Over the last few years, oil and gas companies have been subjected to unprecedented challenges. Geopolitical instability and supply chain dynamics have increased the volatility of oil prices, and regulators are now shifting their focus to the O&G industry with new norms to curb carbon emissions.

Amidst these challenges, O&G players have been under immense pressure to maintain their output, while absorbing the blow of increasing input costs. This has sent leading companies to eliminate all sources of inefficiency and loss – and while digital projects have shown promising results, 70% are yet to move beyond pilots.

Oil and gas analytics at an inflection point

For most O&G players, 2023 marks an inflection point. Reducing cost of prediction and the rise of advanced analytics platforms means that O&G companies no longer need to add expensive developers and data scientists to their factor costs. 

Given the degree of complexity that underpins oil and gas analytics, prototyping use cases and scaling them in production comes with costs that most businesses are not yet ready to handle. IoT-enabled offshore plants produce massive amounts of data (up to 80,000+ data tags) that offer vital information about the condition of assets and insights into processes. While building useful models with thousands of parameters requires significant analytics expertise, analytics platforms can bring fine-tuned models into production from the day of deployment. 

Take a look at how oil and gas analytics will rewrite the definition of efficiency and alter competitive dynamics across the value chain in 2023.

Asset sweating with predictive analytics

O&G being an asset-intensive industry, has always sought to extract maximal value out of its assets. Asset sweating is a key objective for both upstream, and midstream and downstream players. To put the value into perspective,  offshore O&G companies experience ~27 days of unplanned downtime per year, which results in annual losses of up to $88m.

A crucial aspect of asset sweating is optimal equipment maintenance. This is one of the low-hanging fruits for O&G businesses, as it can be achieved by implementing AI-driven predictive maintenance for key assets across all sites. Predictive analytics can be leveraged to predict drill-bit and drill string failure, and mitigate unprecedented plunger lift failure. In upstream, predictive maintenance will reduce unplanned downtime by 20-30%.

Optimizing transport with gas compressor models

In midstream operations, gas compression systems consume a significant amount of energy (amounting up to 5% of the transported gas), and their operating costs account for nearly half of the company’s budget. However, pipeline optimization can bring up to 20% savings in the fuel consumption of the gas compressor train.

What’s more, compressor station failures result in major outages, which further disrupts downstream operations. O&G players can now optimize transport operations with two prominent use cases: it is now possible to predict compressor unit failure with predictive analytics, and machine learning algorithms like deep neural networks (DNN) can be leveraged to optimize compressor performance.

Pipeline risk mitigation with real time data

Pipeline defect detection and monitoring has been a major cost center for midstream and downstream players since decades. In-line inspection and reactive repair measures lead to delayed detection, and this incurs significant losses for businesses.

One of the key challenges in implementing AI for pipeline defect detection is the sparse availability of production data. This, however, can be mitigated by leveraging Cycle-GAN models to generate pseudo-sample data points. ML algorithms can then be applied to these datasets enriched with real-time pipeline metrics to detect cracks, leakages, and fissures immediately.

Corrosion detection with advanced analytics

In O&G operations, production and transport machinery and components are prone to damage from corrosion. The causes of corrosion vary based on the conditions in which an asset operates. But corrosion-related damages are not caused by external factors alone – in fact, internal corrosion leads to 15% incidents in gas transmission pipelines, leading to $3m in damages.

AI techniques like deep-learning can be leveraged to detect corrosion and optimize replacement and anti-corrosion coating timeframes, enabling O&G companies to mitigate both safety risks and losses from leakage and downtime. Recent studies demonstrate that deep Bayesian networks detect corrosion with highest confidence compared to other methods.

O&G analytics drives sustainability

Oil and gas analytics use cases also result in a significant impact on the organization’s carbon footprint, with advancements such as predictive analytics in oil and gas playing a crucial role. For example, manual pipeline monitoring, and dispatch of repair crew to inexact locations results in transport-related GHG emissions. These can be mitigated by monitoring pipelines with real-time data streams, and dispatching the crew to the exact point of failure, when needed. Similarly, process mining helps eliminate inefficiencies, and enables offshore rigs to run at their complete capacity at all times, without reducing the lifetime value of the well. Lastly, asset sweating enables O&G businesses to run assets in their optimal condition at all times. Likewise, by extending the asset lifecycle, businesses are able to minimize their scope 3 emissions and material footprint.

Looking to the horizon

With advancements in computational techniques and sensor technology, oil and gas analytics has come a long way. But one of the most crucial shifts that improved the accessibility to analytics technologies to O&G companies, is the platformization of predictive technologies. This has enabled them to reap the benefits of AI in their operations without having to hire and retain big technology teams. 

With this shift, better uptime, increased production, improved margins, and higher revenues are only an implementation away. Bring these benefits to your bottomline today, with UptimeAI. Contact us now!