Upstream Oil Research Articles

A Systematic Review of Cloud Computing Adoption by Organisations

Cloud computing represents a paradigm shift in information technology outsourcing. The technology renders numerous benefits such as flexibility, cost reduction and scalability. Also, it presents organisations with the opportunity to conform to environmental management system. Several studies have applied different theories to investigate the factors that affect cloud computing adoption by organisations. However, the results are inconclusive. Therefore, this study systematically reviews factors influencing adoption the adoption of cloud computing by organisations. A total of 174 papers were reviewed from different databases such as Science direct, Emerald and IEEE Xplore. Consequently, 37 of these articles have been considered to come up with the most salient factors based on their frequency. The findings indicated that technology readiness, top management support, relative advantage, competitive pressure, compatibility, complexity and data security are the significant factors that affect cloud computing adoption in organisations. This review consolidates literature and can benefit decision makers in organisations who intend to adopt cloud computing technology. Furthermore, it has laid a foundation for a future part of this work which is conceptualising a model for cloud computing adoption in the upstream oil and gas sector.

Guest Editorial: Artificial Intelligence in the E&P Industry: What Have We Learned So Far and Where to Next?

Artificial intelligence (AI) has captivated the imagination of science-fiction movie audiences for many years and has been used in the upstream oil and gas industry for more than a decade (Mohaghegh 2005, 2011). But few industries evolve more quickly than those from Silicon Valley, and it accordingly follows that the technology has grown and changed considerably since this discussion began. The oil and gas industry, therefore, is at a point where it would be prudent to take stock of what has been achieved with AI in the sector, to provide a sober assessment of what has delivered value and what has not among the myriad implementations made so far, and to figure out how best to leverage this technology in the future in light of these learnings. When one looks at the long arc of AI in the oil and gas industry, a few important truths emerge. First among these is the fact that not all AI is the same. There is a spectrum of technological sophistication. Hollywood and the media have always been fascinated by the idea of artificial superintelligence and general intelligence systems capable of mimicking the actions and behaviors of real people. Those kinds of systems would have the ability to learn, perceive, understand, and function in human-like ways (Joshi 2019). As alluring as these types of AI are, however, they bear little resemblance to what actually has been delivered to the upstream industry. Instead, we mostly have seen much less ambitious “narrow AI” applications that very capably handle a specific task, such as quickly digesting thousands of pages of historical reports (Kimbleton and Matson 2018), detecting potential failures in progressive cavity pumps (Jacobs 2018), predicting oil and gas exports (Windarto et al. 2017), offering improvements for reservoir models (Mohaghegh 2011), or estimating oil-recovery factors (Mahmoud et al. 2019). But let’s face it: As impressive and commendable as these applications have been, they fall far short of the ambitious vision of highly autonomous systems that are capable of thinking about things outside of the narrow range of tasks explicitly handed to them. What is more, many of these narrow AI applications have tended to be modified versions of fairly generic solutions that were originally designed for other industries and that were then usefully extended to the oil and gas industry with a modest amount of tailoring. In other words, relatively little AI has been occurring in a way that had the oil and gas sector in mind from the outset. The second important truth is that human judgment still matters. What some technology vendors have referred to as “augmented intelligence” (Kimbleton and Matson 2018), whereby AI supplements human judgment rather than sup-plants it, is not merely an alternative way of approaching AI; rather, it is coming into focus that this is probably the most sensible way forward for this technology.

Real-time Performance Monitoring of Electrical Submersible Pumps in SAGD Process

Electrical submersible pump (ESP) is one of the preferred artificial lift systems in upstream oil production because of its wide operating range and endurance to harsh operating environments. In Alberta Canada, about two-thirds of steam assisted gravity drainage (SAGD) wells are equipped with ESPs. Keeping the ESPs long-term operational is one of the primary challenges faced by the operators. ESP failures are a common problem due to various reasons, such as, harsh operating conditions, improper installations, etc. Therefore, the real-time monitoring of ESP performance is indispensable as it can prevent unscheduled well shutdowns and enable preventative maintenance. In this work, aiming at the development of reliable ESP performance monitoring in SAGD process, a monitoring strategy based on a multivariate statistical technique is proposed. The strategy includes a novel first-principles based feature extraction approach, an online model update strategy and a new health index to evaluate ESP health conditions in real time. Test results on real ESP datasets are presented to highlight the efficacy of the proposed algorithm.

