Study on the Causes of Wellbore Leakage in Carbon Capture, Utilization, and Storage—Enhanced Oil Recovery

Abstract

This study investigates wellbore leakage accidents associated with Carbon Capture, Utilization, and Storage Enhanced Oil Recovery (CCUS-EOR) to identify causal factors, clarify their degrees of influence, hierarchical structures, and substantive roles, while revealing the causal mechanisms behind these incidents to promote the safe development of CCUS-EOR. A distinctive aspect of this research is its integrated framework, which effectively combines the theory of integrated safety management with advanced methodologies such as the Decision-Making Trial and Evaluation Laboratory (DEMATEL), Interpretive Structural Models (ISM), and Cross-Impact Matrix Multiplication (MICMAC) to systematically analyze the interdependencies among risk factors. This comprehensive approach provides a nuanced understanding of the interactions among the 20 identified influencing factors across four domains, organized into a multilayered, three-stage structure. Furthermore, the study uncovers two critical causal pathways for wellbore leakage, namely F17 (lack of supervision and feedback) → F20 (inadequate safety investment) → F16 (lack of education and training) → F3 (weak safety awareness) → F9 (improper material selection) and F13 (high geological activity) → F11 (poor reservoir properties) → F6 (corrosion and aging failure), offering unique insights into risk dynamics that remain underexplored in the existing literature. This study could be enhanced in future research by taking into account a broader spectrum of causal factors, incorporating scenario simulations to facilitate a more comprehensive analysis, and involving a greater number of experts from diverse fields to enrich the insights derived.

1. Introduction

Global warming is one of the most serious climate change issues currently facing all humankind. Carbon dioxide (CO₂) emissions have contributed to a global average temperature increase of 0.74 degrees Celsius over the past century [1]. This challenging environment is also highlighted in the Sustainable Development Goals (SDGs), which recognize that climate change and its adverse impacts require urgent action by the world’s nations to address them [2]. From the Paris Agreement, which puts forward the direction and planning for a “carbon-free future” and “carbon neutrality”, to President Xi Jinping’s proposal of China’s “carbon capping by 2030 and carbon neutrality by 2060”, the “dual carbon” goal has become the common aspiration of all humankind. Countries around the world are paying more and more attention to green and sustainable development, and increasing the research and development and application of low-carbon technologies.

Existing research suggests that only about 1150 billion tonnes of the current global CO₂ emissions budget remains to achieve the 2 °C target of the Paris Agreement, and that global CO₂ emissions exceed 20 percent of this budget over the period 2017–2022. Carbon Capture, Utilization, and Storage (CCUS), as an indispensable part of a carbon-neutral technology portfolio, is an important technological tool for global warming, given the impossibility of abandoning fossil energy altogether [3]. However, the key to the successful implementation of CCUS is the safe and long-term sequestration of captured CO₂ in deep geological formations (deep saline aquifers, depleted reservoirs, etc.) [4]. Near-surface flora and fauna, as well as groundwater, could be harmed to varying degrees in the event of a leakage of sequestered CO₂ [5].

Carbon Capture, Utilization, and Storage-Enhanced Oil Recovery (CCUS-EOR) is a CCUS technology that utilizes CO₂ to enhance oil recovery while simultaneously sequestering CO₂ [6]. CCUS-EOR stands out because of its technological maturity and commercial applicability [7]. Firstly, the reservoir has a large amount of existing equipment installed on the surface and underground that can be repurposed for CO₂ drive and storage with only minor modifications. Secondly, the sealing quality and integrity of the geological structural system ensures CO₂ sequestration. Therefore, under the new application scenarios and in-depth emission reduction needs, CCUS-EOR meets the current urgent global needs to reduce greenhouse gas emissions and helps to realize the unity of energy development and environmental protection goals [8].

When CO₂ is injected and stored in geologically stable formations, the injected CO₂ is completely isolated by geological confinement, such as a capping formation [4]. As a result, the wellbore, which penetrates the formation, is the component most likely to compromise the safe storage of CO₂ [4]. API 65 defines wellbore integrity as the application of technical, operational, and organizational solutions throughout the life cycle of a wellbore to reduce the risk of the uncontrolled release of formation fluids through the wellbore [9]. Any failure of wellbore integrity can result in CO₂ leakage through the wellbore or even escape to the surface [10]. There is a pressing need for a comprehensive investigation into the fractures that compromise well integrity and how attenuation methods can be employed to identify them [11]. In other words, wellbore integrity directly affects the effectiveness and safety of CCUS-EOR. Wellbore leakage not only affects the dynamic storage rate of CO₂ but also easily causes a series of environmental problems such as soil, groundwater, and air pollution near the leakage area [5].

Therefore, this study aims to investigate the causal factors and causal mechanisms of CCUS-EOR wellbore leakage accidents, clarify the causal relationships and hierarchical structure among the causal factors, and reveal the causal mechanisms of wellbore leakage accidents. First, this study identified the causal factors of CCUS-EOR wellbore leakage from four dimensions: humanity, machine, environment, and method. Then, the Decision-Making Trial and Evaluation Laboratory–Interpretive Structural Models–Cross-Impact Matrix Multiplication method (DEMATEL-ISM-MICMAC) was used to identify the key causal factors among these factors and the causal relationships among them, establish a hierarchical model of causal factors, reveal the outbreak process of wellbore leakage accidents, and clarify the substantial roles of each factor in the system. Specifically, DEMATEL effectively maps the causal relationships among various risk factors, elucidating how they collectively influence leakage incidents. ISM organizes these factors into a hierarchical framework, revealing the interaction pathways that exist among them. Meanwhile, MICMAC evaluates the influence and dependence of these factors. Together, this integrated approach provides a comprehensive perspective on the complex dynamics of wellbore leakage, addressing critical gaps in the existing literature and offering actionable insights to ensure safer operational practices in CCUS technologies. Finally, corresponding countermeasures are proposed in each of the four dimensions. The results of this study can help oil companies to develop targeted and sustainable improvement programs.

The unique application of integrated methodologies such as DEMATEL, ISM, and MICMAC significantly enhances our understanding of wellbore leakage in Carbon Capture, Utilization, and Storage Enhanced Oil Recovery (CCUS-EOR). Specifically, DEMATEL effectively maps the causal relationships among various risk factors, elucidating their collective influence on leakage incidents. ISM organizes these factors into a coherent hierarchical framework, highlighting the intricate dependencies and interaction pathways among them. Meanwhile, MICMAC evaluates the influence and dependence of these factors, enabling a nuanced classification that informs targeted risk management strategies. This integrated approach together provides a comprehensive perspective on the complex dynamics of wellbore leakage, addressing critical gaps in the existing literature and offering actionable insights to promote safer operational practices in CCUS technologies.

The paper is structured as follows: Section 2 introduces the relevant literature; Section 3 outlines the research methodology; Section 4 explains the causal factors identified; Section 5 explores the causal mechanisms of these factors on wellbore leakage; and Section 6 suggests countermeasures.

