Artificial Intelligence (AI) is largely helping the oil & gas industry to shape its future. AI is predicted to highly impact the oil and gas industry over the coming years. Artificial intelligence has a number of potential applications in the oil and gas industry, from surveying to planning and forecasting, and facility management to safety. AI is being used for predicting equipment failure and scheduling maintenances in oilfields. A MarketsandMarkets report estimates, the global AI in Oil & Gas Market is expected to grow at a CAGR of 12.66%, from 2017 to 2022, to reach a projected market value of USD 2.58 Billion by 2022.

Artificial Intelligence (AI) technologies are being increasingly used in the Oil and Gas (O&G) industry to optimize production, reduce operational costs and maximize efficiency. According to a Markets and Markets report, AI in the global oil and gas market is expected to grow from an estimated USD 1.57 billion in 2017 to USD 2.85 billion by 2022, at a CAGR of 12.66%. The oil and gas enterprises are seeking novel approaches to address the issues that plague the industry at present. In view of the falling fuel prices, concerns over the environmental impact of energy production and personnel safety, companies are leveraging technological innovations such as AI to optimize processes and maximize the returns on investment. In this report, we present insights and trends related to the AI technologies used in the Oil and Gas industry, through a study of patents related to petroleum exploration and refining technology segments.

Oil and gas (O&G) sources will still supply around 50% of the global energy demand by 2040.a In this article, we make the case for why the Arab region is well positioned for building world-class data science teams to fill the supply shortage of data professionals,5 especially in the O&G field critical to region's economy. This article presents challenges facing O&G industry players, such as governments, regulatory bodies, operators, and investors, and shows how Raisa Energy (with its Egypt-based data science team) is efficiently and effectively solving these challenges. Such challenges aim at assessing the economic viability of an O&G asset that depends on several factors (as shown in the accompanying figure) such as estimating well production, O&G prices, and risks associated with inputs uncertainty. It is worth emphasizing that the challenges presented here are global in nature and yet are tackled with a team fully formed from the region working at a world-class research and development level.

The face of the banking industry has changed in the last few years.

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Digital transformation is no longer a fuzzy buzzword in industry, rather it is now a well understood and a credible approach to achieving business value. With increasing maturation of transformative technologies, it's becoming a lot easier for organizations to chart their approach and digital transformation journeys.

This paper outlines some of the inherent difficulties present in large-scale standards-based semantic interoperability in the Oil and Gas industry. This domain in particularly interesting for semantic interoperability, as the complexity is manifold: data sets are large, span many different domains, are modeled and represented differently in various standards, which have evolved considerably over time. We outline the main challenges with respect to sustained interoperability and advocate that the interoperability scenarios could serve as an interesting test bed for evaluating semantic interoperability techniques.