This paper presents the optimization of the power required to activate a jaw-type blowout preventer (BOP) in the oil industry using an axial piston pump. Experimental and numerical methods were combined to analyze the effects of pressure, flow rate, volumetric efficiency, and clearance leakage on energy consumption. Taguchi methodology with an orthogonal array and the “smaller-is-better” criterion was used in the experiments, while regression analysis provided a predictive model. Optimization was performed using the Grey Wolf Optimizer (GWO) in Python 3.13. The results show that pressure and flow rate significantly affect power consumption, while higher volumetric efficiency leads to notable energy savings. The optimal configuration reduced the power demand to 5.0001 kW. Based on this, reliability models were created to assess deviations from optimal conditions. The study demonstrates the effectiveness of combining statistical and optimization techniques for improving safety systems in the oil industry. The key contribution of this study lies in the integration of experimental Taguchi-based modeling with Grey Wolf Optimizer (GWO) metaheuristic techniques to optimize the energy-efficient activation of jaw-type blowout preventers, representing a novel methodological approach in the field of hydraulic safety systems in the oil industry.

This paper explores the integration of Circular Economy (CE) principles and Industrial Ecology (IE) practices as a means to enhance sustainability and resource efficiency across various industries. The circular economy shifts away from the traditional linear model of “take, make, dispose,” promoting a closed-loop approach that minimizes waste and prolongs the lifecycle of products through reuse, recycling, and remanufacturing. Industrial ecology, on the other hand, emphasizes industrial symbiosis, where waste or by-products from one process serve as inputs for another, mimicking natural ecosystems. The study employs a qualitative methodology, analysing secondary data from industries such as automotive, energy, and electronics, revealing how CE and IE practices lead to significant reductions in resource use, waste generation, and carbon emissions. Results show that industries adopting CE and IE principles achieve improved efficiency and environmental sustainability, with specific examples of resource savings and reductions in CO2 emissions. Despite challenges in implementation, this research highlights the potential of CE and IE to foster innovation, economic growth, and environmental resilience. The paper concludes by stressing the importance of collaboration between governments, businesses, and academia to advance these concepts and address the environmental challenges of the 21st century.

This paper explores the application of predictive maintenance (PdM) to address paraffin deposition in sucker rod pump systems used for oil production. System maintenance has become critical for enhancing efficiency and reducing costs, while PdM, supported by advanced analytics and sensors, enables downtime prediction and maintenance optimization. Paraffin deposition is a significant problem in the oil industry, as it diminishes production capacity and increases expenses. This paper presents the use of the SCADA system, which enables the collection and analysis of data in real time. Furthermore, it proposes diagnostic methods for early detection of paraffin deposition using predictive maintenance, offering timely warnings to prevent production delays. While the proposed framework relies on interpretable statistical and physics-informed predictive models, the results indicate that further improvements could be achieved by integrating advanced artificial intelligence techniques to enhance adaptability, automation, and decision support in predictive maintenance systems.

This paper presents a dashboard-oriented visual analytics workflow for classification-related exploratory analysis based on Information Gain (IG), histogram segmentation, and complementary localized interpretation through the Precise Piecewise Correlation (PPC) method. The workflow is designed to support the construction of a primary dashboard view by prioritizing attributes with stronger relevance to the decision variable and inspecting their class-related behavior within segmented histogram intervals. Rather than introducing a new standalone feature-selection metric, this study formalizes how established analytical components can be integrated into a coherent dashboard framework for structured visual inspection. The proposed workflow was examined on three datasets from different application domains: the Iris dataset, an educational performance dataset, and an Oil and Gas dataset. Across these cases, IG-based prioritization identified attributes that provided clearer class-related structure in the primary dashboard view, while histogram segmentation supported interval-level interpretation of class concentration and overlap. A compact quantitative evaluation further showed that top-ranked IG subsets retained strong discriminative information under standard classification models, whereas lower-ranked subsets generally performed less favorably. Entropy-based segment analysis additionally indicated lower local class uncertainty for higher-ranked attributes. A small user study provided preliminary user-centered support for the interpretability and practical usefulness of the proposed dashboard structure. The results suggest that the proposed workflow can support dashboard-based inspection of class-related patterns across different contexts.

The application of CO₂ for enhanced oil recovery requires reliable operation of the injection system and the integrity of its technical and geological components. The aim of this study is to systematically identify potential causes and failure events that may lead to the loss of safe and effective CO₂ injection in a CO₂-EOR system. Based on a review of relevant scientific literature and the defined system boundaries, a qualitative Fault Tree Analysis (FTA) model was developed. The top event was divided into two main groups of events: loss of effective injection capability and loss of safe CO₂ containment during injection. The developed model includes surface, wellbore, reservoir, and geomechanical failure pathways, including CO₂ supply and compression problems, corrosion, loss of injectivity, loss of well barrier integrity, and unfavorable reservoir pressure response. The results show that FTA enables different failure mechanisms to be integrated into a structured cause-and-effect model and provides a basis for their further quantitative assessment and prioritization.

This review paper examines the impact of gas flaring and direct venting in the oil and gas industry on air quality and climate change. Flaring generates CO₂, NOₓ, SO₂, and fine particulate matter, including black carbon, while venting directly releases methane with a high short-term global warming potential. Due to variable combustion efficiency, unlit flares, and systematic venting, actual emissions often exceed reported inventories. We summarize findings from studies based on satellite assessments and targeted field measurements, including the identification of 'ultra-emitters' and epidemiological evidence of local health effects. Regulatory trends (EU, USA) toward banning routine flaring/venting, introducing mandatory MRV/LDAR programs, and aligning with international initiatives are discussed. Technological mitigation measures are presented (gas capture and reuse, replacement of pneumatic equipment with low-emission alternatives, closed-loop systems, and, where applicable, CCS) along with their medium-term cost-effectiveness. We conclude that the rapid implementation of available solutions is crucial for achieving sustainable development goals, improving air quality, and supporting the energy transition.