2. Literature Review

Identifying the causal factors of wellbore leakage is the basis for studying causal mechanisms. The causal factors can be broadly categorized into equipment and non-equipment factors. Equipment factors mainly focus on wellbore integrity [12], including cementing quality [13], cement sheath integrity [14], and casing sealing [15]. For cementing quality, a perfect cementing process can guarantee the stability of the wellbore, and too short a cementing time or too much retarder can lead to insufficient cement curing, which threatens the stability of the wellbore. In addition, the presence of insufficient cement slurry concentration [16] or insufficient cement return height [17] during cementing operations can lead to insufficient sealing of the wellbore, increasing the risk of wellbore leakage. For the integrity of cement sheath, long-term chemical corrosion and mechanical stretching will easily cause the cement sheath to suffer from decreased compressive strength [14], decreased cementing performance [18], and increased permeability [19], which will damage the integrity of cement sheath. When the permeability of the cement sheath increases due to corrosion, a large amount of dissolved CO₂ liquid will flow to the vicinity of the casing, forming a highly permeable channel to accelerate the casing corrosion rate [19]. For casing sealing, Water Alternating Gas (WAG) is a widely used injection method in CCUS-EOR, but the casing is exposed to the weak acid environment formed by the miscibility of CO₂ and water during the long-term injection process and suffers from serious corrosion damage, which ultimately leads to a reduction in the pressure-bearing performance of the casing and a loss of wellbore integrity [20]. During the injection process, the casing also expands radially under internal pressure, and if the tensile stress on the casing exceeds its tensile strength and tensile damage occurs, the casing sealing capacity will be reduced or failed [21].

Factors contributing to wellbore leaks include not only equipment-level factors, but also non-equipment-level factors, such as wellbore-related operational behavior, the geological environment and the management system during the CCUS-EOR. For the operational behavior. Firstly, injection and extraction operations will cause changes in the wellbore state, for example, injection pressure [22], injection temperature [23], injection rate [24], and injection cycle [24] will affect the stress stretching and chemical corrosion effects on the wellbore. Secondly, operators who lack safety awareness and professional skills may ignore the safety operation procedures, disregard the risk warning signals, and make operation errors or irregularities. Regarding the geological environment, firstly, the reservoir physical properties determine the CO₂ transport speed and range [25], and when the rock porosity and permeability near the injection well are low, there may be a prolonged accumulation of CO₂ in the vicinity of the injection well, leading to the serious corrosion of the injection well [26]. Secondly, a large amount of CO₂ dissolution will cause a decrease in groundwater pH, and contact between the acidified groundwater and the wellbore will lead to corrosion of the wellbore, and the wellbore in the area of wide groundwater distribution is prone to serious corrosion [27]. Regarding the management system, first, traditional regular manual inspections, maintenance, and overhauls cannot achieve the real-time monitoring and early warning of wellbore performance degradation and failure. Outdated overhaul mechanisms will lead to wellbore deterioration or failure without timely detection and repair [17]. Secondly, with the deepening of underground exploration and development, the number of abandoned wells is gradually increasing. Failure to complete a long-term safety and integrity assessment of abandoned wells from the perspective of CO₂ sequestration could easily result in CO₂ leakage through abandoned wells [28].

Through the literature review, we find that there are still two major shortcomings: (1) Existing research focuses on the relationship between the causal factors and wellbore leakage, while ignoring the interaction between the causal factors, such as wellbore being subjected to more serious corrosion effects in reservoirs with poor physical properties. (2) Existing studies focus on the management and improvement of one or several causal factors, while ignoring the risk transfer process among causal factors and the outbreak process of wellbore leakage accidents. Therefore, this study employs an integrated safety management approach that encompasses a broader spectrum of risk factors, including those pertaining to humanity, machine, environment, and management dimensions. Furthermore, the temporal scope of this research is extended to longer phases, allowing for a more comprehensive risk analysis that considers a wider array of factors across an extended timeframe. Finally, this study remedies the deficiencies in causal factors and causal mechanisms of CCUS-EOR wellbore leakage.

3. Methodology

In the study of risk factors and their mechanisms, existing methods, while offering diverse perspectives and tools, still exhibit certain shortcomings. Firstly, qualitative analysis methods, such as case studies and expert interviews, often rely on subjective judgment, which can lead to inconsistencies and biases in the results. Additionally, these methods tend to lack systematic approaches, making it difficult to capture all relevant factors within complex systems comprehensively. Quantitative methods, such as surveys and statistical analyses, can generate substantial amounts of data; however, they often face issues related to sample bias and data quality. Furthermore, quantitative studies may not provide sufficient contextual information when identifying the complex interrelationships among risk factors, potentially leading to the misunderstandings of causal relationships. For example, while methods like system dynamics and game theory can simulate the evolutionary processes of risk, the assumptions made and the choice of parameters in these models often affect the validity and reliability of the results. Additionally, these approaches require a high degree of mathematical and computational proficiency, which can pose challenges for practical implementation. In summary, despite the valuable foundations provided by existing methods for studying risk factors and their evolutionary mechanisms, there is a need to explore more integrated and adaptive approaches to better address the challenges of risk management in complex environments.

Therefore, this study uses a combination of qualitative and quantitative research methods. First, the influential factors related to wellbore leakage were identified through integrated safety management theory, literature reviews, and expert interviews. Then, the causality and centrality of the factors were calculated using the DEMATEL method to determine their relative importance and causality. Third, in order to further analyze the influence transmission paths between the factors, the ISM method was used to draw a hierarchical structure diagram of these factors to reveal the causal mechanism of wellbore leakage. Finally, the MICMAC method is used to calculate the driving force and dependence degree of each factor and determine its relative position in the system, and the research idea is shown in Figure 1.

3.1. Identification of Causal Factors

The theory of integrated safety management was put forward by the American scholar Wright in 1940 as a systematic theory of safety management. Covering the four aspects of humanity, machine, environment, and method, the theory stresses the comprehensiveness and systematicity of safety management, aiming at comprehensively improving the effectiveness and scientificity of safety management by comprehensively considering the elements of these four aspects and in-depth analyses of the reasons for the occurrence of accidents. In recent years, the theory of comprehensive safety management has been applied in many fields such as civil aviation safety management [29] and railway signal safety [30].

CCUS-EOR wellbore leakage involves four aspects: personnel operation, equipment condition, operating environment, and management system. By considering the comprehensive influence of personnel, equipment, environment, and management in an all-round way, it helps to systematically grasp the whole picture of wellbore leakage and ensure the safety of CCUS-EOR. Therefore, this study determines the causal factors of CCUS-EOR wellbore leakage based on the theory of integrated safety management.

3.2. DEMATEL

The Decision-Making Trial and Evaluation Laboratory (DEMATEL) was proposed by Gabus and Fontela of Battelle Laboratory in 1971 [31]; this is a method used to systematically analyze the importance and causal relationship between the elements within a complex problem with the help of matrix computation tools and graph theory and by making full use of experts’ experience and knowledge. DEMATEL is a method to systematically analyze the degree of importance and causal relationship between elements within a problem by making full use of experts’ experience and knowledge. As a method for analyzing the relationship between factors in complex systems, DEMATEL has been widely applied in complex problems, especially in the fields of safety management, such as the risk assessment of gas explosion in coal mines [32], the risk assessment of natural gas pipeline leakage [33], and the analysis of the causes of accidents in construction sites [34]. Specific steps for the use of the DEMATEL method are as follows.

