Uncertainty In Input Parameters Research Articles

Uncertainty evaluation and correlation analysis of single-particle energies in phenomenological nuclear mean field: an investigation into propagating uncertainties for independent model parameters

Based on the Monte Carlo approach and conventional error analysis theory, taking the heaviest doubly magic nucleus $$^{208}\hbox {Pb}$$ as an example, we first evaluate the propagated uncertainties of universal potential parameters for three typical types of single-particle energy in the phenomenological Woods–Saxon mean field. Accepting the Woods–Saxon modeling with uncorrelated model parameters, we found that the standard deviations of single-particle energy obtained through the Monte Carlo simulation and the error propagation rules are in good agreement. It seems that the energy uncertainty of the single-particle levels regularly evoluate with certain quantum numbers to a large extent for the given parameter uncertainties. Further, the correlation properties of the single-particle levels within the domain of input parameter uncertainties are statistically analyzed, for example, with the aid of Pearson’s correlation coefficients. It was found that a positive, negative, or unrelated relationship may appear between two selected single-particle levels, which will be extremely helpful for evaluating the theoretical uncertainty related to the single-particle levels (e.g., K isomer) in nuclear structural calculations.

Developing a process-based and remote sensing driven crop yield model for maize (PRYM–Maize) and its validation over the Northeast China Plain

Spatial dynamics of crop yield provide useful information for improving the production. High sensitivity of crop growth models to uncertainties in input factors and parameters and relatively coarse parameterizations in conventional remote sensing (RS) approaches limited their applications over broad regions. In this study, a process-based and remote sensing driven crop yield model for maize (PRYM–Maize) was developed to estimate regional maize yield, and it was implemented using eight data-model coupling strategies (DMCSs) over the Northeast China Plain (NECP). Simulations under eight DMCSs were validated against the prefecture-level statistics (2010–2012) reported by National Bureau of Statistics of China, and inter-compared. The 3-year averaged result could give more robust estimate than the yearly simulation for maize yield over space. A 3-year averaged validation showed that prefecture-level estimates by PRYM–Maize under DMCS8, which coupled with the development stage (DVS)-based grain-filling algorithm and RS phenology information and leaf area index (LAI), had higher correlation (R, 0.61) and smaller root mean standard error (RMSE, 1.33 t ha–1) with the statistics than did PRYM–Maize under other DMCSs. The result also demonstrated that DVS-based grain-filling algorithm worked better for maize yield than did the harvest index (HI-based method, and both RS phenology information and LAI worked for improving regional maize yield estimate. These results demonstrate that the developed PRYM–Maize under DMCS8 gives reasonable estimates for maize yield and provides scientific basis facilitating the understanding the spatial variations of maize yield over the NECP.

SENSITIVITY RESEARCH ON MODELS OF EARTH ENTRY AND IMPACT EFFECTS BY ASTEROIDS

Asteroid impact on Earth is one of the potential threats to human beings. Engineering models of Earth entry and impact effects by asteroids have been developed in order to estimate ground hazard by planetary defense communities nowadays. However, there are large uncertainties in input parameters of these models, which not only brings trouble to use of the model, but also greatly affects results of hazard estimate. Ranges of input parameters are analyzed, of Which baseline values are taken as maximum probability values or recommended values in literatures. Furthermore, by changing one or more input parameters based on ranges and baseline values, AICA (Asteroid Impact Consequence Analysis) code developed by authors of this paper, are used to study sensitivity of output parameters, such as energy deposition, airburst altitude, radius of 4psi overpressure and 3rd degree burn, to input parameters. The results are given for stony asteroids with diameter of 60~300~m and entry velocity of 12~40~km/s. It is indicated by results that airburst altitude of asteroid trends to decrease, with increase of size, strength at 1st breakup and mass fraction of debris cloud, and with decrease of entry velocity and ablation coefficient. Drag coefficient of asteroid and fragment, as well as strength scaling exponent, has little effect on airburst altitude and ground damage range. Radius of overpressure and thermal radiation damage generally increases with increase of size and entry velocity. If mass fraction of debris cloud is less than 50%, airburst altitude and ground damage range will fluctuate. But a large value for mass fraction of debris cloud, such as 80%, is reasonable according to analysis of many meteor events at present. Luminous efficiency only affects damage range of thermal radiation. Under the computational condition in this paper, radius of 3rd degree burn is less than that of 4-psi overpressure, indicating that the main type of ground hazard is overpressure caused by airburst.

