In the energy field, the decisions need to take into consideration several factors such as the needs of the population, the environment, suitability, capital cost, sustainability, political goals and the actors involved, with their interests and preferences. The lack of homogeneity in all the factors that must be consider makes it necessary to design a process that guides the analysis process of any type of decision-maker. Decision analysis methods have been developed to aid decision-makers identify a problem, determine the criteria to be consider and their importance, recognize the stakeholders that need to be involved and pose the different alternatives to resolve or to best address the problem. These techniques range from simple to more mathematically oriented ones, from single criterion evaluation to multiple criteria, and from purely qualitative or quantitative to mixed techniques. Within the field of decision analysis, multi-criteria techniques are better suited to aid in decision situations in the energy field as these decisions require several considerations beside economic ones. This thesis uses theories and notions of decision analysis to construct a framework to be used in any energy related decision situations by non-experts. The framework tackles common challenges faced by multi-criteria decision analysis methods, including the identification of stakeholders and decision-makers, the aggregation of various decision-makers, preferences and heterogeneous inputs, and the selection of suitable criteria, alternatives and methods.

<p>Background and Motivation</p><p>Unlike what is common in Europe and the rest of the world, Norway has traditionally met most of its stationary energy demand (including heating) with electricity, because of abundant access to hydropower. However, after the deregulation of the Norwegian electricity market in the 1990s, the increase in the electricity generation capacity has been less than the load demand increase. This is due to the relatively low electricity prices during the period, together with the fact that Norway’s energy companies no longer have any obligations to meet the load growth. The country’s generation capacity is currently not sufficient to meet demand, and accordingly, Norway is now a net importer of electricity, even in normal hydrological years. The situation has led to an increased focus on alternative energy solutions.</p><p>It has been common that different energy infrastructures – such as electricity, district heating and natural gas networks – have been planned and commissioned by independent companies. However, such an organization of the planning means that synergistic effects of a combined energy system to a large extent are neglected. During the last decades, several traditional electricity companies have started to offer alternative energy carriers to their customers. This has led to a need for a more comprehensive and sophisticated energy-planning process, where the various energy infrastructures are planned in a coordinated way. The use of multi-criteria decision analysis (MCDA) appears to be suited for coordinated planning of energy systems with multiple energy carriers. MCDA is a generic term for different methods that help people make decisions according to their preferences in situations characterized by multiple conflicting criteria.</p><p>The thesis focuses on two important stages of a multi-criteria planning task:</p><p>- The initial structuring and modelling phase</p><p>- The decision-making phase</p><p>The Initial Structuring and Modelling Phase</p><p>It is important to spend sufficient time and resources on the problem definition and structuring, so that all disagreements among the decision-maker(s) (DM(s)) and the analyst regarding the nature of the problem and the desired goals are eliminated. After the problem has been properly identified, the next step of a multi-criteria energy-planning process is the building of an energy system model (impact model). The model is used to calculate the operational attributes necessary for the multi-criteria analysis; in other words, to determine the various alternatives’ performance values for some or all of the criteria being considered. It is important that the model accounts for both the physical characteristics of the energy system components and the complex relationships between the system parameters. However, it is not propitious to choose/build an energy system model with a greater level of detail than needed to achieve the aims of the planning project.</p><p>In my PhD research, I have chosen to use the eTransport model as the energy system model. This model is especially designed for planning of local and regional energy systems, where different energy carriers and technologies are considered simultaneously. However, eTransport can currently provide information only about costs and emissions directly connected to the energy system’s operation. Details about the investment plans’ performance on the remaining criteria must be found from other information sources. Guidelines should be identified regarding the extent to which different aspects should be accounted for, and on the ways these impacts can be assessed for each investment plan under consideration. However, it is important to realize that there is not one solution for how to do this that is valid for all kind of local energy-planning problems. It is therefore necessary for the DM(s) and the analyst to discuss these issues before entering the decision-making phase.