The Application of a Multidisciplinary Framework for Optimizing the Monitoring System for Geological CO₂ Storage

Abstract

The technical objective of a monitoring system is to provide the means to detect potential irregularities related to the project plan, to provide assurance that the migration of the CO₂ plume stays within the storage unit, and to show that CO₂ behaves in conformance with the model predictions. From an operational point of view, monitoring will also provide data that can be used to optimize the injection schedule relative to the storage capacity and availability of CO₂ to minimize risks and long-term costs. Finally, monitoring is a crucial factor for the public perception of risks related to CO₂ storage, as surveys indicate that adequately designed monitoring can mitigate concerns. The Analytical Hierarchy Process (AHP) is a holistic, transdisciplinary, multi-criteria decision-making framework. The objective of this work is to apply the AHP framework to monitoring-solutions for a synthetic geological storage site of CO₂ to secure the technical, operational, and societal embeddedness of the solutions and gain experience in how this can be applied to a real project. Through this first application of AHP within the field of geological carbon storage, the AHP was found to be a structured and transparent framework for holistic, multi-criteria decision-making (MCDM), where the wisdom and expertise of different domain experts were considered. A further novelty in this study is introducing a measure of spread in assessing the various solution alternatives’ capacity to meet monitoring criteria. This approach was utilized to underscore disparities among respondents’ experiences and to identify potential informational deficiencies in evaluating alternatives and devising the optimal monitoring solution.

1. Introduction

The implementation of the large-scale geological storage of CO₂ has the potential to significantly reduce CO₂ emissions. However, despite significant efforts, estimates indicate that existing carbon capture and storage (CCS) facilities cover a mere 1–10% of projected future needs [1,2]. To secure the widespread use of CCS, each geological CO₂ storage project must be able to show regulators, economic stakeholders, and the public that the project is being executed safely, securely, and according to plan. For this measurement, monitoring and verification are key.

Ideally, the design of a monitoring system allows for the optimal balance between ensuring containment (that the CO₂ remains permanently within the storage complex) and conformance (the consistency of the actual behavior of the injected CO₂ with the modeled behavior) while at the same time providing a solution that is operationally feasible at an affordable cost [3,4]. In addition, the monitoring system is highly important for the perception of CCS initiatives by the public and political and industrial stakeholders [5].

The current economic costs of geological CO₂ storage are significant [6], which may limit the widespread deployment of this technology. The cost may be lowered by implementing lower-cost monitoring systems; however, this must be balanced against the potential environmental impacts and societal criteria like public trust in the fact that CO₂ is stored safely and reliably. A transdisciplinary approach to designing an optimal monitoring system is therefore needed to capture the technical, operational, economic, and societal factors that all contribute to the sustainable deployment of geological CO₂ storage.

Optimizing a solution based on diverse criteria from different disciplines is challenging. It requires a methodology that is both inter- and transdisciplinary and emphasizes a holistic assessment rather than merely comparing a limited set of criteria. Several methods exist to facilitate rational decisions involving multiple, qualitatively different criteria. Among these are ELECTRE [7], PROMETHEE [8], SMARTS [9], and the Analytical Hierarchy Process (AHP) [10]. In this paper, we have chosen to apply the AHP framework since it enables group discussions for selecting the relevant criteria, allows the assessment of alternatives by multiple experts, and, last but not least, is a transparent and structured framework for decision-making. AHP [10] has been applied to many different areas, from politics, engineering, education, and industry to management [11].

The ELECTRE method is more sophisticated than AHP when comparing alternatives for different criteria, with defined thresholds for strong/weak preferences, ways to determine indifference, and veto thresholds. However, the ELECTRE method relies on quantifiable criteria (e.g., the release of polluting gases, number of employees, and pollution of water) while AHP can, in addition, be used for criteria that are not easily quantified (e.g., how well the monitoring system will be able to provide an early warning of irregularities). In those cases, AHP can be used to gather experience and preferences among experts. While quantifiable criteria can be a goal to aim for, criteria that are hard to quantify or even unquantifiable may still be very important to consider.

In this study, we applied AHP to a synthetic but realistic storage site representative of the conditions on the Norwegian Continental Shelf. An important reason for using a synthetic site in this first study is to ensure the full openness of the results without risking revealing information that may be company-sensitive for a real site. The application of AHP is also a learning process for all participants, and one of the primary purposes of this study has been to gain experience and adapt the process as a blueprint for practical application. Hence, this work focuses on the implementation of the methodology and extracting key learnings that are transferable to other specific field applications.

