Reservoir Simulation Studies for Planning Monitoring Schemes for CO2 Storage

2021 ◽  
Author(s):  
Georgios Nikolakopoulos-Skelly ◽  
Marie Ann Giddins ◽  
Rong Xu ◽  
Chioma Ezeogu ◽  
Matthew Jackson
Keyword(s):  
Sensitivity Analysis ◽  
Reservoir Simulation ◽  
Co2 Storage ◽  
Storage Period ◽  
Model Parameters ◽  
Pressure Curve ◽  
Observation Well ◽  
Gas Saturation ◽  
Critical Gas Saturation ◽  
Co2 Plume

Abstract In this paper, we describe an approach to designing monitoring schemes for carbon dioxide sequestration in saline aquifers. Changes in key parameters are investigated over timescales of up to a thousand years. The study addresses movement of the CO2 plume, possible locations for observation wells and the period for which a storage location should be monitored. For the initial sensitivity analysis, we use a simple homogeneous reservoir simulation model to understand how reservoir, operational and model parameters affect the amount of mobile CO2 remaining at different times over the storage period. The parameters with the greatest impact are taken forward to uncertainty studies, which are conducted on two reservoir models with more realistic geological characteristics: one with lateral extensive baffles and one with sand channels. For these cases, we investigate the movement of the CO2 plume and its arrival at possible locations for an observation well. Results from the sensitivity analysis indicate that the most influential parameters are horizontal permeability, dipping angle, critical gas saturation, salinity, the period of injection and the capillary pressure curve. The results from the uncertainty studies indicate that for the two heterogeneous models, a reasonable monitoring period is in the range of 60 to 150 years and that the movement of the plume probably stops after approximately 100 years. The arrival time of CO2 at the observation well can be predicted with greater confidence when the well is in close proximity to the injector and in the direction in which CO2 will preferably move. A correlation analysis on the uncertain parameters shows that the main contributor affecting the amount of mobile CO2 is critical gas saturation, followed by dipping angle and the period of injection. While previous studies focus on how different parameters affect immobilization of CO2, this study aims to develop a methodology to plan long-term monitoring of mobile CO2. Prediction of the expected plume movement can help to determine suitable observation well locations and reasonable timescales for the monitoring process.

Understanding the Effect of Rock Compressibility on CO2 Storage Capacity Estimation in a Depleted Carbonate Gas Reservoir

2021 ◽  
Author(s):  
Prasanna Chidambaram ◽  
Raj Deo Tewari ◽  
Siti Syareena Mohd Ali ◽  
Chee Phuat Tan
Keyword(s):  
Sensitivity Analysis ◽  
Greenhouse Gases ◽  
Simulation Model ◽  
Reservoir Simulation ◽  
Storage Capacity ◽  
Co2 Storage ◽  
History Match ◽  
Hydrocarbon Reservoir ◽  
Rock Compressibility

Abstract Avoiding or reducing greenhouse gases emission in the atmosphere requires extensive application of technologies and one of them is underground CO2 sequestration. Capture and storage of CO2 in depleted hydrocarbon reservoirs can reduce greenhouse gases released into the atmosphere effectively. Hydrocarbon reservoirs are considered one of the ideal geologic storage sites as they have held hydrocarbons over millions of years. Their architecture and properties are well understood due to exploration and production activities from these reservoirs. Storage projects require a large depleted hydrocarbon reservoir with good reservoir properties and are affected by several factors including voidage created by hydrocarbon production, pressure, architecture, formation permeability, aquifer influx, subsidence and compaction, and rock compressibility to name a few. Thus, realistic estimation of the storage capacity of the reservoir is a key step in the evaluation of CO2 storage plan. A good history matched simulation model incorporating the geomechanical parameters is essential to estimate storage capacity of the reservoir. Three major depleted gas reservoirs in Central Luconia field, located in offshore Sarawak, are being evaluated for future CO2 storage. Reservoir simulation is used as a tool to estimate future CO2 storage capacity of these reservoirs. Reliability of forecast from a reservoir simulation model is dependent on the quality of history match achieved. Hence it is believed that CO2 storage capacity estimates obtained from a good history matched simulation model must be reliable. However, during history matching exercise in these reservoirs, it was observed that an acceptable history match could be achieved with a range of rock compressibility values and aquifer influxes. Generally, a constant value of rock compressibility is used in conventional simulation. For example, in order to obtain an acceptable history match, with a lower compressibility, a larger aquifer influx is needed and vice versa. Interestingly, a forecast using these history match cases yield different CO2 storage capacities. A closer evaluation shows that aquifer influx has a strong impact on future CO2 storage capacity. An acceptable quality of history match can be obtained for a range of rock compressibility values when aquifer influx is adjusted along with it. Sensitivity analysis shows that future CO2 storage capacity in depleted hydrocarbon reservoir is sensitive to rock compressibility used in the simulation model. A detailed sensitivity analysis along with multiple history match scenarios is necessary to understand the range in future storage capacity when evaluating CO2 storage plan.