</p><p>The Decision-Making Phase</p><p>Two case studies have been undertaken to examine to what extent the use of MCDA is suitable for local energy-planning purposes. In the two case studies, two of the most well-known MCDA methods, the Multi-Attribute Utility Theory (MAUT) and the Analytical Hierarchy Process (AHP), have been tested. Other MCDA methods, such as GP or the outranking methods, could also have been applied. However, I chose to focus on value measurement methods as AHP and MAUT, and have not tested other methods. Accordingly, my research cannot determine if value measurement methods are better suited for energy-planning purposes than GP or outranking methods are.</p><p>Although all MCDA methods are constructed to help DMs explore their ‘true values’ – which theoretically should be the same regardless of the method used to elicit them – our experiments showed that different MCDA methods do not necessarily provide the same results. Some of the differences are caused by the two methods’ different ways of asking questions, as well as the DMs’ inability to express clearly their value judgements by using one or both the methods. In particular, the MAUT preference-elicitation procedure was difficult to understand and accept for DMs without previous experience with the utility concept. An additional explanation of the differences is that the external uncertainties included in the problem formulation are better accounted for in MAUT than in AHP. There are also a number of essential weaknesses in the theoretical foundation of the AHP method that may have influenced the results using that method. However, the AHP method seems to be preferred by DMs, because the method is straightforward and easier to use and understand than the relatively complex MAUT method.</p><p>It was found that the post-interview process is essential for a good decision outcome. For example, the results from the preference aggregation may indicate that according to the DM’s preferences, a modification of one of the alternatives might be propitious. In such cases, it is important to realize that MCDA is an iterative process. The post-interview process also includes presentation and discussion of results with the DMs. Our experiments showed that the DMs might discover inconsistencies in the results; that the results do not reflect the DM’s actual preferences for some reason; or that the results simply do not feel right. In these cases, it is again essential to return to an earlier phase of the MCDA process and conduct a new analysis where these problems or discrepancies are taken into account.</p><p>The results from an MAUT analysis are usually presented to the DMs in the form of expected total utilities given on a scale from zero to one. Expected utilities are convenient for ranking and evaluation of alternatives. However, they do not have any direct physical meaning, which quite obviously is a disadvantage from an application point of view. In order to improve the understanding of the differences between the alternatives, the Equivalent Attribute Technique (EAT) can be applied. EAT was tested in the first of the two case studies. In this case study, the cost criterion was considered important by the DMs, and the utility differences were therefore converted to equivalent cost differences. In the second case study, the preference elicitation interviews showed, quite surprisingly, that cost was not considered among the most important criteria by the DMs, and none of the other attributes were suitable to be used as the equivalent attribute. Therefore, in this case study, the use of EAT could not help the DMs interpreting the differences between the alternatives.</p><p>Summarizing</p><p>For MCDA to be really useful for actual local energy planning, it is necessary to find/design an MCDA method which: (1) is easy to use and has a transparent logic; (2) presents results in a way easily understandable for the DM; (3) is able to elicit and aggregate the DMs' real preferences; and (4) can handle external uncertainties in a consistent way.</p>

Background and Motivation Unlike what is common in Europe and the rest of the world, Norway has traditionally met most of its stationary energy demand (including heating) with electricity, because of abundant access to hydropower. However, after the deregulation of the Norwegian electricity market in the 1990s, the increase in the electricity generation capacity has been less than the load demand increase. This is due to the relatively low electricity prices during the period, together with the fact that Norway’s energy companies no longer have any obligations to meet the load growth. The country’s generation capacity is currently not sufficient to meet demand, and accordingly, Norway is now a net importer of electricity, even in normal hydrological years. The situation has led to an increased focus on alternative energy solutions. It has been common that different energy infrastructures – such as electricity, district heating and natural gas networks – have been planned and commissioned by independent companies. However, such an organization of the planning means that synergistic effects of a combined energy system to a large extent are neglected. During the last decades, several traditional electricity companies have started to offer alternative energy carriers to their customers. This has led to a need for a more comprehensive and sophisticated energy-planning process, where the various energy infrastructures are planned in a coordinated way. The use of multi-criteria decision analysis (MCDA) appears to be suited for coordinated planning of energy systems with multiple energy carriers. MCDA is a generic term for different methods that help people make decisions according to their preferences in situations characterized by multiple conflicting criteria. The thesis focuses on two important stages of a multi-criteria planning task: - The initial structuring and modelling phase - The decision-making phase The Initial Structuring and Modelling Phase It is important to spend sufficient time and resources on the problem definition and structuring, so that all disagreements