As the Oil and Gas industry exits 2022 with record profits, looming recession concerns and continuing price volatility has brought the focus back on the balance sheets. While upstream players have enjoyed higher free cash flows, their midstream and downstream counterparts are now prioritizing financial health over capacity gains amidst weakening demand.

From this perspective, 2023 represents a crucial opportunity for all businesses in the O&G value chain. While upstream companies have the funds to transform their operations with digital technologies, midstream and downstream organizations can leverage proven use cases to hyper-optimize their operations and eliminate traditional cost centers. In any case, high-ROI investments will be at the top of the agenda.

Predictive technologies have been one of the key areas of focus for O&G organizations, especially because of their potential in optimizing asset maintenance and repair. So, how can organizations measure the ROI of predictive analytics solutions, and nail their projections with complete confidence? Read on to find out.

Predictive analytics in Oil and Gas: play fast, but not loose

In O&G operations, where the stakes of asset failure are much higher, implementing predictive analytics brings a multi-dimensional impact on the bottom line. How? In the following ways:

  1. Reducing unplanned downtime: Predictive technologies can reduce unplanned downtime of assets by up to 30%. Considering offshore organizations average 27d of annual downtime where each day costs them $1.37mn, this represents a major revenue gain opportunity.
  2. Lower maintenance costs: Offshore facilities typically spend $2bn annually on the maintenance of nearly 50,000 maintainable assets. Predictive technologies can optimize these costs down to a minima, while improving the reliability of assets.
  3. Mitigating costly liabilities: O&G companies have repeatedly made multi-million dollar settlements with regulators for exceeding emissions or failing to comply with worker safety norms. Predictive technologies help them eliminate these costly liabilities.
  4. Improving energy efficiency: Suboptimally running equipment increases the energy burden and contributes significantly to Scope 2 emissions. By ensuring that assets run in an optimal state, predictive technologies reduce carbon tax overheads and energy expenses.
  5. Eliminating revenue leakage: By enabling midstream and downstream organizations to detect pipeline leakages in real-time, predictive technologies help minimize revenue loss and prevent hazardous situations.

The ROI on predictive analytics

So, what is the ROI of implementing a predictive analytics solution?

While it is possible to figure out some benefits of predictive analytics solutions into an ROI calculation, estimating the investment costs can prove challenging – especially as it may involve change management, hiring teams with technical skills, and maintenance of the solution itself.

Moreover, some of the intangible benefits can be difficult to measure – improvement in consistency of product/service quality, for example. Nonetheless, predictive analytics solutions like AI-based predictive maintenance can help you recover your investments within months. Considering that some failure predictions can save $750,000, this proves a conservative estimate.

When it comes to advanced analytics in Oil and Gas, outcomes matter

Not all predictive analytics solutions will yield the same outcomes. For example, a predictive maintenance algorithm that is trained to predict the failure of assets in advance, will only help reduce unplanned downtime that would have resulted from the failure.

This, however, is not enough when it comes to complex systems like offshore rigs. Why? Because a team of technicians tasked with investigating the cause of probable failure might still take a while to figure out the root cause. In such a scenario, the ROI on a solution that predicts the probability of crucial failure modes (ways in which a machine may fail) will be much higher than the above solution.

Multiple factors will impact the outcomes of a predictive analytics solution: like the choice of algorithms, how predictions are incorporated into the workflows of technicians, the scope of predictions, model accuracy, or the cost of maintaining it.

Build vs Buy: weighing the pros and cons

For predictive technologies, the build-or-buy question ultimately boils down to capabilities and investments. If your organization employs unique production processes or assets that predictive analytics software cannot cater to, it might be possible to justify building it on your own.

However, for most O&G organizations, buying a proven predictive analytics software will make more sense. It will enable them to bypass the risks of custom development, and leverage economies of scale to achieve the same outcomes for much lower costs.

Given that best-of-breed solutions will employ vibration analysis, computer vision, temperature, heat and humidity sensing, and acoustic analysis to monitor machines, they provide early warning and can predict failure modes of nearly every crucial asset deployed in O&G operations.

Addressing common adoption roadblocks

Implementing a predictive analytics solution can pose unforeseen challenges. Examples include the integration of IoT sensors with aging machines, or the lack of adequate historical data for training the model. If your organization lacks in-house expertise to address these challenges, a trusted solution provider can prove invaluable.

Today, predictive models can be trained with minimal historical data. Often, generative models are used to create new data points which can be added to the training dataset. This is then used to fine-tune a pre-trained model to your plant environment to achieve high accuracy.

Moving ahead

Today, only 24% oil and gas companies are leveraging a predictive maintenance strategy – which indicates that the industry is looking at a massive untapped potential. The use cases of predictive technologies are not limited to maintenance alone. Leading players are already making a compelling case for digital twins, quality assurance, and operations optimization.

However, predictive maintenance solutions represent the lowest-hanging fruit for O&G companies across the value chain – especially with cutting edge platforms like UptimeAI in the market. Bringing real-time predictions based on domain expertise, UptimeAI can increase productivity levels by 30%, and reduce maintenance overheads by 20%.

Bring the power of predictive analytics to your O&G operations today. Contact us now.