Safety culture is a critical determinant of organisational performance, particularly in high-risk industries especially in oil and gas. Understanding stakeholder preferences is essential for developing effective strategies that enhance safety culture. This study utilised the Analytic Hierarchy Process (AHP) to prioritise stakeholder preferences, identifying key elements of safety culture in Malaysia\'s oil and gas sector. This study employed a structured methodology to evaluate safety culture within the oil and gas industry, focusing on 18 sub-elements across three key domains: psychological, behavioural, and situational factors. A diverse sample of industry experts was recruited using purposeful and snowball sampling to ensure a comprehensive representation of stakeholder views. The AHP framework was applied to analyse the data, utilizing structured questionnaires and multicriteria decision-making techniques to prioritize the identified safety culture elements. The AHP analysis identified distinct priorities among different professional groups within the oil and gas sector. Safety and Health Practitioners emphasized practical elements such as safety rules and management commitment, while academicians prioritized knowledge and training. Management personnel highlighted the importance of safety ownership and communication, whereas policymakers focused on broader, policy-oriented aspects. The findings suggest that safety culture improvement initiatives should be tailored to address the specific needs and priorities of each professional group. A nuanced understanding of stakeholder preferences is crucial for developing comprehensive strategies that integrate observable behaviours, situational conditions, and psychological factors, ultimately fostering a robust safety culture in the oil and gas industry.

UNASSIGNED: Inside oil and gas pipelines, native microbial communities and different solid compounds typically coexist and form mixed deposits. However, interactions between these deposits (primarily consisting of mineral phases) and microorganisms in oil and gas systems remain poorly understood. Here, we investigated the influence of magnetite (Fe3O4), troilite (FeS), and silica (SiO2) on the microbial diversity, cell viability, biofilm formation, and EPS composition of an oil-recovered multispecies consortium.
UNASSIGNED: An oilfield-recovered microbial consortium was grown for 2 weeks in separate bioreactors, each containing 10 g of commercially available magnetite (Fe3O4), troilite (FeS), or silica (SiO2) at 40°C ± 1°C under a gas atmosphere of 20% CO2/80% N2.
UNASSIGNED: The microbial population formed in troilite significantly differed from those in silica and magnetite, which exhibited significant similarities. The dominant taxa in troilite was the Dethiosulfovibrio genus, whereas Sulfurospirillum dominated in magnetite and silica. Nevertheless, biofilm formation was lowest on troilite and highest on silica, correlating with the observed cell viability.
UNASSIGNED: The dissolution of troilite followed by the liberation of HS- (H2S) and Fe2+ into the test solution, along with its larger particle size compared to silica, likely contributed to the observed results. Confocal laser scanning microscopy revealed that the EPS of the biofilm formed in silica was dominated by eDNA, while those in troilite and magnetite primarily contained polysaccharides. Although the mechanisms of this phenomenon could not be determined, these findings are anticipated to be particularly valuable for enhancing MIC mitigation strategies currently used in oil and gas systems.

The U.S. Gulf of Mexico contains a complex network of existing, decommissioned, and abandoned oil and gas pipelines, which are susceptible to a number of stressors in the natural-engineered offshore system including corrosion, environmental hazards, and human error. The age of these structures, coupled with extreme weather events increasing in intensity and occurrence from climate change, have resulted in detrimental environmental and operational impacts such as hydrocarbon release events and pipeline damage. To support the evaluation of pipeline infrastructure integrity for reusability, remediation, and risk prevention, the U.S. Gulf of Mexico Pipeline and Reported Incident Datasets were developed and published. These datasets, in addition to supporting advanced analytics, were constructed to inform regulatory, industry, and research stakeholders. They encompass more than 490 attributes relating to structural information, incident reports, environmental loading statistics, seafloor factors, and potential geohazards, all of which have been spatially, and in some cases temporally matched to more than 89,000 oil and gas pipeline locations. Attributes were acquired or derived from publicly available, credible resources, and were processed using a combination of manual efforts and customized scripts, including big data processing using supercomputing resources. The resulting datasets comprise a spatial geodatabase, tabular files, and metadata. These datasets are publicly available through the Energy Data eXchange®, a curated online data and research library and laboratory developed by the U.S. Department of Energy\'s National Energy Technology Laboratory. This article describes the contents of the datasets, details the methods involved in processing and curation, and suggests application of the data to inform and mitigate risk associated with offshore pipeline infrastructure in the Gulf of Mexico.