among the decision-maker(s) (DM(s)) and the analyst regarding the nature of the problem and the desired goals are eliminated. After the problem has been properly identified, the next step of a multi-criteria energy-planning process is the building of an energy system model (impact model). The model is used to calculate the operational attributes necessary for the multi-criteria analysis; in other words, to determine the various alternatives’ performance values for some or all of the criteria being considered. It is important that the model accounts for both the physical characteristics of the energy system components and the complex relationships between the system parameters. However, it is not propitious to choose/build an energy system model with a greater level of detail than needed to achieve the aims of the planning project. In my PhD research, I have chosen to use the eTransport model as the energy system model. This model is especially designed for planning of local and regional energy systems, where different energy carriers and technologies are considered simultaneously. However, eTransport can currently provide information only about costs and emissions directly connected to the energy system’s operation. Details about the investment plans’ performance on the remaining criteria must be found from other information sources. Guidelines should be identified regarding the extent to which different aspects should be accounted for, and on the ways these impacts can be assessed for each investment plan under consideration. However, it is important to realize that there is not one solution for how to do this that is valid for all kind of local energy-planning problems. It is therefore necessary for the DM(s) and the analyst to discuss these issues before entering the decision-making phase. The Decision-Making Phase Two case studies have been undertaken to examine to what extent the use of MCDA is suitable for local energy-planning purposes. In the two case studies, two of the most well-known MCDA methods, the Multi-Attribute Utility Theory (MAUT) and the Analytical Hierarchy Process (AHP), have been tested. Other MCDA methods, such as GP or the outranking methods, could also have been applied. However, I chose to focus on value measurement methods as AHP and MAUT, and have not tested other methods. Accordingly, my research cannot determine if value measurement methods are better suited for energy-planning purposes than GP or outranking methods are. Although all MCDA methods are constructed to help DMs explore their ‘true values’ – which theoretically should be the same regardless of the method used to elicit them – our experiments showed that different MCDA methods do not necessarily provide the same results. Some of the differences are caused by the two methods’ different ways of asking questions, as well as the DMs’ inability to express clearly their value judgements by using one or both the methods. In particular, the MAUT preference-elicitation procedure was difficult to understand and accept for DMs without previous experience with the utility concept. An additional explanation of the differences is that the external uncertainties included in the problem formulation are better accounted for in MAUT than in AHP. There are also a number of essential weaknesses in the theoretical foundation of the AHP method that may have influenced the results using that method. However, the AHP method seems to be preferred by DMs, because the method is straightforward and easier to use and understand than the relatively complex MAUT method. It was found that the post-interview process is essential for a good decision outcome. For example, the results from the preference aggregation may indicate that according to the DM’s preferences, a modification of one of the alternatives might be propitious. In such cases, it is important to realize that MCDA is an iterative process. The post-interview process also includes presentation and discussion of results with the DMs. Our experiments showed that the DMs might discover inconsistencies in the results; that the results do not reflect the DM’s actual preferences for some reason; or that the results simply do not feel right. In these cases, it is again essential to return to an earlier phase of the MCDA process and conduct a new analysis where these problems or discrepancies are taken into account. The results from an MAUT analysis are usually presented to the DMs in the form of expected total utilities given on a scale from zero to one. Expected utilities are convenient for ranking and evaluation of alternatives. However, they do not have any direct physical meaning, which quite obviously is a disadvantage from an application point of view. In order to improve the understanding of the differences between the alternatives, the Equivalent Attribute Technique (EAT) can be applied. EAT was tested in the first of the two case studies. In this case study, the cost criterion was considered important by the DMs, and the utility differences were therefore converted to equivalent cost differences. In the second case study, the preference elicitation interviews showed, quite surprisingly, that cost was not considered among the most important criteria by the DMs, and none of the other attributes were suitable to be used as the equivalent attribute. Therefore, in this case study, the use of EAT could not help the DMs interpreting the differences between the alternatives. Summarizing For MCDA to be really useful for actual local energy planning, it is necessary to find/design an MCDA method which: (1) is easy to use and has a transparent logic; (2) presents results in a way easily understandable for the DM; (3) is able to elicit and aggregate the DMs' real preferences; and (4) can handle external uncertainties in a consistent way.