Step 1. Establish the direct influence matrix $O={\left[o_{ij}\right]}_{n\times n}$. The questionnaire method was used to invite experts to determine the degree of direct influence between the factors according to the Likert four-level scale, and the direct influence matrix $O$ was obtained, in which $o_{ij}$ indicates the degree of direct influence of the factor $F_i$ on the factor $F_j$, and m denotes the number of experts. At this time, the degree of influence of the factor on itself is not taken into account, so that when $i=j$, $o_{ij}=0$.

(1)$O={\displaystyle \frac{1}{m}}{\sum }_{k=1}^m{o}_{ij}\left(k=\mathrm{1,2},\dots ,m\right)$

Step 2. Establish the normative influence matrix $N={\left[n_{ij}\right]}_{n\times n}$.

(2)$N={\displaystyle \frac{1}{\underset{1\le i\le n}{\max }{\sum }_{j=1}^n{o}_{ij}}}O$

Step 3. Establish the integrated influence matrix $T={\left[t_{ij}\right]}_{n\times n}$.

(3)$T=\underset{k\to \mathrm{\infty }}{\lim }\left(N+N^2+\cdots +N^k\right)=N{\left(I-N\right)}^{-1}$

Step 4. Calculate the influence degree, influenced degree, central degree, and cause degree, which are $e_i$, $f$, $c_i$, and $r_i$.

(4)$e_i={\sum }_{j=1}^n{t}_{ij}\left(i=\mathrm{1,2},\dots ,n\right)$

(5)$f_i={\sum }_{i=1}^n{t}_{ij}\left(j=\mathrm{1,2},\dots ,n\right)$

(6)$c_i=e_i+f_i$

(7)$r_i=e_i-f_i$

3.3. ISM

Interpretative Structural Modeling Method (ISM), proposed by Professor Warfield in 1973, is a structural modeling technique that uses computer technology, graph theory, and matrix tools to analyze complex systems, so that the logical relationships of factors are clearly presented in a hierarchical structural diagram [35]. In recent years, ISM has been widely used as an effective system analysis tool in research areas such as barriers to electric vehicle adoption [36], wind farm development risks [37], and urban resilience impact mechanisms [38]. In order to further investigate the structural relationships among the causal factors of the CCUS-EOR wellbore leakage accident, this study combines the DEMATEL method with ISM, so that the integrated influence matrix in the DEMATEL method is converted into a reachability matrix through the corresponding calculations, and then the corresponding multilevel recursive structural model is constructed using the ISM method. The specific steps of using the ISM method are as follows.

Step 1. Establish the overall influence matrix $H={\left[h_{ij}\right]}_{n\times n}$.

(8)$H=I+T$

Step 2. Establish the reachability matrix $R={\left[r_{ij}\right]}_{n\times n}$. Based on the overall influence matrix, the reachability matrix is established by selecting a suitable threshold value λ.

(9)$r_{ij}=\left\{\begin{array}{c}1, if h_{ij}\ge \lambda \left(i,j=\mathrm{1,2},\dots ,n\right)\\ 0, if h_{ij}<\lambda \left(i,j=\mathrm{1,2},\dots ,n\right)\end{array}\right.$

Step 3. Establish the hierarchical structure. Based on the reachability matrix, the reachable set and common set are computed, which are $R\left(F_i\right)$ and $C\left(F_i\right)$. If $R\left(F_i\right)=C\left(F_i\right)$, it means all factors in $R\left(F_i\right)$ can find antecedents in $C\left(F_i\right)$, $F_i$ is a high-level factor. At the same time, the ith factor in $R\left(F_i\right)$ is removed, and this step is repeated until all factors are removed.

3.4. MICMAC

Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) was proposed by Duperrin and Godet in 1973 as a method to analyze the influential position and substantial role of factors in a system [39]. The core idea of the MICMAC method is to classify all factors into four groups, namely autonomous, dependence, association, and independence, based on the calculation of their driving forces and on their active and passive influence. In recent years, the MICMAC method has been applied in research areas such as green technology adoption [40] and urban water management [41]. The specific steps for using the MICMAC method are as follows.

Step 1. Calculate the driving degree $d_i$ and the dependent degree $j_i$. $d_i$ and $j_i$ of each factor can be obtained by calculating the sum of rows and columns of each factor in the reachability matrix.

(10)$d_i={\sum }_{j=1}^n{r}_{ij}\left(i=\mathrm{1,2},\dots ,n\right)$

(11)$j_i={\sum }_{i=1}^n{r}_{ij}\left(i=\mathrm{1,2},\dots ,n\right)$

The DEMATEL method is able to determine the importance of factors and reveal the causal relationship between factors, but it is unable to classify factors into a hierarchical structure. The ISM method focuses on sorting out direct and indirect factors, and constructs a clear hierarchical structure, but it is unable to reflect the substantive relationship between adjacent factors. The MICMAC method is capable of analyzing the differentiated effect of the same hierarchical level of factors on the next hierarchical level, thus compensating for the shortcomings of the ISM. Therefore, the combination of DEMATEL, ISM, and MICMAC is a better choice for systematically analyzing the complex correlations, hierarchical structure and substantive effects among the causal factors of CCUS-EOR wellbore leakage, which can reveal the causal mechanism of CCUS-EOR wellbore leakage completely.

4. Causal Factors of Wellbore Leakage in CCUS-EOR

Based on the framework of integrated safety management theory, this study searched for “Wellbore Leakage” in the core database of Web of Science and obtained a total of 619 articles. Then, the literature related to “CCUS-EOR” was screened, and 145 articles were finally obtained. Among the 145 articles, 27 high-frequency factors such as corrosion and aging failure, poor reservoir properties, and lagging technical standards were initially identified. In addition, this study invited relevant experts, as shown in Table 1, to rank the importance of the preliminarily identified causal factors using a five-point Likert scale, and finally identified 20 important causal factors, as shown in Table 2. This group of 10 experts possesses diverse backgrounds and extensive experience in carbon dioxide enhanced oil recovery (EOR) and storage, justifying their inclusion in the study. Representing universities, companies, government agencies, and professional associations, they combine rich theoretical knowledge with practical insights.

4.1. Insecurity of Humanity

Insecurity of humanity refers to the existence of people in the work process, which can result in not meeting the safety requirements or the standards of the state [42]. In CCUS-EOR wellbore leakage, human error, poor physical condition, weak safety awareness, inadequate emergency capacity, and insufficient professional skills are the five factors in the insecurity of humanity.

