Data-worth Analysis Research Articles

Numerical Prediction Uncertainty and Data Worth Analysis of Solute Transport in an Agricultural Clay Till Setting With Preferential Flow

AbstractWith modern agricultural practices, it is essential to quantify flow and solute transport fluxes by numerical models and associated predictions. A major challenge in modeling preferential flow settings is the ability to constrain the often numerous parameters needed to physically represent these systems. Following this, there is a lack of understanding of what parameters and observations carry the most worth for a model to reduce its prediction uncertainty. Here, first‐order second moment (FOSM) analyses were used for a heavily monitored clay till field with preferential flow to investigate which parameters and observation types contribute the most to reducing the uncertainty of bromide predictions at various depths. Using a multi‐objective regularized optimization approach, a 1‐D preferential flow model was calibrated and subjected to FOSM analyses. Key parameters contributing to the prediction uncertainty of bromide concentrations in 0.25–3 m were limited to the lower boundary condition, the mass transfer coefficient, the hydraulic conductivity of the micro‐ and macropore, the macropore porosity, and the water content at wilting point. The data with the largest worth and ability to reduce the pre‐calibration prediction uncertainty were concentration observations closest to the sought prediction depth, drain concentrations, and averaged water table measurements from the entire field. The post‐calibration prediction uncertainty was increased primarily by removing concentration observations closest to the prediction depths. While this study provided new insights into parameter importance and data worth further research is required to understand if these findings apply broadly to clay till settings (or any soil setting) with preferential flow.

Data-worth analysis for the design of the HotBENT monitoring system

The safety of a geologic repository for heat-generating nuclear waste relies in part on the long-term performance of the clay-based buffer material that surrounds the waste canisters. The sealing and waste-isolation properties of the bentonite may degrade as temperatures rise to high values. To improve the understanding of the evolution of the repository's near field and in particular the effects of increased temperatures on the bentonite buffer, a full-scale, long-duration experiment named HotBENT is underway at the Grimsel Test Site, Switzerland. The HotBENT monitoring system was designed with the support of a modelling-based data-worth analysis in which the contribution of individual sensors to the reduction in estimation or prediction uncertainties is formally evaluated. Two examples are described; the first demonstrates optimization of the monitoring system for the estimation of parameters of a clay-swelling model, whereas the second example shows how to select monitoring data that help improve the quality of model predictions needed for decision support. A data-worth analysis was considered a useful approach to identify the information content of actual and planned monitoring data for the specific objectives of the experiment. The HotBENT monitoring system was eventually designed in part based on the results presented in this paper. Thematic collection: This article is part of the Sustainable geological disposal and containment of radioactive waste collection available at: https://www.lyellcollection.org/topic/collections/radioactive

Data space inversion for efficient uncertainty quantification using an integrated surface and sub-surface hydrologic model

Abstract. It is incumbent on decision-support hydrological modelling to make predictions of uncertain quantities in a decision-support context. In implementing decision-support modelling, data assimilation and uncertainty quantification are often the most difficult and time-consuming tasks. This is because the imposition of history-matching constraints on model parameters usually requires a large number of model runs. Data space inversion (DSI) provides a highly model-run-efficient method for predictive uncertainty quantification. It does this by evaluating covariances between model outputs used for history matching (e.g. hydraulic heads) and model predictions based on model runs that sample the prior parameter probability distribution. By directly focusing on the relationship between model outputs under historical conditions and predictions of system behaviour under future conditions, DSI avoids the need to estimate or adjust model parameters. This is advantageous when using integrated surface and sub-surface hydrologic models (ISSHMs) because these models are associated with long run times, numerical instability and ideally complex parameterization schemes that are designed to respect geological realism. This paper demonstrates that DSI provides a robust and efficient means of quantifying the uncertainties of complex model predictions. At the same time, DSI provides a basis for complementary linear analysis that allows the worth of available observations to be explored, as well as of observations which are yet to be acquired. This allows for the design of highly efficient, future data acquisition campaigns. DSI is applied in conjunction with an ISSHM representing a synthetic but realistic river–aquifer system. Predictions of interest are fast travel times and surface water infiltration. Linear and non-linear estimates of predictive uncertainty based on DSI are validated against a more traditional uncertainty quantification which requires the adjustment of a large number of parameters. A DSI-generated surrogate model is then used to investigate the effectiveness and efficiency of existing and possible future monitoring networks. The example demonstrates the benefits of using DSI in conjunction with a complex numerical model to quantify predictive uncertainty and support data worth analysis in complex hydrogeological environments.