Multiple criteria decision analysis (MCDA) techniques are developed to address challenging classification problems arising in engineering management and elsewhere. MCDA consists of a set of principles and tools to assist a decision maker (DM) to solve a decision problem with a finite set of alternatives compared according to two or more criteria, which are usually conflicting. The three types of classification problems to which original research contributions are made are <ol> <li>Screening: Reduce a large set of alternatives to a smaller set that most likely contains the best choice. </li> <li>Sorting: Arrange the alternatives into a few groups in preference order, so that the DM can manage them more effectively. </li> <li>Nominal classification: Assign alternatives to nominal groups structured by the DM, so that the number of groups, and the characteristics of each group, seem appropriate to the DM. </ol> Research on screening is divided into two parts: the design of a sequential screening procedure that is then applied to water resource planning in the Region of Waterloo, Ontario, Canada; and the development of a case-based distance method for screening that is then demonstrated using a numerical example. <br /><br /> Sorting problems are studied extensively under three headings. Case-based distance sorting is carried out with Model I, which is optimized for use with cardinal criteria only, and Model II, which is designed for both cardinal and ordinal criteria; both sorting approaches are applied to a case study in Canadian municipal water usage analysis. Sorting in inventory management is studied using a case-based distance method designed for multiple criteria ABC analysis, and then applied to a case study involving hospital inventory management. Finally sorting is applied to bilateral negotiation using a case-based distance model to assist negotiators that is then demonstrated on a negotiation regarding the supply of bicycle components. <br /><br /> A new kind of decision analysis problem, called multiple criteria nominal classification (MCNC), is addressed. Traditional classification methods in MCDA focus on sorting alternatives into groups ordered by preference. MCNC is the classification of alternatives into nominal groups, structured by the DM, who specifies multiple characteristics for each group. The features, definitions and structures of MCNC are presented, emphasizing criterion and alternative flexibility. An analysis procedure is proposed to solve MCNC problems systematically and applied to a water resources planning problem.

Abstract The modeling and analysis of multilateral negotiations are studied under the assumption that reaching an agreement is the main objective of the negotiators. A new methodology and associated definitions are proposed to predict the outcomes of such negotiations. The general objective of the new methodology is to study movements from one state to another in multilateral negotiations, to predict stable agreements, and to study their properties. The assumptions that the set of possible agreements is discrete and specified in advance make the negotiation problems considered here distinctive. Each decision maker has two concerns: first, achieving an alternative that is as preferable as possible; second, building support for this alternative among the other decision makers. In summary, this research consists of a systematic investigation of multilateral negotiations with the following general characteristics: • Decision makers in the negotiation seek a resolution that is not only feasible but also stable (enduring). Of course, each negotiator tries to attain the most preferable agreement for himself or herself. • If an agreement is reached, it must be an alternative from a pre-specified list, and all of the decision makers must accept the agreement. • Decision makers can possess different levels of power (or legitimacy) in support of an agreement, so the negotiation is not necessarily symmetric. Moreover, the analysis makes use of the decision makers’ preference orders over the proposed alternatives only, and does not require cardinal representations of their preferences. New concepts including State, Acceptability, Feasibility, Stability, and Fallback Distance are defined to pave the way for the proposed methodology. It is based on four types of movements, from unstable states toward stable ones, including preferential improvement, agglomeration, disloyalty move, and strategic disimprovement. Some criteria and algorithms are proposed to measure the likelihood of different moves and different outcomes. An important theorem shows that all four types of movement are mutually exclusive. The evolution of a negotiation from its status quo to the most likely outcomes is illustrated, using a tree. Several applications demonstrate that the proposed methodology can be applied to identify the most likely outcomes of a multilateral negotiation. Sensitivity analyses can be applied in several different ways to assess whether sudden or unforeseen changes in the model affect the conclusions. Several methods can be used from the literature for predicting the outcome of a negotiation. Social Choice Rules, Fallback Bargaining