Generating candidate networks for optimization: The CO2 capture and storage optimization problem

We develop a new framework for spatially optimizing infrastructure for CO₂ capture and storage (CCS). CCS is a complex and challenging problem: domestically deploying CCS at a meaningful scale will require linking hundreds of coal-fired power plants with CO₂ sequestration reservoirs through a dedicated and extensive (many tens-of-thousands of miles) CO₂ pipeline network. We introduce a unique method for generating a candidate network from scratch, from which the optimization model selects the optimal set of arcs to form the pipeline network. This new generation method can be applied to any network optimization problem including transmission line, roads, and telecommunication applications. We demonstrate the model and candidate network methodology using a real example of capturing CO₂ from coal-fired power plants in the US Midwest and storing the CO₂ in depleted oil and gas fields. Results illustrate the critical need to balance CCS investments with generating a candidate network of arcs.     

Designing a cost-effective CO2 storage infrastructure using a GIS based linear optimization energy model

Large-scale deployment of carbon capture and storage needs a dedicated infrastructure. Planning and designing of this infrastructure require incorporation of both temporal and spatial aspects. In this study, a toolbox has been developed that integrates ArcGIS, a geographical information system with spatial and routing functions, and MARKAL, an energy bottom-up model based on linear optimization. Application of this toolbox led to blueprints of a CO₂ infrastructure in the Netherlands. The results show that in a scenario with 20% and 50% CO₂ emissions reduction targets compared to their 1990 level in respectively 2020 and 2050, an infrastructure of around 600 km of CO₂ trunklines may need to be built before 2020. Investment costs for the pipeline construction and the storage site development amount to around 720 m€ and 340 m€, respectively. The results also show the implication of policy choices such as allowing or prohibiting CO₂ storage onshore on CO₂ Capture and Storage (CCS) and infrastructure development. This paper illustrates how the ArcGIS/MARKAL-based toolbox can provide insights into a CCS infrastructure development, and support policy makers by giving concrete blueprints over time with respect to scale, pipeline trajectories, and deployment of individual storage sites.     

Valuation of R&;D Sequential Exchange Options Using Monte Carlo Approach

This article describes a methodology for evaluating R&D investment projects using Monte Carlo method. R&D projects generally involve multiple phases with or without overlapping. R&D investments are made often in a phased manner, with the commencement of subsequent phase being dependent on the successful completion of the preceding phase. This is known as sequential investment. Moreover, each stage creates an opportunity (option) for subsequent investment. Therefore, R&D projects can be considered as ‘Compound Options’ in which investments present uncertainty both in the gross project value and in their costs. It is possible to use exchange options to value the R&D investment opportunities. In this paper, we propose to evaluate the European and American Real Compound exchange options through Monte Carlo simulations. We also provide a set of numerical experiments to provide evidence for the accuracy of the proposed methodology.      

Efficient screening of enhanced oil recovery methods and predictive economic analysis

Oil demand for economic development around the world is rapidly increasing. Moreover, oil production rates are getting a peak in mature reservoirs and tending to decline in the near future, which has led to considerable researches on enhanced oil recovery (EOR) methods. Therefore, an efficient technical and economical screening to appropriate selection of EOR methods can make savings in time and cost. The purpose of this communication is to present a method to select an efficient EOR process and investigate its economic parameters. A database of reservoir parameters of rock and fluid properties along with successful EOR techniques has been collected and analyzed. First, an artificial neural network (ANN) was developed to classify the EOR methods technically. Then, an economical EOR screening model was designed, and then, future cash flows on the use of EOR methods were predicted. The results show that the ANN system can select proper EOR methods and classify them. Moreover, the obtained results indicate that the economic analysis performed in this study is efficient and useful to predict future cash flows.     

A comparison study of multi-component Lattice Boltzmann models for flow in porous media applications

A comparison study of three different multi-component Lattice Boltzmann models is carried out to explore their capability of describing binary immiscible fluid systems. The Shan–Chen pseudo potential model, the Oxford free energy model and the colour gradient model are investigated using the multi-relaxation time scheme (MRT) algorithm to study the flow of binary immiscible fluids. We investigate Poiseuille flow of layered immiscible binary fluids and capillary fingering phenomena and evaluate the results against analytical solutions. In addition, we examine the capability of the various models to simulate fluids with significant viscosity and density contrast and suitable interface thickness. This is of great importance for large scale flow in porous media applications, where it is important to minimise the interfacial thickness from a computational point of view. We find that the Shan–Chen model can simulate high density ratios up to 800 for binary fluids with the same viscosity. Imposing a viscosity contrast will lead to highly diffusive interfaces in the Shan–Chen model and therefore this will affect significantly the numerical stability. The Free Energy model and the colour gradient model have similar capabilities on this point: they can simulate binary fluids having the same density but with significant viscosity contrast. This is of great importance to study the flow of water, supercritical CO₂ and oil in porous media, for CO₂ storage and Enhanced Oil Recovery (EOR) operations.     