Data worth analysis within a model-free data assimilation framework for soil moisture flow

Abstract. Conventional data worth (DW) analysis for soil water problems depends on physical dynamic models. The widespread occurrence of model structural errors and the strong nonlinearity of soil water flow may lead to biased or wrong worth assessment. By introducing the nonparametric data worth analysis (NP-DWA) framework coupled with the ensemble Kalman filter (EnKF), this real-world case study attempts to assess the worth of potential soil moisture observations regarding the reconstruction of fully data-driven soil water flow models prior to data gathering. The DW of real-time soil moisture observations after Gaussian process training and Kalman update was quantified with three representative information metrics, including the trace, Shannon entropy difference and relative entropy. The sequential NP-DWA framework was examined by a number of cases in terms of the variable of interest, spatial location, observation error, and prior data content. Our results indicated that, similarly to the traditional DW analysis based on physical models, the overall increasing trend of the DW from the sequential augmentation of additional observations within the NP-DWA framework was also susceptible to interruptions by localized surges due to never-experienced atmospheric conditions (i.e., rainfall events). The difference is that this biased DW in the former is caused by model structural errors triggered by contrasting scenarios, which is difficult to be compensated for by assimilating more prior data, while this performance degradation in the NP-DWA can be effectively alleviated by enriching training scenarios or the appropriate amplification of observational noise under extreme meteorological conditions. Nevertheless, a substantial expansion of the prior data content may cause an unexpected increase in the DW of future potential observations due to the possible introduction of ensuing observation noises. Hence, high-quality and representative small data may be a better choice than unfiltered big data. Compared with the observations in the surface layer with the strongest time variability, the soil water content in the middle layer robustly exhibited remarkable superiority in the construction of model-free soil moisture models. We also demonstrated that the DW assessment performance was jointly determined by 3C, i.e., the capacity of potential observation realizations to capture actual observations, the correlation of potential observations with the variables of interest and the choice of DW indicators. Direct mapping from regular meteorological data to soil water content within the NP-DWA mitigated the adverse effects of nonlinearity-related interference, which thus facilitated the identification of the soil moisture covariance matrix, especially the cross-covariance.

Analyzing Groundwater Recharge and Vadose Zone Dynamics by Combining Soil Moisture and Groundwater Level Data with a Numerical Model in Subarctic Conditions

Quantitative estimates on the magnitude and dynamics of vadose and groundwater zones in subarctic conditions are rare. Furthermore, knowledge on the constraints of different data on numerical groundwater recharge estimates (i.e., data worth analyses) is limited. We built a process-based three-dimensional hydrological model to describe and analyze the dynamics and magnitude of groundwater recharge, vadose zone, and related key hydrological components in subarctic conditions. Three-year time series of daily groundwater level and soil moisture (five different depths) data were used to identify plausible model realizations. Thereafter, long-term simulations over 6 years were conducted. The model adequately corresponded with the magnitude and dynamics of the observations, even though the simulations and observations occasionally differed. Simulations with several different plausible parameterizations showed how soil moisture and groundwater level fluctuations, as well as recharge estimates, are sensitive to water retention parameter variations, whereas precipitation and evapotranspiration controlled the recharge magnitude. Recharge showed clear interannual variability (annual mean 0.28–0.60 m). The fraction of annual mean recharge over precipitation was 39%–66% during our simulation period. We showed how groundwater level data can set more constraints on the recharge estimates than soil moisture data, and how both of the data types together constrained the recharge estimates the most. We showed how the vadose zone had modest dynamics and storage properties (typically 2%–8% of the total soil water) in terms of the bulk soil water storage. However, the average amount of the air-filled pore space (0.70 m) and water (0.38 m) in the vadose zone were proportional to annual precipitation and evapotranspiration, respectively. Thus, the vadose zone can form an important boundary between the soil surface and groundwater zone, and its role is not highly sensitive to hydrometeorological fluctuations. Data worth analyses provide a potential opportunity to move toward increasingly reliable model-based groundwater recharge estimates and system understanding.