Breaking the Bond: Primary Markets and Carbon-Intensive Financing

To align capital flows with the goals of the Paris Agreement, financial institutions must decarbonise primary market transactions, as these continue to provide new capital to the real economy that can create carbon lock-in and the risk of stranded assets. In this paper we define a new metric, Primary Market Carbon Exposure (PMCE), as the proportion of primary market transactions that occur in carbon-intensive sectors. We calculate PMCE for US corporate bond exchange-traded funds (ETFs) and find that these funds systematically partake in carbon-intensive primary market transactions, with a PMCE of 14% from 2015 to 2020, despite tracking indexes that rebalance monthly. High yield ETFs have a higher PMCE than investment grade ETFs and provide more financing to upstream oil & gas. To avoid becoming capital providers of last resort for carbon-intensive sectors, ETF providers need to reduce PMCE in line with Paris Agreement carbon budgets. For policymakers, not only can passive funds contribute to carbon lock-in, but ETFs directly bought by central banks are financing carbon-intensive sectors. We demonstrate this for ETFs bought by the Federal Reserve in 2020.

Turnaround project risk assessment using hybrid fuzzy SWARA and EDAS method: case of upstream oil process industries in Iran

PurposeThe purpose of this paper is to propose a new framework for assessing the risks of turnaround projects in upstream oil process plants.Design/methodology/approachThis study represents a new hybrid framework for turnaround project risk assessment. First, according to experts’ opinions, the project risks were identified using interviews and brainstorming. The most important risks selected by experts and a hybrid multiple-attribute decision-making (MADM) method used to assess and prioritize them. The proposed MADM method uses fuzzy step-wise weight assessment ratio analysis (SWARA) and fuzzy evaluation based on distance from average solution (EDAS) methods based on trapezoidal fuzzy numbers.FindingsIn this research, 28 usual risks of turnaround projects are identified and 10 risks are then selected as the most important ones. The findings show, that among the risks of upstream oil industry turnaround projects from the perspective of experts, the risk of timely financing by the employer, with an appraisal score of 0.83, has the highest rank among the risks and the risk of machine and equipment failure during operation, with an appraisal score of 0.04, has the lowest rank.Research limitations/implicationsThe risk analysis based on inputs collected from the experts in the Iranian upstream oil industry, and so the generalization of the results is limited to the context of developing countries, especially oil producer ones. However, the proposed risk analysis methodology and key insights developed can be useful for researchers and practitioners in any other process industry everywhere.Originality/valueA novel framework for risk assessment is introduced for turnaround projects in the oil industry using MADM methods. There is no literature on using MADM methods for turnaround project risk analysis in the oil and gas industries. Furthermore, this paper presents a hybrid fuzzy method based on SWARA and EDAS.

Executives’ Decision Processes at the Front End of Major Projects: The Role of Context and Experience in Value Creation

The front end of projects is strategically important; yet, how project concepts are identified, evaluated, and selected at the pre-project stage is poorly understood. This article reports on an inductive multiple-case study of how executives made such decisions in major upstream oil and gas projects. The findings show that in such a high-risk context, often an experienced executive makes these decisions alone and he creates value by facilitating growth. We identified three value-creating decision processes that varied by the executives’ risk approach and decision context. These processes depart from the formal project management prescriptions and the strategic decision-making literature.

The distribution of petroleum resources in Malaysia: unpacking federalism

Abstract This article examines the concept of federalism in the oil and gas industry in Malaysia. The petroleum industry is one of the 12 National Key Economic Areas (NKEAs) to enhance national growth under Malaysia’s Economic Transformation Programme (ETP). Due to its economic significance, the petroleum industry was given priority by the Government of Malaysia. The development of the oil and gas industry in Malaysia can be divided into two stages, ie the period before 1974 and after 1974. Prior to 1974, the ownership of hydrocarbon resources was placed under the jurisdiction of the respective 13 states forming part of Malaysia. In 1974, the Petroleum Development Act (PDA) was passed by the Parliament of Malaysia. Pursuant to the PDA, a national oil company has been established in the form of a public listed company which is called Petroliam Nasional Berhad (Petronas). The national oil company was granted the entire ownership and the exclusive rights, powers, liberties and privileges of exploring, winning and obtaining petroleum onshore and offshore Malaysia. Each state permanently conferred its ownership, rights, powers, liberties and privileges in the petroleum by executing the vesting instrument specified in the PDA. This article examines the rights of ownership of the Federation and the various states of Malaysia with regards to the hydrocarbon resources at the time of the formation of the Federation of Malaysia, and the gradual changes in the ownership of the hydrocarbon resources from the states to the absolute ownership of the Federal government in 1974. Furthermore, this article examines the legislation regulating petroleum resources and the role of the states and federation in the upstream oil and gas industry prior to 1974 and after the enactment of the PDA.