Firstly, CCUS-EOR has strict operating procedures, and if the staff makes mistakes or violates the regulations, it may directly lead to wellbore leakage [43]. Secondly, fatigue operation will significantly increase the probability of misoperation, and timely and effective response of staff to accidental danger signals is crucial to reduce the risk of wellbore leakage [42]. Thirdly, if the safety awareness of the staff is weak, it is very easy to produce the phenomenon of omission of inspection and checking, which will increase the possibility of wellbore leakage [43]. Fourthly, if the staff does not have effective emergency response capability, they will not be able to control the development of the situation in a timely manner, and the damage caused by wellbore leakage may be aggravated by improper behavior [44]. Fifth, professional skills are the basis of practical operation, if the staff’s professional skills are insufficient, it will inevitably lead to misoperation [42].

4.2. Insecurity of Machine

Insecurity of machine is a state of machine that does not meet safety requirements or standards during operation [23]. In CCUS-EOR wellbore leakage, corrosion and aging failure, design and construction defects, improper maintenance, improper material selection, and quality workmanship defects are five factors in insecurity of machine.

Firstly, CCUS-EOR has strict operating procedures, and if the staff makes mistakes or violates the regulations, it may directly lead to wellbore leakage [9]. Secondly, fatigue operation will significantly increase the probability of misoperation, and the timely and effective response of staff to accidental danger signals is crucial to reduce the risk of wellbore leakage [23]. Thirdly, if the safety awareness of the staff is weak, it is very easy to produce the phenomenon of omission of inspection and checking, which will increase the possibility of wellbore leakage [45]. Fourthly, if the staff does not have effective emergency response capability, they will not be able to control the development of the situation in a timely manner, and the damage caused by wellbore leakage may be aggravated by improper behavior [9]. Fifth, professional skills are the basis of practical operation, if the staff’s professional skills are insufficient, it will inevitably lead to misoperation [23].

4.3. Insecurity of Environment

Insecurity of environment is a condition that exists in the workplace or the surrounding environment that does not meet safety requirements or standards and may pose a hazard or affect the safety of man and machine [46]. In CCUS-EOR wellbore leakage, poor reservoir properties, fluctuating water table, high geological activity, unfavorable climatic conditions, and poor operating environment are the five factors in insecurity of environments.

Firstly, the casing and cementing cement ring were put in during the early drilling of the well, and after a long period of development and production, their own strength and anti-corrosion capacity have significantly decreased, making them highly susceptible to failure and rupture [47]. Secondly, the wellbore design and construction is not up to standard and does not have sufficient integrity and stability, including cementing not reaching high enough in the wellhead [13]. Third, if there is a lack of monitoring of the equipment or a lack of clear maintenance procedures, staff may not be able to understand the operating status of the equipment and carry out maintenance in a timely manner [48]. Fourth, the improper selection of wellbore materials or equipment with pressure-bearing, air-sealing, and corrosion protection properties below the minimum performance requirements [49]. Fifth, devices such as tubulars and columns are important players in the stable operation of the wellbore, and if the wellbore equipment is defective in quality workmanship, it may lead to problems such as failure of the wellbore seal [42].

4.4. Insecurity of Method

Insecurity of method is a flaw or deficiency in a business, organization, or project method system that may result in the method system being ineffective in preventing risks, controlling safety issues, or overseeing implementation [50]. In the CCUS-EOR wellbore leakage, lack of education and training, lack of supervision and feedback, inadequate operational procedures, inadequate emergency system, and inadequate safety investment are five factors in insecurity of method.

First, lax education and training can lead to a lack of professional skills required for the project and a lack of awareness of the need to protect the safety of the wellbore [51]. Secondly, inadequate supervision and feedback is manifested by an imperfect supervisory mechanism that fails to clarify the necessary supervisory responsibilities, authority, and processes to effectively supervise the project [45]. Third, the lack of clarity in processes such as long-term inspections will be detrimental to the discovery of possible safety hazards and increase the likelihood of wellbore leaks occurring [52]. Fourth, the lack of a sound emergency protection mechanism may expand the scope of the impact of the accident and increase the damage caused by the accident [50]. Fifth, failure to adequately invest funds and manpower, etc., in effective safety management may result in insufficient safety management capability [46].

5. Causal Mechanisms of Wellbore Leakage in CCUS-EOR

5.1. Causality and Centrality Analysis

A total of 10 experts were invited to this study, as shown in Table 1. The invited experts scored the direct influence relationship between the factors according to a four-point Likert scale. In order to ensure the reliability of the research results, the experts’ opinions were tested for reliability with the help of SPSS.23.0 software, and the Cronbach’s α was obtained at 0.937, which is greater than 0.80, and the research results are highly reliable.

Further, the ratings of each expert on the factors are summed and averaged, and the direct influence matrix is established with the arithmetic mean of the influence relationship, see Table 3. In this paper, according to the modeling steps of the DEMATEL method, the matrix is then normalized with Formula (1), and then the integrated influence matrix T is obtained with Formula (2), see Table 4. According to the integrated influence matrix T, the degree of influence, degree of influence, degree of centrality, and degree of causality of the factors can be obtained. In order to present the obtained results visually, a right-angle coordinate system is constructed with the center degree as the horizontal coordinate and the cause degree as the vertical coordinate, and the calculated influencing factors are plotted on the coordinate system, see Figure 2.

Table 5 shows that the top three factors in terms of influence are F₂₀, F₁₆, and F₁₇, which have a greater influence on the other factors. The top three factors in terms of being influenced are F₆, F₃, and F₁₁, which are more influenced by other factors.

The higher the degree of centrality, the more critical the role of the factor in the system. As can be seen from Table 5 and Figure 2, the top five factors in terms of centrality are F₂₀, F₃, F₁₆, F₆, and F₁₁, indicating that these five factors are key factors and are at the center.

The higher the degree of cause, the greater the influence of the factor on other factors. Factors with a cause degree greater than 0 are called cause factors and factors with a cause degree less than 0 are called effect factors. As can be seen from Table 5 and Figure 2, the top three factors in terms of causality are F₈, F₁₈, and F₁₄, while F₁, F₂, and F₁₂ have the lowest causality and are most likely to be affected by other factors.

Insufficient investment in safety initiatives may result in outdated technologies and inadequate infrastructure, which heightens the risk of incidents. Similarly, the absence of robust training and supervision mechanisms can lead to increased human error, compounding the risks associated with operations. Conversely, the factors most influenced by others include corrosion and aging failure, weak safety awareness, and poor reservoir properties. For instance, the effects of corrosion and aging can be exacerbated by inadequate maintenance practices, while weak safety awareness often stems from insufficient education and training initiatives.

The degree of centrality of these factors, particularly those at the center such as inadequate safety investment, weak safety awareness, and others, underscores their pivotal role in the risk management framework. High centrality indicates that any interventions aimed at improving these factors will have a widespread impact on the overall risk landscape. On the other hand, the causality analysis indicates that factors like inadequate maintenance, operational procedures, and unfavorable climatic conditions are primary drivers of risk, with a significant influence on the other factors in the system.

In conclusion, a multifaceted approach that integrates adequate safety investment, robust education and training programs, and effective supervision is essential for minimizing the risks associated with CCUS-EOR and ensuring operational integrity.