An integrated experimental design framework for optimizing solute transport monitoring locations in heterogeneous sedimentary media

This study develops an integrated framework to guide the monitoring network optimization and duration selection for solute transport in heterogeneous sand tank experiments. The method is designed based on entropy and data worth analysis. Numerical models are applied to approach prior observation datasets and to support optimization analysis. Several candidate monitoring locations are synthetically assumed in numerical models. Entropy analysis considers local scale heterogeneity in experiment and identifies stable monitoring locations through extracting maximum information and minimizing optimization redundancies. Data worth analysis quantifies the potential of observation data to reduce the uncertainty of key parameters and selects the monitoring locations with higher data worth. Final monitoring network comprises of optimized monitoring locations obtained based on entropy and data worth analysis. A lab-scale tracer experiment is presented to explore the applicability of the proposed framework. Results show that the optimized monitoring network can accurately characterize the distribution of contaminant plumes in 3D domains and provides estimation of key flow and transport parameters (e.g., hydraulic conductivity and dispersivity). With the extension of experiment time, the total information of monitoring network is maximized, while the uncertainty of key parameters is minimized. The recommended experimental duration is the time by which both joint entropy and parameter variation coefficients are stabilized. Our developed methodology can be used as a flexible and powerful tool to design more complex transport experiments at different spatiotemporal scales.

Streambed water flux characterization through a Deep-Learning-Based approach considering data worth analysis: Numerical modeling and sandbox experiments

• An approach named CNN-DWA is proposed for estimating streambed water fluxes. • High-utility data are chosen through data worth analysis for CNN model training. • A few chosen data points provide similar information content to entire dataset. • CNN-DWA achieves more accurate flux estimations than the previously used IES. Quantifying streambed water fluxes (SWFs) is a prerequisite for studying the transport and fate of contaminants in the hyporheic zone. One approach, the heat tracing technique combined with analytical or numerical heat transport models has been widely used to quantify SWFs. However, both analytical and numerical methods are limited. For example, analytical methods are built upon rarely met assumptions, while the inversion performance of numerical methods can be impeded by uncertainties from different sources. To overcome these limitations, we introduce a new approach that combines convolutional neural networks (CNNs) with the Bayesian data worth analysis (DWA) framework for estimating SWFs. In this approach, a synthetic dataset is first generated through numerical simulations of groundwater flow and heat transport in streambeds. Then high-utility temperatures are chosen from the synthetic dataset through Bayesian DWA and further used to train the CNN model. We assess the capability of Bayesian DWA in improving the SWF estimation accuracy of CNN models through both numerical and sandbox experimental cases. Results show that the CNN model trained on temperatures with higher utility generally gives more accurate SWF estimations; use of a few high-utility temperature measurements can provide near similar constraint on the CNN model training and SWF estimation as compared to the full time series. Furthermore, a comparison to the iterative ensemble smoother algorithm shows the proposed inversion approach can estimate the flux field in the sandbox more accurately. The proposed CNN-based inversion approach considering DWA (CNN-DWA) would be beneficial more broadly for other hydrological problems.

Application of an iterative source localization strategy at a chlorinated solvent site

This study presents an inverse modeling strategy for organic contaminant source localization. The approach infers the hydraulic conductivity field, the dispersivity, and the source zone location. Beginning with initial observed data of contaminant concentration and hydraulic head, the method follows an iterative strategy of adding new observations and revising the source location estimate. Non-linear optimization using the Gauss-Levenberg-Marquardt Algorithm (PEST++) is tested at a real contaminated site. Then a limited number of drilling locations are added, with their positions guided by the Data Worth analysis capabilities of PYEMU. The first phase of PEST++, with PYEMU guidance, followed by addition of monitoring wells provided an initial source location and identified four additional drilling locations. The second phase confirmed the source location, but the estimated hydraulic conductivity field and the Darcy flux were too far from the measured values. The mismatch led to a revised conceptual site model that included two distinct zones, each with a plume emanating from a separate source. A third inverse modelling phase was conducted with the revised site conceptual model. Finally, the source location was compared to results from a Geoprobe@ MiHPT campaign and historical records, confirming both source locations. By merging measurement and modeling in a coupled, iterative framework, two contaminant sources were located through only two drilling campaigns while also reforming the conceptual model of the site.