Predicting employees’ engagement in environmental behaviours with supply chain firms

PurposeThe aim of this study is to investigate the employee perceptions of organizational support for environmental behaviours and its impact on innovative environmental behaviours and frequency of involvement in upstream oil and gas supply chain management. The study also examines a new area where environmental work culture has been introduced as a facilitator on the relationship between employees’ perceptions and engagement in supply chain management.Design/methodology/approachSelf-administered questionnaires were used for collecting data from supply chain managers in production arrangement contractor and service provider company in Kuala Lumpur. Partial least squares was used for data analysis.FindingsThe findings reveal that supervisory support for environmental initiatives and environmental training is positively related to employees’ perception of organizational support for environmental behaviours, while rewards provided by the organization for environmental behaviours are not associated with it. The employees’ perceptions of organizational support for environmental behaviours have a significant impact on employees’ engagement in environmental behaviours in both forms of employees’ frequency of involvement and employees’ innovative environmental behaviours. The findings also show that environmental work culture moderates positively the impacts of organization’s support practices (supervisory support for environmental initiatives and rewards provided by the organization for environmental) on employees’ perception of organizational support for environmental behaviours.Originality/valueThe study critically examines the possible impact of enablers of engagement in environmental behaviours and how employees’ perceptions of organizational support reflect their engagement towards environmental behaviours of the organizational practices. The findings are useful for supply chain management practitioners in terms of exerting environmental behaviours and facilitating employees’ environmental behaviours in the upstream oil and gas supply chain management sector.

Survive, Revive, Thrive: Chapter 3: Getting Oriented

Do what you can, with what you have, where you are. - Theodore Roosevelt, 26th US President Getting Oriented - The Only Wrong Direction Is Backwards I begin this column by saying that after attending several recent SPE events filled with students and professionals, all virtual of course, that I have seen the very best of us as members of SPE. To a person, you all have been nothing short of inspiring. Our members know that there is a better tomorrow on the horizon and diligently prepare for the days when our industry begins to thrive again. On the other hand, the industry consolidations and workforce reductions continue, and frankly, I don’t know of anyone whose life and career has not been touched by this pandemic-induced crisis. It’s an axiom of life that if you cannot control it, then you must control how you deal with it. I believe operating under this axiom will serve us well collectively and individually as we push through the pandemic. I want to repeat a passage from my remarks at the President’s Session on Member Monday at ATCE in late October. “As an industry, we are as relevant as ever, even more so now. And further, our industry continues to provide economic growth, as well as access to cleaner water, better food, and affordable transportation. I am a believer, a true believer, I believe in what we do, in who we are, and how important our contributions are to society … I want to thank each of you for your time and effort in support of SPE. We are who we are because of what you have done, and we are who we will become because of your commitment to serve this industry and our global society. The world needs what we do - now, more than ever.” I want you to know that I believe in our mission and our future. As we “Get Oriented,” I want to assure you that as a discipline, we will become stronger, and as an organization, SPE will become more focused on content generation and delivery than at any time in its history. Most importantly, I want to assure you that SPE will continue to serve its members and its global community as the preeminent technical organization in upstream oil and gas. It is imperative that you “Get Oriented” with what you can do and will do in the future as SPE continues to evolve.

Model Selection for Machine Learning Algorithm on Decision Making in Oil and Gas Upstream Project Malaysia

Model selection is a crucial element in data analysis to get reliable and reproducible statistical inferences or predictions. It is a long history of model selection method arising from research in statistics, information theory, and signal processing. The purpose of this study is to address the problems related to big data in contributing the strategies to make decisions on new investments for upstream Oil and Gas projects in Malaysia. It also discusses the use of machine learning methods for big data processing and highlights current scenarios in a model selection perspective. Machine learning algorithms have proven to work well for statistics used to make decisions. The selection of the machine learning algorithm model does not make drastic assumptions about data, and it can help optimise the exploration process and allow the computer to analyse large amounts of data quickly and accurately. The results show that k-fold cross-validation of the developed model options intended to make subsequent decisions because it is an integral portion of big data processing to gather unexpected new insights, discover new knowledge and improve efficiency.

