-parallel fractures, occurs although the mechanism of parting is mostly unknown. The wider HFTS2 project will make use of this fracture description to improve hydraulic fracture modeling (geomechanics and engineering), to help explain pressure depletion and fiber optics observations (reservoir depletion and frac geometry), to help verify indirect fracture diagnostic techniques such as microseismic monitoring (geophysics), and to help calibrate horizontal well image logs.

58 GEOSCIENCES↗
DOE OSTI2020

Equation of State of TiN at High Pressures and Temperatures: A Possible Host for Nitrogen in Planetary Mantles

Nitrogen, the most abundant element in Earth's atmosphere, is also a primary component of solid nitride minerals found in meteorites and on Earth's surface. If they remain stable to high pressures and temperatures, these nitrides may also be important reservoirs of nitrogen in planetary interiors. We used synchrotron X-ray diffraction to measure the thermal equation of state and phase stability of titanium nitride (TiN) in a laser-heated diamond anvil cell at pressures up to ~70 GPa and temperatures up to ~2500 K. TiN maintains the cubic B1 (NaCl-type) crystal structure over the entire pressure and temperature range explored. It has K 0 = 274(4) GPa, K 0 ' = 3.9(2), and γ 0 = 1.39(4) for a fixed V 0 = 76.516(30) Å 3 (based on experimental measurements), q = 1, and θ 0 = 579 K. Additionally, we collected Raman spectra of TiN up to ~60 GPa, where we find that the transverse acoustic (TA), longitudinal acoustic (LA), and transverse optical (TO) phonon modes exhibit mode Grüneisen parameters of 1.66(17), 0.54(15), and 0.93(4), respectively. Based on our equation of state, TiN has a density of ~5.6–6.4 g/cm 3 at Earth's lower mantle conditions, significantly more dense than both the mantle of the Earth and the estimated densities of the mantles of other terrestrial planets, but less dense than planetary cores. Furthermore, we find that TiN remains stable against physical decomposition at the pressures and temperatures found within Earth's mantle, making it a plausible reservoir for deep planetary nitrogen if chemical conditions allow its formation.

58 GEOSCIENCES↗
DOE OSTI2022

LANL Activities on Mechanistic Approach to Analyzing and Improving Unconventional Hydrocarbon Production

Hydrocarbon production from shale reservoirs is inherently inefficient and challenging since these are low permeability plays. In addition, there is a limited understanding of the fundamentals and the controlling mechanisms, further complicating how to optimize these plays. Herein, we summarize our experimental and computational efforts fully and partially supported by the fundamental shale portfolio to reveal unconventional shale fundamentals and devise development strategies to enhance extraction efficiency with a minimal environmental footprint. Integrating these fundamentals with machine learning, we outline a pathway to improve the predictive power of our models, which enhances the forecast quality of production, thereby improving the economics of operations in unconventional reservoirs. For instance, we have developed science informed workflows and platforms for optimizing pressure-drawdown at a site, which allow operators to make reservoir-management decisions that optimize recovery in consideration of future production. Recently, our work relies on the hybridization of physics-based prediction and machine learning, whereby accurate synthetic data (combined with available site data) can enable the application of machine learning methods for rapid forecasting and optimization. Consequently, the workflow and platform are readily extendable to operations at other sites, plays, and basins.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2025

CO2 Plume Imaging with Accelerated Deep Learning-based Data Assimilation Considering Multiple Realizations: Application to the Illinois Basin-Decatur Carbon Sequestration Project

We propose a fast and efficient deep learning workflow for near real-time data assimilation, forecasting and visualization of CO2 plume evolution in saline aquifer and demonstrate its application at a field site. Unlike the previous work, this study incorporates the impact of spatial heterogeneity using multiple realizations. In the proposed workflow, a neural network model utilizes available monitoring data such as downhole pressure measurements as input and predicts the propagating pressure ‘front’ using the diffusive time of flight (DTOF) map which is considered as representative reservoir image of the flow field. The DTOF is the arrival time of pressure front propagation, which can be computed by the Fast Marching Method rapidly without flow simulations. Reservoir model calibration can be implemented by selecting the training data samples that describe the predicted DTOF map based on observed data. The power and efficacy of our workflow is demonstrated by application to the Illinois Basin-Decatur Project.