A framework for parameter estimation using sharp-interface seawater intrusion models

Sharp-interface seawater intrusion models present shorter run times than variable density codes, which makes them practical for regional, decision-support groundwater modeling. Although parameter estimation and uncertainty analyses are essential steps for model-based decision making, their implementation in seawater intrusion models has remained limited. Few guidelines are available and it is unclear which observations should be used, what processing they require and what weighting strategy should be used. A framework has been developed for parameter estimation using a regional sharp-interface decision-support model applied to a real-world example in the Magdalen Islands (Quebec, Canada). This framework included the assimilation of head observations collected from shallow wells, deep open wells and pumping wells, as well as freshwater-seawater interface observations derived from deep open wells, TDEM (time-domain electromagnetic) and ERT (electrical resistivity tomography) geophysical surveys. A model was developed using MODFLOW-SWI2 in which fast model run times allowed the estimation of numerous parameters, including a heterogeneous hydraulic conductivity field with pilot points. Following parameter estimation with PEST, the uncertainty of several model forecasts, i.e. the volume of freshwater and the interface elevation near municipal pumping wells, was examined with the first-order second moment (FOSM) approach and a data worth analysis was carried out. While the observations presented a low signal-to-noise ratio, parameter estimation was effective to reduce the uncertainty of model forecasts. Interface observations, and particularly geophysical observations, were most useful to reduce predictive uncertainties. Head and interface observations from deep open wells were biased and could not be suitably reproduced by the model. The framework developed here is relatively straightforward and could be implemented more systematically. The study also provides recommendations to guide future data collection strategies in coastal aquifers.

Robust Data Worth Analysis with Surrogate Models.

Highly detailed physically based groundwater models are often applied to make predictions of system states under unknown forcing. The required analysis of uncertainty is often unfeasible due to the high computational demand. We combine two possible solution strategies: (1) the use of faster surrogate models; and (2) a robust data worth analysis combining quick first-order second-moment uncertainty quantification with null-space Monte Carlo techniques to account for parametric uncertainty. A structurally and parametrically simplified model and a proper orthogonal decomposition (POD) surrogate are investigated. Data worth estimations by both surrogates are compared against estimates by a complex MODFLOW benchmark model of an aquifer in New Zealand. Data worth is defined as the change in post-calibration predictive uncertainty of groundwater head, river-groundwater exchange flux, and drain flux data, compared to the calibrated model. It incorporates existing observations, potential new measurements of system states ("additional" data) as well as knowledge of model parameters ("parametric" data). The data worth analysis is extended to account for non-uniqueness of model parameters by null-space Monte Carlo sampling. Data worth estimates of the surrogates and the benchmark suggest good agreement for both surrogates in estimating worth of existing data. The structural simplification surrogate only partially reproduces the worth of "additional" data and is unable to estimate "parametric" data, while the POD model is in agreement with the complex benchmark for both "additional" and "parametric" data. The variance of the POD data worth estimates suggests the need to account for parameter non-uniqueness, like presented here, for robust results.

Efficient multi-objective calibration and uncertainty analysis of distributed snow simulations in rugged alpine terrain

In mountainous terrain, reliable snow simulations are crucial for many applications. However, except in highly instrumented research catchments, meteorological data are usually limited, and so the interpolated spatial fields used to force snow models are uncertain. Moreover, certain potentially important processes cannot presently be simulated at catchment scales using entirely physical algorithms. It is therefore often appropriate to introduce empirical parameters into otherwise physically-based snow models. Many opportunities to incorporate snow observations into the parameter estimation process now exist, but they remain to be fully exploited. In this context, a novel approach to the calibration of an energy balance-based snow model that additionally accounts for gravitational redistribution is presented. Several important parameters were estimated using an efficient, gradient-based method with respect to two complementary observation types – Landsat 8-derived snow extent maps, and reconstructed snow water equivalent (SWE) time-series. When assessed on a per-pixel basis, observed patterns were ultimately reproduced with a mean accuracy of 85%. Spatial performance metrics compared favourably with those previously reported, whilst the temporal evolution of SWE at the stations was also satisfactorily captured. Subsequent uncertainty and data worth analyses revealed that: i) the propensity for model predictions to be erroneous was substantially reduced by calibration, ii) pre-calibration uncertainty was largely associated with two parameters which modify the longwave component of the energy balance, but this uncertainty was greatly diminished by calibration, and iii) a lower elevation SWE series was particularly valuable, despite containing comparatively few observations. Overall, our work demonstrates that contemporary snow models, observation technologies, and inverse approaches can be combined to both constrain and quantify the uncertainty associated with simulations of alpine snow dynamics.