scholarly journals A Sensitivity Analysis of the SPACSYS Model

Agriculture ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 624
Author(s):  
Yan Shan ◽  
Mingbin Huang ◽  
Paul Harris ◽  
Lianhai Wu
Keyword(s):  
Sensitivity Analysis ◽  
Wheat Yield ◽  
Climatic Conditions ◽  
Water Dynamics ◽  
Ecological Modelling ◽  
Soil Conditions ◽  
Model Parameters ◽  
Nutrient Inputs ◽  
Nutrient Pool ◽  
Wheat Field

A sensitivity analysis is critical for determining the relative importance of model parameters to their influence on the simulated outputs from a process-based model. In this study, a sensitivity analysis for the SPACSYS model, first published in Ecological Modelling (Wu, et al., 2007), was conducted with respect to changes in 61 input parameters and their influence on 27 output variables. Parameter sensitivity was conducted in a ‘one at a time’ manner and objectively assessed through a single statistical diagnostic (normalized root mean square deviation) which ranked parameters according to their influence of each output variable in turn. A winter wheat field experiment provided the case study data. Two sets of weather elements to represent different climatic conditions and four different soil types were specified, where results indicated little influence on these specifications for the identification of the most sensitive parameters. Soil conditions and management were found to affect the ranking of parameter sensitivities more strongly than weather conditions for the selected outputs. Parameters related to drainage were strongly influential for simulations of soil water dynamics, yield and biomass of wheat, runoff, and leaching from soil during individual and consecutive growing years. Wheat yield and biomass simulations were sensitive to the ‘ammonium immobilised fraction’ parameter that related to soil mineralization and immobilisation. Simulations of CO2 release from the soil and soil nutrient pool changes were most sensitive to external nutrient inputs and the process of denitrification, mineralization, and decomposition. This study provides important evidence of which SPACSYS parameters require the most care in their specification. Moving forward, this evidence can help direct efficient sampling and lab analyses for increased accuracy of such parameters. Results provide a useful reference for model users on which parameters are most influential for different simulation goals, which in turn provides better informed decision making for farmers and government policy alike.

scholarly journals Global Sensitivity Analysis of a Homogenized Constrained Mixture Model of Arterial Growth and Remodeling

2021 ◽  
Author(s):  
Sebastian Brandstaeter ◽  
Sebastian L. Fuchs ◽  
Jonas Biehler ◽  
Roland C. Aydin ◽  
Wolfgang A. Wall ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Model Parameters ◽  
Computational Studies ◽  
Growth And Remodeling ◽  
Global Sensitivity ◽  
Output Variability ◽  
Constrained Mixture ◽  
The One ◽  
Influential Parameters

AbstractGrowth and remodeling in arterial tissue have attracted considerable attention over the last decade. Mathematical models have been proposed, and computational studies with these have helped to understand the role of the different model parameters. So far it remains, however, poorly understood how much of the model output variability can be attributed to the individual input parameters and their interactions. To clarify this, we propose herein a global sensitivity analysis, based on Sobol indices, for a homogenized constrained mixture model of aortic growth and remodeling. In two representative examples, we found that 54–80% of the long term output variability resulted from only three model parameters. In our study, the two most influential parameters were the one characterizing the ability of the tissue to increase collagen production under increased stress and the one characterizing the collagen half-life time. The third most influential parameter was the one characterizing the strain-stiffening of collagen under large deformation. Our results suggest that in future computational studies it may - at least in scenarios similar to the ones studied herein - suffice to use population average values for the other parameters. Moreover, our results suggest that developing methods to measure the said three most influential parameters may be an important step towards reliable patient-specific predictions of the enlargement of abdominal aortic aneurysms in clinical practice.

scholarly journals Gaussian Processes Proxy Model with Latent Variable Models and Variogram-Based Sensitivity Analysis for Assisted History Matching

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4290
Author(s):  
Dongmei Zhang ◽  
Yuyang Zhang ◽  
Bohou Jiang ◽  
Xinwei Jiang ◽  
Zhijiang Kang
Keyword(s):  
Sensitivity Analysis ◽  
Gaussian Processes ◽  
Latent Variable ◽  
History Matching ◽  
Latent Variable Models ◽  
High Dimensional ◽  
Model Parameters ◽  
Variable Model ◽  
Assisted History Matching ◽  
Proxy Models

Reservoir history matching is a well-known inverse problem for production prediction where enormous uncertain reservoir parameters of a reservoir numerical model are optimized by minimizing the misfit between the simulated and history production data. Gaussian Process (GP) has shown promising performance for assisted history matching due to the efficient nonparametric and nonlinear model with few model parameters to be tuned automatically. Recently introduced Gaussian Processes proxy models and Variogram Analysis of Response Surface-based sensitivity analysis (GP-VARS) uses forward and inverse Gaussian Processes (GP) based proxy models with the VARS-based sensitivity analysis to optimize the high-dimensional reservoir parameters. However, the inverse GP solution (GPIS) in GP-VARS are unsatisfactory especially for enormous reservoir parameters where the mapping from low-dimensional misfits to high-dimensional uncertain reservoir parameters could be poorly modeled by GP. To improve the performance of GP-VARS, in this paper we propose the Gaussian Processes proxy models with Latent Variable Models and VARS-based sensitivity analysis (GPLVM-VARS) where Gaussian Processes Latent Variable Model (GPLVM)-based inverse solution (GPLVMIS) instead of GP-based GPIS is provided with the inputs and outputs of GPIS reversed. The experimental results demonstrate the effectiveness of the proposed GPLVM-VARS in terms of accuracy and complexity. The source code of the proposed GPLVM-VARS is available at https://github.com/XinweiJiang/GPLVM-VARS.

