A numerical study of dynamic capillary pressure effect for supercritical carbon dioxide-water flow in porous domain

This is the accepted version of the following article: DAS, D.B. ... et al., 2014. A numerical study of dynamic capillary pressure effect for supercritical carbon dioxide-water flow in porous domain. AIChE Journal, 60 (12), pp. 4266-4278, which has been published in final form at http://dx.doi.org/10.1002/aic.14577Numerical simulations for core-scale capillary pressure (Pc)–saturation (S) relationships have been conducted for a supercritical carbon dioxide-water system at temperatures between 35°C and 65°C at a domain pressure of 15 MPa as typically expected during geological sequestration of CO2. As the Pc-S relationships depend on both S and time derivative of saturation (∂S / ∂t) yielding what is known as the ‘dynamic capillary pressure effect’ or simply ‘dynamic effect’, this work specifically attempts to determine the significance of these effects for supercritical carbon dioxide-water flow in terms of a coefficient, namely dynamic coefficient (τ). The coefficient establishes the speed at which capillary equilibrium for supercritical CO2-water flow is reached. The simulations in this work involved the solution of the extended version of Darcy’s law which represents the momentum balance for individual fluid phases in the system, the continuity equation for fluid mass balance, as well as additional correlations for determining the capillary pressure as a function of saturation, and the physical properties of the fluids as a function of temperature. The simulations were carried for 3D cylindrical porous domains measuring 10 cm in diameter and 12 cm in height. τ was determined by measuring the slope of a best-fit straight line plotted between (i) the differences in dynamic and equilibrium capillary pressures (Pc,dyn – Pc,equ) against (ii) the time derivative of saturation (dS/dt), both at the same saturation value. The results show rising trends for τ as the saturation values reduce, with noticeable impacts of temperature at 50% saturation of aqueous phase. This means that the time to attain capillary equilibrium for the CO2-water system increases as the saturation decreases. From a practical point view, it implies that the time to capillary equilibrium during geological sequestration of CO2 is an important factor and should be accounted for while simulating the flow processes, e.g., to determine the CO2 storage capacity of a geological aquifer. In this task, one would require both the fundamental understanding of the dynamic capillary pressure effects for supercritical CO2-water flow as well as τ values. These issues are addressed in this article

Stochastic description of infiltration between aquifers

Aim of this work is to propose a stochastic description of the leakage between two aquifers separated by a semi-permeable layer with low hydraulic conductivity. The source of uncertainty here considered is the random fluctuation of the phreatic surface of surficial aquifer, originated from random rainfall events. The study focuses on an area surrounding a pumping well penetrating the deep aquifer and impacting its piezometric level, where infiltration from the surficial aquifer can be more harmful. Closed form expressions for the leakage between the surficial and the deep aquifer are used to obtain the long-term probability distribution of leakage flow rate, assuming the shallow phreatic surface dynamics modeled with a Poisson- driven stochastic process. A sensitivity analysis is performed to verify the variability of the probability distribution of leakage within the range of feasible parameter values, then the stochastic model is applied to three field cases where time series of the piezometric levels of the phreatic aquifer are available. Results show that the induced variability of the discharge flowing between aquifers is remarkable and that in general it cannot be neglected despite the low hydraulic conductivity of the semi-permeable layer. The proposed probabilistic model is a useful tool for evaluating the risk associated to contaminant transport into deep aquifers and its fate in relation to groundwater withdrawal

Simulation of muon radiography for monitoring CO2 stored in a geological reservoir

Current methods of monitoring subsurface CO2, such as repeat seismic surveys, are episodic and require highly skilled personnel to acquire the data. Simulations based on simplified models have previously shown that muon radiography could be automated to continuously monitor CO2 injection and migration, in addition to reducing the overall cost of monitoring. In this paper, we present a simulation of the monitoring of CO2 plume evolution in a geological reservoir using muon radiography. The stratigraphy in the vicinity of a nominal test facility is modelled using geological data, and a numerical fluid flow model is used to describe the time evolution of the CO2 plume. A planar detection region with a surface area of 1000 m2 is considered, at a vertical depth of 776 m below the seabed. We find that 1 year of constant CO2 injection leads to changes in the column density of ≲1%, and that the CO2 plume is already resolvable with an exposure time of less than 50 days

Estimating geological CO2 storage security to deliver on climate mitigation

Carbon capture and storage (CCS) can help nations meet their Paris CO2 reduction commitments cost-effectively. However, lack of confidence in geologic CO2 storage security remains a barrier to CCS implementation. Here we present a numerical program that calculates CO2 storage security and leakage to the atmosphere over 10,000 years. This combines quantitative estimates of geological subsurface CO2 retention, and of surface CO2 leakage. We calculate that realistically well-regulated storage in regions with moderate well densities has a 50% probability that leakage remains below 0.0008% per year, with over 98% of the injected CO2 retained in the subsurface over 10,000 years. An unrealistic scenario, where CO2 storage is inadequately regulated, estimates that more than 78% will be retained over 10,000 years. Our modelling results suggest that geological storage of CO2 can be a secure climate change mitigation option, but we note that long-term behaviour of CO2 in the subsurface remains a key uncertainty

Multi-source statistics:Basic situations and methods

Many National Statistical Institutes (NSIs), especially in Europe, are moving from single‐source statistics to multi‐source statistics. By combining data sources, NSIs can produce more detailed and more timely statistics and respond more quickly to events in society. By combining survey data with already available administrative data and Big Data, NSIs can save data collection and processing costs and reduce the burden on respondents. However, multi‐source statistics come with new problems that need to be overcome before the resulting output quality is sufficiently high and before those statistics can be produced efficiently. What complicates the production of multi‐source statistics is that they come in many different varieties as data sets can be combined in many different ways. Given the rapidly increasing importance of producing multi‐source statistics in Official Statistics, there has been considerable research activity in this area over the last few years, and some frameworks have been developed for multi‐source statistics. Useful as these frameworks are, they generally do not give guidelines to which method could be applied in a certain situation arising in practice. In this paper, we aim to fill that gap, structure the world of multi‐source statistics and its problems and provide some guidance to suitable methods for these problems

Forecasting with Big Data: A Review

Big Data is a revolutionary phenomenon which is one of the most frequently discussed topics in the modern age, and is expected to remain so in the foreseeable future. In this paper we present a comprehensive review on the use of Big Data for forecasting by identifying and reviewing the problems, potential, challenges and most importantly the related applications. Skills, hardware and software, algorithm architecture, statistical significance, the signal to noise ratio and the nature of Big Data itself are identified as the major challenges which are hindering the process of obtaining meaningful forecasts from Big Data. The review finds that at present, the fields of Economics, Energy and Population Dynamics have been the major exploiters of Big Data forecasting whilst Factor models, Bayesian models and Neural Networks are the most common tools adopted for forecasting with Big Data