CO2 plume imaging↗
DOE OSTI2022

Modeling Subsurface Performance of a Geothermal Reservoir Using Machine Learning

Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells—increasing or decreasing the fluid flow rates across the wells—and drilling new wells at appropriate locations. The latter is expensive, time-consuming, and subject to many engineering constraints, but the former is a viable mechanism for periodic adjustment of the available fluid allocations. In this study, we describe a new approach combining reservoir modeling and machine learning to produce models that enable such a strategy. Our computational approach allows us, first, to translate sets of potential flow rates for the active wells into reservoir-wide estimates of produced energy, and second, to find optimal flow allocations among the studied sets. In our computational experiments, we utilize collections of simulations for a specific reservoir (which capture subsurface characterization and realize history matching) along with machine learning models that predict temperature and pressure timeseries for production wells. We evaluate this approach using an “open-source” reservoir we have constructed that captures many of the characteristics of Brady Hot Springs, a commercially operational geothermal field in Nevada, USA. Selected results from a reservoir model of Brady Hot Springs itself are presented to show successful application to an existing system. In both cases, energy predictions prove to be highly accurate: all observed prediction errors do not exceed 3.68% for temperatures and 4.75% for pressures. In a cumulative energy estimation, we observe prediction errors that are less than 4.04%. A typical reservoir simulation for Brady Hot Springs completes in approximately 4 h, whereas our machine learning models yield accurate 20-year predictions for temperatures, pressures, and produced energy in 0.9 s. This paper aims to demonstrate how the models and techniques from our study can be applied to achieve rapid exploration of controlled parameters and optimization of other geothermal reservoirs.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

LLNL Kimberlina 1.2 NUFT Simulations June 2018 (v2)