The structure of this work is as follows: Section 2 begins by describing the methodology used to identify societal requirements essential for the successful deployment of technological innovations. We then present the AHP method applied to multi-criteria decision-making (MCDM). This is followed by a discussion of the regulatory requirements that the monitoring solution must fulfill. Finally, we outline the methodology for ranking these solution alternatives within the AHP framework. Section 3 presents the results of the initial AHP application. It begins with the identified monitoring criteria through the transdisciplinary approach and the principles used to establish different solution alternatives, followed by the stakeholders’ assessments and the ranking of various solution alternatives. Section 4 discusses the implementation, key insights, and potential improvements for using AHP in MCDM before concluding the study in Section 5.

2. Materials and Methods

2.1. Societal Embeddedness Framework

The societal embeddedness level (SEL) is a novel methodology designed to assess the societal requirements necessary for the successful deployment of technological innovations, particularly those impacting the environment [12,13]. Unlike existing readiness tools that primarily focus on technological factors, the SEL emphasizes societal aspects, which are crucial for understanding challenges that might delay or hinder the adoption of new technologies. By aligning technological maturity with societal readiness, SEL enhances the chances of successful implementation.

The SEL serves researchers and technology developers by offering a structured approach to (1) assess the current societal embeddedness of a technology, (2) identify societal challenges that need resolution, and (3) monitor and evaluate societal readiness throughout the development process. It complements the Technology Readiness Level (TRL) framework, which measures technological maturity. While low TRL stages require minimal societal readiness, advancing to higher TRLs demands a correspondingly higher SEL. Any misalignment between TRL and SEL—such as a high TRL but low SEL—indicates unresolved societal challenges that must be addressed to achieve full integration and deployment readiness. The correspondence between TRL and SEL is illustrated in Figure 1.

The SEL framework uses a transdisciplinary, holistic, multidimensional framework for its assessment of the societal embeddedness of a technology. It incorporates the perspectives of various disciplines (in the case of CCS, for instance, engineers, geologists, mathematicians, chemists, and social scientists) and relevant stakeholder groups (e.g., industrial, political, and civil society actors). The SEL methodology distinguishes four dimensions: (1) environment, (2) stakeholder involvement, (3) policy and regulations, and (4) market and financial resources. These four dimensions are the primary factors determining a technological innovation’s societal embeddedness. While other aspects, such as organization, capacity, expertise, and skills, are required for the successful deployment of a technological innovation, they are not included as separate dimensions. It is important to note that the four dimensions do not operate in isolation but instead influence and interact with one another. Additionally, the methodology considers the iterative character of technological innovation processes, wherein progress and setbacks frequently alternate.

For the assessment of a particular type of technology, the SEL can draw upon data generated using a variety of empirical research methods. For this study, the assessment of CCS was conducted based on internationally comparative surveys, stakeholder and expert interviews, and literature reviews in Norway, the Netherlands, Greece, and Germany [13]. This mixed methods approach enabled us to study CCS perception multidimensionally with a focus on different societal groups and data types. We used literature reviews to establish the political, governmental, and societal contexts of CCS in respective countries to understand the framings in which CCS has been discussed. The representative surveys were designed as informed choice questionnaires [14,15,16], aiming to capture the perception of the public in the respective countries. We conducted semi-structured interviews to gather insights into the perspectives of both local and national stakeholders regarding CO₂ storage projects in each country. The selection of interviewees was informed by stakeholder mappings conducted in the four countries and focused on individuals from key groups: politicians, governmental authorities, industry representatives, environmental non-governmental organizations (eNGOs), and scientists. Interviewees were either directly involved in local cases or possessed expertise in CCS or CO₂ storage monitoring, enabling them to speak to at least one of the abovementioned dimensions.

This broad, multidimensional, and internationally comparative empirical approach yielded a large amount of quantitative and qualitative data. We analyzed and summarized the most relevant results for a CO₂ storage monitoring system and presented them to expert and stakeholder workshops for further discussion and scrutiny in an iterative transdisciplinary research process (for details, see [13,17]). This enabled us to discuss conflicting points of view and to identify the most relevant factors for the societal embeddedness of a monitoring system.

Additionally, this long-term inter- and transdisciplinary process enabled us to bridge the gap between social scientific research and the more technical Analytical Hierarchy Process. Over the course of more than a year, an interdisciplinary group of researchers met to discuss the technical and social scientific insights of the project work to develop a common language and understand the requirements for translating social scientific results into quantitative information for Analytical Hierarchy Process.

2.2. The Analytical Hierarchy Process

The main idea of the Analytical Hierarchy Process (AHP) is to break down the overall decision goal (i.e., defining the optimal monitoring solution for a given site) into sets of criteria grouped into a hierarchy representing the various aspects of the monitoring solution that need to be considered. The criteria can span multiple disciplines and are weighed based on their perceived importance for achieving the primary goal. In practice, the weighting of the criteria is site-dependent and depends on the technical risks and the public perception of risks associated with CO₂ storage.