Connectionist approaches for solubility prediction of n-alkanes in supercritical carbon dioxide

Carbon dioxide injection is a known promising and economical technology for improving oil recovery. Despite its immense effect on oil recovery, the application of this technique in modern recovery industry has been limited due to poor solubility of n-alkanes in supercritical CO₂. Therefore, it is very consequential to investigate the solubility of different n-alkanes in supercritical CO₂. Since experimental methods for measuring the solubility of n-alkanes in supercritical CO₂ at different temperatures and pressures are not economical and usually take a long time, feasibility of applying intelligent tools in the solubility prediction of different n-alkanes in supercritical CO₂ at pressures up to 45.9 MPa was conducted in this study. For this purpose, two models including an artificial neural network and an adaptive neuro-fuzzy interference system (ANFIS) both trained with particle swarm optimization (PSO) algorithm were used for simulating this process. Calculated mole fractions of n-alkanes in supercritical CO₂ from ANFIS–PSO model were excellently consistent with actual measured values. Moreover, comparison between these models and Chrastil semiempirical correlation show superiority and accuracy of the proposed ANFIS–PSO approach. Results of this study indicate that ANFIS–PSO method is a powerful technique for predicting solubility of n-alkanes in supercritical CO₂.

     

Relative importance,specific investment and ownership in interorganizational systems

Implementation and maintenance of interorganizational systems (IOS) require investments by all the participating firms. Compared with intraorganizational systems, however, there are additional uncertainties and risks. This is because the benefits of IOS investment depend not only on a firm’s own decisions, but also on those of its business partners. Without appropriate levels of investment by all the firms participating in an IOS, they cannot reap the full benefits. Drawing upon the literature in institutional economics, we examine IOS ownership as a means to induce value-maximizing noncontractible investments. We model the impact of two factors derived from the theory of incomplete contracts and transaction cost economics: relative importance of investments and specificity of investments. We apply the model to a vendor-managed inventory system (VMI) in a supply chain setting. We show that when the specificity of investments is high, this is a more critical determinant of optimal ownership structure than the relative importance of investments. As technologies used in IOS become increasingly redeployable and reusable, and less specific, the relative importance of investments becomes a dominant factor. We also show that the bargaining mechanism—or the agreed upon approach to splitting the incremental payoffs—that is used affects the relationship between these factors in determining the optimal ownership structure of an IOS.

A workflow for handling heterogeneous 3D models with the TOUGH2 family of codes: Applications to numerical modeling of CO2 geological storage

This paper is addressed to the TOUGH2 user community. It presents a new tool for handling simulations run with the TOUGH2 code with specific application to CO₂ geological storage. This tool is composed of separate FORTRAN subroutines (or modules) that can be run independently, using input and output files in ASCII format for TOUGH2. These modules have been developed specifically for modeling of carbon dioxide geological storage and their use with TOUGH2 and the Equation of State module ECO2N, dedicated to CO₂-water-salt mixture systems, with TOUGHREACT, which is an adaptation of TOUGH2 with ECO2N and geochemical fluid-rock interactions, and with TOUGH2 and the EOS7C module dedicated to CO₂-CH₄ gas mixture is described. The objective is to save time for the pre-processing, execution and visualization of complex geometry for geological system representation. The workflow is rapid and user-friendly and future implementation to other TOUGH2 EOS modules for other contexts (e.g. nuclear waste disposal, geothermal production) is straightforward. Three examples are shown for validation: (i) leakage of CO₂ up through an abandoned well; (ii) 3D reactive transport modeling of CO₂ in a sandy aquifer formation in the Sleipner gas Field, (North Sea, Norway); and (iii) an estimation of enhanced gas recovery technology using CO₂ as the injected and stored gas to produce methane in the K12B Gas Field (North Sea, Denmark).     

Influence of CO2 leakage from oil-producing wells on crop growth based on improved CASA model

As a significant application of CO₂ geological storage technology, CO₂-enhanced oil recovery (CO₂-EOR) considers impacts on ecosystems due to the risk of CO₂ leakage from oil-producing wells. It is critical to clarify the relationship between CO₂ leakage and vegetation growth, because large numbers of wells are situated in the midst of cropland. In this study, we calculated the net primary productivity (NPP) of crops to examine the correlation between oil-producing wells and crop growth using Pleiades’ high-spatial resolution remote-sensing imaging in a CO₂-EOR experimental area in Shandong Province, China, basing our methods on the improved Carnegie–Ames–Stanford approach (CASA) model. Atmospheric CO₂ concentrations at varying distances from one of the producing wells were collected by wireless sensors and hand-held equipment, and analysis was conducted in regard to the impact of CO₂ leakage from producing wells on crop growth according to a simple sampling method. The results indicated that increased CO₂ concentration resulting from leakage from producing wells has a slightly positive influence on crop productivity, with the influence ranges correlated with wind direction and velocity. The variance of NPP resulting from increased CO₂ concentration and wind direction ranged from 5.0 to 33.7%.     