Methane emissions from the global oil and gas value chain are a major contributor to climate change, and their mitigation could avoid 0.1 °C of warming by 2050. Here, we synthesize nearly a decade of research encompassing thousands of multiscale methane measurements along the oil and gas value chain (production to end use) to better constrain estimates of methane emissions from Canada\'s energy sector and to identify research gaps contributing to uncertainty in current estimates. We find that total value chain methane emissions are 2,600 (2,100-3,700) kt, which broadly agrees with Canada\'s latest official inventory that now includes atmospheric measurement data in some of their oil and gas methane estimates. Accurate understanding of emission magnitudes is critical because Canada committed to a 75% reduction of oil and gas methane emissions by 2030. We also identify and discuss information gaps in both emissions and activity data, namely, from the midstream, downstream, and end-use sectors. While they make up a smaller portion of the total inventory, accurate quantification of these emissions is still important and could point to more cost-effective mitigation solutions. This work emphasizes the need for frequent, comprehensive measurements to better constrain the climate impacts of the oil and gas sector and to validate reductions and commitments pledged by industry and governments.

Global Li production will require a ∼500 % increase to meet 2050 projected energy storage demands. One potential source is oil and gas wastewater (i.e., produced water or brine), which naturally has high total dissolved solids (TDS) concentrations, that can also be enriched in Li (>100 mg/L). Understanding the sources and mechanisms responsible for high naturally-occurring Li concentrations can aid in efficient targeting of these brines. The isotopic composition (δ7Li, δ11B, δ138Ba) of produced water and core samples from the Utica Shale and Point Pleasant Formation (UPP) in the Appalachian Basin, USA indicates that depth-dependent thermal maturity and water-rock interaction, including diagenetic clay mineral transformations, likely control Li concentrations. A survey of Li content in produced waters throughout the USA indicates that Appalachian Basin brines from the Marcellus Shale to the UPP have the potential for economic resource recovery.

Hydraulic fracturing (HF) has substantially boosted global unconventional hydrocarbon production but has also introduced various environmental and operational challenges. Understanding the interactions between abundant and diverse microbial communities and chemicals, particularly polymers used for proppant delivery, thickening, and friction reduction, in HF water cycles is crucial for addressing these challenges. This review primarily examined the recent studies conducted in China, an emerging area for HF activities, and comparatively examined studies from other regions. In China, polyacrylamide (PAM) and its derivatives products became key components in hydraulic fracturing fluid (HFF) for unconventional hydrocarbon development. The microbial diversity of unconventional HF water cycles in China was higher compared to North America, with frequent detection of taxa such as Shewanella, Marinobacter, and Desulfobacter. While biodegradation, biocorrosion, and biofouling were common issues across regions, the mechanisms underlying these microbe-polymer interactions differed substantially. Notably, in HF sites in the Sichuan Basin, the use of biocides gradually decreased its efficiency to mitigate adverse microbial activities. High-throughput sequencing proved to be a robust tool that could identify key bioindicators and biodegradation pathways, and help select optimal polymers and biocides, leading to more efficient HFF systems. The primary aim of this study is to raise awareness about the interactions between microorganisms and polymers, providing fresh insights that can inform decisions related to enhanced chemical use and biological control measures at HF sites.

Enhancing the management and monitoring of oil and gas processes demands the development of precise predictive analytic techniques. Over the past two years, oil and its prediction have advanced significantly using conventional and modern machine learning techniques. Several review articles detail the developments in predictive maintenance and the technical and non-technical aspects of influencing the uptake of big data. The absence of references for machine learning techniques impacts the effective optimization of predictive analytics in the oil and gas sectors. This review paper offers readers thorough information on the latest machine learning methods utilized in this industry\'s predictive analytical modeling. This review covers different forms of machine learning techniques used in predictive analytical modeling from 2021 to 2023 (91 articles). It provides an overview of the details of the papers that were reviewed, describing the model\'s categories, the data\'s temporality, field, and name, the dataset\'s type, predictive analytics (classification, clustering, or prediction), the models\' input and output parameters, the performance metrics, the optimal model, and the model\'s benefits and drawbacks. In addition, suggestions for future research directions to provide insights into the potential applications of the associated knowledge. This review can serve as a guide to enhance the effectiveness of predictive analytics models in the oil and gas industries.