After the relevant monitoring criteria against which the solution is to be evaluated are defined, the next step is to define a selection of realistic monitoring solutions (i.e., different monitoring designs). This step can be taken by a group of experts with knowledge of the capabilities of different monitoring technologies while considering the relevant risks specific to the storage site.

In the evaluation process, another set of experts scores the solution alternatives according to their abilities to fulfill the individual criteria. This provides a rational basis for deciding on an optimal monitoring solution for the site. The method for the evaluation process and an explanation of how the ranking is computed are provided in Section 2.4.

2.3. Regulatory Requirements on the Monitoring Solutions

The national regulatory requirements for implementing CCS in Europe follow the directive on the geological storage of carbon dioxide provided by the EU (https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32009L0031 (accessed on 13 May 2025)). The aim of this directive is to establish a legal framework for the “environmentally safe geological storage of carbon dioxide (CO₂)” to contribute to the fight against climate change. The purpose is the permanent containment of CO₂ “to prevent and, where this is not possible, eliminate as far as possible negative effects and any risk to the environment and human health”.

The directive provides a minimum set of requirements, which means that Member States may adopt more stringent rules on the national level. However, Schütz, Omar, and Carpentier [18] found no significant new mandatory monitoring phases, monitoring aims, or requirements for specific monitoring technologies in a recent study on national legislation. Hence, for this work, we chose the criteria against which the monitoring solution was to be evaluated to be based on EU regulations as specified in the CCS directive 2009/31/EC.

The EU regulations identify different storage phases: pre-injection, during injection, and after (post) the storage site is sealed. In the pre-injection phase, the suitability of a geological formation for use as a storage site is defined through characterization and the assessment of the potential storage site by the following factors:

I.

Building a detailed geological earth model and dynamic flow model predicting, inter alia, the evolution of pressure, temperature, the areal and the vertical extent of CO₂, storage capacity, seismicity, and surface elevation;

II.

Conducting a site-specific risk assessment characterizing the potential for leakage from the storage complex.

The regulations require that geological formation be selected as a storage site only if, under the proposed conditions of use, there is no significant risk of leakage and no significant environmental or health risks that exist. For a description of the possible workflow for a containment risk assessment, we refer to [19].

In this study, we are concerned with evaluating a framework for optimizing the monitoring system for the injection- and post-injection phases. It is assumed that the initial site description, model building, and risk assessment have been previously conducted. Hence, the evaluation of the monitoring solutions will build on the information available from the data collection and analysis performed in the pre-injection phase, the relevant regulations, and the SEL of the involved technologies.

Annex II of directive 2009/31/EC gives clear directions related to “the choice of monitoring technology”, underlining the use of a situational, site-by-site approach. This choice “shall be based on best practice available at the time of design”. Hence, certain features “shall be considered and used as appropriate”, for example, that they “can detect the presence, location, and migration paths of CO₂ in the subsurface and at the surface” for the specific geological settings at hand [18].

Each monitoring alternative should ideally be designed to provide the information required to ensure conformance, containment, and the detection of significant irregularities. (Here, “significant irregularity” means any irregularity in the injection or storage operations or in the condition of the storage complex itself, which implies the risk of leakage or risk to the environment or human health.) However, they are not designed to answer additional requirements arising in the case that any significant irregularities should be disclosed either operationally or in understanding the subsurface and flow dynamics.

If monitoring shows evidence of a “significant irregularity” as represented by any of the above risk factors that may occur, the risk assessment and the monitoring plan should be updated accordingly. Outlining the requirements for such triggered monitoring plans is deemed outside the scope of this study. In principle, the proposed workflow can also be applied to this task. However, the requirements and solution alternatives for the triggered monitoring solution will have to be designed according to what caused the trigger.

2.4. Ranking Alternative Solutions According to Their Ability to Meet the Requirements

Following the recommended practice of AHP, the rankings of the alternatives based on the identified criteria are performed pairwise on the following scale: “Equal”, “Slight”, “Strong”, “Very strong”, and “Extreme”. These rankings are associated with the numerical values 1, 3, 5, 7, and 9, respectively, with the reciprocal values for the inverse comparison. If, for example, the respondent preferred alternative I slightly over alternative II, this would be indicated by a checkmark in the box with the heading “slight”, as shown in

Table 1. An example of the questionnaire answering sheet where a slight preference for alternative I over alternative II is indicated by a checkmark.

	Extreme	Very Strong	Strong	Slight	Equal	Slight	Strong	Very Strong	Extreme		No Opinion
Alt I				X						Alt II
	Preference for Alt I					Preference for Alt II

. Such pairwise assessment is assumed to be easier for humans to perform than ranking multiple alternatives simultaneously.