Statistical Taylor series expansion: An approach for epistemic uncertainty propagation in Markov reliability models

In this paper we develop a new Taylor series expansion method for computing model output metrics under epistemic uncertainty in the model input parameters. Specifically, we compute the expected value and the variance of the stationary distribution associated with Markov reliability models. In the multi-parameter case, our approach allows to analyze the impact of correlation between the uncertainty on the individual parameters the model output metric. In addition, we also approximate true risk by using the Chebyshev’ inequality. Numerical results are presented and compared to the corresponding Monte Carlo simulations ones.

Improved modelling of pressure-dependent permeability behaviour in coal based on a new workflow of petrophysics, hydraulic fracturing and reservoir simulation

Many coal seam gas (CSG) reservoirs (also known as coalbed methane) can have low permeability, require stimulation to produce economic rates and often exhibit pressure-dependent permeability (PDP) behaviour. Defining PDP behaviour in coal using reservoir simulation is a non-unique problem based on the uncertainty in coal properties and input parameters. Recent research demonstrated that an integrated analysis coupling of a diagnostic fracture injection test analysis, hydraulic fracture modelling and reservoir simulation can better characterise PDP behaviour in order to evaluate stimulation effectiveness in coals (Johnson et al. 2020). The present work aims to improve the recently developed model by including multilayer and permeability anisotropy effects. A reservoir model with multiple coal layers is established in a pressure-dependent reservoir simulator, based on the image log interpretations. Permeability anisotropy in the formation is realised by introducing heterogeneous distribution of permeability in different directions. Modelling results indicate effects of aspect ratio between multilayers on the pressure distribution and production history. A lower permeability anisotropy ratio yields better well productivity, and higher stimulation is required to increase the stimulated reservoir volume to maximise gas recovery. The improved model and workflow are applicable to other CSG fields for defining key variables where hydraulic fracturing performance has been unable to overcome limitations based on pressure dependency, often accompanied by low-permeability behaviour. This workflow has applications in Australia and many areas (e.g. China and India) exhibiting low-permeability and PDP behaviour and where only typically collected field data is available.

Effect of Treatment with the PD-1/PD-L1 Inhibitors on Key Health Outcomes of Cancer Patients.

Recent studies have shown that treatment with the programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) inhibitor class could significantly improve survival outcomes in several oncology indications. However, there is some clinical uncertainty. This study aimed to obtain high-level estimates of the impact of treatment with PD-1/PD-L1 inhibitor class to oncology treatment on key health outcomes in real-world situations and to inform public health policy decisions about cancer care after reducing uncertainties around new immuno-oncology therapy options in South Korea. A model was developed to estimate the impact of PD-1/PD-L1 inhibitors on outcomes in situations wherein both anti-PD-1/PD-L1s and standard of care (SOC) were available versus SOC only. A partitioned survival model was utilized to estimate the impact of introducing anti-PD-1/PD-L1s on outcomes, including life-years gained, quality-adjusted life-years gained, progression-free survival-years obtained, and grade 3 or higher adverse events avoided for six indications over 5 years. An exponential distribution was fitted to the survival function of the SOC based on visual inspection. Outcomes associated with anti-PD-1/PD-L1s were estimated using a piecewise modeling approach with Kaplan-Meier analysis followed by best-fitting survival analysis. The incident number of patients and market share of anti-PD-1/PD-L1s during 2020-2024 were projected using published literature and Korean market survey data. Sensitivity analyses were performed to test the uncertainty of input parameters. During the next 5-year period (2020-2024), introducing the anti-PD-1/PD-L1 class led to a gain of 22,001 life-years (+ 31%), 19,073 quality-adjusted life-years (+ 38%), and 22,893 progression-free survival-years (+ 82%); it also avoided 3610 adverse events (- 11%) compared with SOC alone. Most adverse events associated with anti-PD-1/PD-L1s were attributed to combination therapy with cytotoxic chemotherapy (91%). In a scenario wherein the time to reimbursement of the anti-PD-1/PD-L1s was accelerated by 1 year, the life-years gained increased by 14% compared with the base-case scenario. Anti-PD-1/PD-L1 therapy is expected to provide marked survival benefits for patients with cancer. This study demonstrated the potentially beneficial health impacts of utilizing the anti-PD-1/PD-L1 class at the population level. The findings could inform health policy decision makers about cancer care and ultimately enhance population health through rapid access to innovative cancer drugs.