Procedures, and Bankruptcy Solutions are applied to the current negotiations over the legal status of the Caspian Sea to predict or recommend the most appropriate resolution among the proposed alternatives. In addition, the applicability of Graph Model for Conflict Resolution and its associated decision support system (DSS), GMCR II, are briefly discussed. Reasons why these methods are not appropriate when reaching an agreement is the main objective of decision makers (DMs) are then put forward. Based on the conceptual model for multilateral negotiations proposed in this thesis, a practical Negotiation Support System (NSS) is designed and implemented in Microsoft Access using Microsoft Visual Basic. This NSS increases the speed and accuracy of calculations. In the output of this NSS, all movements from initial states to subsequent states and their associated likelihoods are clearly illustrated, and all stable agreements are distinguished. Two real-world multilateral negotiations, over the legal status of the Caspian Sea and over the Epton site brownfield redevelopment project in Kitchener, Ontario, Canada, are modeled and analyzed using the proposed methodology. To measure DMs’ weights quantitatively in the Caspian Sea negotiations, eleven criteria that can be considered to be important determinants of countries’ capabilities are discussed, evaluated, and integrated using a Multiple Criteria Decision Analysis model. The Data Envelopment Analysis (DEA) method is employed to find the most favourable set of relative importance of different criteria for each country. Applying the proposed methodology indicates that unanimous agreements over the division of the Caspian Sea, either based on the International Law of the Seas or based on Soviet maps, are most likely as the enduring legal status of the Caspian Sea. The objective of applying the proposed methodology to actual negotiations over the redevelopment of a brownfield project is to ensure that the new methodology is flexible enough to model more real-world cases. Moreover, we wanted to test how well the actual outcomes of the real world negotiations match the most likely outcomes identified by the methodology. The results show that the decisions on the use of the Epton site followed the most likely path described and predicted by the model. This thesis is multidisciplinary in nature. It utilizes different branches of knowledge, including applied mathematics (game theory), computer science and programming, international relations, and environmental management. However, negotiation modeling and analysis in this thesis is developed from a systems engineering perspective.

Multiple criteria decision analysis (MCDA) techniques are developed to address challenging classification problems arising in engineering management and elsewhere. MCDA consists of a set of principles and tools to assist a decision maker (DM) to solve a decision problem with a finite set of alternatives compared according to two or more criteria, which are usually conflicting. The three types of classification problems to which original research contributions are made are <ol> <li>Screening: Reduce a large set of alternatives to a smaller set that most likely contains the best choice. </li> <li>Sorting: Arrange the alternatives into a few groups in preference order, so that the DM can manage them more effectively. </li> <li>Nominal classification: Assign alternatives to nominal groups structured by the DM, so that the number of groups, and the characteristics of each group, seem appropriate to the DM. </ol> Research on screening is divided into two parts: the design of a sequential screening procedure that is then applied to water resource planning in the Region of Waterloo, Ontario, Canada; and the development of a case-based distance method for screening that is then demonstrated using a numerical example. <br /><br /> Sorting problems are studied extensively under three headings. Case-based distance sorting is carried out with Model I, which is optimized for use with cardinal criteria only, and Model II, which is designed for both cardinal and ordinal criteria; both sorting approaches are applied to a case study in Canadian municipal water usage analysis. Sorting in inventory management is studied using a case-based distance method designed for multiple criteria ABC analysis, and then applied to a case study involving hospital inventory management. Finally sorting is applied to bilateral negotiation using a case-based distance model to assist negotiators that is then demonstrated on a negotiation regarding the supply of bicycle components. <br /><br /> A new kind of decision analysis problem, called multiple criteria nominal classification (MCNC), is addressed. Traditional classification methods in MCDA focus on sorting alternatives into groups ordered by preference. MCNC is the classification of alternatives into nominal groups, structured by the DM, who specifies multiple characteristics for each group. The features, definitions and structures of MCNC are presented, emphasizing criterion and alternative flexibility. An analysis procedure is proposed to solve MCNC problems systematically and applied to a water resources planning problem.