The approach taken to transform the pairwise rankings into a ranking of the three alternatives is shown below. For each criterion, $k$, the pairwise rankings given by each respondent, $l$, are assembled into a matrix $A_{k,l}$:

$$A_{k,l}=\left[\begin{array}{ccc}1& z_{12}& z_{13}\\ 1/z_{12}& 1& z_{23}\\ 1/z_{13}& 1/z_{23}& 1\end{array}\right]$$

(1)

Here $z_{ij}$ is the ranking given by the respondent between alternative i and alternative j, in the sense that alternative i is evaluated to be $z_{ij}$ better than alternative j. Reciprocal values were then added below the diagonal. The best approximation of the ranking between the alternatives is given by the principal eigenvalue, ${\lambda }_{max,k,l}$, and eigenvector, $v_{k,l}$, such that

$$A_{k,l}{v}_{k,l}={\lambda }_{max,k,l}{v}_{k,l}$$

(2)

The normalized principal eigenvector, $\overline{v_{k,l}}$, where the elements sum to one, is then defined as the priority vector, or the ranking, between the alternatives for criterion $k$ and respondent $l$.

If the responses are entirely consistent, the matrix $A_{k,l}$ in (1) will have rank 1. In other words, all rows in $A$ will be scalar multiples of each other. However, in practice, most assessments are not completely consistent. Inconsistencies are partly due to the discrete scale used and partly due to the nature of human judgment being approximate. Say, for example, that a respondent is asked to pairwise evaluate alternatives I, II, and III. If they answer $z_{12}=5$ (I = 5 II) and $z_{13}=3$, then they ought to answer that $z_{23}=3/5$ (II = 3/5 III) to be completely consistent, but since 3/5 is not on the scale, a slight inconsistency is necessarily introduced.

For a consistent matrix, $A$, the principal eigenvalue ${\lambda }_{max}$ is equal to $n$, where $n$ is the number of alternatives to evaluate. If, however, $A$ is inconsistent, it can be shown that ${\lambda }_{max}\ge n$. Based on this, Saaty [10] introduced a consistency index to measure the level of inconsistency as follows:

$$CI={\displaystyle \frac{{\lambda }_{max}-n}{n-1}}$$

(3)

The average consistency index of randomly generated comparison matrices is then computed, which is called the random consistency index, $RI$. For $n=3$, which is the case here, $RI=0.4914$ [20]. The consistency ratio, $CR$, can then be computed as follows:

$$CR={\displaystyle \frac{CI}{RI}}$$

(4)

Using the example above where the respondent evaluates I = 5 II and I = 3 III, the two most consistent possibilities that they can choose, II = III or II = 1/3 III, give consistency ratios, CR, of 2.5% or 3.3%, respectively, through the procedure of applying Equations (2)–(4). Some inconsistency is allowed, however, and handling this is part of the AHP.

Saaty [10] recommends that for $CR<10\%$, the judgment is considered acceptable. Otherwise, there may be a reason to ask the respondent to reconsider the judgment or simply exclude the response from further processing. The first part of the processing consists, therefore, of excluding inconsistent responses.

The aggregation of the results for each criterion incorporating feedback from all respondents can be performed in several ways. Since the priority vectors are vectors of ratios, a geometric mean is recommended over the more standard arithmetic mean [21]. The geometric mean is less sensitive to large outliers than the arithmetic mean. The geometric mean is also used to aggregate the results of sub-criteria into results for their parent criteria. The aggregated priority vectors were re-normalized to sum to one since the geometric mean does not preserve the lengths of the aggregated results.

A measure of the spread is also beneficial when interpreting the results. This spread is something that is usually omitted in the AHP literature, but we recommend it to be included since it can be used to detect disagreements between the responses. As a robust measure of the spread, we used the interquartile range, which is the interval between the 25% and 75% quantiles. Note that when there are strong outliers, the geometric mean may sometimes lie outside the interquartile range, although these occurrences are rare.

3. Results

The AHP was applied by first identifying and grouping criteria for the monitoring solution. The criteria were identified based on literature studies [3,22,23], studies of monitoring plans for planned and existing storage sites [4,24,25,26,27], and through a collaborative effort within the Digimon project [28] involving stakeholders from different disciplines. The criteria were later harmonized by aligning them with EU regulations.

3.1. Identified Criteria

The identified list of criteria and associated sub-criteria for [4,24,25] evaluating a CO₂ storage monitoring system are outlined below. The criteria cover the requirements identified through the SEL framework in addition to technical and operational constraints.

The descriptions of the individual criteria are meant to be technology-agnostic. Any references to specific technologies that may be used to fulfill the different criteria are avoided as much as possible, as this may introduce bias in the evaluation of the monitoring alternatives.

Figure 2 summarizes the defined criteria as a hierarchy, and each sub-criterion is further described in the following sections.