5.2. Hierarchical Model Analysis

According to the research idea, the threshold λ is introduced to the established integrated influence matrix T to exclude the less influential indicators, so as to simplify and obtain the relationship matrix, and then the reachability matrix. It is worth noting that the introduction of different thresholds expresses different logical relationships. Generally speaking, the selection of threshold λ based on expert experience alone lacks objectivity. In this study, the sum of the mean and standard deviation in the statistical distribution is used as the value of λ, which effectively reduces the subjective influence.

Here, α and β are the mean and standard deviation of all elements of the integrated impact matrix, respectively. α = 0.732 and β = 0.059 are calculated to obtain the threshold λ = α + β = 0.732 + 0.059 = 0.791, and the reachability matrix R is finally obtained according to Equation (5), which is shown in Table 6.

According to the reachability matrix R, the reachable set, prior set, and common set are calculated, and then the hierarchy is divided, and finally the table on CCUS-EOR wellbore leakage causal factors hierarchy is constructed, see Table 7, and the ISM model diagram is plotted, see Figure 3.

As can be seen from the figure, the causal factors of CCUS-EOR wellbore leakage are characterized by multi-level progression and are divided into five layers from bottom to top. Based on the influence relationship between the layers, the factors at Level 1 (surface layer) are identified as the direct causal factors of the accident, the factors at Level 2–4 (transition layer) are identified as the transitional causal factors, and the factors at Level 5 (bottom layer) are identified as the essential causal factors.

Surface factors are the direct causal factors for leakage in the CCUS-EOR wellbore. The surface factors consist of F₁, F₉, F₇, and F₆. This indicates that during wellbore operations, the level of personnel operation and the condition of the equipment used have a direct impact on wellbore leakage accidents. Therefore, for oil companies to quickly reduce the risk of wellbore leakage, they can start from the surface factors to find a breakthrough point, while the surface factors are susceptible to the combined influence of other factors, and also need to consider the joint enhancement and regulation of the surface factors with the transition layer and the bottom layer factors.

Transitional tier factors convey risk through their influence on surface tier factors and act as hubs in CCUS-EOR wellbore spills. Transitional layer factors involve three layers, with Layer 4 factors F₂₀, F₁₉, and F₁₈ driving continuous improvement in education and training, emergency response capabilities and maintenance of Layer 3 factors. Meanwhile, Tier r 3 factors F₁₆ and F₈, among others, play a role in the professional skills and safety awareness of personnel and the stable operation of equipment and quality processes, respectively. Layer 2 factors F₃ and F₁₁, etc., further transfer the influence of the bottom layer factors upwards to the surface layer factors, which ultimately lead to the occurrence of wellbore leakage accidents. Thus, the transition layer factors indirectly have an impact on wellbore leakage by transferring the influence of the underlying factors to the surface factors.

The factor located at the bottom is F₁₇, which is the most fundamental element contributing to the leakage of the CCUS-EOR wellbore. In order to ensure the safe and stable operation of the wellbore, it is inevitably required to identify and correct potential wellbore problems in a timely manner and establish feedback and improvement mechanisms. Effective regulatory feedback can supervise the improvement of safety investment, emergency system, and operation process to ensure the effectiveness and safety [53].

5.3. Causal Path Analysis

The causal mechanism is the process in which some factors in an abnormal state within a system continue to stimulate the remaining factors through the correlation between the factors, ultimately leading to the outbreak of an accident [54]. Therefore, clarifying the correlation between factors, revealing the causal mechanism and cutting off the conduction path of key factors are effective means to prevent accidents. The combination of DEMATEL calculation results and ISM model can intuitively reveal the complex correlation between the factors of the system as well as the causal paths of the key factors, as shown in Table 8. Column 3 of the table is the relative order of the centrality of the factors at the same level; columns 4 and 5 clearly list which factors are affected by the upper level of the factors in the ISM model and which factors are affected by the lower level; and the discriminating condition of the key factors is the maximum centrality at the same level. Column 6 is the maximum centrality at the same level, which is the maximum centrality at the same level. The condition for the key factors in column 6 is that the centrality of the same tier is the greatest.

Finally, eight key factors were obtained, which were F₃, F₆, F₉, F₁₁, F₁₃, F₁₆, F₁₇, and F₂₀, and the correlation of the key factors was analyzed to obtain two systematic critical causation paths, namely (1) F17 → F20 → F16 → F3 → F9 and (2) F13 → F11 → F6, which are collated and plotted in Figure 4 to illustrate the critical causation paths of the leakage of the wellbore of CCUS-EOR.

Among the key factors, F₁₇ and F₁₃ are the most central underlying factors leading to the occurrence of wellbore leakage accidents, and F₆ and F₉ are the most direct surface factors leading to the occurrence of accidents. Therefore, to reduce the risk of wellbore leakage, we should consider starting from strengthening the engineering site selection and establishing the supervision mechanism, improving the system of reservoir geological body evaluation standards, and strictly implementing a system of safety responsibility during production. Secondly, we should also investigate and optimize the key causal paths of the system one by one, block the risk transfer process, and attempt to avoid the loss of wellbore integrity due to improper material selection and corrosion and aging failure. In addition, targeted prevention and control strategies can be adopted according to the importance and correlation of each factor in the system, so as to improve the system layer by layer, starting from the bottom factor, and to realize the positive cycle of the system.

5.4. Driving Force and Dependency Analysis

The MICMAC was used to obtain the CCUS-EOR Wellbore Leakage Accident Causal Factors Driving Force Dependence Matrix, and its mean value was used as the quadrant dividing line to divide all the factors into four quadrants, namely, association, dependence, autonomy, and independence, and was plotted to obtain the Wellbore Leakage Accident Causal Factors MICMAC model, which is shown in Figure 5.

As can be seen from the figure, the factors located in Quadrant I are the correlating factors, specifically including F₃, F₄, F₅, and F₁₆. The correlating factors have strong driving force and dependence, and are located in Layers 2 and 3 in the ISM multilevel hierarchical structural modeling diagram, which play an intermediary role in the whole system of transmitting the driving force of the lower factors to the upper factors, which is in line with the results of the ISM model.

Factors located in Quadrant II are dependent factors, specifically F₁, F₆, F₇, F₉, F₁₁, and F₁₂. Dependent factors possess strong dependence but weak driving force, are susceptible to other factors, and are mainly located in layer 1 in the ISM model, which is consistent with the ISM model.

The factors located in Quadrant III are autonomous factors, specifically F₂, F₈, F₁₀, F₁₅, and F₁₉. The autonomous factors are less dependent and less driven, and they can be considered separately during CCUS-EOR.

The factors located in Quadrant IV are independent factors, including F₁₃, F₁₄, F₁₇, F₁₈, and F₂₀, which are strongly driven but weakly dependent, and are mainly located in Layers 4 and 5 of the ISM model, which is consistent with the results of the ISM model. The independent factors have a strong driving effect on the factors above them, and these factors are not easily influenced indirectly by moderating other factors, so the independent factors should be regarded as the key focus point when preventing CCUS-EOR wellbore leakage.

