Hybrid Framework for Investigating Digital Transformation Barriers in the Oil and Gas Sector

Abstract

The oil and gas (O&G) sector depends on information analysis, making digital transformation vital. Industry-specific factors like environmental, regulatory, operational, and security challenges shape its tech adoption. This paper identifies and examines the barriers and drivers of digital transformation in O&G through a literature review and expert insights, leading to a questionnaire assessment. Interpretive Structural Modeling (ISM) was employed to create a structural model that illustrates the interactions between these barriers, helping experts understand how to achieve digitization within the industry. The questionnaire analysis identified the key barriers based on their RII values: “Experiments requiring significant time for validation of new technologies”, “Technical complexities of new technologies”, “Security concerns”, “Insufficient strategies”, and “Organizational culture and resistance to change”. The ISM indicated that the “Technical complexities of new technologies” and the “Lack of readily deployable technologies” are the most influential barriers. Addressing these is crucial for O&G companies to unlock digitalization potential.

1. Introduction

Digital transformation utilizes digital technologies to reshape business models, generating new value and opportunities [1]. It focuses on enhancing performance, safety, cost reduction, and efficiency through digitization [2,3]. Digitalization has significantly surpassed the conventional annual productivity improvement range of 3–5%, demonstrating a clear potential to reduce costs by over 25% [4]. The United Nations’ 7th Sustainable Development Goal calls for “affordable, reliable, sustainable, and modern energy for all”, requiring rapid energy transitions and digital innovation to enable flexible power systems [5].

Prestidge [6] highlights the critical role of digital adoption to enhance competition and performance. This adoption varies due to the O&G sector’s unique nature. Digital solutions are crucial for improving efficiency, saving time, and accelerating decision-making. The O&G industry is projected to remain dominant up to 2060, accounting for about 55% of global energy consumption despite the rise of renewables [7,8]. Consequently, the sector is rapidly digitizing, with real-time analytics enhancing equipment availability and reducing delays [2]. To maintain competitiveness, businesses are adopting advanced technologies such as big data, IoT, cloud storage, digital twins, and AI to improve operations and add value [7,9,10]. The O&G industry is still in the early stages of adopting digital applications, behind other industries such as banking, tourism, media, and telecommunications [11]. Digital transformation encounters various barriers. Identifying these barriers can be challenging, particularly due to difficulties in accessing information and reports in the highly confidential O&G sector [12]. However, several previous studies have addressed this challenge by identifying alternative methods to obtain reliable data while ensuring its security and confidentiality [13,14,15]. Additionally, there is a notable lack of conducting investigation on digitization within the O&G sector when compared to other industries. Moreover, the number of digitization experts in O&G organizations is limited, as digital transformation in this industry remains in its early stages.

Given the O&G industry’s unique operational complexity, market instability, legacy systems, and workforce limitations, this paper focuses on exploring the primary barriers and their interdependencies in the digitalization efforts of the O&G sector. This study explores the barriers to digital transformation in the O&G sector. It aims to aid organizations in overcoming these challenges, optimizing resource allocation, and achieving a competitive advantage. It offers insights for policy formulation and supports decision-making processes to facilitate successful digital transformation within the industry.

2. Literature Survey

2.1. The Role of Digital Technologies in Improving the O&G Industry

Digitized technologies provide significant advantages to the O&G industry, including environmental protection, improved operational efficiency, and enhanced system reliability. As natural extraction declines, embracing technological advancements in exploration and production is vital for sustaining growth in petroleum fuels. Digitalization aims to improve processes, cut costs, and optimize operations [6,16]. Many organizations are adopting digital transformation tools like AI, robotics, and carbon capture and storage. Shukla and Karki [17] found that robotics in the O&G sector primarily focuses on inspecting pipes and tanks, with reliable autonomous models emerging as viable options despite the need for skilled operators.

Digitalization seeks operational excellence by creating a more efficient and responsive IT service environment. It involves implementing emerging technologies like data science, data center automation, and cloud computing, which integrate separate organizational sectors to enhance agility and drive innovation. This transformation boosts productivity and supports data-driven decision-making. Organizations can combine data from economics, commerce, and geophysical sciences with digital technology to enable advanced analytics and unlock previously untapped value through tools ranging from basic visualization to automated machine learning [6]. The O&G industry has faced many challenges, prompting technical, organizational, and administrative changes. Currently, the sector is under pressure from rising energy demand and environmental concerns, necessitating enhanced production efficiency and sustainability. Embracing digital transformation in O&G operations is now viewed as a strategic imperative. Developing an effective digitization strategy in the O&G industry is challenging due to extensive legacy systems and overlapping operational processes. Samoun [18] notes that structural complexities and reliance on partnerships for procurement, services, and engineering prevent smooth digital adoption. Additionally, the costs of implementing complex operational and management systems to meet standards pose a significant challenge. Peskova et al. [19] emphasize the necessity for each O&G company to create a strategic transformation plan aligned with both short-term and long-term objectives.

2.2. Previous Studies

Throughout this century, the O&G industry has progressed significantly [20]. Research has explored various aspects of this history, including investigations into digital transformation and barriers to digitization within the O&G sector.

Tung et al. [7] stressed that O&G firms must align their business models with technological advancements, necessitating the restructuring of organizational frameworks to meet digitalization demands. While most O&G workers recognize the importance of adopting digital technology, leaders encounter significant organizational challenges when implementing these initiatives. The study highlights three key pillars for successful digital transformation: human, process, and technological applications, with the human factor being the most significant barrier. Leaders should enhance training and knowledge sharing, especially when organizational experience is limited. They also identify major challenges in the O&G sector, including adherence to digitization strategies, traditional processes, resistance to change, budget constraints, and technical obstacles, all of which can impact safety. The limitation of this study lies in its focus on a single company, which may restrict the generalizability of the findings to other organizations. Further, limitations related to technological maturity and cybersecurity risks are discussed, suggesting the findings might not fully capture long-term impacts or variations in global practices.