3.1.1. Conformance Monitoring

Showing conformance [24,29] is primary because it ensures that the CO₂’s behavior in the reservoir is understood and that the model-based flow predictions and risk assessments leading to the license of being able to inject CO₂ are valid.

Following the analytic hierarchy process, this criterion is divided into the sub-criteria presented below.

• Map the area and vertical extent of CO₂ vs. time.

  ○

  This criterion concerns the tracking of the CO₂ plume location and movement. Being able to trace the movement of the injected CO₂ is key to ensuring that reality and model predictions align.

• Map the pressure field over time.

  ○

  The CO₂ injection will alter the fluid pressure and stress field within the reservoir. Monitoring the pressure evolution is key to ensuring that reality and model predictions align. Moreover, mapping the pressure plume is critical to inform the connectivity within the storage unit and enables the prediction of the risk of fault reactivation and the opening of new migration paths in the storage complex.

• Determine CO₂ phase behavior and its state.

  ○

  Determining the phase behavior and state of the CO₂ is key to predicting the plume migration, injectivity, and storage capacity of the reservoir.

• Quantify CO₂ trapping mechanisms and rates.

  ○

  In geological storage in saline aquifers, CO₂ is primarily structurally trapped as a free phase within the brine-saturated host rock. Residual trapping, which involves capillarity-driven trapping at the scale of the pores in the rock, is generally considered the second most important trapping mechanism. Other trapping mechanisms involve solubility trapping and mineral trapping. See ref. [30] for an overview.

  ○

  To confirm structural trapping, a thorough understanding of the connectivity across the field, including the possible compartmentalization of sand and communication across faults, is required. In addition, the viscous, gravity, and capillary forces determining the flow dynamics must be understood to accurately predict the evolution of the CO₂ plume.

• Utilize mature technologies.

  ○

  A monitoring setup with technology components considered mature may be given preference over a monitoring setup with less mature technologies, at least during the transition phase before the less mature technologies have been demonstrated successfully in a full-scale system over a certain period.

• Provide flexibility in the monitoring solution.

  ○

  A monitoring solution that can be adapted to changing conditions may be given preference over a more rigid solution. A flexible solution may consist of sensors that are mobile or can be adapted for different purposes.

3.1.2. Containment Monitoring

The purpose of containment monitoring is to ensure that CO₂ stays within the storage unit. This means that potential irregularities and major deviations related to the model predictions can be detected, such as unexpected migration paths or indications that the storage has a lower capacity than expected.

• Monitor injectivity and storage capacity.

  ○

  The storage capacity of saline aquifers is governed by the pore volume available for CO₂ storage and the density of CO₂ at reservoir conditions, which are both controlled by the temperature and pressure in the reservoir. In addition, connectivity across the field is another key parameter affecting the storage volume available. The degree of communication across faults is a frequent source of uncertainty.

  ○

  The ability to inject the volumes of CO₂, as determined by the storage capacity, depends on the injectivity. This capacity of the well(s) to push the CO₂ through the matrix into the storage volume is controlled by the transmissibility, connectivity, permeability, and pressure gradient in the well, together with the skin factor [31].

• Detecting significant irregularities.

  ○

  Permanent downhole pressure sensors provide data that can be used for the observation and optimization of the injection process.

  ○

  In addition, technologies are required that can cover a wide area to capture information on previously undetected potential leakage pathways across the area dimensions of the complete storage complex.

• Detect leakage.

  ○

  Leakage is defined here as the migration of injected CO₂ outside of the storage complex. The risk of leakage may, for example, increase due to faults and fractures, inadequately sealed wells, or the loss of well integrity [32].

• Provide real-time information for early warnings.

  ○

  To detect abrupt changes in the operations or the condition of the storage complex itself and enable the implementation of mitigating actions in the case of significant irregularities, the timeliness of the information obtained from monitoring is key.

• Provide an assessment of the safety and integrity of the storage complex in the short and long term.

  ○

  This criterion addresses the requirement of the monitoring solution to provide sufficient information to validate the assessment of whether the stored CO₂ will be completely and permanently contained in the storage unit.

3.1.3. Cost

The cost of a monitoring system includes all costs associated with acquiring the sensors, installing them, operating them, and maintaining them. It also includes the cost of processing and interpreting the data from the sensors.

• Provide competitive equipment and installation costs.

  ○

  This cost is a one-time investment to purchase and install the sensors and the infrastructure needed to keep a working monitoring system up and running.

• Provide competitive operation and maintenance costs.

  ○

  This criterion refers to the running cost to maintain and operate the sensors and the infrastructure.

• Provide competitive costs for data processing and interpretation.

  ○

  Data processing and analysis have a cost in terms of person-hours, data storage, and CPU hours. Different data types require the processing of different complexities depending on the purpose and nature of the measurements. Moreover, translating the data into knowledge about the subsurface requires expertise and dedicated tools for analysis tailored to the information source at hand.

