Review on the challenges and strategies in oil and gas industry's transition towards carbon neutrality in China

In light of carbon-neutral pledge, the oil and gas industry has been facing several critical new challenges in China. The current status and new challenges in terms of market mechanism reform, supply-consumption balance and key technology innovation in China's oil and gas industry are reviewed in the present study, and new strategies and roadmaps are proposed to cope with the challenges. The study found that (i) the oil and gas market faces challenges such as incomplete pricing mechanisms, unclear subject rights, and the lack of recognition and trading of carbon assets. (ii) the trade-off between short-term supply security and long-term low-carbon supply is the most critical issue. (iii) in addition to typical challenges such as immature technology and lack of funding support, the unclear multiple technology coupling development mode also poses problems for the low-carbon transformation of the oil and gas industry. To address these new challenges, comprehensive strategies and roadmaps for China's oil and gas industry towards carbon neutrality are proposed and discussed in the aspects of participating in market transactions, restructuring production and consumption, deploying key technology innovations, and planning enterprise strategies. The present study is expected to provide a blueprint for the future development of China's oil and gas industry towards carbon neutrality.

Risk Matrix as a Tool for Risk Analysis in Underwater Operations in the Oil and Gas Industry

The study is a cross-sectional design assessment of the likelihood, frequency and severity of hazards associated with underwater operations in the Niger Delta. Five oil and gas companies were used for this study selected by a purposive method given that they had the highest number of workers involved in underwater operations. A sample size of 418 was computed to which the questionnaires were administered with response rate of 95.93%. Data analyses were carried out to cover descriptive statistics, analysis of variance and Pearsonal correlation coefficients. The 4 by 4 risk assessment matrix for the likelihood and consequences showed that 8 out of 20 underwater hazards were categorized as having very high risk according to their risk ratings. The eight hazards categorized based on their risk IDs were H01, H03, H04, H08, H10, H11, H12, and H15. The 4 by 4 risk matrix for frequency and consequences revealed that two hazards (Piracy & bandit attack/kidnapping (H01) and Other main vessels/heavy object dropping or falling load/collision (H08)) were identified to be of very high risk.

Big Data analytics in oil and gas industry: An emerging trend

This paper reviews the utilization of Big Data analytics,as an emerging trend,in the upstream and downstream oil and gas industry.Big Data or Big Data analytics refers to a new technology which can be employed to handle large datasets which include six main characteristics of volume,variety,velocity,veracity,value,and complexity.With the recent advent of data recording sensors in exploration,drilling,and production operations,oil and gas industry has become a massive data intensive industry.Analyzing seismic and micro-seismic data,improving reservoir characterization and simulation,reducing drilling time and increasing drilling safety,optimization of the performance of production pumps,improved petrochemical asset management,improved shipping and transportation,and improved occupational safety are among some of the applications of Big Data in oil and gas industry.Although the oil and gas industry has become more interested in utilizing Big Data analytics recently,but,there are still challenges mainly due to lack of business support and awareness about the Big Data within the industry.Furthermore,quality of the data and understanding the complexity of the problem are also among the challenging parameters facing the application of Big Data.

Application of machine learning and artificial intelligence in oil and gas industry

Oil and gas industries are facing several challenges and issues in data processing and handling.Large amount of data bank is generated with various techniques and processes.The proper technical analysis of this database is to be carried out to improve performance of oil and gas industries.This paper provides a comprehensive state-of-art review in the field of machine learning and artificial intelligence to solve oil and gas industry problems.It also narrates the various types of machine learning and artificial intelligence techniques which can be used for data processing and interpretation in different sectors of upstream oil and gas industries.The achievements and developments promise the benefits of machine learning and artificial intelligence techniques towards large data storage capabilities and high efficiency of numerical calculations.In this paper a summary of various researchers work on machine learning and artificial intelligence applications and limitations is showcased for upstream and sectors of oil and gas industry.The existence of this extensive intelligent system could really eliminate the risk factor and cost of maintenance.The development and progress using this emerging technologies have become smart and makes the judgement procedure easy and straightforward.The study is useful to access intelligence of different machine learning methods to declare its application for distinct task in oil and gas sector.