Development of a methodology for evaluating computer integrated manufacturing (CIM) implementation performance

Competitive pressures of global markets has stimulated significant interest in upgrading of process technologies in manufacturing sectors of the economy. In response to these pressures, manufacturing firms have made substantial investments in advanced manufacturing technologies involving the pervasive use of computers to integrate the various functions of the enterprise. Although there are varying reports of the performance of these computer integrated systems, there has not been any consistent method used for evaluating implementation performance from one project and organization to the next. In order to compare Computer Integrated Manufacturing (CIM) implementation performance, effective procedures for evaluating individual projects are necessary. This paper presents an approach to evaluating CIM systems performance for comparison across a spectrum of projects.     

An Approach to Evaluating E-Business Information Systems Projects

Capital investments in the e-business infrastructure add complexity to the IT payoff question because e-business interorganizational investments are deployed across multiple platforms, projects, vendors and partners. Traditional MIS research has been devoted to measuring the payoff from information technology (IT) investment without any specific focus on e-business. Flaws in the mature MIS performance measures seem to yield weak guidance for managers when evaluating IT success, thus diluting the potential for these measures to be adapted for use in the new e-business environment. A review of the conventional MIS payoff literature indicates that these measures may be incomplete, inaccurate, or inefficient for application to electronic commerce investments. This paper brings four new points to the e-business IT investment evaluation dialogue: (1) we first explore performance measurement validity flaws in our long established measures; (2) the paper discusses a two-by-two matrix delineating the gap between the quantitative versus qualitative performance measures of management information systems (MIS) initiatives; (3) sample e-business payoff techniques are discussed and reviewed in light of these historical imperfections, and (4) fourth, the paper proposes and describes an innovative framework derived from production theory economics for future research in evaluating e-business MIS implementations.     

Spatially-explicit water balance implications of carbon capture and sequestration

Implementation of carbon capture and sequestration (CCS) will increase water demand due to the cooling water requirements of CO₂ capture equipment. If the captured CO₂ is injected into saline aquifers for sequestration, brine may be extracted to manage the aquifer pressure, and can be desalinated to provide additional freshwater supply. We conduct a geospatial analysis to determine how CCS may affect local water supply and demand across the contiguous United States. We calculate baseline indices for each county in the year 2005, and project future water supply and demand with and without CCS through 2030. We conduct sensitivity analyses to identify the system parameters that most significantly affect water balance. Water supply changes due to inter-annual variability and projected climate change are overwhelmingly the most significant sources of variation. CCS can have strong local effects on water supply and demand, but overall it has a modest effect on water balances.     

云存储服务中可证明数据持有及恢复技术研究

云存储可以实现任意地点、任意时间、任意数据访问及保障法规遵从的需求等.对存储需求不可预测、需要廉价存储的用户来说,按需购买存储容量的云存储与一次性购买整套存储系统相比显然会带来更多的方便和效益,且云存储在为用户节省投资的同时也节约了社会资源与能源.但是,因为云存储的安全性、可靠性及服务水平等还存在众多问题亟待解决,所以云存储还未得到人们的广泛认可与使用.当用户将数据存放在云存储中,他们最关心的是数据是否完整无误;如果出现故障,是否可以恢复其数据.所以,在云存储中只有让用户可以验证存储服务提供者正确地持有其数据,且当检测到错误时可实现数据恢复,他们才可放心地使用云存储.综述了可证明数据持有及恢复技术在国内外的研究现状,讨论了云存储服务的安全性与可靠性需求,并研究云存储服务对可证明数据持有及恢复方案的特殊要求,从而明确在云存储环境下可证明数据持有及恢复技术的研究方向.     