3.1.4. Societal Perception/Public and Stakeholder Perceptions

A carbon storage monitoring system should adhere to the perceptions and expectations that the public and stakeholders have towards CO₂ storage monitoring to strengthen the societal embeddedness of CCS. Previous research has shown that the relevance of CCS technology is related to public perception of its implementation (e.g., [33,34,35]; for overviews of the growing literature, see [36,37]). We added to this research by focusing on CO₂ storage monitoring systems based on our empirical research and identifying the following list of expectations:

• Minimize environmental impact.

  ○

  Public surveys conducted in Germany, Norway, Greece, and the Netherlands revealed that minimizing the environmental impact of monitoring systems is a significant criterion in how people perceive monitoring options. Between 82 and 87 percent of respondents rated the low environmental impact as an important qualifier for a monitoring system. This criterion focuses on whether monitoring systems and techniques have adverse effects on the environment. For instance, some sensors may require seabed preparation before installation, which could temporarily disturb marine vegetation. Other methods, such as active seismic surveys, have the potential to cause stress, avoidance behavior, or even hearing loss in local marine animals. However, the scientific literature on the extent of these impacts remains inconclusive [38,39].

• Facilitate data access and ensure external supervision.

  ○

  Interviews have highlighted that transparent data access and independent, trusted supervision are crucial for socially embedded monitoring technologies. This is supported by open answers in our survey on requirements for a monitoring system that stressed the high importance of transparency. It also aligns with previous research emphasizing transparency in CCS communication (e.g., [36,40,41,42]). Data should be provided in an easily understandable format for non-experts to prevent misunderstandings and be incorporated into public engagement strategies, as demonstrated in Japan’s Tomakomai CCS project [43].

  ○

  Regarding supervision, public trust and operator interests must both be considered. Regional differences in public trust exist; for example, about 65 percent of Norwegian respondents trust government actors as monitoring operators for a CO₂ storage site. In the Netherlands (48 percent), Greece (30 percent), and Germany (22 percent), trust in governmental actors as monitoring operators is lower. For scientists (more than 70 percent) and environmental NGOs (42–57 percent), we found comparable levels of trust across regions [13], though industrial stakeholders in CCS expressed in interviews that they did not view environmental NGOs as trusted actors. These findings fit nicely with previous research that highlighted the complexities of trust in the context of CCS and other energy technologies (e.g., [41,44,45]).

• Provide reliable measurements of plume movement and leakage.

  ○

  The surveys show that the reliable measurement and prediction of plume behavior is a core characteristic that the public expects from a carbon storage monitoring system (more than 82 percent in all countries). Like the measurement of plume movement, the detection and prediction of leakages are seen as principal elements of CO₂ storage monitoring systems by the public and stakeholders alike.

• Provide an early warning system and security concept.

  ○

  Survey and interview results suggest that the monitoring system should be linked to a warning system and a security concept to address unexpected plume behavior. A large majority of the survey respondents (83 percent in Greece, 88 percent in The Netherlands, 89 percent in Germany, and 90 percent in Norway) consider this an important element. The security concept includes risk assessment, mitigation actions, and safety measures for significant irregularities.

  ○

  Although the warning system is primarily tied to CO₂ storage site operations, findings emphasize the need for transparency in linking monitoring, warnings, and security measures. This could involve explaining existing safety measures, such as multiple barriers or stopping injections as a key precaution.

• Public engagement.

  ○

  Based on the survey and interview results, a socially embedded monitoring system should be developed and setup by experts. Most respondents in all countries state that the primary responsibility for information gathering, defining how monitoring is conducted, and who is responsible for the monitoring should lie with experts. At the same time, the results show that these processes should be open to public participation. This can best be summarized by framing the monitoring system as designed and operated by experts but making it open to public concerns and interests.

3.2. Definition of Storage Site and Monitoring Alternatives

Storage sites will vary with different geological and operational conditions, each with site-specific risks and monitoring requirements. It is not intended to cover all possible geological storage formations in this work. To illustrate the proposed framework for the system design, we chose a typical generic brine-filled geological structure on the Norwegian Continental Shelf. A description of the sample injection site, together with relevant risk factors, is provided in Appendix A.

Three alternative monitoring solutions representing different combinations of technologies and data types were setup for the analysis. The alternatives vary in maturity (high vs. low TRL), operational settings and cost, and public engagement. Details of the three alternatives are provided in Appendix B. Note, however, that these alternatives are viewed as a starting point. Through the AHP workflow, we seek to explore new solutions and build experience and competence in their capabilities (weaknesses and strengths as monitoring tools).