6. Suggestions

6.1. Improve the Ability of Employee

Human factors, including safety awareness, emergency response capability, and professional skills, are recognized as interrelated key elements that significantly influence overall risk. The interaction of these factors with others creates a compounding effect on operational safety, underscoring the importance of adopting a holistic risk management approach. Firstly, oil companies should enhance employees’ awareness of the wellbore leakage issue by organizing regular safety training courses, promoting safety knowledge, and publicizing real accident cases. This will help instill a deeper understanding of the potential risks and reinforce the importance of adhering to safety protocols [42]. Secondly, it is essential to strengthen training in employees’ professional skills by providing comprehensive operational and emergency response training. This will improve their ability to act effectively during critical situations, thereby enhancing overall operational safety. In addition, the formulation of detailed operating procedures is crucial to ensuring safe production, as these procedures should encompass every aspect of wellbore operations to minimize the risk of accidents caused by operational errors. Companies should involve employees in the development of these procedures to foster a sense of ownership and accountability. Furthermore, fostering an organizational culture that prioritizes safety and encourages open communication about safety concerns will further enhance employees’ safety awareness. By implementing these measures, oil companies can significantly reduce operational risks and promote a safer working environment.

6.2. Enhance the Maintenance of Equipment

Unsafe mechanical conditions, including corrosion and deterioration failures, design and construction deficiencies, and poor material selection, are significant risk factors that affect operational safety. These factors are dependent on other elements within the risk management framework, forming a chain of influences that may increase safety risks. By recognizing these dependencies, the operational safety of CCUS-EOR operations can be enhanced, and the risk of accidents minimized. Firstly, oil companies should develop a detailed equipment inspection plan that outlines the frequency, scope, and standards of inspections to promptly detect potential issues and hidden dangers in the equipment. Timely identification allows for effective repairs before problems escalate [23]. Secondly, it is essential for oil companies to establish a comprehensive equipment maintenance plan that specifies maintenance cycles and procedures for different types of equipment. This approach will help extend the service life of the equipment while minimizing risks associated with corrosion and aging failures. In addition, oil companies should prioritize the proper selection of materials used in their equipment and infrastructure. Employing corrosion-resistant and high-durability materials can mitigate issues related to improper material selection. Regular assessments of the equipment’s health, including evaluations of corrosion rates, aging levels, and performance degradation, should be conducted. Aging equipment should be replaced proactively to further reduce the risk of failure and ensure safe operational practices. By integrating these measures, oil companies can create a safer and more reliable environment for CCUS-EOR operations.

6.3. Strengthen the Inspection of Environment

High levels of geological activity and unfavorable climatic conditions can significantly affect the operational safety of CCUS-EOR projects. Furthermore, poor reservoir properties and fluctuating water tables may be influenced by these driving factors, exacerbating overall risks and compromising the effectiveness of CO₂ injection and storage. By identifying the drivers and dependencies among environmental factors, companies can enhance their risk management strategies and improve the safety and reliability of CCUS-EOR operations. To this end, oil companies should establish comprehensive evaluation criteria for assessing CCUS-EOR reservoir geological bodies to thoroughly investigate and analyze the geological and engineering conditions of oil reservoirs. This should include a detailed characterization of reservoir properties, such as porosity and permeability, to ensure effective CO₂ storage [55]. Secondly, oil companies should conduct thorough investigations of the surrounding environment, including geological features, hydrological characteristics, and meteorological conditions. Understanding these attributes will help anticipate how geological activity and climatic variations could impact operations and enhance overall resilience [56]. In addition, oil companies should establish a comprehensive environmental monitoring system that continuously tracks key indicators, including water quality, soil conditions, and air quality. This system is essential for the early detection of potential CO₂ leakage and other environmental impacts, thereby strengthening environmental protection measures. Regular updates to monitoring protocols in response to changes in geological activity or climatic conditions will further enhance the company’s ability to manage risks effectively and sustain safe operational practices in CCUS-EOR projects.

6.4. Establish the System of Management

Management factors are primarily recognized as driving elements that significantly influence the safety and effectiveness of CCUS-EOR operations. Issues such as lack of supervision and feedback, inadequate operational procedures, and insufficient safety investment create a context that elevates operational risks. By addressing these factors, companies can enhance management effectiveness and improve safety. First, oil companies should regularly monitor and inspect safety conditions, assess potential hazards, and implement timely measures to mitigate the risks of wellbore leakage. This monitoring should be coupled with robust supervision and structured feedback systems that encourage open communication about safety concerns, enabling continuous improvement in operational practices. Second, establishing comprehensive operating procedures with detailed standards and safety practices is crucial to ensure that employees fully understand and comply with them. This includes integrating education and training programs that enhance employees’ knowledge of operational protocols and safety measures. Regular training sessions should be held to update staff on best practices and new developments in safety. Lastly, increasing safety investment is essential for maintaining equipment integrity and supporting safety initiatives. Companies should allocate adequate resources for state-of-the-art safety equipment, ongoing employee training, and the development of a robust emergency response plan. This plan should encompass clear disposal procedures and emergency measures, ensuring a quick and effective reaction to incidents. By adopting these strategies, companies can significantly improve the safety and reliability of CCUS-EOR operations.

7. Conclusions

In recent decades, global warming has become a major and urgent issue of concern and CO₂ emission reduction has become a common demand of all of humanity. As a technology that can not only improve oil recovery but can also reduce CO₂ emissions, CCUS-EOR has received extensive attention from countries all over the world. Although many oil companies have invested a lot of human and financial resources to build CCUS-EOR, they still face many safety challenges, and wellbore leakage is one of the most prominent problems. To reduce the risk of wellbore leakage, it is important to accurately identify the causal factors and mechanisms, which is an important prerequisite for finding effective solutions.

This study identifies 20 causal factors of CCUS-EOR wellbore leakage based on integrated safety management theory, the literature, and expert interviews. Secondly, the DEMATEL method was used to analyze the interactions between the factors, with special attention to the centrality and causality of all factors. Third, the ISM method was used to demonstrate that the causal factors of the wellbore leakage accident showed a multilayered order structure distribution of five levels and three orders. Fourth, the DEMATEL results were combined with the ISM model to identify two critical paths for the occurrence of wellbore leakage, revealing the process of accident outbreak. Fifth, the MICMAC method was used to analyze the substantial role relationship of each factor in the system, and the results are consistent with the ISM model. Finally, four major countermeasures were proposed to reduce the risk of wellbore leakage, namely improving the safety ability of employees, focusing on the maintenance of equipment, strengthening environmental inspections, and assessing and improving management systems.

