AI-Driven Cost Optimization in Oil and Gas Projects (Published)
The oil and gas sector, a cornerstone of the global energy supply, is poised at the brink of a technological renaissance as it embraces Artificial Intelligence (AI). This paradigm shift is driven by the industry’s imperative to enhance efficiency, bolster safety, and notably, optimize costs amidst volatile market conditions and escalating operational complexities. This paper presents an in-depth exploration of the multifaceted applications of AI technologies in streamlining cost management across various phases of oil and gas projects—from exploration to distribution. At the heart of this exploration is the elucidation of AI’s transformative role in predictive analytics, automation, and decision support systems. By harnessing the power of machine learning algorithms, the industry can predict equipment failures, optimize maintenance schedules, and ensure uninterrupted production, thereby significantly reducing downtime costs. Furthermore, AI-driven data analytics enable the identification of patterns and insights from vast datasets, leading to more informed and cost-effective decision-making. The paper also delves into the integration of AI in operational domains such as drilling optimization, where AI algorithms analyze geological data to determine optimal drilling locations and parameters, thus minimizing the risk of costly non-productive time. Similarly, in the realm of supply chain management, AI facilitates dynamic routing and inventory control, curtailing logistical expenses. Highlighting case studies and empirical data, the paper underscores the tangible benefits realized by early adopters of AI in the industry, showcasing a potential reduction in operational costs and an increase in efficiency that often surpasses traditional methodologies. This detailed examination not only charts a course for future AI-driven endeavors in the oil and gas sector but also serves as a clarion call for stakeholders to navigate the intricacies of digital transformation strategically. In conclusion, the paper posits that the integration of AI stands as a beacon of innovation, promising not only cost optimization but also a sustainable and resilient future for the oil and gas industry. The findings herein aim to galvanize industry leaders, policymakers, and technologists to foster a collaborative ecosystem where AI can flourish, driving the industry towards a new horizon of operational excellence.
Keywords: AI-Driven, cost optimization, gas projects, oil
Carbon Intensity During Oil and Gas Production Process (Published)
This report focuses on reviewing the types of carbon intensity metrics, and the use of such metrics across the oil and gas sector, to monitor progress towards transitioning away from fossil fuel production. Producers are under pressure to respond to challenging conditions resulting from increasing climate policy, tightening markets and a move away by investors. A number of commentators are suggesting that production may have peaked, given these emerging trends, and the post Covid-19 pandemic. From a combination of review and modeling, this report provides some key insights on carbon intensity metrics and the impact of different carbon intensities on future production, which are pertinent to the future strategies of the oil and gas sector –
Narrow-scoped metrics that only include upstream emissions are insufficient for producers reporting on progress towards climate goals. The carbon intensity of the final product also needs to be considered, given that it is increasingly subject to increased demand-side policy e.g. in relation to carbon pricing, bans on the sale of internal combustion engines (ICEs) etc.
Given that climate targets are expressed in absolute terms, the relative measure of progress provided by carbon intensity metrics is insufficient to guide progress towards net-zero emissions. As shown by the modelling, there is a significant decline in the levels of production permitted under climate targets by 2050.
Given the need for diversification, metrics that account for scope 3 emissions will be important, to help monitor the transformation away from oil and gas. As discussed in this report, a number of IOCs appear to be making small steps in this direction, although their key business focus very much remains on oil & gas. As the IEA (2020a) has reported, less than 1% of capital expenditure is being spent outside of core business areas.
However, cleaner operations are also important. Therefore, scope 1&2 metrics are still useful for minimising upstream emissions. The modelling highlights the impact for example of high carbon intensity gas resources (due to methane emissions) on their production levels. Unconventional resources, which tend to require more energy input per unit of extraction, and are more costly, appear unlikely to be exploited in our Paris-aligned case.
Any assertion that higher carbon intensity production upstream can be offset by lower emissions downstream (e.g. via higher vehicle efficiency standards) is not supported by the modelling. This is particularly the case where these oil products areexported abroad to regions with low efficiency forms of transportation/limited environmental regulation.
National oil companies (NOCs) have more potential to achieve emission reduction from operational emissions, although the incentives to do so might be lower (with far less scrutiny and reporting). Diversification is also likely to be more of a challenge for NOCs, due to the reliance of public budgets on revenues gained. However, a number of high-producing countries are vigorously exploring diversification strategies. Such strategies could include massively increasing support for renewable industries, and focusing on areas such as hydrogen production and CCS applications.
For the large NOC producers, with the lowest-cost conventional reserves, it is likely that they may be able to continue producing for the longest time, as climate policy stringency increases. However, given that NOCs hold the largest reserves, risks of stranding will be greater in absolute terms.
Keywords: GAS, carbon intensity, oil, production process