3.3. Performance Evaluation by Means of the AHP

Finally, the monitoring alternatives were evaluated according to the identified criteria by a group of senior experts, the steering committee, and the technical advisory board of the DigiMon project [28]. Descriptions of the monitoring alternatives and descriptions of the identified criteria were given to all the experts, together with a questionnaire for evaluating the alternatives for each criterion by pairwise rankings according to the scale in Table 1 The respondents were also encouraged to provide comments to accompany the assessments.

Even though we present some of the evaluation results below, the primary purpose of this study has been to gain experience with the AHP methodology and its applicability to design the monitoring of geological CO₂ storage facilities. The specific ranking of the monitoring solutions presented here cannot easily be transferred to other monitoring sites since each site’s particularities influence the evaluations and, hence, require its own application of the framework.

For this study, we assumed the equal weighting of all sub-criteria. In a practical application on a real site, the criteria should be given weights according to their perceived importance to fulfill the main criteria. However, this weighting is best left to developers and operators based on site-specific technical risks, economic conditions, and regional societal embeddedness levels (SELs) of CO₂ storage projects, which were deemed to be outside the scope of this study.

With equal weighting of all the sub-criteria, we obtained the ranking of the monitoring alternatives, together with spreads represented by their interquartile ranges displayed in

Table 2. The overall ranking of the alternatives, assuming equal weighting of the underlying sub-criteria.

	Alternative I	Alternative II	Alternative III
Geometric mean	0.23	0.32	0.45
Interquartile range	[0.11, 0,41]	[0.17, 0.56]	[0.34, 0.72]

. Alternative III can be deemed the preferred solution, although its interquartile range overlaps somewhat with the interquartile ranges of alternatives I and II. The difference between alternatives I and II is arguably not significant.

Alternative III scored better than the alternatives on conformance, containment, and societal acceptance criteria. The aggregated opinion was that its environmental impact was lower, while there was some disagreement among the respondents on this point. Seismic surveys, of which there were fewer for alternative III, were generally assumed to have negative impacts on fish and mammals. Alternatives II and III scored better on “facilitate data access and ensure external supervision” since these alternatives included the statement “data will be shared with research institutes and universities by request”, providing more transparency, and such transparency may be advisable to ensure public trust. Alternatives II and III were preferred for the criterion “detect leakage”, mainly due to the added DTS measurements along the well. In the cost category of the criteria, alternative III scored better than the alternatives on “equipment and installation cost” but scored slightly worse on “operation and maintenance cost” and “cost of data processing and interpretation”. Weighing these criteria based on their expected lifespan costs might give a different ranking for the cost aspect.

Two examples of aggregated sub-criteria results are shown in Figure 3. The interquartile ranges are shown with filled boxes, and the min/max are indicated with whiskers. The large spread on alternatives I and III for the criterion “map the areal and vertical extent of CO₂ vs. time” in Figure 3a can probably be explained by disparities of experiences among the respondents regarding which is the preferred alternative. Panel meetings between the respondents might have revealed the reason for this and potentially revealed knowledge gaps that need further investigation. However, this limited study did not permit panel meetings. For the criterion “provide early warning system and security concept” in Figure 3b, alternative III is clearly the preferred alternative for the aggregated result, reducing the need to discuss this criterion in panel meetings unless it has a large weight on the overall assessment.

4. Discussion

The Analytical Hierarchy Process (AHP) has been demonstrated in the evaluation of three alternative solutions for monitoring geological CO₂ storage in a synthetic brine-filled site representative of the Norwegian Continental Shelf.

Defining the most important criteria and sub-criteria that a monitoring solution must fulfill is a crucial part of the process. It is advantageous if this is performed collaboratively between people from different disciplines and representing different stakeholders to ensure that all relevant aspects are considered. In this case of designing monitoring solutions for geological CO₂ storage, the required criteria span technical and operational aspects, economic aspects, and societal acceptance aspects.

A group of experts familiar with the site, its specific risks, and relevant monitoring technologies should define the set of alternative monitoring solutions to be evaluated. In this application of AHP, we recommend viewing the solution alternatives as starting points. The outcome of evaluating the strengths and weaknesses of the aspects of the proposed solutions can be used to define new alternative solutions in an iterative process.

In the evaluation process, experts gave the monitoring alternatives scores according to their ability to fulfill the criteria with the overall goal of securing the measurement, monitoring, and verification (MMV) of the CO₂ storage project in an economically and societally sustainable way. From these scores, individual rankings of the monitoring alternatives for each criterion and an overall ranking were computed, providing a rational basis for deciding on an optimal monitoring solution for the site.

Some criteria, like cost criteria and technological readiness criteria, may be quantifiable. Quantified criteria might not need additional expert opinions to be evaluated but could rather be assessed in an objective way, like the method used in ELECTRE [7]. Then, the work of the experts would focus on evaluating alternatives to the unquantifiable criteria, where the gathering of experience and personal assessment would be the important outcome. This avenue of combining the strengths of different methods can be further investigated in future work.