This dataset contains the output 6,000, 3-dimensional reactive multi-phase flow and transport aquifer simulations of brine and CO2 leakage into a protective aquiver in California’s San Joaquin Valley and input data files detailing the geologic mesh, aquifer physical properties and CO2 and brine injection rates. This data set was generated as an ongoing effort with the US DOE National Risk Assessment Partnership (NRAP) to evaluate the effectiveness of monitoring techniques to detect brine and CO2 leakage from legacy wells into underground sources of drinking water overlaying a CO2 storage reservoir. Each simulation contains a unique set of input parameters, generated stochastically. The outputs consist of these upper three geologic layers (from top): the Etchegoin, Macoma-Chanac, Santa Margarita-McLure formations. These simulations span the several distances (1, 3 and 6 km or wells W31-0.2, W31-0.5 and W31-1.0, respectively) from the CO2 injector, initiated from bottom hole pressure and saturation to calculate wellbore leakage from the storage reservoir, with low and high regional groundwater gradients and wellbore leakage into 5 leaky nodes. The dataset includes 1,000 unique simulations for each distance, which each contain a unique aquifer heterogeneity, aquifer and caprock permeability, and two model generations are included with a high permeability (prod07) and hybrid permeability (prod09). The range of permeability distributions is listed in Table 1. Each model generation consists of 3,000 simulations. Included in the dataset are the leakage rates determined from 2D wellbore models which utilize the pressure and CO2 saturation from LBL's reservoir simulations, NUFT mesh files with distributed lithology, NUFT rocktab files which describe the material properties for the geologic layers and the NUFT input files and post-processed output 'ntab' files. Each ntab file contains spatial (rows) and temporal (columns) model output tables for each model cell, the locations (x,y,z) and dimensions for each cells (dx, dy, dz). Table 1. Permeability distribution ranges for prod07 and prod09 model generations Geologic Layer: Permeability Range (log10 m^2) prod07 prod09 Etchegoin -12.92 to -10.92 -13.70 to -11.44 Macoma-Chanac -12.72 to -10.72 -13.50 to -11.24 Santa Margarita-McLure -12.70 to -10.70 -13.48 to -11.22 The input files used to generate the model include which are included in the dataset are: Time series of CO2 leakage input into the model (ex: Q_brn.W31-0.2.sim1000.layers123.tab) Time series of CO2 leakage input into the model (ex: Q_CO2.W31-0.2.sim1000.layers123.tab) Physical properties of the aquifer materials detailing the aquifer porosity, solid density, partitioning coefficients, permeabilities and van-Genuchten parameters detailed in a NUFT rocktab file: (ex: sim1000.usnt.rocktab) Numerical mesh and geologic data assigned to each model cell detailed in a NUFT genmsh format (ex: sim1000.mesh_k16.prod07.trans.genmsh) The primary output parameters are: pH (use absolute value) Change in TDS (mg/kg) Change in Pressure (Pa) Change CO2 gas saturation (fraction range 0.0-1.0) for example, the directory /p/lscratchh/mansoor1/nrap/kimberlina/prod09/mainfiles/sim1000/W31- 0.2 contains: sim1000.W31-0.2.trans.pH.red.ntab sim1000.W31-0.2.no_bg.trans.TDS.red.ntab sim1000.W31-0.2.usnt.P.deltabg.red.ntab sim1000.W31-0.2.usnt.CO2_sat.deltabg.red.ntab Each row in the NTAB files consist of model output per numerical grid cell. Each output file contains 33 columns (variables), including the information of numerical records, geologic location and sizes and the simulated parameter values over time. The first 13 variables are about numerical records and relative geologic information for a simulation grid: 1. index: simulation index 2. i: the ith grid of x-axis 3. j: the ith grid of y-axis 4. k: the ith grid of z-axis 5. element_ref: element reference 6. nuft_ind: nuft index 7. x: grid location in the x axis direction 8. y: grid location in the y axis direction 9. z: grid location in the z axis direction 10. dx: grid length in the x axis direction 11. dy: grid length in the y axis direction 12. dz: grid length in the z axis direction 13. volume: volume of the simulation grid The remainder (14, 15, 16...) variables are the simulated parameter values over time, take Pressure as an example, are: 14. 0.0y: initial pressure per cell. 15. 10.0y: simulated pressure at the end of the 10th year. 16. 20.0y: simulated pressure at the end of the 20th year. ... (repeated for every 10 years until 200 years)... The model extends 10,000 m, 5,000 m and 1,411 m in the x,y and z dimensions, respectively. The mesh consists of 164,832 cells with mesh dimensions of 101 x 51 x 32 (nx, ny, nz), with cell dimensions ranging from 100 m laterally (along x and y-axis) and model layers are as designated in the z-axis: Layer 1: atmosphere (1e-30 m thick) Layer 2: upper caprock (10 m thick) Layers 3-13: Etchegoin (536.23 m thck) Layers 14-27: Macoma-Chanac (679.04 m thick) Layers 28-32: Santa Margarita-McLure (185.94 m thick) The wellbore is placed along node i=51, j=26, and extends vertically along 5 nodes from the top to the bottom of the model. Special instructions when extracting files: Each Gzip archive (ex: prod07.sim1000-sim00099.tar.gz) contains 100 simulations. Gzip archives should be transferred into base directories (ie. In Linux: mkdir prod07; mv prod07.*.tar.gz prod07/.) before extracting, or files will be overwritten. Each sub-simulation tree should have the following file structure pattern (using the linux 'tree' command): |-- prod07 | |-- sim0001 | |-- W31-0.2 | | |-- Q_brn.W31-0.2.sim0001.layers123.tab | | |-- Q_co2.W31-0.2.sim0001.layers123.tab | | |-- sim0001.W31-0.2.no_bg.trans.TDS.red.ntab | | |-- sim0001.W31-0.2.trans.pH.red.ntab | | |-- sim0001.W31-0.2.usnt.CO2_sat.deltabg.red.ntab | | |-- sim0001.W31-0.2.usnt.P.deltabg.red.ntab | |-- W31-0.5 | | |-- Q_brn.W31-0.5.sim0001.layers123.tab | | |-- Q_co2.W31-0.5.sim0001.layers123.tab | | |-- sim0001.W31-0.5.no_bg.trans.TDS.red.ntab | | |-- sim0001.W31-0.5.trans.pH.red.ntab | | |-- sim0001.W31-0.5.usnt.CO2_sat.deltabg.red.ntab | | |-- sim0001.W31-0.5.usnt.P.deltabg.red.ntab | |-- W31-1.0 | | |-- Q_brn.W31-1.0.sim0001.layers123.tab | | |-- Q_co2.W31-1.0.sim0001.layers123.tab | | |-- sim0001.W31-1.0.no_bg.trans.TDS.red.ntab | | |-- sim0001.W31-1.0.trans.pH.red.ntab | | |-- sim0001.W31-1.0.usnt.CO2_sat.deltabg.red.ntab | | |-- sim0001.W31-1.0.usnt.P.deltabg.red.ntab | |-- sim0001.mesh_k16.prod07.trans.genmsh Disclaimer This document was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor Lawrence Livermore National Security, LLC, nor any of their employees makes any warranty, expressed or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or Lawrence Livermore National Security, LLC. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States government or Lawrence Livermore National Security, LLC, and shall not be used for advertising or product endorsement purposes. Lawrence Livermore National Laboratory is operated by Lawrence Livermore National Security, LLC, for the U.S. Department of Energy, National Nuclear Security Administration under Contract DE-AC52-07NA27344. This report was reviewed and released as LLNL-MI-753464.

