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At least 793 records · Page 44
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DOE OSTI2021

Verification of Coupled Hydraulic Fracturing Simulators Using Laboratory-Scale Experiments

In this work, we aim to verify the predictions of the numerical simulators, which are used for designing field-scale hydraulic stimulation experiments. Although a strong theoretical understanding of this process has been gained over the past few decades, numerical predictions of fracture propagation in low-permeability rocks still remains a challenge. Against this background, we performed controlled laboratory-scale hydraulic fracturing experiments in granite samples, which not only provides high-quality experimental data but also a well-characterized experimental set-up. Using the experimental pressure responses and the final fracture sizes as benchmark, we compared the numerical predictions of two coupled hydraulic fracturing simulators—CSMP and GEOS. Both the simulators reproduced the experimental pressure behavior by implementing the physics of Linear Elastic Fracture Mechanics (LEFM) and lubrication theory within a reasonable degree of accuracy. The simulation results indicate that even in the very low-porosity (1–2 %) and low-permeability (10 -18 m 2 - 10 -19 m 2 ) crystalline rocks, which are usually the target of EGS, fluid-loss into the matrix and unsaturated flow impacts the formation breakdown pressure and the post-breakdown pressure trends. Therefore, underestimation of such parameters in numerical modeling can lead to significant underestimation of breakdown pressure. The simulation results also indicate the importance of implementing wellbore solvers for considering the effect of system compressibility and pressure drop due to friction in the injection line. The varying injection rate as a result of decompression at the instant of fracture initiation affects the fracture size, while the entry friction at the connection between the well and the initial notch may cause an increase in the measured breakdown pressure.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

A deep learning-based direct forecasting of CO 2 plume migration

Accurate and timely forecasts of CO 2 plume evolution in geological reservoirs are crucial for CO 2 migration detection, leakage risk assessment, and operation decision support. Conventional forecasting usually adopts a two-step strategy, first calibrating reservoir model parameters against observations using iterative inverse modeling (or history matching) and then applying the calibrated model for predictions. This method impedes real-time forecasts due to the heavy computational demand in inverse modeling and may suffer from poor prediction accuracy because of the limited observation data. In this work, we propose a deep learning-based latent space mapping framework to forecast CO 2 plume migration directly by avoiding the inverse modeling. We first use the convolutional autoencoder to map the high-dimensional complex plume extents onto low-dimensional latent space. Next, we use neural networks to learn the relationship between the observation variables and the prediction latent variables. And then for given observation data, we infer the prediction values directly. This one-step direct forecasting is computationally efficient which requires a few number of parallelizable reservoir simulations and it can provide accurate predictions with limited observations by learning the observation-prediction relationship in the reduced dimension. Therefore, our proposed method enables an in-time forecast of dynamic CO 2 plume distributions. In this work, we demonstrate the effectiveness and accuracy of our method in predicting the CO 2 plume migration using four metrics such as plume area, centroid movement distance, and plume spreading in the primary and secondary directions. And the spatio-temporal evolution patterns of plume migration under diverse geological complexities are also accurately quantified.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Uncertain characterization of reservoir fluids due to brittleness of equation of state regression

Equations of state (EoS) play a central role in modeling the phase equilibrium of fluid mixtures. Their parameterization involves fitting a model to experimental data, i.e., solving a nonlinear, non-convex, multivariate optimization problem. The latter requires one to select design variables, domains of definition for each variable, and weights assigned to individual measurements. We demonstrate that subjective choices of an optimization algorithm and an initial guess also impact the regression process. Consequently, EoS predictions are fundamentally uncertain even after the EoS tuning to a limited set of experimental data points. We demonstrate this observation for two hydrocarbon reservoir fluids, in which five properties of the heaviest carbon fraction are treated as design variables. While all the optimization algorithms and initial guesses match experimental data for the gas and liquid properties, the resulting EoS parameterizations lead to dramatically different predictions of the fluid’s thermophysical behavior in the unsampled pressure and temperature regions. In conclusion, we propose the probabilistic treatment of design variables to quantify the predictive uncertainty of the resulting fluid models.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Self-re-adhering alkali-activated cement composite and its ability to mitigate corrosion of carbon steel in 300 °C hydrothermal environment

Cement-steel interface is a weak point of cement sheath integrity under the environments of high-temperature geothermal wells. This paper presents carbon steel (CS) adherence behaviors of 300 °C-autoclaved alkali-activated Calcium-Aluminate-Cement (CAC)/fly ash F (FAF) (Thermal Shock Resistant Cement, TSRC), granulated blast furnace slag (GBFS)/SiO 2 , and Ordinary Portland Cement (OPC)/SiO 2 blends. The composites’ ability to preserve the bond under the conditions of thermal shock (TS) and strong acid attack (pH 0.6 H 2 SO 4 /brine at 90 °C) as well as their ability to recover the damaged bond and provide steel corrosion protection after additional short-time 5-day curing at 300 °C were evaluated. GBFS/SiO 2 sheath underwent catastrophic failure in the first TS cycle; OPC/SiO 2 lost 78 % and TSRC 51 % of the bond strength in 6 cycles. Only TSRC-CS bond survived 30 days of strong acid exposure; OPC/SiO 2 bond failed after 18 days and GBFS/SiO 2 after 20 days. Addition of micro-glass fibers (MGF) to TSRC further improved its bond strength and CS corrosion protection. Samples of TSRC-CS cured for 30 days at 300 °C before the bond damage recovered 49 % of the damaged bond strength after additional short-time curing. The CS protected by the re-adhered TSRC showed low corrosion rate of 0.13 mm/year (TSRC-MGF), 0.2 mm/year (TSRC) vs. 0.64 mm/year for OPC/SiO 2 protected CS.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Rheological investigation of effect of high temperature on geothermal drilling fluids additives and lost circulation materials