The Impact of the Covid-19 Pandemic on Iranian Oil and Gas Industry Planning:A Survey of Business Continuity Challenges

The Covid-19 pandemic has severely affected various aspects of life,and its compounding and cascading impacts have been observed in most industries and firms.The oil and gas(O&G)industry was among the first to experience the impacts as the pandemic began due to the global economic recession and a sharp decline in demand for oil.The pandemic revealed major risk management and business continuity challenges and uncovered some of the vulnerabilities of the O&G industry and its major companies during a prolonged global disaster.Examining and understanding how the Covid-19 pandemic impacted the O&G sector in different countries,considering their unique circumstances,can provide important lessons for managing the current and future similar events.This study investigated various impacts of the Covid-19 pandemic on the O&G industry using Iran's Pars Oil and Gas Company(POGC)as a case study.Data were collected through indepth interviews with key managers of the company.Qualitative methods,specifically thematic analysis,were used to analyze the data.Findings of this study provide further insights into how the pandemic impacted the operations,risks,and business continuity of the POCG.The results show that the pandemic caused significant operational,financial,and legal impacts by disrupting routine maintenance,reducing the availability of human resources under the public health measures and mobility restrictions,increasing processing and delivery times,increasing costs and decreasing revenues,and delaying contractual obligations.

Fuzzy logic applied to value of information assessment in oil and gas projects

The concept of value of information(VOI)has been widely used in the oil industry when making decisions on the acquisition of new data sets for the development and operation of oil fields.The classical approach to VOI assumes that the outcome of the data acquisition process produces crisp values,which are uniquely mapped onto one of the deterministic reservoir models representing the subsurface variability.However,subsurface reservoir data are not always crisp;it can also be fuzzy and may correspond to various reservoir models to different degrees.The classical approach to VOI may not,therefore,lead to the best decision with regard to the need to acquire new data.Fuzzy logic,introduced in the 1960 s as an alternative to the classical logic,is able to manage the uncertainty associated with the fuzziness of the data.In this paper,both classical and fuzzy theoretical formulations for VOI are developed and contrasted using inherently vague data.A case study,which is consistent with the future development of an oil reservoir,is used to compare the application of both approaches to the estimation of VOI.The results of the VOI process show that when the fuzzy nature of the data is included in the assessment,the value of the data decreases.In this case study,the results of the assessment using crisp data and fuzzy data change the decision from"acquire"the additional data(in the former)to"do not acquire"the additional data(in the latter).In general,different decisions are reached,depending on whether the fuzzy nature of the data is considered during the evaluation.The implications of these results are significant in a domain such as the oil and gas industry(where investments are huge).This work strongly suggests the need to define the data as crisp or fuzzy for use in VOI,prior to implementing the assessment to select and define the right approach.

Empirical Investigation of Treatment of Sour Gas by Novel Technology: Energy Optimization

The sour gas sweetening is one of the main processes in gas industries. Gas sweetening is done through chemical processes. Therefore, it requires high cost and energy. The results show that increasing the operating temperature increases the mass transfer coefficient and increases the mass transfer rate. Theoretical and experimental data show that sulfur removal in 4.5 W magnetic field is desirable. The increase in sulfur removal percentage in the magnetic field of 4.5 W and 6.75 W is about 16.4% and 15.2%, respectively. According to the obtained results, the effect of temperature increase from 18.8°C to 23.4°C is more evident than the effect of temperature change from 23.4°C to 32.2°C. Because more thermal energy is needed to provide higher temperatures. Therefore, the temperature of 23.4°C is reported as the optimal temperature. The results of this research show that the percentage of sulfur removal is also high at this temperature.

Mississippi River Delta: Land Subsidence and Coastal Erosion

The Mississippi River Delta is a major center for transportation, industry, human population and ecosystem services. Critical areas included energy production, navigation, fisheries, flood protection of coastal communities, and restoration of damaged habitats. Complex environmental management in a great river system requires broad-base complex science, engineering and monitoring. A major national and state objective has become the restoration of the Mississippi River Delta that is threatened by subsidence, flooding, storm surges, compaction, oil extraction and gas extraction. The primary objectives of the paper are to document the landscape and geological properties of the Mississippi River Delta which have contributed to the successful resource and economic development of a historically-rich region of North America and to document the natural resource and environmental risks to the Mississippi River Delta. Economic and urban development of the Mississippi River Delta by the oil and gas industry and creation of levees by the USACE has contributed to land subsidence problems. Environmental challenges include land subsidence as a result of the pumping of vast amounts of oil and gas, the lack of sediment deposition in the Mississippi River Delta as a result of a system of levees, coastal erosion impacts of hurricanes, disposal of untreated and treated wastewater, periodic flooding and water pollution.