Analysing India’s Open Acreage Licensing Policy: problems and perspectives

Abstract In recent decades, India has increasingly felt the need to reduce its dependence on imported crude oil by augmenting domestic production. In such pursuance, India recently enacted the Hydrocarbon Exploration Licensing Policy (HELP) to attract private investments in India’s upstream oil and gas sector. One of the salient features of HELP is the Open Acreage Licensing Policy (OALP), which sought to liberalize auctioning and licensing process of upstream oil and gas sector in India. This article analyses the salient features of the OALP to argue that it has remedied most of the problems associated with previous licensing regimes. Even though India has significantly liberalized the auction and licensing process, the fiscal terms governing the revenue sharing between the operator and the government have served as a deterrent to make India an attractive destination for foreign investors. Therefore, this article suggests that India must offer more attractive fiscal terms to oil companies to incentivize them to undertake the risk of investing in India’s upstream sector.

Applications of Chelating Agents in the Upstream Oil and Gas Industry: A Review

Chelating agents show very effective performance in different applications in the upstream oil and gas industry. This study presents a critical review of the application of chelating agents in acid...

A magic formula for economic development? Global market integration and spatial polarization in extractive industries

ABSTRACT The World Bank, World Trade Organization and others promote integration into global markets as a certain path towards economic development. Some researchers share this optimism, arguing that development is the record of how one thing leads to another, once peripheral locations have plugged into global networks. Comparing resource peripheries in South America, Southeast Asia and Sub-Saharan Africa with regard to the upstream oil and gas sector, we call for a more nuanced assessment. Against the backdrop of 38 narrative, open-ended interviews, we show that there is spatial polarization between ‘gateways’ and peripheral sites in Southeast Asia. Argentinean and Ghanaian case studies reveal that local firms usually provide generic services, with little prospects of venturing into higher value-adding activities. We conclude that at least the oil and gas sector is not suitable for fulfilling very optimistic expectations on development.

Evaluasi Proses Pengadaan Barang Menggunakan Metode Value Stream Mapping pada Perusahaan Minyak dan Gas

Upstream oil and gas industry is one industry that has an important role in the economic movement in Indonesia. Products produced by this industry, namely oil and gas, are one of the largest energy sources in Indonesia. High demand must be supported by rapid exploration and production activities. This activity must be supported by other divisions, one of which is the procurement of goods and services. The main problem currently faced by companies is the length of the procurement process causing the duration of the procurement process to be long. There are 2 factors that cause the duration of procurement duration, namely technical bid evaluation by the user and pre-qualification activities. This research uses quantitative methods by calculating process cycle efficiency and value stream mapping. The results of this study show using the offline method, the resulting process efficiency cycle is 60.59% and the duration of procurement is 67 days. Using the combination of online and offline methods, the process cycle efficiency increased to 73.06% with a procurement duration of 59 days. This shows that the online method can reduce the duration of procurement by up to 8 days with a process cycle efficiency increasing by 12.47%. However, both methods have their advantages and disadvantages so it is proposed that the two methods can be combined to produce less time.

Practical Applications of Net Energy Analysis of Upstream Oil and Gas Processes

Abstract Net energy analysis (NEA) has traditionally been utilized by nonindustry actors, such as academics, economists, and regulators. It has not been widely accepted as a beneficial method within the oil and gas industry with regard to oil extraction systems. This research describes several NEA metrics, such at the energy return on energy investment (EROI), and the energy intensity ratio (EIR), and suggests several practical benefits to oil and gas owner/operators of applying them. The benefits are primarily realized by integrating NEA into economic analysis at the field level, facility level, and well level. The high level impact of energy on both capital and operational (OPEX and CAPEX) expenditures is explored. The case is made that NEA can be used to illuminate the drivers behind energy intensive oil and gas extraction processes, and thus can be used to reveal important economic risks and opportunities.