Use of Global Sensitivity and Data-Worth Analysis for an Efficient Estimation of Soil Hydraulic Properties

Accurate simulation of flow and contaminant transport processes through unsaturated soils requires adequate knowledge of the soil parameters. This study deals with the hydraulic characterization of soils using laboratory experiments. A new strategy is developed by combining global sensitivity analysis (GSA) and Bayesian data-worth analysis (DWA) to obtain efficient data that ensure a good estimation of the soil properties. The strategy is applied for the estimation of soil properties from a laboratory infiltration experiment. Results of this study show that GSA allows identification of regions and periods of high sensitivity of each parameter and thereby, the observations prone to contain information for a successful calibration. Further, the sensitivity depicts a nonlinear behavior with regions of high influence and regions of weak influence inside the parameter space. Bayesian DWA, performed a priori, allows to quantify the improvement of the posterior uncertainty of the estimated parameters when adding a type of measurement. The results reveal that an accurate estimation of the soil properties can be obtained if the target parameter values are located in the regions of high influence in the parameter space.

Optimal Design and Prediction-Independent Verification of Groundwater Monitoring Network

In this study, we developed a workflow that applies a complex groundwater model for purpose-driven groundwater monitoring network design and uses linear uncertainty analysis to explore the predictive dependencies and provide insights into the veracity of the monitoring design. A numerical groundwater flow model was used in a probabilistic modelling framework for obtaining the spatial distribution of predicted drawdown for a wide range of plausible combination of uncertain parameters pertaining to the deep sedimentary basin and groundwater flow processes. Reduced rank spatial prediction was used to characterize dominant trends in these spatial drawdown patterns using empirical orthogonal functions (EOF). A differential evolution algorithm was used to identify optimal locations for multi-level piezometers for collecting groundwater pressure data to minimize predictive uncertainty in groundwater drawdown. Data-worth analysis helps to explore the veracity of the design by using only the sensitivities of the observations to predictions independent of the absolute values of predictions. A 10-bore monitoring network that collects drawdown data from multiple depths at each location was designed. The data-worth analysis revealed that the design honours sensitivities of the predictions of interest to parameters. The designed network provided relatively high data-worth for minimizing uncertainty in the drawdown prediction at locations of interest.

An iterative strategy for contaminant source localisation using GLMA optimization and Data Worth on two synthetic 2D Aquifers

A contaminant source localisation strategy was developed considering unknown heterogeneous hydraulic conductivity field, unknown dispersivity and unknown location of a continuous contaminant source. The Gauss-Levenberg-Marquardt algorithm is combined with a data worth analysis to estimate the unknown parameters and identify the best locations of additional measurements. The data collection strategy is iterative, based on the ability of the additional dataset to decrease the uncertainties on the contaminant source location. Two 2D synthetic models are considered. The method is first illustrated with a simple model and a more complex model is then considered to evaluate the ability of the approach to locate the contaminant source from hydraulic heads and concentration data. This approach is parsimonious in terms of model runs and applicable to real cases. The results give a good estimate of the source location and the dispersivity, with acceptable NRMSE for each case. New observations introduced at each iteration decrease the standard deviation of the source location and improve the NRMSE. The estimated hydraulic conductivity field presents the same features as the original field.