Abstract The modeling and analysis of multilateral negotiations are studied under the assumption that reaching an agreement is the main objective of the negotiators. A new methodology and associated definitions are proposed to predict the outcomes of such negotiations. The general objective of the new methodology is to study movements from one state to another in multilateral negotiations, to predict stable agreements, and to study their properties. The assumptions that the set of possible agreements is discrete and specified in advance make the negotiation problems considered here distinctive. Each decision maker has two concerns: first, achieving an alternative that is as preferable as possible; second, building support for this alternative among the other decision makers. In summary, this research consists of a systematic investigation of multilateral negotiations with the following general characteristics: • Decision makers in the negotiation seek a resolution that is not only feasible but also stable (enduring). Of course, each negotiator tries to attain the most preferable agreement for himself or herself. • If an agreement is reached, it must be an alternative from a pre-specified list, and all of the decision makers must accept the agreement. • Decision makers can possess different levels of power (or legitimacy) in support of an agreement, so the negotiation is not necessarily symmetric. Moreover, the analysis makes use of the decision makers’ preference orders over the proposed alternatives only, and does not require cardinal representations of their preferences. New concepts including State, Acceptability, Feasibility, Stability, and Fallback Distance are defined to pave the way for the proposed methodology. It is based on four types of movements, from unstable states toward stable ones, including preferential improvement, agglomeration, disloyalty move, and strategic disimprovement. Some criteria and algorithms are proposed to measure the likelihood of different moves and different outcomes. An important theorem shows that all four types of movement are mutually exclusive. The evolution of a negotiation from its status quo to the most likely outcomes is illustrated, using a tree. Several applications demonstrate that the proposed methodology can be applied to identify the most likely outcomes of a multilateral negotiation. Sensitivity analyses can be applied in several different ways to assess whether sudden or unforeseen changes in the model affect the conclusions. Several methods can be used from the literature for predicting the outcome of a negotiation. Social Choice Rules, Fallback Bargaining Procedures, and Bankruptcy Solutions are applied to the current negotiations over the legal status of the Caspian Sea to predict or recommend the most appropriate resolution among the proposed alternatives. In addition, the applicability of Graph Model for Conflict Resolution and its associated decision support system (DSS), GMCR II, are briefly discussed. Reasons why these methods are not appropriate when reaching an agreement is the main objective of decision makers (DMs) are then put forward. Based on the conceptual model for multilateral negotiations proposed in this thesis, a practical Negotiation Support System (NSS) is designed and implemented in Microsoft Access using Microsoft Visual Basic. This NSS increases the speed and accuracy of calculations. In the output of this NSS, all movements from initial states to subsequent states and their associated likelihoods are clearly illustrated, and all stable agreements are distinguished. Two real-world multilateral negotiations, over the legal status of the Caspian Sea and over the Epton site brownfield redevelopment project in Kitchener, Ontario, Canada, are modeled and analyzed using the proposed methodology. To measure DMs’ weights quantitatively in the Caspian Sea negotiations, eleven criteria that can be considered to be important determinants of countries’ capabilities are discussed, evaluated, and integrated using a Multiple Criteria Decision Analysis model. The Data Envelopment Analysis (DEA) method is employed to find the most favourable set of relative importance of different criteria for each country. Applying the proposed methodology indicates that unanimous agreements over the division of the Caspian Sea, either based on the International Law of the Seas or based on Soviet maps, are most likely as the enduring legal status of the Caspian Sea. The objective of applying the proposed methodology to actual negotiations over the redevelopment of a brownfield project is to ensure that the new methodology is flexible enough to model more real-world cases. Moreover, we wanted to test how well the actual outcomes of the real world negotiations match the most likely outcomes identified by the methodology. The results show that the decisions on the use of the Epton site followed the most likely path described and predicted by the model. This thesis is multidisciplinary in nature. It utilizes different branches of knowledge, including applied mathematics (game theory), computer science and programming, international relations, and environmental management. However, negotiation modeling and analysis in this thesis is developed from a systems engineering perspective.