High temperatures (HT) and the corrosive environment make the design of a geothermal drilling fluid a highly complex task. The presence of HT exacerbates the problems due to thermal degradation. Geothermal aquifers usually combine high-temperature and complex chemistry, making them highly corrosive. Although geothermal drilling fluids are exposed to thermal degradation, research of the effect on mud rheological properties in temperatures near or above 149°C (300°F) is limited. In this study, a High Pressure-High Temperature (HPHT) rheology setup was used to measure drilling fluids' properties up to 204.4°C (400°F). Rheological behavior of viscosity, density, lost circulation, and alkalinity control additives were analyzed at HT. It was found experimentally that Bentonite alone is thermally stable at HT. In contrast, Caustic Soda showed a tendency to gelate at temperatures above 93.3°C (200°F), adversely affecting mud rheology. Alkalinity control materials to replace Caustic Soda were evaluated. Among them, Lime presented the best thermal stability at 204.4°C with a range of variation of 0.0024 Pa.s (2.4 cP) on apparent viscosity after three consecutive tests. Besides, it was tested 11 different lost circulation materials (LCM) for understanding their rheological behavior at HT. Further, it was found that HT has less impact on fine granular materials rheological behavior with an average increase of 17.7% compared with the baseline. In contrast, flaky, fibrous, and coarse granular materials presented a more apparent impact in WBM rheology with a deviation of 166% compared with the baseline.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Implications of rootless geothermal models: Missing processes, parameter compensation, and imposter convection

Numerical models of geothermal systems commonly capture only the top of a reservoir. Deeper areas of the reservoir are simplified to a boundary at the base of the model domain. Commonly, the basal boundary is given either a heat source or a source of mass and enthalpy. Here we developed and present simple numerical experiments which demonstrate that these approaches do not produce the correct model behavior in comparison to a model that captures the entire convecting domain with a heat flux only. We describe a variety of incorrect types of model behavior that arise directly from the choice of boundary condition, independent of the specific parameterization of the model. Heat sources are sensitive to the thickness of the domain and parameters take unphysical values to compensate for the reduced height. The combined mass/heat boundary can produce temperatures that show similarities to circulating geothermal systems, but with incorrect fluid flow and a strong boundary layer focused at the base of the simulated clay cap. These errors likely cause parameters to adjust their values to compensate for the incorrect physics. We highlight these issues and show an example from a developed reservoir model. Initial calibrations to natural state temperature were unsuitable for history matching. The 3D model required parameter adjustments to achieve a more realistic production model. Appropriate mitigation measures should be considered to reduce parameter compensation and improve decision-support models.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

De-risking superhot EGS development through 3D play fairway analysis: Methodology development and application at Newberry Volcano, Oregon, USA

The DErisking Exploration for multiple geothermal Plays in magmatic ENvironments (DEEPEN) project aims to de-risk exploration of geothermal plays in magmatic systems, with a focus on superhot and supercritical geothermal systems. This article presents advancements in geoscientific assessment of superhot enhanced geothermal system (EGS) resources as part of the DEEPEN project, particularly in exploration and identification of these systems. While there exists a general consensus on key components of conventional hydrothermal systems, there has not been a prior consensus on such components for EGS, let alone superhot EGS. The DEEPEN project identified these key components for superhot EGS plays, and used them to modify the traditional geothermal play fairway analysis (PFA) approach to identify favorable areas in superhot EGS plays in 3D. This was done through modeling both favorability and uncertainty separately for each key component of a superhot EGS play, incorporating diverse 3D geoscientific datasets, including geologic features, models produced from direct observations, single inversions, and joint inversions. The PFA is applied to Newberry Volcano as a form of validation. Finally, the results appear to validate the methodology, aligning with expectations derived from conceptual modeling, and highlighting the area targeted for EGS stimulation well NWG 55-29 as favorable, and suggesting potential additional areas worthy of exploration at Newberry.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Multiscale spatial analysis of fracture nodes in two dimensions

Spatial arrangement of fractures as a function of scale is an important component of fracture quantification for inferential and predictive modeling. Available methods that analyze fracture spatial arrangement are based on one-dimensional spacing data; therefore, they are limited to semi-parallel fractures. Such methods cannot be applied to fracture networks in higher dimensions, particularly when fractures have different orientations. Here to characterize fracture arrangements in two dimensions, we propose using Ripley’s K-function, as a method of point pattern analysis, to quantify spatial arrangement of fracture nodes. Fracture nodes, such as barycenters, intersection points, and tips, are point-based representations of fracture locations and connectivity within the fracture network. We introduce formulations for isotropic as well as directional analyses of spatial arrangement. In addition, we derive formulations for edge correction in circular and rectangular study domains. Finally, we demonstrate applications of Ripley’s K-function on two natural fracture datasets. Our proposed method supports quantification and characterization of fracture spatial arrangements that allow practitioners to build representative models of fractures in the subsurface.

02 PETROLEUM↗