Prestidge [6] examined challenges and solutions related to digitization in the O&G sector, identifying internal barriers like organizational structure, cultural dynamics, strategic gaps, and economic feasibility, alongside external barriers such as limited funding, workforce skills, and inadequate infrastructure.

Moşteanu [21] added that challenges include data isolation, workforce development, solution compatibility, resistance to digital transition, ineffective measurement of new technology efficiency, and incompatibility among digitized technologies. Nevertheless, the study’s limitations include a lack of detailed data on organizations that have restructured to integrate new technologies, insufficient correlation of required skills and activities for digital implementation, and limited focus on employee reallocation and requalification processes. These gaps suggest the need for further research to better understand the practical and workforce implications of digital transformation.

Kohli and Johnson [22] analyzed a case study to identify challenges that emerging industries face when digitalizing core processes and services. They noted the need for technical expertise, increased costs, conflicts in management, clashes between technology-oriented and business-focused cultures, budget constraints, and skill gaps between business experts and technology engineers. The study’s limitations include its reliance on a single case study, limiting generalizability and applicability to other firms. The solutions presented are context-specific and may not address broader workforce challenges or the integration of emerging technologies. It focuses on leadership roles without exploring operational-level impacts and is influenced by specific market conditions during a period of turbulence. Additionally, it lacks comprehensive industry comparisons and an analysis of barriers to digitization across latecomer industries.

Additionally, Bailie and Chinn [23] highlighted further barriers to digital adoption, including human resource limitations, the financial burden of new technologies, data management challenges, implications for existing processes, required cultural shifts, change management, leadership dynamics, strategic planning, effective communication, technology complexity, and cybersecurity concerns. The study’s limitations include a lack of comprehensive analysis of barriers, reliance on general trends and speculative future applications, and insufficient focus on segment-specific challenges within the O&G industry. It provides limited depth on cultural adaptation strategies, talent management for digital transformation, and actionable insights into cybersecurity risks and mitigation.

Kane et al. [24] identified key barriers to digitalization, such as conflicting priorities, lack of a comprehensive strategy, misunderstandings about digitalization, security concerns, limited collaboration, reluctance to take risks, and inadequate skills.

Parsoya [25] added that many technologies in the O&G industry remain underutilized due to insufficient knowledge and awareness. Nevertheless, the study does not include empirical evidence or detailed exploration of practical challenges, such as resource constraints and workforce adaptation.

Ismail et al. [26] emphasized that these challenges are common obstacles for businesses in the sector. Digital transformation disrupts companies’ value chains, organizational hierarchies, and operational protocols, but adaptation remains crucial. Historically, O&G companies have undergone major structural changes in response to challenges. For instance, Mitchell and Mitchell [27] noted that the sector’s structural stability was significantly affected in 2008 when demand exceeded supply, resulting in the elimination of structural surpluses.

Sarabdeen et al. [28] emphasized the important role of digital technology in addressing climate change by improving environmental quality. However, they identified differing impacts within the digital realm: internet access has a negligible effect on environmental quality, whereas mobile access shows a slightly negative influence. These observations indicate that although digital innovations offer notable environmental advantages, some aspects of their implementation could hinder sustainability objectives.

Since the 1980s, O&G firms have used sensors to collect vast amounts of data across industries, as noted by Snook [29]. However, while they excel in data collection, many struggle to effectively analyze and utilize it due to limited expertise in data analytics. Although 66% of respondents in an upstream O&G digitalization survey view analytics as crucial, only 13% are confident in their firm’s analytics capabilities.

Baaziz and Quoniam [30] identified key areas where the O&G industry could benefit from big data analytics, including process improvement, optimization, safety, and real-time decision-making. Although the industry acknowledges its potential, most firms are still in the early stages of adoption, with efforts remaining largely experimental.

Suppliers of technological assets and service providers offer valuable data to O&G firms [31], with their equipment, such as sensors, generating initial data. However, suppliers often hesitate to provide open, standardized, and accessible data, fearing product commoditization. Prestidge [6] recommended that O&G firms collaborate with their partners to implement these requirements and establish an open data platform.

Digital transformation in the energy sector, including O&G, is often slow due to delayed adoption of new technologies [32]. Tung et al. [7] noted that introducing new technology requires careful evaluation and adjustments to current processes. This delay is largely due to the industry’s culture and reluctance to abandon long-standing practices [8,24], with many organizations hesitant to take risks associated with sudden changes [33,34]. However, to stay competitive, the industry must embrace innovation and experiment with new approaches, gaining valuable insights even from failed attempts. Carvajal et al. [35] highlighted that downstream sector organizations have successfully reorganized their digitization efforts by adopting sensors to improve drilling operations and integrating them with supply chain information systems to optimize and accelerate processes.

The IT infrastructure of the O&G industry is lagging due to outdated data processing technology that fails to meet increasing demands. Legacy systems, such as the “Highway Addressable Remote Transducer (HART) protocol”, “SCADA”, and “Fieldbus”, are still used in modern centralized distributed control systems [18].

The use of such old protocols can be due to the existing infrastructure, in which a big investment was already made. The replacement of such a system can be costly. In addition, the existing system is inexpensive compared with modern systems and has proven to be reliable, so there is no good reason to replace it. Consequently, the upstream O&G sector remains in the early stages of digitization. Singh [34] identified budgeting limitations as a significant challenge to digital transformation, emphasizing the need for alignment between strategy and the business model. These challenges are primarily due to two factors: (i) market price instability and (ii) high operating costs.

