The existence of external two-fold pressure regarding competitiveness and sustainable development in a capital-intensive industry supports the need for sustainable performance. However, endeavors to create a sustainable framework to measure the performance of the oil and gas (O&G) industry are mostly devoted to the production and supply chain of petrochemical products and rarely focus on a maintenance perspective. Motivated by such scarcity, the goal of this research was to discuss and articulate the performance assessment framework by integrating concepts of maintenance and sustainability in the O&G industry. This study proposed the use of a range of performance measures for assessing sustainability on offshore production and drilling platforms. The conceptual framework consists of four aspects of sustainability categorized into technical, environmental, social, and economic dimensions. Each measure was assigned according to its relevance at the strategic, tactical, and functional levels of maintenance decision making. The conceptual framework resulted in hierarchical clusters of twelve strategic indicators. These indicators consist of conventional measures as well as new ones relating to the safety and reliability on offshore platforms. The potential contribution of the present study is found in its intention to empower a better understanding of sustainable maintenance and encourage those making decisions about practical implementation within the O&G industry. This paper culminates with directions for future studies.

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In response to the growing demand for the global energy supply chain, wind power has become an important research subject among studies in the advancement of renewable energy sources. The major concern is the stochastic volatility of weather conditions that hinder the development of wind power forecasting approaches. To address this issue, the current study proposes a weather prediction method divided into two models for wind speed and atmospheric system forecasting. First, the data-based model incorporated with wavelet transform and recurrent neural networks is employed to predict the wind speed. Second, the physics-informed echo state network was used to learn the chaotic behaviour of the atmospheric system. The findings were validated with a case study conducted on wind speed data from Turkmenistan. The results suggest the out-performance of physics-informed model for accurate and reliable forecasting analysis, which indicates the potential for implementation in wind energy analysis.

In response to the growing demand for the global energy supply chain, wind power has become an important research subject among studies in the advancement of renewable energy sources. The major concern is the stochastic volatility of weather conditions that hinder the development of wind power forecasting approaches. To address this issue, the current study proposes a weather prediction method divided into two models for wind speed and atmospheric system forecasting. First, the data-based model incorporated with wavelet transform and recurrent neural networks is employed to predict the wind speed. Second, the physics-informed echo state network was used to learn the chaotic behaviour of the atmospheric system. The findings were validated with a case study conducted on wind speed data from Turkmenistan. The results suggest the out-performance of physics-informed model for accurate and reliable forecasting analysis, which indicates the potential for implementation in wind energy analysis.

No abstract