An individual weighting of the solution criteria was intentionally omitted here. Although a site risk analysis can inform the weighing between the different technical criteria, the weighing between the technical, economic, and societal aspects of the monitoring solution is, to a certain degree, a decision best left to developers and operators based on financial considerations and regional societal acceptance factors.

Even though considerable effort has been made to define clear criteria, some of them still have possibly conflicting interpretations, as was revealed in the evaluation process. One example is the criterion “Quantify CO₂ trapping mechanisms and rates”, which, in retrospect, should have been divided into sub-criteria for the different trapping mechanisms. Such ambiguity of the criteria should be avoided, and where possible, the criteria should be divided into sub-criteria with less ambiguous interpretations.

While the descriptions of the criteria and the alternatives must be as unambiguous as possible and provide sufficient information for the experts to make their assessments, there is a risk that providing too much information can bias the results. One should keep in mind the fact that the purpose of AHP is to collect the experts’ information and knowledge based on their own experience and expertise without coloring their judgment with excessive details.

Criteria with a low spread in the assessment scores indicate agreement among the respondents in that they have a common understanding of the requirements for fulfilling the criteria and have sufficient information to provide the assessments. Hence, the ranking of the alternatives for these criteria is quite reliable. On the other hand, the respondents showed significant disagreements on other criteria, which indicate either a lack of knowledge or different experiences among the respondents. In such cases, panel meetings might be an option to either come to a consensus or to reveal and fill in knowledge gaps through additional information-gathering processes or even new research projects. Such stepwise assessments are particularly important for criteria with large weights.

In the current implementation, some sub-criteria under the societal perception criterion were found to be repetitions of sub-criteria under the technical criteria, hence playing an important role in achieving both. The current implementation of the AHP framework does not handle the sharing of sub-criteria between multiple main criteria, but this is something that could be explored in future applications of the framework, possibly with different weights for each main criterion. To the degree that aspects of the criteria should be assessed differently in different contexts, this should be made clear in the formulation of the criteria.

This study represents the first application of the AHP framework for monitoring system design for synthetic geological CO₂ storage. Hence, the results provided here should be regarded as illustrative of the approach. In general, AHP is a dynamic framework in that when new information about risks and concerns is revealed, new criteria can easily be added to the hierarchy. We found that the AHP is a structured and transparent framework for decision-making. Here, it was used to bring together technical, economic, and social aspects in a holistic approach. As a side effect, which is not to be underestimated, it has encouraged the development of a common language and a shared understanding of CO₂ storage monitoring among experts and stakeholders from different fields and backgrounds.

5. Conclusions

The application of the Analytical Hierarchy Process (AHP) for evaluating monitoring solutions for geological CO₂ storage highlights its effectiveness as a structured decision-making tool. This process has shown its capability to guide the synthesis of new alternatives, combining the strengths of existing solutions in an iterative manner toward optimal decision-making. Panel discussions between experts are a good opportunity to update the monitoring alternatives and reformulate criteria to include updated information. While this study did not employ explicit weighting between technical, economic, and societal criteria, it underscores the importance of context-specific considerations in future applications. The flexibility of AHP allows for its dynamic adaptation to include new information and risk factors, thus enhancing its relevance and applicability across evolving project scenarios.

Site-specific risk factors need to be considered. If the framework is applied to a specific storage site, it will be good practice to revisit the criteria identified and potentially reformulate them to ensure that site-specific aspects are adequately covered. The starting point of monitoring alternatives should also be designed based on site-specific risks and conditions.

The assessment of unquantifiable criteria by expert groups will include subjectivity based on prior experience and personal preference. This can be alleviated by panel discussions to reveal knowledge gaps, which can be closed over time.

Beyond its evaluative capabilities, AHP has proven invaluable in building a shared understanding and common language among stakeholders from diverse domains. This aspect is crucial for collaborative efforts in complex projects like CO₂ storage. This study’s insights, such as the need for clear and precise criteria definitions and the potential inclusion of panel discussions to resolve stakeholder disagreements, can inform enhancements in the AHP framework. Additionally, future refinements consider allowing sub-criteria to intersect multiple main criteria, thereby reflecting the interconnected nature of project factors. Overall, AHP stands out as a transparent and holistic approach, facilitating balanced decision-making while encouraging continuous learning and adaptation.

Future research directions will include combining the strengths of different multi-criteria decision-making frameworks. For quantifiable criteria like cost and technological readiness, an objective evaluation like ELECTRE could be used, while unquantifiable criteria would still be assessed by a group of experts, as was performed here. Another research direction would be to investigate the value of panel discussions to resolve disagreements and reveal knowledge gaps. Applying the AHP framework to a real CO₂ storage monitoring project would provide further evidence of its feasibility and effectiveness in practice.