Current global market conditions are marked by uncertainty, with factors like economic slowdowns and political conflicts making oil prices unpredictable. These prices directly impact O&G firms’ profitability and investment decisions [36]. Thus, organizations are more likely to invest in new fields if they expect prices to rise, while expected price drops may delay exploration and development projects. Bailie and Chinn [23] stated that operations in the O&G industry are expensive, with high capital costs and numerous variables affecting operating expenses. Increasing project complexity, regulatory requirements, and labor costs further drive-up expenses. Combined with fluctuating global prices, these factors make digitalization more costly and less economically feasible compared to other sectors.

3. Research Methodology

This study employed a research methodology consisting of several key steps. An extensive literature review identified barriers to digitization in the O&G industry, complemented by insights from field experts. An online survey was distributed to O&G professionals to assess the perceived importance of these barriers, with data undergoing statistical analysis for significance. Interpretive Structural Modeling (ISM) was then utilized to explore relationships among the barriers and create a structural model of their interactions. Finally, the findings were discussed, and conclusions and recommendations were made based on the survey analysis and ISM results.

3.1. Identifying Barriers to Digital Transformation

A comprehensive review of the literature addressing the obstacles to digital transformation within the O&G sector revealed a wide range of challenges across operations and regions. Despite this diversity, several barriers consistently surfaced. To explore the barriers to digital transformation in the O&G sector, semi-structured interviews were conducted with seven experts with 15 to 33 years of industry experience in roles such as senior data analysts and project engineers. These experts were specifically chosen for their direct involvement in digital transformation initiatives and leadership roles in system transformation projects. The interviews used a semi-structured format to balance consistency in core questions with the ability to gain insight into emerging themes. The interview questions were carefully developed based on a thorough review of the literature. The interviews, each lasting approximately one hour, were conducted in person. All participants were briefed on the study’s objectives, with informed consent obtained before the interviews commenced. Experts highlighted the need to address safety concerns, challenges at brownfield sites, investment in new systems with adequate data transfer infrastructure, and competition for funding between digitalization and expansion projects.

Thematic analysis was used to identify and interpret patterns related to digital transformation barriers in the interview data. To ensure a detailed and accurate representation of the expert discussions, the interviews were first conducted. The research team became familiar with the responses through repeated readings, allowing them to fully engage themselves in the information. Next, an initial coding process was carried out, which involved systematically identifying and labeling meaningful data segments. These codes were then classified into larger categories to capture common themes and insights. The themes were iteratively refined through discussions and cross-referenced with findings from the literature to ensure validity and coherence. To enhance the reliability of the findings, the participants reviewed the identified themes to confirm the accuracy and relevance. The final analysis identified 20 key barriers to digital transformation in the O&G sector, which formed the basis of the study’s findings and discussions. These barriers, summarized in Table 1, are classified by their causes and organizational implications, including administrative, cultural, technical, financial, security, human resources, existing infrastructure, and other factors.

Table 1. Description of the identified barriers to digitalization in O&G industry.