The outcome range problem in interval linear programming

Quantifying extra functions, herein referred to as outcome functions, over optimal solutions of an optimization problem can provide decision makers with additional information on a system. This bears more importance when the optimization problem is subject to uncertainty in input parameters. In this paper, we consider linear programming problems where input parameters are described by real-valued intervals, and we address the outcome range problem which is the problem of finding the range of an outcome function over all possible optimal solutions of a linear program with interval data. We give a general definition of the problem and then focus on a special class of it where uncertainty occurs only in the right-hand side of the underlying linear program. We show that our problem is computationally hard to solve and also study some of its theoretical properties. We then develop two approximation methods to solve it: a local search algorithm and a super set based method. We test the methods on a set of randomly generated instances. We also provide a real case study on healthcare access measurement to show the relevance of our problem for reliable decision making.

PRS15 Cost-Utility Analysis of Fixed Dose Combination (FDC) of Indacaterol Acetate (IND) Glycopyrronium Bromide (GLY) and Mometasone Furoate (MF) As a Maintenance Treatment in Adult Asthma Patients not Adequately Controlled with a Maintenance Combination of a long-Acting beta2-Agonist and a High Dose of an Inhaled Corticosteroid who Experienced One or More Asthma Exacerbations in the Previous Year

To evaluate the cost-utility of FDC of IND/GLY/MF relative to a combination of salmeterol/fluticasone (SF) and tiotropium (Tio) or SF or IND/MF in adult asthma patients, from the Italian Health Service (NHS) perspective. A two-state and four-week cycle Markov model was used to estimate lifetime clinical outcomes and costs. Patients entered the model in stable disease and could experience a non-fatal exacerbation event. The exacerbation rate is dependent upon the therapy a patient is receiving, as per the IND/GLY/MF clinical trials. The impact of each type of exacerbation is accounted by applying a utility decrement, obtained from literature, and a treatment cost. Utility values were obtained from the EQ-5D questionnaires in the IND/GLY/MF clinical trials. Lifetime costs considered in the analysis were drugs and exacerbation management. Probabilistic sensitivity analyses were carried out, with the aim of evaluating impact of uncertainty in input parameters. IND/GLY/MF is associated with higher quality of life (+0.254 QALY) than SF+Tio, with an incremental cost of -€3,213.90. The incremental cost-utility ratio (ICUR) indicates dominance. At a threshold of €5,000 per QALY, IND/GLY/MF has nearly 100% probability of being cost-effective. IND/GLY/MF is associated with higher quality of life (+0.214 QALY) than SF, with an incremental cost of €2,547.76. ICUR results in €11,897 per QALY. At a threshold of €20,000 per QALY, IND/GLY/MF has nearly 100% probability of being cost-effective. IND/GLY/MF is associated with higher quality of life (+0.337 QALY) than IND/MF, with an incremental cost of €4,745.91. ICUR results of €14,088. At a threshold of €20,000 per QALY, IND/GLY/MF has nearly 100% probability of being cost-effective. The results indicate that IND/GLY/MF is cost-effective among the considered comparisons in a representative cohort of adult asthma patients in Italy.