Assessment of regional inter-basin groundwater flow using both simple and highly parameterized optimization schemes

The need for regional-scale integrated hydrological models for the purpose of water resource management is increasing. Distributed physically based coupled surface-subsurface models are usually complex and contain a large amount of spatio-temporal information that leads to a relatively long forward runtime. One of the main challenges with regard to regional-scale inverse modeling relates to parameterization and how to adequately exploit the information embedded in the existing observational data while avoiding parameter identifiability issues. This study examined and compared the calibration of a “highly parameterized” model with a “classical” unit-based parameterization scheme in which the dominant geological features were assumed to be known. The physically based coupled surface-subsurface model MIKE SHE was used for conducting the study of five river basins (4,900 km2) in central Jutland in Denmark, characterized by heterogeneous geology and a considerable amount of groundwater flux across topographical catchment boundaries. The results indicated that introducing more flexibility in the parameter estimation process through a regularized approach significantly improved the model performance, in particular head and water balance errors. The highly parameterized calibration results additionally provided very useful insights into the model deficiencies in terms of conceptual model structure and incorrectly imposed boundary conditions. Furthermore, the results from data-worth analysis indicated that the highly parameterized model has more effectively utilized the information in the dataset compared to a traditional unit-based calibration approach.

Thermal Evolution near Heat-Generating Nuclear Waste Canisters Disposed in Horizontal Drillholes

We consider the disposal of spent nuclear fuel and high-level radioactive waste in horizontal holes drilled into deep, low-permeable geologic formations using directional drilling technology. Residual decay heat emanating from these waste forms leads to temperature increases within the drillhole and the surrounding host rock. The spacing of waste canisters and the configuration of the various barrier components within the horizontal drillhole can be designed such that the maximum temperatures remain below limits that are set for each element of the engineered and natural repository system. We present design calculations that examine the thermal evolution around heat-generating waste for a wide range of material properties and disposal configurations. Moreover, we evaluate alternative layouts of a monitoring system to be part of an in situ heater test that helps determine the thermal properties of the as-built repository system. A data-worth analysis is performed to ensure that sufficient information will be collected during the heater test so that subsequent model predictions of the thermal evolution around horizontal deposition holes will reliably estimate the maximum temperatures in the drillhole. The simulations demonstrate that the proposed drillhole disposal strategy can be flexibly designed to ensure dissipation of the heat generated by decaying nuclear waste. Moreover, an in situ heater test can provide the relevant data needed to develop a reliable prediction model of repository performance under as-built conditions.

Data-Worth Assessment for a Three-Dimensional Optimal Design in Nonlinear Groundwater Systems.

Groundwater model predictions are often uncertain due to inherent uncertainties in model input data. Monitored field data are commonly used to assess the performance of a model and reduce its prediction uncertainty. Given the high cost of data collection, it is imperative to identify the minimum number of required observation wells and to define the optimal locations of sampling points in space and depth. This study proposes a design methodology to optimize the number and location of additional observation wells that will effectively measure multiple hydrogeological parameters at different depths. For this purpose, we incorporated Bayesian model averaging and genetic algorithms into a linear data-worth analysis in order to conduct a three-dimensional location search for new sampling locations. We evaluated the methodology by applying it along a heterogeneous coastal aquifer with limited hydrogeological data that is experiencing salt water intrusion (SWI). The aim of the model was to identify the best locations for sampling head and salinity data, while reducing uncertainty when predicting multiple variables of SWI. The resulting optimal locations for new observation wells varied with the defined design constraints. The optimal design (OD) depended on the ratio of the start-up cost of the monitoring program and the installation cost of the first observation well. The proposed methodology can contribute toward reducing the uncertainties associated with predicting multiple variables in a groundwater system.

Sequential data-worth analysis coupled with ensemble Kalman filter for soil water flow: A real-world case study