No.	Barrier	Brief Description	Source	Classification
1	Poor data management systems	Speedy access to the data is important to identifying the source of the issue or formulating a plan to address it. The O&G industry heavily depends on data and information received from fields, processes, forecasts, and markets. In some cases, obtaining real-time data is critical for developing the necessary models to digitize systems, especially during operational processes. The historical challenge of integrating and transferring this data efficiently has led to obstacles and delays in accessing information.	Expert	Existing infrastructure
2	Insufficient strategies	The O&G industry is pursuing digital transformation through a singular, substantial shift. It seeks a substantial and rapid departure from the present state to realize digital transformation. Failing to consider the consequences of this abrupt transition for both organizations and employees could lead to numerous disruptions, causing delays in implementing the necessary changes. Moreover, negative experiences from previous unsuccessful attempts can influence management decisions regarding supporting other initiatives.	[6,33]	Administrative
3	Lack of appropriate communication and lack of teamwork	A disruption in the seamless transfer of data between any two stages may have an impact on the transformation process. The lack of communication between the concerned organizations, including contractors, vendors, service providers, and other partners in this sector, threatens to disrupt the transition to digitization as a whole. This may be attributed to reasons related to organizational structure, communication protocols, and legal matters as well.	[24] & Expert	Cultural
4	Experiments requiring significant time for validation of new technologies	Often, when integrating new technology, a trial phase is necessary to demonstrate its suitability for the business. In the O&G sector, where approval processes are rigorous and procedures lengthy, these trials can be time-consuming. Subsequently, the team must await the results and evaluations.	[7]	Administrative
5	The complexity of some technologies	In the realm of digitalization, many of the introduced technologies rely on components, concepts, and software that are unfamiliar to workers in the O&G sector and unrelated to their usual backgrounds. This poses challenges for the sector, especially in instances where there is no prior experience.	[23]	Technical
6	Existing culture and resistance to change	Cultures evolve over time, often becoming accepted norms. This transformation demands workers to practice creativity, innovation, and adaptability, attributes not commonly adopted in the culture of O&G companies. Consequently, employees often resist change and experimentation, retaining long-standing practices. Many companies are hesitant to take such bold steps, fearing the associated risks. For management, instilling a new culture can prove challenging when employees are entrenched in established methods. Convincing people to accept change can be difficult when they are accustomed to traditional business practices.	[6,10,34]	Cultural
7	Inadequate training	To ensure successful digital transformation, the O&G industry requires various forms of digital expertise currently lacking. Training programs have yet to adapt to the rapid pace of change, and the existing infrastructure falls short in adequately educating the workforce.	[7,23]	Human resources
8	Security concerns	In O&G operations, a significant amount of confidential data is involved, necessitating its transfer throughout the digital transformation process. The ongoing information revolution has increased the risks associated with information security. The growing dependence on digitization exposes companies to various security risks, particularly when granting access to multiple levels of employees. The necessity for rigorous information security measures may influence decisions regarding digital transformation in certain scenarios.	[6,7,23,33,34,35]	Security
9	Budget limitations	Funding plays a crucial role in driving technological progress; without it, transformation remains unseen. The current budgeting procedures are ill suited to the requirements of digitalization projects. Moreover, there is a lack of alignment among management regarding goals, complicating the endorsement of budgets for these projects. This deviation makes it challenging to reach consensus on priorities and assess the value generated.	[6,23,34]	Administrative
10	Misunderstanding of uncertainties and requirements	People in the O&G sector are conscious of an issue that could potentially be addressed through digital transformation, yet they lack clarity on the form this change should assume.	[37]	Others
11	Poor definition of digital transformation metrics and absence of measures for digitalization performance	The lack of clear definitions for digitalization and its business benefits creates uncertainty. Moreover, there is a lack of standards to evaluate digitization, its types, and how the system performs pre- and post-transformation. With such uncertainty surrounding digital transformation, convincing stakeholders in the O&G industry of its values can be difficult.	[38]	Administrative
12	Poor leadership and mismanagement of change	As a company implements digital transformation, it may encounter confusion as old procedures, roles, and expectations evolve. This demands leadership capable of adopting necessary changes to foster innovation and empowering individuals to communicate and engage in the necessary adjustments. Change can be driven without rising risk, but only through effective leadership across the organization.	[5,10] & Expert	Human resources
13	Aging facilities and legacy systems	The existing established systems within the O&G sector pose significant challenges for renovation. A considerable portion of these systems date back to before the current digital revolution, rendering them outdated and not prepared for modern digital utilization. Incorporating sensors into equipment instrumentation to enable the running of process and equipment data is essential. However, numerous pieces of equipment in the field are outdated and necessitate replacement with newer counterparts outfitted with sensors. Implementing these changes requires extensive modifications, potentially impacting the economic viability of digitalization initiatives.	[2,9,39]	Existing infrastructure
14	Lack of communication infrastructure	Insufficient, unreliable infrastructure and communication networks pose a challenge for widespread digital transformation deployment in the O&G sector, given the dispersed nature of facilities. While various technologies like SCADA, Point-to-Multi-Point Wireless (e.g., VSAT, Radio Communication), and Fiber Optic Cable (FOC) exist, none provide a comprehensive solution. Network coverage requires a tailored approach combining different technologies, with FOC being the preferred option despite its high implementation costs.	Expert	Existing infrastructure
15	Inadequacy of resources	The presence of dedicated teams experienced in digital transformation initiatives is insufficient to supplant prior methods. The organization lacks the necessary digital expertise to effectively utilize technology for enhancing productivity. This necessitates O&G companies to invest in attracting talents from backgrounds previously not essential and integrating them with a skilled workforce to facilitate knowledge transfer and expedite transformation.	[15]	Human resources
16	Difficulties in reaching accurate information	Information within the O&G sector is often classified, hindering easy sharing, and lacks mechanisms for capturing accuracy. Additionally, coordination is lacking, leaving the sector vulnerable to information gaps.	Expert	Administrative
17	The aversion to risk	Management in the O&G industry tends to prioritize eliminating anticipated potential threats rather than creating strategies to counter them. While acknowledging fears is valid and can be constructive, it is important for them to recognize that taking no action also carries risks.	[23]	Cultural
18	Inability to fulfil safety requirements with new digitized technologies	The failure of safety systems in hydrocarbon facilities can have severe impacts on both human lives and assets. Therefore, the design of safety systems necessitates redundancy and multiple layers of protection. However, achieving this level of robustness can be challenging with certain new digitized technologies that rely on wireless communication, particularly those designed to minimize interference from workers. For instance, some IR4.0 technologies are predominantly wireless, posing a challenge in their application for control purposes, with their usage often restricted to monitoring functions.	Expert	Technical
19	Economic of scale	After successfully completing experiments, the challenge lies in securing the budget for scaling up. This is primarily due to the economics of scale. Can it be deployed effectively across the entire company?	Expert	Financial
20	Lack of readily deployable technologies	The absence of proven technologies that fulfil the operational needs of O&G fields and can be implemented without trial and error is evident.	Expert	Others

3.2. Questionnaire Survey Design

The survey was developed using Google Forms and distributed online to O&G professionals for efficient data collection. This approach enabled respondents to fill out the questionnaire survey at their convenience and facilitated the subsequent data analysis process. The survey had three sections: the first explained its purpose and objectives, the second asked four general questions about participants’ education, experience, and job roles, and the third asked participants to rate the frequency of 20 identified barriers on a weighted scale. Respondents rated each barrier on a scale of 5 to 1 to determine its importance regarding its occurrence or impact on digital transformation in the O&G sector. The scale was defined as follows: Extremely Important (5; 100%), Very Important (4; 80%), Important (3; 50%), Somewhat Important (2; 20%), and Not Important (1; 0%), reflecting each barrier’s influence on digitalization efforts.

Statistical analysis of the feedback used the Relative Importance Index (RII), a simple and effective tool for ranking the barriers discussed in this paper. The RII ranges from zero (minimal importance) to one (maximal importance). The RII is calculated using Equation (1) [40].

$$\mathrm{Relative} \mathrm{Importance} \mathrm{Index} (\mathrm{RII})={\displaystyle \frac{ {\sum }_{i=1}^5\left(a_i\right)\left(x_i\right)}{5{\sum }_{i=1}^5{(x}_i)}}\times 100\%$$

(1)

where

• i is the response category index;
• a_i is the weight given to i response; and
• x_i is the variable expressing the frequency of i.