Many Oil and Gas (O&G) fields in the North Sea have produced their economically recoverable reserves and have entered the decommissioning phase or are close to cessation of production. The subsequent O&G decommissioning process involves a range of stakeholders with specific interests and priorities. This range of inputs to the process highlights the necessity for the development of multi-criteria decision frameworks to help guide the decision-making process. This study presents bottom-up formulations for the economic, environmental, and safety risk criteria to support the multi-criteria decision analysis within the Comparative Assessment (CA) of O&G pipeline decommissioning projects in the North Sea. The approach adapts current guidelines in the O&G industry and considers a range of parameters to provide estimations for the costs, energy usage, greenhouse gas emissions, and safety risks. To verify the effectiveness of the proposed bottom-up formulations, the longest oil export pipeline in the Brent field, PL001/N0501 is selected as a case study. The numerical results revealed the consistency of the results obtained from the proposed approach with those reported in the technical documents by industry. In most cases, the formulations provide estimates with less than 10% differences for the costs, energy usage, emissions, and safety risks. Based on the proposed multi-criteria formulations, the study also presents the use of an immersive decision-making environment within a marine simulator system to help inform the decision-making process by stakeholders.

Quantifying and controlling fugitive methane emissions from oil and gas facilities remains essential for addressing climate goals, but the costs associated with monitoring millions of production sites remain prohibitively expensive. Current thinking, supported by measurement and simple dispersion modelling, assumes single-digit parts-per-million instrumentation is required. To investigate instrument response, the inlets of three trace-methane (sub-ppm) analyzers were collocated on a facility designed to release gas of known composition at known flow rates between 0.4 and 5.2 kg CH4 h-1 from simulated oil and gas infrastructure. Methane mixing ratios were measured by each instrument at 1 Hertz resolution over nine hours. While mixing ratios reported by a cavity ring-down spectrometer (CRDS)-based instrument were on average 10.0 ppm (range 1.8 to 83 ppm), a mid-infrared laser absorption spectroscopy (MIRA)-based instrument reported short-lived mixing ratios far larger than expected (range 1.8 to 779 ppm) with a similar nine-hour average to the CRDS (10.1 ppm). We suggest the peaks detected by the MIRA are likely caused by a micrometeorological phenomenon, where vortex shedding has resulted in heterogeneous methane plumes which only the MIRA can observe. Further analysis suggests an instrument like the MIRA (an optical-cavity-based instrument with cavity size ≤10 cm3 measuring at ≥2 Hz with air flow rates in the order of ≤0.3 slpm at distances of ≤20 m from the source) but with a higher detection limit (25 ppm) could detect enough of the high-concentration events to generate representative 20 min-average methane mixing ratios. Even though development of a lower-cost, high-precision, high-accuracy instrument with a 25 ppm detection threshold remains a significant problem, this has implications for the use of instrumentation with higher detection thresholds, resulting in the reduction in cost to measure methane emissions and providing a mechanism for the widespread deployment of effective leak detection and repair programs for all oil and gas infrastructure.

There is a growing need to understand the potential ecological impacts of contaminants in offshore oil and gas infrastructure, especially if that infrastructure is to be left in situ as a decommissioning option. Naturally occurring radioactive material (NORM) is one type of contaminant found in solid deposits on internal surfaces of infrastructure that poses potential ecological harm if released into the marine environment. Microbes are important components of marine sediment ecosystems because they provide ecosystem services, yet the impacts of NORM contamination to these communities are not well understood. The present study aimed to investigate the response of benthic microbial communities to NORM-contaminated scale, collected from an offshore oil and gas system, via controlled laboratory microcosm studies. Changes to microbial communities in natural sediment and sediments spiked with NORM at radium-226 activity concentrations ranging from 9.5 to 59.8 Bq/kg (in partial equilibria with progeny) over 7 and 28 days were investigated using high-throughput sequencing of environmental DNA extracted from experimental sediments. There were no significant differences in microbial community composition between control and scale-spiked sediments over 7 and 28 days. However, we observed a greater presence of Firmicutes in the scale-mixed treatment and Chloroflexi in the scale-surface treatments after 28 days. This could suggest selection for species with contaminant tolerance or potential resilience to radiation and metal toxicity. Further research is needed to explore microbial tolerance mechanisms and their potential as indicators of effects of radionuclide-contaminated sediments. The present study demonstrated that microcosm studies can provide valuable insights about the potential impacts of contamination from oil and gas infrastructure to sediment microbial communities. Environ Toxicol Chem 2024;43:1648-1661. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.