Identification of Chaboche\u2013Lemaitre combined isotropic\u2013kinematic hardening model parameters assisted by the fuzzy logic analysis

A very good knowledge of material properties is required in the analysis of severe plastic deformation problems in which the classical material processing methods are accelerated by the application of the additional cyclic load. A general fuzzy logic-based approach is proposed for the analysis of experimental and numerical data in this paper. As an application of the fuzzy analysis, the calibration of Chaboche–Lemaitre model hardening parameters of PA6 aluminum is considered here. The experimental data obtained in a symmetrical strain-controlled cyclic tension–compression test were used to estimate the material’s hardening parameters. The numerically generated curves were compared to the experimental ones. For better fitting of numerical and experimental results, the optimization approach using the least-square method was applied. Unfortunately, commonly accepted calibration methods can provide various sets of hardening parameters. In order to choose the most reliable set, the fuzzy analysis was used. Primarily selected values of hardening parameters were assumed to be fuzzy input parameters. The error of the hysteresis loop approximation for each set was used to compute its membership function. The discrete value of this error was obtained in the defuzzification step. The correct selections of hardening parameters were verified in ratcheting and mean stress relaxation tests. The application of the fuzzy analysis has improved the convergence between experimental and numerical stress–strain curves. The fuzzy logic allows analyzing the variation of elastic–plastic material response when some imprecisions or uncertainties of input parameters are taken into consideration.

Estimation of Domain of Attraction for Discrete-Time Positive Interval Type-2 Polynomial Fuzzy Systems With Input Saturation

This article focuses on expanding the estimation of the domain of attraction (DOA) for discrete-time positive nonlinear systems subject to input saturation and parameter uncertainties. To facilitate analysis and design, the interval type-2 (IT2) polynomial fuzzy model is used to represent the nonlinear plant and capture uncertainties. Combining with the IT2 polynomial fuzzy controller, the discrete-time positive IT2 polynomial fuzzy-model-based (PIT2PFMB) control system is formed to facilitate analysis. To enlarge the estimation of DOA of the discrete-time PIT2PFMB system, polyhedron is used to characterize the DOA with the help of linear copositive Lyapunov function (LCLF). Referring to the nonconvex conditions derived by LCLF, an effective convexification method is proposed in this article. For comparison purposes, the saturation-dependent-Lyapunov-function-based method is extended to the PIT2PFMB control system by adding the corresponding positivity conditions. In addition, this article attempts to enlarge the estimation of the DOA by improving the IT2 membership-function-dependent (IT2MFD) method and extending it to all conditions, including the stability conditions and the DOA estimation conditions. Finally, an example with simulation results is given to verify the effectiveness of all the methods proposed in this article for expanding the estimation of the DOA.

Data-Driven Uncertainty Quantification for Cardiac Electrophysiological Models: Impact of Physiological Variability on Action Potential and Spiral Wave Dynamics.

Computational modeling of cardiac electrophysiology (EP) has recently transitioned from a scientific research tool to clinical applications. To ensure reliability of clinical or regulatory decisions made using cardiac EP models, it is vital to evaluate the uncertainty in model predictions. Model predictions are uncertain because there is typically substantial uncertainty in model input parameters, due to measurement error or natural variability. While there has been much recent uncertainty quantification (UQ) research for cardiac EP models, all previous work has been limited by either: (i) considering uncertainty in only a subset of the full set of parameters; and/or (ii) assigning arbitrary variation to parameters (e.g., ±10 or 50% around mean value) rather than basing the parameter uncertainty on experimental data. In our recent work we overcame the first limitation by performing UQ and sensitivity analysis using a novel canine action potential model, allowing all parameters to be uncertain, but with arbitrary variation. Here, we address the second limitation by extending our previous work to use data-driven estimates of parameter uncertainty. Overall, we estimated uncertainty due to population variability in all parameters in five currents active during repolarization: inward potassium rectifier, transient outward potassium, L-type calcium, rapidly and slowly activating delayed potassium rectifier; 25 parameters in total (all model parameters except fast sodium current parameters). A variety of methods was used to estimate the variability in these parameters. We then propagated the uncertainties through the model to determine their impact on predictions of action potential shape, action potential duration (APD) prolongation due to drug block, and spiral wave dynamics. Parameter uncertainty had a significant effect on model predictions, especially L-type calcium current parameters. Correlation between physiological parameters was determined to play a role in physiological realism of action potentials. Surprisingly, even model outputs that were relative differences, specifically drug-induced APD prolongation, were heavily impacted by the underlying uncertainty. This is the first data-driven end-to-end UQ analysis in cardiac EP accounting for uncertainty in the vast majority of parameters, including first in tissue, and demonstrates how future UQ could be used to ensure model-based decisions are robust to all underlying parameter uncertainties.