Streamlining Digital Modeling and Building Information Modelling (BIM) Uses for the Oil and Gas Projects

The oil and gas industry is a technology-driven industry. Over the last two decades, it has heavily made use of digital modeling and associated technologies (DMAT) to enhance its commercial capability. Meanwhile, the Building Information Modelling (BIM) has grown at an exponential rate in the built environment sector. It is not only a digital representation of physical and functional characteristics of a facility, but it has also made an impact on the management processes of building project lifecycle. It is apparent that there are many similarities between BIM and DMAT usability in the aspect of physical modeling and functionality. The aim of this study is to streamline the usage of both DMAT and BIM whilst discovering valuable practices for performance improvement in the oil and gas projects. To achieve this, 28 BIM guidelines, 83 DMAT academic publications and 101 DMAT vendor case studies were selected for review. The findings uncover (a) 38 BIM uses; (b) 32 DMAT uses and; (c) 36 both DMAT and BIM uses. The synergy between DMAT and BIM uses would render insightful references into managing efficient oil and gas’s projects. It also helps project stakeholders to recognise future investment or potential development areas of BIM and DMAT uses in their projects.     

Pricing real abandonment options on several R&;D investment projects

Much previous research has shown that the R&D investments can be evaluated by real growth options approach. But few studies have been done on real abandonment options for R&D projects which may not succeed. The contribution of this paper is not only to derive a more general closed-form solution for evaluating real abandonment options, but to put backup project consideration into our model for reality. We show that both Black-Scholes’s and Stulz’s models are special cases of our model under some specifications of parameters. From the simulation results, we explore that the higher the percentage of recovering salvage value, the more investment projects should be carried out. We hope that the results in this study could provide a useful reference for the manager, to make better decisions regarding backup projects.     

Real options analysis of the timing of IS investment decisions

Many information systems (IS) investments belong to a class of capital budgeting problem where there is an option: the investment may be made straight away or delayed for some period. A real options analysis could allow decision-makers to add value to these investment decisions by providing a framework that explicitly recognises uncertainty. This paper uses options pricing theory to determine the optimal timing of IS investments and to explore the effect of different investment review cycles. The findings provide support for the common industry practice of demanding short payback periods for IS investments.     

A process-focused decision framework for analyzing the business value potential of IT investments

A key consideration during investment decision making is the overall business value potential of an information technology (IT) solution. The complexity of the contemporary IT landscape is increasing. As information systems and technologies become more advanced and interconnected, they often impact multiple business processes in the organization. This in turn increases the complexity of IT investment decisions. This paper describes a decision framework for considering investments in information technologies that impact multiple business processes in the organization. The decision framework is illustrated via a case study of a small business that invested in mobile and wireless computing. The microcosm of the small business serves to illustrate some aspects of the business value derived from information technology investments that are often challenging to isolate in more complex organizational environments. The decision framework can support managers to analyze the overall business value returns arising from the ‘ripple effect’ of an IT investment on core and ancillary business processes. In the decision framework, the business value ripple effect is analyzed via a vertical dimension that emanates from core business processes to ancillary processes, and a horizontal dimension that extends over time.

Real options-based evaluation model for transformable plant designs in the process industry

Transformable plant designs are emerging in the process industry as an alternative production concept to conventional large-scale plant designs. While transformable plant designs come at the disadvantage of higher costs per unit due to lower scale effects and higher investment costs per capacity unit installed, their major advantage are the flexible choices of capacity, product, and location which allow for quick adjustments to market changes. The value of such flexibility is not captured in traditional methods of investment appraisal such as the net present value method. When using these methods flexible technologies may appear less attractive than they actually are. More recently, the real options approach has been proposed as a tool to value investments in flexible technologies. In this paper we present an evaluation model for the economic assessment of investments in transformable plant designs that is able to capture different kinds of flexibility in a setting with multiple sources of uncertainty. The implementation of the model in two case studies shows that the flexibility value is a significant driver of the overall value of transformable plant designs.     

Best mix of primary energy resources by renewable energy and fossil fuel with CCS in view of security,stability and sustainability——A vision on hydrogen supply chain by organic chemical hydride method

The best mix scenario by renewable energy and fossil fuel with or without CCS(Carbon Dioxide Capture and Storage) would be a solution to compromise Greenhouse Gases emission issue caused by carbon dioxide(CO2),and depletion of crude oil and natural gas reserves.As fossil fuel with pre-combustion CCS means hydrogen manufacturing and also hydrogen can be produced via electrolysis with renewable energy,it is desirable to establish transportation and storage systems of hydrogen as a clean energy.In this paper a...     

Robust Monte Carlo Method for R&D Real Options Valuation

This paper is devoted to developing a robust numerical analysis of least squares Monte Carlo (LSM) in valuing R&D investment opportunities. As it is well known, R&D projects are characterized by sequential investments and therefore they can be considered as compound options involving a set of interacting American-type options. The basic Monte Carlo simulation takes a long time and it is computationally intensive and inefficient. In this context, LSM method is a powerful and flexible tool for capital budgeting decisions and for valuing R&D investments. In particular way, numerical tests are performed to examine the optimal choice of basis function and polynomial degree in terms of reduction of the execution time, accuracy and improvement in the simulation.