Comparative Analysis of Indonesia Gross Split PSC with Fiscal Terms of Several Southeast Asian Countries

The implementation of the Gross Split PSC in the upstream oil and gas industry in Indonesia has been running since 2017 with the hope of being able to accelerate the decision-making process and increase attractiveness for oil and gas investors. This study is to analyze the implementation of Indonesia Gross Split PSC compared to other fiscal terms in Southeast Asia region from an economics perspective. The comparisons reviewed are between Indonesia Gross Split PSC and the following fiscal terms: Indonesia Cost Recovery PSC, Malaysia R/C PSC, Thailand Concession, and Vietnam PSC. Fields used as input for analysis are producing oil fields with small-scale recoverable reserves according to RF-2005 / SPE, namely Block X (early production fields) and Block Y (terminated fields). From economics calculations and comparisons, sensitivity and profitability characteristics, specifically applied to the field conditions under review, it is concluded that Indonesia Gross Split PSC has improved economics indicators compared to Indonesia Cost Recovery PSC so that Indonesia Gross Split PSC has an economics level indicator that is better than PSC Indonesia Cost Recovery when compared to Malaysian R/C PSC, Thailand Concession and Vietnam PSC.

Big data analytics in upstream oil and gas industries for sustainable exploration and development: A review

This paper reviews how upstream oil and gas organizations can rapidly dissect an expansive volume of data by taking steps to amalgamate all the data by combining the operational data and drilling rig sensor data to real-time windows using advanced analytics to mine the data for formation evaluation in the reservoir. Examination and impending the data to complex models progressively are carried out using multiple linear regressions and support vector machines to predict uncertainty. They can create strategic bits of knowledge that assist in execution while anticipating issues that help to increase drilling and production performances. Over all big data analytics helps for the sustainable development of oil and gas Industries.

Achieving Agility and Dynamic Capabilities on Sustainable Performance: Evidence from the Upstream Oil and Gas Sector

Purpose: This paper aims to seize the understanding of the effect of TMT commitment (TMC), leadership agility development (LAD), field agile leader (FAL), and operational capabilities (OC) on the field unit performance (FUP) in Indonesia oil and gas sector. Design/methodology/approach: Research instruments were developed and distributed throughout field operation units in the Indonesian oil and gas sector, resulting in 175 data from field operation leaders across Indonesia's operating oil and gas companies. Structural equation modeling (SEM) LISREL were used to examine the developed hypotheses. Findings: This research finds that TMT commitment and leadership agility development on field agile leaders, directly and indirectly, play a significant role in the Indonesian oil and gas sector. Leadership agility development has a strong influence on the field of an agile leader. Furthermore, field agile leaders influence operational capabilities, which then affects unit performance significantly. Practical Implications: The findings have several implications for professionals in the oil and gas sector. The finding of this research also describes the pivotal role of leadership agility development to make the leader more resilient and agile. Originality/value: The proposed model will describe the input-process-output phases in creating value. This research also contributes to how leadership agility can be promoted and organizational agility and development.

Forest Conversion in Upstream Oil and Gas Industry Area

Forest conversion is the root of the problem that can hamper investment, state revenue, and potentially cause state losses. The purpose of this study was to analyze forest conversion in the upstream oil and gas industry area and the factors that influence forest conversion in the upstream oil and gas industry area based on spatial analysis. This study used systematic sampling with the point grid method measuring 1 kilometers * 1 kilometers. Rokan Block had 5,125 sampling points. This study used raster-based GIS analysis and logistic regression with the Wald Test. The results of the study showed that the Rokan Block Area experienced a fairly severe forest conversion in 2000-2014 influenced by the total population and The 1986 TGHK. The novelty of this study is that the active role of communities around the forests that still upholds the concern for the forest must be preserved, because they will be the front guard in securing forest encroachment, and upstream oil and gas industry are not the main economic activities that cause forest conversion

Tenure Amnesty for the Upstream Oil and Gas Industry in the Forest Area

The overlap of the operational area of ??the upstream oil and gas industry with forest areas in the Rokan Block as the main contributor to 40 percent of the national oil, threatens the contribution that can be given to national oil needs. This study aimed to analyze the implementation of tenure amnesty to resolve tenurial conflicts in the upstream oil and gas and forest areas. Descriptive analysis was carried out by using the IRAC reasoning scheme, namely issue (I), rule of law (R), analysis (A), and conclusion (C) as forming policy analysis. The findings of this study showed that there were two solutions to resolve tenure conflicts in r upstream oil and gas industrial land and forest areas, namely IPPKH (Borrowing and Use of Forest Areas Permits) and tenure amnesty. Tenure amnesty is better mechanism than IPPKH both socially, economically and institutionally. The presence of the upstream oil and gas industry through tenure amnesty in forest areas, can improve the performance of ecosystem management through harmonization of operations that is followed up by collaborative program.