Hydrological Modelling in Data Sparse Environment: Inverse Modelling of a Historical Flood Event

We dealt with a rather frequent and difficult situation while modelling extreme floods, namely, model output uncertainty in data sparse regions. A historical extreme flood event was chosen to illustrate the challenges involved. Our aim was to understand what the causes might have been and specifically to show how input and model parameter uncertainties affect the output. For this purpose, a conceptual model was calibrated and validated with recent data rich time period. Resulting model parameters were used to model the historical event which subsequently resulted in a rather poor hydrograph. Due to the bad model performance, a spatial simulation technique was used to invert the model for precipitation. Constraints, such as taking the precipitation values at historical observation locations in to account, with correct spatial structures and following the observed regional distributions were used to generate realistic precipitation fields. Results showed that the inverted precipitation improved the performance significantly even when using many different model parameters. We conclude that while modelling in data sparse conditions both model input and parameter uncertainties have to be dealt with simultaneously to obtain meaningful results.

Local and regional evaluation of liquefaction potential index and liquefaction severity number for liquefaction-induced sand boils in pohang, South Korea

An earthquake of moment magnitude 5.4 occurred on November 15, 2017 in Pohang, South Korea. This earthquake is the second largest instrumented earthquake ever recorded in South Korea and caused extensive damage to the ground and infrastructures. Hundreds of liquefaction-induced sand boils were observed in the rice fields near the epicenter. We calculate the liquefaction potential index (LPI) and liquefaction severity number (LSN) using the existing borehole data from the area, which includes 166 soil borings, to predict local and regional sand boils. Three of the soil borings considered in the dataset are within 47 m from the sand boils, and one is within 174 m from the sand boil. We provide detailed soil profiles at 20 sites (four of which are considered to be at liquefied sites) and soil properties at three sites (two of which are at liquefied sites). The calculated LPI and LSN values are in good agreement with the actual sand boil locations at 19 out of the 20 sites. Then, we use the existing borehole data to produce a regional liquefaction map using interpolation methods. As is common in regional seismic hazard maps, geotechnical and ground motion data are limited; therefore, we assess the uncertainty of important input parameters, such as the peak ground acceleration, water table, and N-value. For the regional maps, the maximum accuracy of the LPI is 69% at an LPI threshold of 2, and that of the LSN is 71% at an LSN threshold of 10. We report some ground settlements in the areas with high LPI and LSN values. Furthermore, we propose models for predicting the sizes and occurrence probabilities of sand boils.

Probabilistic evaluation of three-dimensional seismic active earth pressures using sparse polynomial chaos expansions

This work presents a probabilistic analysis of three-dimensional (3D) seismic active earth pressures acting on rigid retaining walls that are designed to support cohesive-frictional soil masses. The analysis is implemented using sparse polynomial chaos expansions (SPCE) to represent the physical model responses. The deterministic evaluation of 3D seismic active earth pressures is conducted with the application of the kinematic approach of limit analysis combined with a pseudo-static method. All of the input parameters are deemed as random variables. After the SPCE metamodels are determined, the method of Monte Carle simulations is applied to perform the probabilistic analysis of 3D seismic active earth pressures. The effects of uncertainty levels and correlation relationships of input parameters are investigated. Several illustrative examples of the reliability-based design of retaining walls are shown. Results indicate that uncertainties in input parameters possess a notable effect on 3D seismic active earth pressures, suggesting the need to investigate the problem from a stochastic perspective. In most cases, the soil cohesion, internal friction angle, and seismic coefficient are found to be three of the parameters that affect the final response the most significantly. It is also indicated that considering 3D effects and soil cohesion has a favorable effect on increasing the stability of retained slopes.