4. Findings and Discussion

4.1. Profile of Respondents

A total of 112 participants from Saudi Arabia completed the survey. As the second-largest holder of oil reserves, Saudi Arabia accommodates 17% of the world’s proven petroleum reserves [41]. Analysis of the second section showed a diverse range of educational backgrounds and work experience. Over 76% of participants held engineering degrees, reflecting the industry’s technical focus. Most had a bachelor’s degree (54%), followed by master’s degrees (31%), with only 3% holding a PhD. More than 75% had 5 to 15 years of experience, 15% had 15 to 20 years, 7% had over 25 years, and only 2% had 20 to 25 years. This indicates a younger workforce, driven by initiatives promoting learning and competition and supporting digital adoption and leadership development [7]. Most respondents were engineers, with 58% in technical or project roles and 17% in management, highlighting their scientific backgrounds and relatively limited practical experience.

4.2. RII Calculations

The Relative Importance Index (RII) for each barrier was determined using Equation (1), as illustrated in Table 2. RII values ranged from 0.618 to 0.725, indicating that all barriers could significantly impede digital transformation in the O&G sector. The top four barriers identified were security concerns, the time required to validate new technologies, organizational culture and resistance to change, and the technical complexities of new technologies. The fifth barrier, “aging facilities and legacy systems”, also had a notable RII value close to the top four. In contrast, barriers related to safety requirements, data management, and team communication were ranked as less impactful.

Table 2. Rank of identified barriers.

No.	Identified Barriers	All Respondents		Respondents with 10 Years of Experience or More
		RII	Rank	RII	Rank
B1	Poor data management systems	0.63	19	0.636	17
B2	Insufficient strategies	0.671	9	0.7	4
B3	Lack of appropriate communication and lack of teamwork	0.641	18	0.66	16
B4	Experiments requiring significant time for validation of new technologies	0.725	1	0.74	1
B5	The technical complexities of new technologies	0.704	4	0.72	2
B6	Existing culture of organization and resistance to change	0.714	3	0.7	4
B7	Inadequate training	0.68	6	0.668	14
B8	Security concerns	0.725	1	0.716	3
B9	Budget limitations	0.666	11	0.68	11
B10	Misunderstanding of uncertainties and requirements	0.677	8	0.68	11
B11	Poor definition of digital transformation metrics and absence of measures for digitalization performance	0.666	11	0.684	9
B12	Poor leadership and mismanagement of change	0.652	14	0.668	14
B13	Aging facilities and legacy systems	0.693	5	0.692	7
B14	Lack of communication infrastructure	0.645	17	0.636	17
B15	Inadequacy of resources	0.652	14	0.684	9
B16	Difficulties in reaching accurate information	0.652	14	0.6	19
B17	The aversion of risk	0.679	7	0.688	8
B18	Inability to fulfil safety requirements with new digitized technologies	0.618	20	0.58	20
B19	Economic of scale	0.659	13	0.676	13
B20	Lack of readily deployable technologies	0.671	9	0.696	6

The survey analysis showed that nearly half of the participants had fewer than five years of experience in the O&G sector. Typically, more experience improves the ability to identify and assess workplace challenges, which is crucial for evaluating barriers to digital transformation. In a complex sector like O&G, fewer than 10 years of experience may limit understanding of these challenges, potentially impacting assessment accuracy. To improve reliability, the study re-evaluated the data, focusing on the 50 professionals with 10 or more years of experience.

This led to a recalculation of the RII and a revision of the barrier rankings, as depicted in Table 2. The new RII values ranged from 0.58 to 0.74, indicating that all identified barriers could significantly impede digital transformation in the O&G sector. The ranking shifted, with the two most significant barriers now being the time required to validate new technologies and the technical complexities of those technologies. Security concerns ranked third, followed by insufficient strategies in fourth place. The calculated Spearman coefficient of rank correlation was 0.881, indicating a very high level of agreement on the barrier rankings between all respondents and those with 10 or more years of experience.

4.3. The Interpretive Structural Modeling Approach (ISM)

The RII technique primarily ranks the identified barriers under the assumption of their independence [42], which may not be accurate. To address this issue, the Interpretive Structural Modeling (ISM) approach is utilized to examine the complex interrelationships among these identified barriers and develop a structural model that reflects their interactions [43,44]. The ISM approach has been applied in various contexts, including identifying factors that facilitate off-site construction adoption [42] and exploring barriers to sustainable tourism development [45,46].

In addition to the RII calculations, the ISM model has been utilized to assist experts in understanding how to achieve digitization in the O&G industry. The ISM methodology works as follows:

• Step One: Gather, summarize, and prioritize the barriers impacting digitization in the O&G sector. This step is completed, with results shown in Table 2.
• Step Two: Experts examine the relationships among these barriers to create a Structural Self-Interaction Matrix (SSIM) that illustrates their interactions.
• Step Three: Convert the upper triangular SSIM into a binary format to form the Initial Reachability Matrix (IRM).
• Step Four: Assess the IRM for transitivity, ensuring that if Barrier A influences Barrier B, and Barrier B influences Barrier C, then Barrier A should also influence Barrier C. This results in the Final Reachability Matrix (FRM).
• Step Five: Use the FRM to create a partitioning table, isolating barriers and assigning levels, which is crucial for the ISM model.
• Step Six: Develop an initial ISM model based on the partitioning table. Check for inconsistencies or logical errors, repeating the process as needed until a satisfactory model is achieved.