On-line negotiation and bargaining systems can work effectively on the Internet based on the prerequisite that user utility functions are known while undergoing transactions. However, this prerequisite is hard to meet due to the variety and anonymous nature of Internet surfing. Therefore, how to rapidly and precisely construct a user¡¦s utility function is an essential issue. This research proposes a radial basis function (RBF) network, a neural network, to model a user¡¦s utility function in order to rapidly and precisely model user utility function. We verify the feasibility of the method through experiments, and compare the performance of RBF networks in prediction performance, time expenses, and subjects¡¦ perceptions with the Multiple Regression (MR), SMARTS, and SMARTER methods. The results show that the RBF network method is feasible in these criteria. Not only the RBF network needs less time to construct the users¡¦ utility function than the SMARTS method does, but also it can model user utility functions more precisely than the MR, SMARTS, and SMARTER methods.

Carbon dioxide capture and storage (CCS) is considered one of the key technologies for reducing atmospheric emissions of CO₂ from human activities (IPCC, 2005). The scale of potential deployment of CCS is enormous spanning manufacturing, power generation and hydrocarbon extraction worldwide. Uncertainty, cost-benefit challenges, market barriers and failures, and promotion and regulation of infrastructure are the main obstacles for deploying CCS technology in a broad scale. In a CCS project, it is the operator’s responsibility to guarantee the CO₂ containment while complying with environmental regulations and CO₂ contractual requirements with the source emitter. Acquiring new information (e.g. seismic, logs, production data, etc.) about a particular field can reduce the uncertainty about the reservoir properties and can (but not necessarily) influence the decisions affecting the deployment of a CCS project. The main objective of this study is to provide a decision-analysis framework to quantify the Value of Information (VOI) in a CCS project that faces uncertainties about permeability values in the reservoir. This uncertainty translates into risks of CO₂ migration out of the containment zone (or lease zone), non-compliance with contractual requirements on CO₂ storage capacity, and leakage of CO₂ to sources of Underground Source of Drinking Water (USDW). The field under analysis has been idealized based on a real project located in Texas. Subsurface modeling of the upper Frio Formation (injection zone) was conducted using well logs, field-specific GIS data, and other relevant published literature. The idealized model was run for different scenarios with different permeability distributions. The VOI was quantified by defining prior scenarios based on the current knowledge of a reservoir, contractual requirements, and regulatory constraints. The project operator has the option to obtain more reliable estimates of permeability, which will help to reduce the uncertainty of the CO₂ behavior and storage capacity of the formation. The accuracy of the information gathering activities is then applied to the prior probabilities (Bayesian inference) to infer the value of such data. / text

In decision analysis, continuous uncertainties (i.e., the volume of oil in a reservoir) must be approximated by discrete distributions for use in decision trees, for example. Many methods of this process, called discretization, have been proposed and used for decades in practice. To the author’s knowledge, few studies of the methods’ accuracies exist, and were of only limited scope. This work presents a broad and systematic analysis of the accuracies of various discretization methods across large sets of distributions. The results indicate the best methods to use for approximating the moments of different types and shapes of distributions. New, more accurate, methods are also presented for a variety of distributional and practical assumptions. This first part of the work assumes perfect knowledge of the continuous distribution, which might not be the case in practice. The distributions are often elicited from subject matter experts, and because of issues such as cognitive biases, may have assessment errors. The second part of this work examines the implications of this error, and shows that differences between some discretization methods’ approximations are negligible under assessment error, whereas other methods’ errors are significantly larger than those because of imperfect assessments. The final part of this work extends the analysis of previous sections to applications to the Project Evaluation and Review Technique (PERT). The accuracies of several PERT formulae for approximating the mean and variance are analyzed, and several new formulae presented. The new formulae provide significant accuracy improvements over existing formulae. / text