aquifer↗
DOE OSTI2021

NRAP-Open-IAM: FutureGen2 Component Models

This report describes the development and testing of three component models for NRAP-Open-IAM, the National Risk Assessment Partnership’s open-source integrated assessment model. The FutureGen2 Lookup Table Reservoir component model is based on interpolation of data from a set of lookup tables. The lookup tables contain pressures and saturations predicted by multiphase flow simulations performed with a reservoir simulator. The FutureGen2 Above Zone Monitoring Interval (AZMI) component is a surrogate model that can be used to estimate the impact that carbon dioxide (CO 2 ) and brine leaks from the CO 2 storage reservoir at the FutureGen 2.0 site might have had on overlying aquifers or monitoring units were a leak to occur. The model estimates the size of “impact plumes” according to five metrics: pH, Total Dissolved Solids (TDS), pressure, dissolved CO 2 and temperature. The FutureGen2 Aquifer component is similar, but is limited to four metrics: pH, Total Dissolved Solids (TDS), pressure, and dissolved CO 2 . The input parameters for each model are the same, but the Aquifer component is applicable to depths between 100 m and 700 m and the AZMI component is applicable from depths between 700 m and 1050 m.

42 ENGINEERING↗
DOE OSTI2026

Energy to Communities Program Short Introduction: Evaluation of Enhanced Geothermal System for Cornell District Heating [Slides]

Through the U.S. Department of Energy's Energy to Communities (E2C) program, NLR, other national laboratory experts, and select organizations provide Expert Match - free, short-term technical assistance to address near-term energy challenges and questions. Expert Match is for community stakeholders who have decision-making power or influence in their community but need access to additional energy expertise to inform key upcoming decisions. This Expert Match request supported Cornell University (Itaca, NY) community efforts in developing Enhanced Geothermal System (EGS) reservoir performance, by simulating fracture flow to estimate pressure drop, flow distribution, and heat extraction efficiency in fractured rock. Thermal drawdown modeling was used to assess long-term resource sustainability, while system optimization studies refined key parameters such as well configuration and flow rate to improve overall reservoir efficiency.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Changes in rock matrix compressibility during deep CO 2 storage

Geologic carbon storage projects aim to permanently trap large volumes of CO 2 in reservoir rock sealed with low permeability layers. As high-pressure supercritical or liquid CO 2 is injected, hydromechanical and chemical processes caused by the reaction between the rock and acidic mixture of brine and CO 2 are initiated. The compressibility of reservoir rock needs to be properly characterized in order to assess the deformation and stability of the host formations, and there are a number of factors to be considered, including the matrix structure, solid, pores, and fluid. Here this study triggers from a fundamental question whether CO 2 treatment affects the compressibility of the rock matrix and its dominant composing solid minerals. Three different reservoir representatives are selected: Berea sandstone for silica-rich rock, and Apulian limestone and Indiana limestone for calcite-rich rock. Quartz and calcite are the main composing minerals of the reservoir rock, and their crystals are separately examined. Experimental methods are introduced for high-pressure CO 2 treatment of water-saturated materials, and measurements of the unjacketed and solid compressibilities are conducted. No change in the solid compressibility of the sandstone and quartz after CO 2 treatment is observed, whereas it increases by 18–21% for the limestones and by 15% for calcite. The latter observation is inconsistent with the ultrasonic wave velocities measurements and is believed to be due to the local dissolution of the calcite crystal surface. Our results show that only the solid matrix of the limestones becomes more compressible after CO 2 treatment. Consequent microimaging and mercury intrusion porosimetry analyses allowed observations of dissolution and precipitation of calcite, and creation of new connected and non-connected pores. Finally, the changes in limestone solid compressibilities and pore structure could significantly affect the rock properties and behavior during and after CO 2 injection and should be accounted for in the reservoir models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Predicting Geologic Behavior in Carbon Storage Projects Using Graph Neural Network