Artificial intelligence in oil and gas upstream: Trends, challenges, and scenarios for the future

We analyze how artificial intelligence changes a significant part of the energy sector,the oil and gas industry.We focus on the upstream segment as the most capital-intensive part of oil and gas and the segment of enormous uncertainties to tackle.Basing on the analysis of AI application possibilities and the review of existing applica-tions,we outline the most recent trends in developing AI-based tools and identify their effects on accelerating and de-risking processes in the industry.We investigate AI approaches and algorithms,as well as the role and availability of data in the segment.Further,we discuss the main non-technical challenges that prevent the in-tensive application of artificial intelligence in the oil and gas industry,related to data,people,and new forms of collaboration.We also outline three possible scenarios of how artificial intelligence will develop in the oil and gas industry and how it may change it in the future(in 5,10,and 20 years).

A minimalist approach for detecting sensor abnormality in oil and gas platforms

Compressors play an important role in day-to-day operation in most oil and gas platforms,especially in the case for maintaining gas pressure in transportation pipe.Its complex problem to detect the sensors health and abnormality as the sensor reading would reflect the various states of the compressor.In ideal situation,sensor readings offer vast amounts of information on compressor health and could possibly indicate early fault of machines.Furthermore,due to harsh site and process operating conditions,sensors are often found to have drifted or failed,and there is no standard methodology to predict abnormality apart from applying emerging industrial smart sensor technologies.In this paper,we investigate a minimalist approach for detecting abnormality of compressor's shaft's RPM sensor.As the sensors in the compressor are correlated,we first use the outputs of other sensors to predict the shaft's RPM using regression-based models(neural networks and multiple linear regression).Second,we calculate the histogram of residuals by taking the difference between the predicted sensor value and the actual sensor value plus the abnormality in terms of bias/miscalibration and noise.The histogram of residuals can be used for sensor abnormality monitoring.In general,sensor states can be monitored by observing the shifting of the mean in the histogram of residuals.The sensor readings contaminated with noise can be seen by a shifted mean whose value is between the normal condition mean and the biased condition mean.This method is compact and would be relevant to monitor irregularity of the sensors.

Risk perception and safety analysis on petroleum production system of three gas fields in Bangladesh

Accidents are common in the petroleum industry.The risk of accidents can be easily minimized by understand-ing the harm early in the production system.This study presents a perception-based risk and safety analysis of petroleum production systems.Data were collected from three fields operated by Sylhet Gas Fields Limited in Bangladesh.The Statistical Package for the Social Sciences(SPSS)software was used to analyze the data.The results were then subjected to a frequency analysis,an analysis of variance(ANOVA),and a reliability analysis.The frequency analysis indicated the overall safety situation,and the ANOVA models and reliability analysis sub-stantiated the results.A chi-squared test indicated the association between the datasets.The outcomes of the risk matrix indicated various risk levels,such as low,moderate,and high.According to the implicit risks,necessary measures were recommended for the industry’s future.

Review of application of artificial intelligence techniques in petroleum operations

In the last few years,the use of artificial intelligence(AI)and machine learning(ML)techniques have received considerable notice as trending technologies in the petroleum industry.The utilization of new tools and modern technologies creates huge volumes of structured and un-structured data.Organizing and processing of these information at faster pace for the performance assessment and forecasting for field development and management is continuously growing as an important field of investigation.Various difficulties which were faced in predicting the operative features by utilizing the conventional methods have directed the academia and industry toward investigations focusing on the applications of ML and data driven approaches in exploration and production operations to achieve more accurate predictions which improves decision-making processes.This research provides a review to examine the use cases and application of AI and ML techniques in petroleum industry for optimization of the upstream processes such as reservoir studies,drilling and production engineering.The challenges related to routine approaches for prognosis of operative parameters have been evaluated and the use cases of performance optimizations through employing data-driven approaches resulted in enhancement of decision-making workflows have been presented.Moreover,possible scenarios of the way that artificial intelligence will develop and influence the oil and gas industry and how it may change it in the future was discussed.