Cost-effectiveness of ticagrelor in patients with type 2 diabetes and coronary artery disease with a history of PCI: an economic evaluation of THEMIS-PCI using a Swedish healthcare perpective

Abstract Background The Effect of Ticagrelor on Health Outcomes in diabEtes Mellitus patients Intervention Study (THEMIS) evaluated ticagrelor compared to placebo for the prevention of myocardial infarction (MI), stroke and cardiovascular (CV) death in 19 220 patients with type 2 diabetes (T2DM) and stable coronary artery disease (CAD) with no prior myocardial infarction (MI) or stroke. THEMIS-PCI was a pre-specified subgroup of 11 154 patients who had a history of percutaneous coronary intervention (PCI) when entering the study. In THEMIS, ticagrelor reduced CV death, MI or stroke, although with an increase in major bleeding compared to aspirin alone, and there was a significant interaction between a prior history of PCI and the net benefit of ticagrelor. In the THEMIS-PCI population, ticagrelor plus aspirin provided a favourable net clinical benefit with a significant 15% reduction in all-cause death, MI, stroke, fatal bleed, or intracranial haemorrhage. Objective The objective of this analysis was to estimate the cost-effectiveness of ticagrelor for the prevention of CV events based on the results of the THEMIS-PCI population using a lifetime horizon from a Swedish healthcare perspective. Methods A lifetime Markov state transition model was developed with health states aligned to the THEMIS trial endpoints. Health state transitions were informed by parametric survival equations fitted to patient level data from THEMIS-PCI population. Treatment discontinuation rates were informed by the THEMIS-PCI population, with all patients assumed to discontinue treatment with ticagrelor after four years. The incidence of bleeding and dyspnoea were modelled as adverse events. Costs (2019 Euros) and utility data were derived from the published literature and the THEMIS-PCI population, respectively, and discounted at 3.0% annually. Probabilistic (PSA) and deterministic sensitivity analysis (DSA) were conducted to quantify uncertainty of key input parameters. Results Treatment with ticagrelor plus aspirin over four years resulted in estimated Quality Adjusted Life Year (QALY) gains of 0.09 at an incremental cost of €1,891 compared to aspirin alone. The estimated incremental cost-effectiveness ratio (ICER) was €19,959/QALY. PSA indicated that ticagrelor was cost-effective in 93% of simulations using a willingness-to-pay threshold of €47,000/QALY and DSA showed that cost-effectiveness was robust to changes in key input parameters (ICER range: €16,504 to €25,012/QALY). Conclusion Based on the results of the THEMIS trial, dual antiplatelet therapy with ticagrelor plus aspirin is likely to be a cost-effective treatment compared with aspirin alone for the prevention of CV events in patients with T2DM and CAD with a history of PCI. Funding Acknowledgement Type of funding source: Private company. Main funding source(s): AstraZeneca

Adaptive neural network finite-time command filtered tracking control of fractional-order permanent magnet synchronous motor with input saturation

In this paper, the finite-time position tracking control for the fractional-order permanent magnet synchronous motor with input saturation, load disturbance and parameter uncertainties is considered. Based on the command filtered backstepping method, a novel adaptive neural network finite-time controller is presented. First, the neural network is introduced to approximate the uncertain function. Then, by using a command filter at the output side of the virtual signal, the issue of “explosion of complexity” is avoided. In addition, an adaptive law is applied to eliminate the approximation error and filtering error. Meanwhile, by utilizing the terminal sliding mode control technique, the finite-time signal tracking is achieved. Furthermore, an auxiliary design system is constructed to cope with the input saturation constraint. The proposed scheme not only possesses the superiorities of the classical command filtered backstepping, but also holds quick finite-time convergence property. Numerical simulations and experiment results are included to reveal the validity of the proposed design.

Uncertainty and sensitivity analysis by Monte Carlo simulation: Recovery of trans-resveratrol from grape cane by pressurised low polarity water system

Abstract Mechanistic models used to describe heat, mass and momentum transfers involve output uncertainties, which arise from the assumptions made during the model development, and from the uncertainties in model input parameters. The objective of this study was to address the uncertainties and global sensitivities of the input parameters of a model which was developed for the pressurised low polarity water extraction (PLPW) system. Monte Carlo analysis with 1000 simulation was applied with a defined noise for each input parameters to visualise the uncertainty in the model predictions (total extraction time and extract concentration). Six sensitivity methods (scatter plot, standardised regression coefficient, correlation coefficient, Kruskal-Wallis test, differential analysis, and semi-variogram) were evaluated and compared to obtain the input parameters that were responsible for the output uncertainty. It was found that two (extraction solvent flow rate and particle porosity) out of the nine parameters were mainly responsible for the uncertainty. Results of the uncertainty and sensitivity analyses can be used to build reliable mechanistic models, interpret the model outputs, and prioritise future experimental efforts for PLPW system.