4.3.1. Developing the SSIM Matrix

The SSIM matrix is constructed by analyzing the relationships among barriers through the use of symbolic indicators: V indicates barrier i influences barrier j; A indicates barrier j influences barrier i; X indicates mutual influence; and O indicates no influence. Experts assessed these relationships, with only three available during the survey preparation. The experts facilitated the initial SSIM matrix development. The ISM methodology is iterative; each matrix is checked for errors, and the process is repeated if discrepancies are found. Only the final results are reported, and the developed SSIM matrix is presented in Table 3.

Table 3. SSIM Matrix.

	B1	B2	B3	B4	B5	B6	B7	B8	B9	B10	B11	B12	B13	B14	B15	B16	B17	B18	B19	B20
B1	X	V	V	O	O	O	O	O	O	O	O	O	O	O	A	V	O	O	O	O
B2		X	A	A	A	O	O	O	O	O	A	A	O	O	O	O	O	O	O	A
B3			X	O	O	V	O	O	O	V	V	A	O	O	O	O	O	O	O	O
B4				X	A	O	O	O	O	V	V	V	O	O	V	O	O	O	O	A
B5					X	V	O	O	O	O	O	O	O	O	V	O	O	O	O	O
B6						X	A	O	O	A	A	A	O	O	O	O	O	O	O	O
B7							X	V	O	V	V	A	O	O	V	V	O	O	O	O
B8								X	O	O	O	O	O	O	A	O	O	O	O	O
B9									X	A	A	A	A	O	O	O	A	O	A	O
B10										X	X	A	O	O	A	O	O	O	O	O
B11											X	A	O	O	A	O	O	O	O	O
B12												X	V	V	V	O	A	O	A	A
B13													X	O	O	O	V	O	V	O
B14														X	O	V	O	O	O	O
B15															X	V	O	O	O	O
B16																X	O	O	O	O
B17																	X	O	O	O
B18																		X	O	O
B19																			X	O
B20																				X

4.3.2. The IRM Matrix

Developing the IRM matrix requires factoring the contextual relationships in the SSIM matrix using the following transformation rules:

• If cell (i, j) has the symbolic code V, then cell (i, j) will be assigned 1, and cell (j, i) will be assigned 0.
• If cell (i, j) has the symbolic code A, then cell (i, j) will be assigned 0, and cell (j, i) will be assigned 1.
• If the cell (i, j) has the symbolic code X, then both cells are assigned 1.
• If the cell (i, j) has the symbolic code O, then both cells are assigned 0.

These rules were followed to fill all cells. Table 4 presents the IRM matrix.

Table 4. The IRM Matrix.

	B1	B2	B3	B4	B5	B6	B7	B8	B9	B10	B11	B12	B13	B14	B15	B16	B17	B18	B19	B20
B1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
B2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
B3	0	1	1	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	0	0
B4	0	1	0	1	0	0	0	0	0	1	1	1	0	0	1	0	0	0	0	0
B5	0	1	0	1	1	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0
B6	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
B7	0	0	0	0	0	1	1	1	0	1	1	0	0	0	1	1	0	0	0	0
B8	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
B9	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
B10	0	0	0	0	0	1	0	0	1	1	1	0	0	0	0	0	0	0	0	0
B11	0	1	0	0	0	1	0	0	1	1	1	0	0	0	0	0	0	0	0	0
B12	0	1	1	0	0	1	1	0	1	1	1	1	1	1	1	0	0	0	0	0
B13	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	1	0	1	0
B14	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0
B15	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	1	0	0	0	0
B16	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
B17	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	1	0	0	0
B18	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
B19	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	1	0
B20	0	1	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1

4.3.3. The FRM Matrix

The transitivity relations between barriers were further examined. A transitivity relationship implies that if barrier A results in barrier B, and barrier B results in barrier C, then barrier A results in barrier C, too. The transitivity relationships were examined, and the IRM was transferred into the FRM matrix, as shown in Table 5.

Table 5. FRM matrix.

	B1	B2	B3	B4	B5	B6	B7	B8	B9	B10	B11	B12	B13	B14	B15	B16	B17	B18	B19	B20	Driving Power
B1	1	1	1	0	0	1 *	0	0	1 *	1 *	1 *	0	0	0	0	1	0	0	0	0	8
B2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1
B3	0	1	1	0	0	1	0	0	1 *	1	1	0	0	0	0	0	0	0	0	0	6
B4	1 *	1	1 *	1	0	1 *	1 *	1 *	1 *	1	1	1	1 *	1 *	1	1 *	1 *	0	1 *	0	17
B5	1 *	1	1 *	1	1	1	1 *	1 *	1 *	1 *	1 *	1 *	1 *	1 *	1	1 *	1 *	0	1 *	0	18
B6	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1
B7	1 *	1 *	1 *	0	0	1	1	1	1 *	1	1	0	0	0	1	1	0	0	0	0	11
B8	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1
B9	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1
B10	0	1 *	0	0	0	1	0	0	1	1	1	0	0	0	0	0	0	0	0	0	5
B11	0	1	0	0	0	1	0	0	1	1	1	0	0	0	0	0	0	0	0	0	5
B12	1 *	1	1	0	0	1	1	1 *	1	1	1	1	1	1	1	1 *	1 *	0	1 *	0	16
B13	1 *	1 *	1 *	0	0	1 *	1 *	1 *	1	1 *	1 *	1 *	1	1 *	1 *	1 *	1	0	1	0	16
B14	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	2
B15	1	1 *	1 *	0	0	1 *	0	1	1 *	1	1	0	0	0	1	1	0	0	0	0	10
B16	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1
B17	1 *	1 *	1 *	0	0	1 *	1 *	1 *	1	1 *	1 *	1	1 *	1 *	1 *	1 *	1	0	1 *	0	16
B18	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1
B19	1 *	1 *	1 *	0	0	1 *	1 *	1 *	1	1 *	1 *	1	1 *	1 *	1 *	1 *	1 *	0	1	0	16
B20	1 *	1	1 *	1	0	1 *	1 *	1 *	1 *	1 *	1 *	1	1 *	1 *	1 *	1 *	1 *	0	1 *	1	18
Dependent Power	10	14	11	3	1	14	8	10	14	13	13	7	7	8	9	12	7	1	7	1	170

* Cells filled after the transitivity rule is applied.

