Doubling your engineering team won’t double the output

As E&P companies expand their development programs, the demand on their engineering teams to run production forecasts and build reliable development plans grows exponentially. At first glance, the solution seems straightforward – scale the engineering team by hiring more skilled professionals. However, the reality is not so simple. History has shown us that this conventional approach can lead to a bubble that eventually bursts, leaving companies grappling with inefficiencies and missed opportunities.

So, what is this elusive “bubble” I’m referring to? It’s the point at which an operator realizes that simply increasing the size of the reservoir engineering team is not the most sustainable solution. Fortunately, a new era of possibilities has emerged – one that embraces automated workflows and harnesses the power of machine learning.

In this blog, we will explore the limitations of doubling the engineering team’s size and the game-changing role of AI in reshaping the field of reservoir engineering. 

What does it mean to scale your reservoir engineering team?

Growing a team and scaling it are both crucial but distinct aspects. Growing a team is just about increasing headcount, but scaling means more OUTPUT. Scaling a team involves obtaining the required resources, skills, and organizational capability to handle a larger workload and more responsibilities. In essence, it’s about preparing the team to effectively manage increased demands. But how do we achieve effective scaling? The answer is simple: Machine Learning (ML) and automation. 

Data-Driven Approach to Forecasting:

Traditional methods for well forecasting have historically relied on a laborious process of manually filtering through extensive historical well data in search of analogous wells. The subsequent step involves averaging the production profiles of these comparable wells to derive a type curve, which serves as the basis for forecasting future well performance. Although this method is not inherently flawed, its major drawbacks lie in its manual nature, personal bias, and time-intensive requirements – meaning that for more type curves, you need to hire additional engineers.

However, most operators understand that doubling the team size may not necessarily lead to a proportional increase in output. This is where Artificial Intelligence (AI) comes into play, offering a more efficient and effective solution to scale the team’s capabilities. 

Machine learning algorithms can significantly reduce the time and effort required for data analysis and forecasting by reservoir engineers to focus on higher-value tasks and decision-making processes. The energy space produces large amounts of data, including historical production data, completions data, subsurface data and operations data. By utilizing automated workflows, even a small engineering team can discover hidden patterns and opportunities that bigger teams might miss using traditional methods.

How Novi Labs can help you scale your engineering output: Automation and Scalability

Scalability sounds great in theory, but it often takes a robust strategy to turn the idea into reality. Automation makes scalability highly achievable by essentially increasing the output without increasing the need for human resources. 

In the context of reservoir engineering, automation can mean actions such as automatically building datasets, training models, and configuring forecasts. This boosts productivity and improves decision-making quality. Traditional methods like type curves or numerical simulations fall short in accurately planning well forecasts, as they cannot efficiently evaluate multiple development scenarios at the same level as automation powered by machine learning.

Here are 5 key advantages and contributions of ML in this field:

1. Analytics-ready datasets: ML algorithms can efficiently handle large and diverse datasets, automating the process of data preparation, cleaning, and integration. This capability is particularly valuable in the oil & gas industry, where data from various sources need to be cleaned, combined and processed to make informed decisions.
2. Pattern recognition: ML models can detect complex patterns and relationships in the data, including hidden correlations between reservoir characteristics and production performance.
3. Forecasting accuracy: By leveraging historical production data and reservoir characteristics, ML models can offer more accurate and reliable production forecasts compared to traditional analytical methods such as type curves or numerical simulation. These forecasts help in making well-informed decisions regarding development  planning and resource allocation.
4. Optimization: ML algorithms can optimize reservoir development strategies by analyzing various parameters simultaneously. This enables engineers to identify the best combination of factors to maximize oil recovery and reduce operational costs.
5. Continuous learning: As more data becomes available over time, ML models can be updated and retrained to improve their performance continually. This adaptability ensures that the models remain relevant and effective throughout the field’s life cycle.

Conclusion

Scaling your engineering team effectively is possible with AI-driven approaches to forecasting. By leveraging automation & machine learning, E&P operators can achieve more accurate production forecasts, scale their team’s output, and maintain a competitive edge over their peers.

Interested in the new data-driven approach, but unsure of where to get started? Feel free to reach out to our team, and we’ll gladly share additional resources to help you get started.

Mohamed is a digital marketing specialist fueled by a passion for energy independence through technology. He has a successful history of leading marketing initiatives at ShaleProfile and currently serves as the Head of Marketing at Novi Labs.