Investigating the impact of the estimation error of fracture intensity (P32) on the evaluation of in-situ rock fragmentation and potential of blocks forming around tunnels

The purpose of this work is to highlight the impact of input parameters uncertainty in discrete fracture network (DFN) models and their engineering applications. We show how the error of an input parameter, here the volumetric discontinuity intensity P32, impacts the DFN model and two important rock mechanics engineering applications: the in-situ fragmentation size distribution and the potential of formation of removable blocks around tunnels, as two key parameters at block cave mining designs. The volumetric discontinuity intensity (P32) is estimated by two different approaches: the first one estimates P32 directly from 1D data and it is straightforward to implement, while the second one is based on the simulation of DFN models and needs both 1D and 2D data sets, which makes it less flexible and time consuming. The estimated values of P32 obtained from the direct approach are found to be more accurate than those by the simulation approach, with significant impacts observed in the constructed discrete fracture network models and in the estimation of the in-situ fragmentation size distribution and potential of formation of removable blocks around tunnels.

Probabilistic risk-based Area of Review (AoR) determination for a deep-saline carbon storage site

Regulatory oversight of a geologic carbon sequestration (GCS) project relies on iterative estimations, throughout the project lifetime, of the area where increased risks to underground sources of drinking water (USDWs) may occur due to injection of CO2. This area, referred to as Area of Review (AoR), is typically delineated by predicting the migration of fluid between the reservoir and the lowermost USDW via an open wellbore using predictions from physics-based reservoir simulators. The inherent uncertainty in input parameters used in reservoir modeling therefore affects the accuracy of determining the AoR for a project. Furthermore, the standard analytical approaches for calculating a critical pressure to delineate the risk area yield an infinite AoR for cases where the injection reservoir is overpressured relative to the USDW. A methodology is presented here to better characterize the risk to USDWs while accounting for the uncertainty in reservoir modeling, with an application to a permitted GCS project with an overpressured injection formation, FutureGen 2.0. The methodology is demonstrated using the National Risk Assessment Partnership’s open-source integrated assessment model (NRAP-Open-IAM) to develop a probabilistic estimate of impact risk to USDW quality. CO2 and pressure predictions from the reservoir modeling conducted using the STOMP-CO2 simulator for the FutureGen 2.0 site are used in a NRAP-Open-IAM model with reservoir, wellbore, and aquifer components to: (1) assess the extent of potential leakage into the USDW for the predicted reservoir pressure conditions; (2) evaluate the extent of potential impact using “no-net-degradation” thresholds; and (3) account for uncertainty in reservoir permeabilities.

Robust optimisation of water flooding using an experimental design-based surrogate model: A case study of a Niger-Delta oil reservoir

Reservoir simulations are often applied in oil field development to predict and optimise the performance of the production process and improve the decision-making process. This process is made complex by the need to account for uncertainties in the simulation model input parameters. The classical Markowitz optimisation model is an approach that can be applied for reservoir performance optimisation considering uncertainty. However, numerous full-physics simulations are required for the optimisation procedure to work. To overcome this challenge, we deploy a surrogate model of total oil production for the robust optimisation of waterflooding in a Niger-Delta reservoir. Using this approach, we reduce computational costs and consider four uncertain geological variables in a robust application of the optimisation routine. To adequately capture the uncertainties in the geological model, we apply 100 realisations of the reservoir model. In our presented case, we show that by applying a proxy model of the cumulative oil production in the study, the computational costs of the optimisation routines are significantly reduced. Overall, we perform more than 10000 proxy model evaluations requiring 4 days and saving 417 days of computation time which is required if full-physics simulations were applied in the optimisation routine. As a result, we can make decisions accounting for uncertainty and the potential implications for variation in the development and operation of the field.