4.3.4. Levels Portioning

The portioning table includes the reachability set (all barriers influenced by a given barrier, including itself), the antecedent set (all barriers influencing that barrier, including itself), and the intersection set (barriers common to both sets). Barriers are assigned levels through an iterative process that examines both sets in each iteration. Barriers with identical reachability and antecedent sets are extracted and assigned levels based on the iteration number. The FRM is then reduced, and this process continues until all barriers are levelled. Due to the lengthy nature of this iterative procedure, all iterations are condensed in Table 6.

Table 6. Partitioning table.

	Reachability Set	Antecedent Set	Intersection Set	Level
B1	B1	B1, B4, B5, B7, B12, B13, B15, B17, B19, B20	B1	L4
B2	B2	B1, B2, B3, B4, B5, B7, B10, B11, B12, B13, B15, B17, B19, B20	B2	L1
B3	B3	B1, B3, B4, B5, B7, B12, B13, B15, B17, B19, B20	B3	L3
B4	B4	B4, B5, B20	B4	L8
B5	B5	B5	B5	L9
B6	B6	B1, B3, B4, B5, B6, B7, B10, B11, B12, B13, B15, B17, B19, B20	B6	L1
B7	B7	B4, B5, B7, B12, B13, B17, B19, B20	B7	L6
B8	B8	B4, B5, B7, B8, B12, B13, B15, B17, B19, B20	B8	L1
B9	B9	B1, B3, B4, B5, B7, B9, B10, B11, B12, B13, B15, B17, B19, B20	B9	L1
B10	B10, B11	B1, B3, B4, B5, B7, B10, B11, B12, B13, B15, B17, B19, B20	B10, B11	L2
B11	B10, B11	B1, B3, B4, B5, B7, B10, B11, B12, B13, B15, B17, B19, B20	B10, B11	L2
B12	B12, B13, B17, B19	B4, B5, B12, B13, B17, B19, B20	B12, B13, B17, B19	L7
B13	B12, B13, B17, B19	B4, B5, B12, B13, B17, B19, B20	B12, B13, B17, B19	L7
B14	B14	B4, B5, B12, B13, B14, B17, B19, B20	B14	L2
B15	B15	B4, B5, B7, B12, B13, B15, B17, B19, B20	B15	L5
B16	B16	B1, B4, B5, B7, B12, B13, B14, B15, B16, B17, B19, B20	B16	L1
B17	B12, B13, B17, B19	B4, B5, B12, B13, B17, B19, B20	B12, B13, B17, B19	L7
B18	B18	B18	B18	L1
B19	B12, B13, B17, B19	B4, B5, B12, B13, B17, B19, B20	B12, B13, B17, B19	L7
B20	B20	B20	B20	L9

The portioning table displays the barriers assigned to levels, with Level 1 barriers at the top of the ISM model and the highest-ranked barriers at the bottom. The ISM model, derived from the portioning table, is illustrated in Figure 1.

Figure 1. The ISM model.

5. Discussion

As shown in Figure 1, the ISM identifies two main influential barriers driving the current state of digitization in the O&G sector: “Lack of readily deployable technologies” (B20) and “The complexity of some technologies” (B5), both hindering adoption. These barriers directly impact “Experiments requiring significant time for validation” (B4). Addressing these challenges helps management tackle “Poor leadership and mismanagement of change” (B12), as well as factors like “Aversion to risk” (B17), “Economies of scale” (B19), and “Aging facilities and legacy systems” (B13). Based on these findings, decision-makers can develop plans to enhance digitization adoption. Workforce training will address (B7) “Inadequate training”, while establishing a robust communication infrastructure will resolve (B14) “Lack of communication infrastructure”.

Moreover, improving (B3) “Lack of appropriate communication and teamwork” will lead to a better understanding of the uncertainties and risks referenced in (B10) “Misunderstanding of uncertainties and requirements”. This will contribute to a well-defined digital transformation strategy, including clear standards, key performance indicators, and measures of success or failure, addressing (B11) “Poor definition of digital transformation standards and absence of performance measures for digitalization”.

Agreeing on digitization enables effective strategies to address (B2) “Insufficient strategies” for transformation. It includes developing cybersecurity measures for (B8) “Security concerns” and ensuring individual safety (B18), especially with wireless networks. This agreement also helps secure funding to overcome (B9) “Budget limitations” and promotes a positive culture to combat (B6) “Organizational resistance to change”.

5.1. MICMAC Analysis

Figure 2 illustrates the categorization of barriers through MICMAC analysis, highlighting their driving and dependent powers. The barrier driving power is calculated as the sum of its own row and the rows it influences in the FRM matrix, while the dependent power is the sum of its column and the columns that influence it. Both powers are displayed alongside the FRM matrix in Table 5.

Figure 2. The MICMAC analysis.

The barriers are categorized into the following groups based on their driving and dependent powers:

• Autonomous barriers: Low driving and dependent power (B14, B18).
• Driving barriers: High driving power and low dependent power (B4, B5, B7, B12, B13, B15, B17, B19, B20).
• Linkage barriers: High driving and dependent power (none identified).
• Dependent barriers: Low driving power and high dependent power (B1, B2, B3, B6, B8, B9, B10, B11, B16).