Given the high cost of data acquisition in soil water problems, it is becoming increasingly essential to collect the measurements as cost-efficient as possible. By introducing the data-worth analysis framework coupled with Ensemble Kalman Filter (EnKF), this real-world case study attempts to assess the worth of potential soil moisture observations before data collection. A field experiment was implemented to demonstrate the feasibility of quantifying the effect of future data on uncertainty reduction under real circumstances in a sequential way. The data worth of future observations is defined regarding soil hydraulic parameter estimation or soil moisture profile retrieval. Four information measures, including the trace (Tr), Shannon entropy difference (SD), relative entropy (RE) and degrees of freedom for signal (DFS), are introduced to quantify the information content. The sequential data worth analysis framework is examined by a number of cases, including under different irrigation intensities, with different prior data (existing observations that have already been collected), and with data of various depths and different measurement errors. We demonstrated the ability, and the challenge as well, of quantifying the data worth sequentially. Our results showed that data worth assessment regarding soil moisture profile retrieval is more difficult than that regarding parameter identification. Variance-type and covariance-type metrics have relatively loose accuracy requirement on potential observations (future possible observations to be collected), while mean-covariance-type metrics require higher accuracy. The vertical covariance of soil moisture is susceptible to the effect of atmospheric boundary condition, which eventually imposes a challenge on the quantification of data worth with covariance involved indices. The match between the expected and reference data worth can be improved by assimilating more prior data. However, more prior data cannot compensate for the damage from possible model structural error due to the changed scenarios between the prior stage and the posterior or preposterior stage. Shallow soil moisture data generally has larger data worth than deep observations in our study, but evaluating data worth with shallow data is subject to considerable uncertainty if covariance-type or mean-covariance-type index is employed. Smaller measurement error does not always lead to improved data worth estimation.

Analyzing the impact of reaction models on the production of hydrocarbons from thermally upgraded oil shales

Reaction parameters significantly affect oil production from shales by means of heating and in-situ upgrading. In this study, we perform numerical simulations of two chemical reaction models, which are mainly used in the research of kerogen pyrolysis and subsequent hydrocarbon decomposition in organic-rich porous media. They are the Braun and Burnham model and Wellington model. In these forward numerical simulations, we present the influence of the two reaction models on hydrocarbon production. The Braun and Burnham reaction model shows more vigorous kerogen and subsequent decomposition reactions and more hydrocarbon production than the Wellington model. A local sensitivity analysis identifies the reaction parameters with the highest influence on productivity, and the most sensitive outputs. A data-worth analysis identifies the most valuable observation data to be measured for the best prediction of total hydrocarbon production. We find that the most valuable observation data is the cumulative production of heavy oil in the Braun and Burnham model and of light oil in the Wellington model, respectively. Once we determine the maximum allowable prediction uncertainty and the expected measurement uncertainty, the observation data to be measured for the minimization of prediction uncertainty can be obtained.

Simulations of carbon dioxide push-pull into a conjugate fault system modeled after Dixie Valley—Sensitivity analysis of significant parameters and uncertainty prediction by data-worth analysis

Characterizing the faults and fractures that provide flow pathways for efficient geothermal energy production is critical for design of sustainable geothermal energy production. Both natural faults and stimulated fractures in enhanced geothermal systems (EGS) are difficult to image and map by seismic methods because hot brine filling the fractures and faults does not create a strong seismic property contrast relative to surrounding rock. We investigate here the technical feasibility of using supercritical CO2 (scCO2) injection into faults in a single-well push-pull scenario to characterize the hydraulic properties of the fault zone by emplacing scCO2 that can serve as a contrast fluid for seismic monitoring. We develop a conceptual and numerical reservoir model of two intersecting faults based on the Dixie Valley geothermal system in Nevada, USA. The 2D conceptual model consists of a system with a main fault and an intersecting conjugate fault. The corresponding numerical model is discretized using irregular grid blocks with fine discretization around the slip plane, gouge, and damage zones. We perform forward modeling along with sensitivity and data-worth analyses of scCO2 push-pull to investigate the CO2 distribution in the fault gouge during 30 days of push (injection) and 30 days of pull (production). Formal sensitivity analysis is conducted to determine the most controlling unknown parameters in the fault zones. Using the selected set of unknown parameters and output responses, we perform data-worth analysis to reveal the most valuable output response to be measured for the best prediction of CO2 distribution in the fault zones and its uncertainty. From the results of data-worth analysis, we determine the optimal properties to target in monitoring, their locations, and the minimum observation time. Our results provide information on the optimal design of scCO2 push-pull testing in a conjugate fault system modeled after Dixie Valley that can be used to enhance monitoring by active seismic and well-logging methods to better characterize the transmissive fault(s).