This study aims to fill the gap in the causal factors and causal mechanisms of CCUS-EOR wellbore leakage and provide countermeasures to reduce the risk of CCUS-EOR wellbore leakage. Oil companies can use the results of this study to determine the strength and direction of their investment in safety management personnel, equipment, funds, etc., to optimize and block the risk transfer process in a timely manner and to effectively prevent wellbore leakage. However, there are still some aspects of this study that need further improvement. Firstly, the current research has identified a limited number of causal factors associated with wellbore leakage, which may lead to an incomplete analysis of risks. Future studies should aim to expand this list of causal factors by incorporating a broader range of influencing elements, such as social and economic factors, as well as technological advancements. Secondly, the absence of scenario simulations in the current research limits the ability to analyze specific conditions and their impacts on wellbore leakage. Future investigations should focus on developing detailed scenario simulations to explore how different operational and environmental conditions may influence leakage risks.

Author Contributions

Conceptualization, Y.Z.; methodology, Y.Z.; validation, Y.Z. and Q.L.; formal analysis, Y.Z.; investigation, Y.Z., M.P. and Q.L.; data curation, Z.Z. and M.P.; writing—original draft preparation, Y.Z.; writing—review and editing, Z.Z. and M.P.; funding acquisition, M.P. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of Southwest Petroleum University.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data will be made available on request.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Research method, content and result.

Figure 2. Centrality degree−causality degree.

Figure 3. The ISM model of causal factors of wellbore leakage in CCUS-EOR.

Figure 4. Critical failure paths of causal factors of wellbore leakage in CCUS-EOR.

Figure 5. The MICMAC model of causal factors of wellbore leakage in CCUS-EOR.

Table 1

Expert profiles.

Number	Age	Year of Experience	Organization	Position
1	50	21	University	Professor
2	44	15	University	Professor
3	42	16	University	Professor
4	41	11	University	Professor
5	54	22	Company	Director
6	49	18	Company	Senior engineer
7	46	15	Company	Senior engineer
8	43	12	Government	Director
9	38	8	Government	Deputy director
10	43	11	Association	Director

Table 2

The causal factors system of wellbore leakage in CCUS-EOR.

Dimension	Code	Causal Factor
Insecurity of humanity	F1	Human operator error
	F2	Poor physical condition
	F3	Weak safety awareness
	F4	Inadequate emergency capacity
	F5	Insufficient professional skills
Insecurity of machine	F6	Corrosion and aging failure
	F7	Design and construction defects
	F8	Inadequate maintenance
	F9	Improper material selection
	F10	Quality workmanship defects
Insecurity of environment	F11	Poor reservoir properties
	F12	Fluctuating water table
	F13	High geological activity
	F14	Unfavorable climatic conditions
	F15	Poor operating environment
Insecurity of management	F16	Lack of education and training
	F17	Lack of supervision and feedback
	F18	Inadequate operational procedures
	F19	Inadequate emergency system
	F20	Inadequate safety investment

Table 3

The direct influence matrix O.

O	F1	F2	F3	F4	F5	F6	F7	F8	F9	F10	F11	F12	F13	F14	F15	F16	F17	F18	F19	F20
F1	0	0.9	0.9	0.2	0.9	0.1	0.8	0.7	0.4	0.9	1	0.3	1	1.2	0.2	1.1	0.5	0.5	1	0.3
F2	3.2	0	1	0.1	0.1	1	0.3	0.2	1.2	0	1.1	0.1	1	0.1	1	0.2	1.1	0.2	0.3	1
F3	2.3	0.4	0	0.8	0	0.5	1.9	0.6	1.9	0.6	0.6	0.7	0.9	0.5	0.5	0.6	0.8	0.7	1	0.6
F4	0.6	0.5	2.1	0	2.4	0.4	0.7	1	0.7	0.4	0.9	0.3	0.5	0.6	0.7	0.6	0.6	0.7	0.8	0.6
F5	3.3	0.2	0.7	0.5	0	0.4	2.5	0.3	2.3	0.2	0.5	0.5	0.8	0.3	0.2	0.5	0.6	0.4	0.6	0.4
F6	0.7	0.6	0.6	1	0.8	0	0.9	0.7	0.8	0.6	0.9	0.4	1.1	0.8	0.8	1	0.7	0.9	0.9	1
F7	0.3	0.5	1	0.6	0.7	0.8	0	0.7	0.7	0.7	1	0.8	0.8	0.9	0.6	1	0.3	0.7	0.9	0.9
F8	0.5	0.4	0.6	0.5	0.6	0.5	0.8	0	0.7	2.2	0.7	0.7	1	0.9	1.2	0.8	1	0.7	0.9	0.7
F9	0.6	1	0.8	0.5	0.6	0.8	0.9	0.8	0	0.7	1	0.7	0.8	1.1	0.5	0.9	1.1	0.3	1	0.8
F10	0.1	0.3	0.7	0.3	0.4	2.4	0.8	0.4	0.9	0	1	0.5	0.9	0.8	0.5	0.9	0.9	0.9	0.8	0.7
F11	0.2	0.4	0.9	0.4	0.3	2.8	0.6	0.5	0.7	1	0	0.6	0.9	0.7	1.3	0.7	0.8	1	0.8	0.6
F12	0.1	0.9	1	0.1	0.8	3.2	0.2	0.5	1.1	0.3	0.4	0	0.3	0.3	0.1	1	0.3	0.9	0.3	0.9
F13	0.6	0.9	0.3	1	0.7	0.1	0.6	1.1	0.4	0.9	1.7	2	0	1.1	0.7	0.6	0.9	0.9	0.4	0.7
F14	0.4	0.6	0.6	0.7	0.4	0.1	0.8	0.7	0.6	0.6	3.3	3	0.3	0	0.4	0.7	0.7	0.4	0.7	0.5
F15	0.1	3.1	0.3	1	0.2	0.3	1	0.5	0.5	1.2	0.4	0.3	1	0.5	0	0.6	1	1.3	0.6	1.1
F16	0.3	1	1.8	0.6	2.1	0.5	0.4	0.9	0.4	0.7	0.3	1	0.7	0.7	1	0	0.9	0.8	0.9	0.7
F17	0.3	0.9	0.4	0.8	1	0.5	0.3	0.8	0.5	0.8	0.1	0.3	0.5	0.8	1.1	1.3	0	1.6	1.3	2.2
F18	0.4	0.8	0.6	1.8	1.1	0.6	0.7	2.1	0.6	1.2	0.1	0.6	0.8	0.8	0.6	0.5	0.9	0	0.5	0.7
F19	0.7	0.8	0.8	2	0.9	0.4	0.6	0.7	0.5	0.9	0.3	0.8	1	1.1	0.5	0.5	1.1	0.8	0	0.9
F20	0.4	0.7	0.4	1.5	0.7	0.3	0.5	1	0.5	0.8	0.2	0.7	0.7	1	2.2	1.7	0.8	0.7	1	0

Table 4

The integrated influence matrix T.