Barriers in the driving quadrant are highly influential and should be prioritized, as they are key for advancing digitization in the O&G sector. Conversely, barriers in the dependent quadrant generally indicate the system’s efficiency once established.

5.2. Proposed Strategies

Based on the RII results, ISM analysis, and study insights, the following recommendations are proposed to accelerate digitalization in the O&G sector. The recommendations are tailored to address the barriers in the ISM model:

• The barriers at levels 8 and 9: The top barrier identified by professionals with over 10 years of experience and presented by the ISM model is the “Time-consuming validation of new technologies” (B4). To address this, it is essential to manage the “Technical complexities of new technologies” (B5) and overcome the “Lack of readily deployable technologies” (B20).
• Level 7 barriers: Many digital projects struggle to demonstrate economic viability (B19) during assessment, which is crucial for securing funding. Analysts should refine models to capture all positive impacts, including value creation and cost reduction, requiring a deep understanding of digitalization’s benefits. While valid concerns like the fear of the unknown (B17) must be addressed, management should recognize that inaction poses a greater threat to survival (B12). Achieving zero risk is unrealistic; the focus should be on assessing and mitigating risks (B17).
• The barriers at Levels 3, 4, 5, and 6: Transitioning to new technologies can be challenging due to digitization complexities. Establishing training programs (B7) aligned with digital transformation is essential to ensure the workforce can effectively use new technologies and minimize disruptions. O&G companies should recruit specialized experts (B15) to lead implementation and facilitate knowledge sharing. Adopting effective knowledge transfer (B1 and B3) mechanisms, such as web-based systems, will support new practices and ongoing training efforts.
• The barriers at Levels 1 and 2: Installing untested technologies is not advisable. To expedite experimentation, employees need support and flexibility in decision-making (B14). Establishing benchmarks and partnerships can foster innovation (B10 and B11). Collaborating with technology leaders and involving them in R&D helps design solutions tailored to O&G needs. Agreements with suppliers or manufacturers can facilitate faster information exchange, enabling more experimentation and quicker implementation to realize the benefits of new technologies (B16).
• The barriers at Levels 1 and 2: O&G companies should invest in security frameworks (B8), focusing on training and awareness to promote collective responsibility for information security. This may involve reassessing regulations to strengthen authority and impose precise penalties for violations. Enhancing security during technology deployment will support digitalization while ensuring safer data transfer. Cybersecurity is critical for maintaining operational safety (B18) and protecting data confidentiality. Additionally, the sector should seek proven technologies that meet operational needs and enable developing sufficient strategies (B2) without extensive experimentation
• Economics of scale (B19): Moving into fully digitized system entails a high amount of investment. With the cybersecurity concerns are relieved (B8), and the presence of enough strategies for transformation (B2), adequacy of resources (B15), proper training (B7), and all other barriers resolved, the determining factor for transformation is based on the proper balance between benefits incurred relative to the high expected expenditure. O&G companies should be aware that over the short term, benefits from transformation cannot be realized. Nevertheless, digital transformation is a strategic decision, and the incurred benefits should be expected to be realized in the long run.

6. Conclusions

The O&G sector must adopt digital technologies to remain competitive and meet rising demand. Digitalization is essential for flexibility in market challenges and field management. Existing barriers should not hinder digital transformation goals. This study identified and prioritized 20 barriers to digital transformation in Saudi Arabia’s O&G sector, based on expert consultation and research. The barriers span administration, culture, economics, technical issues, structure, procedures, and human factors. All had high RII values, with the most significant being lengthy technology validation, complexity, security concerns, resistance to change, aging facilities, and lack of deployable technologies.

The ISM approach is utilized to analyze the relationships between barriers and develop a structural model of their interactions. It reveals that “Lack of readily deployable technologies” (B20) and “Technical complexities of new technologies” (B5) are the main obstacles to digitalization. Consequently, any new technology requires extensive testing, with “Experiments requiring significant time for validation” (B4) before adoption.

This paper provides recommendations to speed up digitalization in the O&G sector, derived from RII results and ISM analysis. A key barrier is the “Time-consuming validation of new technologies”, which can be addressed by managing “Technical complexities” and overcoming the “Lack of readily deployable technologies”. Enhancing security during technology deployment will also aid digitalization and ensure safer data transfer. However, overcoming these barriers requires significant changes to established policies and standards. The transformation will be costly, demanding employee training, cultural acceptance, procedural adjustments, facility upgrades, and coordination across organizations, all requiring high-level approvals. Integrating digital transformation into corporate strategies is essential for aligning with investment plans.

Despite the useful insights provided by this study, several limitations need to be indicated. First, the sample size of participants may be increased. The study’s focus on the Saudi O&G sector may limit the findings’ applicability to other countries or regions with different regulatory, operational, and cultural contexts. Furthermore, the study identifies several challenges but does not evaluate the practical feasibility or cost-effectiveness of the proposed recommendations, such as the implementation of new technologies or employee training programs. The dynamic and rapidly evolving nature of digital technologies in the O&G sector also presents a challenge, as the identified barriers may change over time as technology advances. To overcome these limitations, future research can be carried out to

• Develop strategies and frameworks to address the cultural resistance and promote digital transformation in O&G.
• Examine the impacts of different digital technologies (such as IoT and AI) on safety protocols and security frameworks in the O&G sector.
• Investigate how technology validation processes can be streamlined without compromising safety or functionality, possibly by using simulations or digital twins.
• Explore how training programs can be optimized to equip the workforce with the necessary skills for digital transformation.