This study was invited to presented at NVIDIA's GTC conference to highlight the potential of Graph Neural Network as a novel and promising methodology for predicting pressure and saturation evolution in carbon storage projects. Carbon capture and storage (CCS) technology plays a pivotal role in mitigating greenhouse gas emissions, facilitating the transition to a low-carbon future. Effective management of subsurface reservoirs is essential to ensure the safe and efficient storage of captured carbon dioxide (CO₂). Accurate predictions of pressure and saturation over time are critical for evaluating the long-term performance and integrity of CCS projects. In recent years, Graph Neural Network (GNN) has emerged as a powerful framework for analyzing complex data in graph-structured domains. This abstract explores the application of GNN to forecast pressure and saturation evolution in carbon storage projects. Traditional numerical simulations of subsurface reservoirs have proven successful in providing pressure and saturation forecasts. However, these simulations involve massive amounts of computational effort and require extensive domain expertise for proper model calibration and validation. Graph Neural Operator offers an alternative approach that harnesses the inherent graph structure of reservoirs, where nodes represent reservoir grid cells and edges represent the geological connectivity between them.

Shih, Chung Yan↗
DOE OSTI2020

Petrophysical and geomechanical properties of gas hydrate-bearing sediments recovered from Alaska North Slope 2018 Hydrate-01 Stratigraphic Test Well

Knowledge of petrophysical and geomechanical properties of gas hydrate-bearing sediments are essential for predicting reservoir response due to gas production from gas hydrate reservoirs. That information will be critical parameters for designing production well completion such as specification of depressurization pump, water storage tank, and mesh size of sand screen. In December 2018, Stratigraphic Test Well Hydrate-01 was drilled in the western part of the Prudhoe Bay Unit, Alaska North Slope as part of the technical planning effort for a future long-term production test by collaborative team of DOE/NETL, USGS, and MH21-S (Boswell et al., 2020, Collett et al., 2020, Okinaka et al., 2020). Data set of logging-while-drilling (LWD) were acquired (Haines et al., 2020, Suzuki et al., 2019) and core sampling depth was determined on-site.Side-wall pressure coring was conducted to recover gas hydrate-bearing sediments from two reservoir sections named Unit-B and Unit-D. A total of 34 cores were successfully recovered by 5 runs of a wire-line deployed pressure corer (CoreVault® System - Halliburton). Core analysis scheme of this project are shown in Figure 1. All cores were quenched in liquid nitrogen while at high pressure in the laboratory of Stratum Reservoir, LLC. at Anchorage (Figure 1, a)). And the cores were removed from the pressure corer autoclave with temperature support of dry ice and stored under liquid nitrogen at atmospheric pressure. 19 damaged cores were processed to index property measurements directly such as grain size, grain density. 4 of another 17 cores were depressurized and trimmed for making a plug to analyze petrophysical properties of host sediments. Unsteady-state permeability test was conducted to obtain relative water permeability to gas and core scale NMR T2 distribution measurement was performed for evaluating pore size distribution at Houston (Figure 1, b)). Remained high quality 13 cores were preserved with gas hydrate for advanced laboratory analysis. National Institute of Advanced Industrial Science and Technology, as a part of the Japanese National Hydrate Research Program (MH21-S, funded by Ministry of Economy, Trade and Industry), received the samples at Sapporo, Japan for advanced core analysis. High-resolution X-ray CT was used to analyze the quality of the samples, which showed undisturbed lithological layers. Cores were lathed into cylindrical shape and distributed for multi property measurements (Figure 1, c)).<p>As a result, sediment from Unit-D is categorized as silty sand at ~37% porosity with ~80% gas hydrate saturation. An average hydration number n = 6.16 was measured by Raman spectroscopy. An average intrinsic permeability of ~400 mD and in situ effective permeability (with hydrate) on the order of ~10 mD. The Unit B recovered cores consisted of well sorted sand at ~40% porosity with ~95% gas hydrate saturation. An average intrinsic permeability of ~1 Darcy and in situ effective permeability on the order of ~30 mD was measured for the Unit B cores. There was a small permeability reduction due to porosity loss with increasing effective stress that simulated consolidation behavior along with depressurization in the highly permeable sandy sediment. The apparent minimum change in porosity and permeability may be caused by the low compressibility of quartz sand grains in the recovered cores. XRD and thermal conductivity analysis also suggested high quartz content. Triaxial compression tests established internal friction angles based on the Mohr-Coulomb's failure criterion, which are 40° for hydrate-bearing sediment and 29.8° for hydrate free sediment.</p>

Yoneda, Jun↗