O	F1	F2	F3	F4	F5	F6	F7	F8	F9	F10	F11	F12	F13	F14	F15	F16	F17	F18	F19	F20
F1	0.62	0.65	0.68	0.59	0.64	0.64	0.67	0.61	0.65	0.65	0.69	0.60	0.67	0.65	0.58	0.68	0.64	0.61	0.66	0.63
F2	0.80	0.61	0.68	0.59	0.60	0.68	0.64	0.59	0.69	0.60	0.69	0.58	0.67	0.59	0.64	0.64	0.67	0.60	0.62	0.68
F3	0.86	0.74	0.75	0.75	0.72	0.79	0.85	0.73	0.85	0.75	0.79	0.74	0.79	0.73	0.72	0.78	0.77	0.74	0.78	0.78
F4	0.77	0.73	0.85	0.68	0.83	0.76	0.78	0.73	0.78	0.72	0.78	0.69	0.74	0.71	0.71	0.75	0.74	0.72	0.75	0.75
F5	0.89	0.69	0.76	0.69	0.68	0.74	0.85	0.67	0.84	0.69	0.75	0.69	0.74	0.68	0.66	0.74	0.72	0.68	0.72	0.72
F6	0.76	0.74	0.77	0.75	0.75	0.74	0.78	0.72	0.78	0.73	0.79	0.70	0.78	0.73	0.72	0.78	0.75	0.74	0.76	0.78
F7	0.68	0.67	0.73	0.67	0.68	0.73	0.67	0.66	0.71	0.68	0.73	0.67	0.70	0.68	0.66	0.72	0.67	0.67	0.70	0.72
F8	0.74	0.74	0.77	0.72	0.74	0.79	0.78	0.68	0.78	0.83	0.78	0.73	0.78	0.74	0.75	0.78	0.78	0.74	0.76	0.78
F9	0.74	0.74	0.76	0.70	0.72	0.77	0.76	0.70	0.71	0.72	0.78	0.71	0.74	0.73	0.69	0.76	0.76	0.69	0.75	0.76
F10	0.68	0.68	0.73	0.67	0.68	0.84	0.73	0.66	0.74	0.66	0.75	0.67	0.73	0.69	0.67	0.74	0.73	0.70	0.71	0.73
F11	0.73	0.74	0.79	0.72	0.72	0.91	0.77	0.71	0.77	0.76	0.73	0.72	0.77	0.72	0.76	0.77	0.76	0.75	0.75	0.77
F12	0.62	0.64	0.68	0.59	0.64	0.81	0.63	0.60	0.69	0.60	0.64	0.57	0.62	0.59	0.58	0.67	0.62	0.63	0.61	0.67
F13	0.75	0.76	0.76	0.74	0.75	0.78	0.76	0.75	0.76	0.76	0.84	0.80	0.72	0.75	0.72	0.77	0.77	0.75	0.73	0.77
F14	0.73	0.73	0.77	0.72	0.72	0.79	0.77	0.71	0.77	0.73	0.92	0.85	0.73	0.67	0.70	0.77	0.75	0.71	0.74	0.76
F15	0.72	0.86	0.73	0.72	0.69	0.74	0.76	0.69	0.74	0.74	0.74	0.67	0.75	0.69	0.66	0.74	0.75	0.74	0.71	0.77
F16	0.78	0.78	0.86	0.74	0.84	0.79	0.78	0.74	0.79	0.75	0.77	0.75	0.78	0.73	0.75	0.74	0.78	0.75	0.77	0.79
F17	0.76	0.78	0.78	0.76	0.78	0.78	0.76	0.75	0.78	0.76	0.75	0.71	0.76	0.74	0.76	0.82	0.73	0.80	0.80	0.87
F18	0.75	0.75	0.78	0.79	0.77	0.78	0.78	0.80	0.78	0.78	0.75	0.72	0.77	0.73	0.72	0.76	0.77	0.69	0.74	0.77
F19	0.76	0.75	0.79	0.80	0.76	0.76	0.76	0.72	0.76	0.75	0.76	0.73	0.77	0.74	0.70	0.76	0.78	0.73	0.70	0.78
F20	0.77	0.78	0.79	0.80	0.77	0.78	0.78	0.76	0.78	0.77	0.77	0.74	0.78	0.76	0.83	0.85	0.79	0.75	0.79	0.75

Table 5

The influence, influenced, centrality and causality degree of factors.

Factors	Influence Degree	Influenced Degree	Centrality Degree	Causality Degree
F1	12.80	14.91	27.70	−2.11
F2	12.88	14.58	27.46	−1.70
F3	15.41	15.23	30.64	0.17
F4	14.98	14.20	29.18	0.79
F5	14.58	14.46	29.04	0.13
F6	15.05	15.40	30.46	−0.35
F7	13.82	15.06	28.88	−1.24
F8	15.17	13.99	29.16	1.18
F9	14.69	15.14	29.83	−0.45
F10	14.21	14.43	28.63	−0.22
F11	15.11	15.18	30.29	−0.07
F12	12.70	14.05	26.75	−1.35
F13	15.19	14.80	29.99	0.39
F14	15.02	14.06	29.08	0.97
F15	14.61	13.98	28.59	0.63
F16	15.46	15.01	30.47	0.46
F17	15.44	14.76	30.20	0.69
F18	15.20	14.19	29.39	1.01
F19	15.07	14.55	29.62	0.53
F20	15.61	15.05	30.66	0.56

Table 6

The reachability matrix R.

O	F1	F2	F3	F4	F5	F6	F7	F8	F9	F10	F11	F12	F13	F14	F15	F16	F17	F18	F19	F20
F1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
F2	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
F3	1	0	1	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0
F4	0	0	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
F5	1	0	0	0	1	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0
F6	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
F7	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
F8	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0
F9	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
F10	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0
F11	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0
F12	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0
F13	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	0	0	0	0
F14	0	0	0	0	0	0	0	0	0	0	1	1	0	1	0	0	0	0	0	0
F15	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
F16	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
F17	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1
F18	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0
F19	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
F20	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	1

Table 7

The hierarchy of causal factors of wellbore leakage in CCUS-EOR.

Layer	Level	Causal Factors
Surface Layer	Level 1	F1, F6, F7, F9
Transition Layer	Level 2	F2, F3, F5, F10, F11, F12
	Level 3	F4, F8, F13, F14, F15, F16
	Level 4	F18, F19, F20
Bottom Layer	Level 5	F17

Table 8

Results of the DEMATEL-ISM synthesis analysis.

Level	CausalFactors	CentralityDegree	Upper LevelFactors	Lower LevelFactors	Key Factors
Level 1	F1	4		F1, F3, F5	F6, F9
	F6	1		F10, F11, F12
	F7	3		F3, F5
	F9	2		F3, F5
Level 2	F2	5	F1	F15	F3, F11
	F3	1	F1, F7, F9	F4, F16
	F5	3	F1, F7, F9	F4, F16
	F10	4	F6	F8
	F11	2	F6	F13, F14
	F12	6	F6	F13, F14
Level 3	F4	3	F3, F5	F18, F19, F20	F13, F16
	F8	4	F10	F18
	F13	2	F11, F12
	F14	5	F11, F12
	F15	6	F2	F20
	F16	1	F3, F5	F20
Level 4	F18	3	F4, F8	F17	F20
	F19	2	F4	F17
	F20	1	F4, F15, F16	F17
Level 5	F17	1	F18, F19, F20		F17

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