scholarly journals Cost-effectiveness of TAS-102 plus bevacizumab versus TAS-102 monotherapy in patients with metastatic colorectal cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Kiyoaki Sugiura ◽  
Yuki Seo ◽  
Takayuki Takahashi ◽  
Hideyuki Tokura ◽  
Yasuhiro Ito ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Health Care ◽  
Sensitivity Analysis ◽  
Cost Effectiveness ◽  
Combination Therapy ◽  
Metastatic Colorectal Cancer ◽  
Cost Effective ◽  
Model Parameters ◽  
Combination Regimen ◽  
The Cost

Abstract Background TAS-102 plus bevacizumab is an anticipated combination regimen for patients who have metastatic colorectal cancer. However, evidence supporting its use for this indication is limited. We compared the cost-effectiveness of TAS-102 plus bevacizumab combination therapy with TAS-102 monotherapy for patients with chemorefractory metastatic colorectal cancer. Method Markov decision modeling using treatment costs, disease-free survival, and overall survival was performed to examine the cost-effectiveness of TAS-102 plus bevacizumab combination therapy and TAS-102 monotherapy. The Japanese health care payer’s perspective was adopted. The outcomes were modeled on the basis of published literature. The incremental cost-effectiveness ratio (ICER) between the two treatment regimens was the primary outcome. Sensitivity analysis was performed and the effect of uncertainty on the model parameters were investigated. Results TAS-102 plus bevacizumab had an ICER of $21,534 per quality-adjusted life-year (QALY) gained compared with TAS-102 monotherapy. Sensitivity analysis demonstrated that TAS-102 monotherapy was more cost-effective than TAS-102 and bevacizumab combination therapy at a willingness-to-pay of under $50,000 per QALY gained. Conclusions TAS-102 and bevacizumab combination therapy is a cost-effective option for patients who have metastatic colorectal cancer in the Japanese health care system.

scholarly journals Numerical Simulation and Optimization of CO2 Sequestration in Saline Aquifers

2012 ◽  
Author(s):  
Zheming Zhang ◽  
Ramesh Agarwal
Keyword(s):  
Numerical Simulation ◽  
Co2 Sequestration ◽  
Storage Capacity ◽  
Co2 Storage ◽  
Injection Well ◽  
Co2 Injection ◽  
Great Promise ◽  
Saline Aquifer ◽  
Simulation And Optimization ◽  
Co2 Plume

With recent concerns on CO2 emissions from coal fired electricity generation plants; there has been major emphasis on the development of safe and economical Carbon Dioxide Capture and Sequestration (CCS) technology worldwide. Saline reservoirs are attractive geological sites for CO2 sequestration because of their huge capacity for sequestration. Over the last decade, numerical simulation codes have been developed in U.S, Europe and Japan to determine a priori the CO2 storage capacity of a saline aquifer and provide risk assessment with reasonable confidence before the actual deployment of CO2 sequestration can proceed with enormous investment. In U.S, TOUGH2 numerical simulator has been widely used for this purpose. However at present it does not have the capability to determine optimal parameters such as injection rate, injection pressure, injection depth for vertical and horizontal wells etc. for optimization of the CO2 storage capacity and for minimizing the leakage potential by confining the plume migration. This paper describes the development of a “Genetic Algorithm (GA)” based optimizer for TOUGH2 that can be used by the industry with good confidence to optimize the CO2 storage capacity in a saline aquifer of interest. This new code including the TOUGH2 and the GA optimizer is designated as “GATOUGH2”. It has been validated by conducting simulations of three widely used benchmark problems by the CCS researchers worldwide: (a) Study of CO2 plume evolution and leakage through an abandoned well, (b) Study of enhanced CH4 recovery in combination with CO2 storage in depleted gas reservoirs, and (c) Study of CO2 injection into a heterogeneous geological formation. Our results of these simulations are in excellent agreement with those of other researchers obtained with different codes. The validated code has been employed to optimize the proposed water-alternating-gas (WAG) injection scheme for (a) a vertical CO2 injection well and (b) a horizontal CO2 injection well, for optimizing the CO2 sequestration capacity of an aquifer. These optimized calculations are compared with the brute force nearly optimized results obtained by performing a large number of calculations. These comparisons demonstrate the significant efficiency and accuracy of GATOUGH2 as an optimizer for TOUGH2. This capability holds a great promise in studying a host of other problems in CO2 sequestration such as how to optimally accelerate the capillary trapping, accelerate the dissolution of CO2 in water or brine, and immobilize the CO2 plume.

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