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Data Science for Oil & Gas

Dataiku Product Joy Looney

One of the most complex challenges for upstream operators is responsibly maximizing the efficiency of their wells. Each organization has a different strategy, and yet they all are balancing objectives like increasing production, capital efficiency, and mitigating risk to drive safe, efficient, profitable production. So let’s explore some of the challenges that oil and gas companies are facing in pursuit of these targets and take a look at how data science, machine learning, and AI offer solutions to these complex problems.

→ Watch Now: How Schlumberger Incorporates Data Science Into Their Processes

What's The Hold Up?

External Challenges

One of the main challenges that oil and gas companies face is the challenge to meet the booming energy demand of the world’s urban population. Navigating the ebb and flow nature of the global commodity market means maximizing efficiency in every aspect of the value chain. Organizations must minimize the pressures that accompany market uncertainty and assure preparedness in the face of volatility.  

Internal Challenges

Unidentified operational constraints present major time and money risks for oil and gas companies. When projects lack precision and transparency, outcomes become both unpredictable and inefficient. Even when problem areas are clearly identified, operators often struggle with prioritizing which of these challenges to address first. 

And further still, those challenges and opportunities continuously change, bringing to light new elements that need to be examined. As just one example, the desire to better understand the subsurface significantly increased well instrumentation in recent years, which in turn creates an overwhelming amount of data that is proving difficult to align and understand in context.  

Oof — those are some heavy-weight challenges. So, how can oil and gas companies get that leap in efficiency? 

Here’s How The Right Data Science Application Can Help:

The problems oil and gas companies face can be summarized as inflexibility, uncertainty, and friction. We will look at how data science solutions lighten the load of these challenges and help organizations achieve efficiency objectives by introducing agility, clarity, and usability. 

Agility 

Predictive maintenance informs human decision-making to help decrease downtime on wells for needed repairs – reducing impending infrastructure concerns and improving both availability and productivity.

Providing engineers with access to data and predictive solutions via an AI platform such as Dataiku’s allows for agile decision-making processes. This agility becomes extremely useful as companies continuously navigate new data points from the field.

Clarity

Increased visibility of overall operations that comes from a 360-degree view of facilities allows for an improved maintenance plan that mitigates human risk, minimizes various safety concerns, and yields productivity. 

Machine learning informed decisions can identify operational constraints and also provide recommendations to engineers in the field for faster decision-making. Among the machine learning solutions is smart pattern prediction, which helps companies decide on which problems need to be prioritized. The biggest lift to operations comes from getting the right data to the right people quickly, saving time and money. 

Similar to how data science applications can be used to provide forecasting models for analysts and optimization recommendations for personnel in charge of the production process, applications can be used to improve exploration endeavors as well. Complex geospatial data is used in exploration efforts, but when using models in AI-based systems such as Dataiku, these inputs – including rich historical data – can be quickly analyzed to discern key information and highgrade development prospects. 

Ultimately, applying data science within an organization means having access to the valuable insights that oil and gas companies use to enhance integrated pore-to-pipeline solutions, boosting hydrocarbon recovery and overall profitability.

Usability 

Data applications that don’t translate to tangible results for an organization are a waste of time and money. Even in organizations that have already embarked on new AI applications, there is often a significant gap that needs to be bridged between machine learning and domain expertise. Communication needs to be — you guessed it — efficient. This is why it is critical for operators to choose a platform that will help align their SMEs and data professionals. 

Democratization of data in your organization matters, and your data platform of choice needs to be able to support this. Dataiku was designed with pervasive collaboration in mind. This means the insights are not only easy to reach with an interface that is friendly for everyone from business analysts to engineers to data scientists, these insights are easily shared as well. End-to-end efficiency means oil and gas companies can unlock a better way to plan, develop and produce the most efficient wells possible!

Check out this video to see how Dataiku has helped Schlumberger, the world’s leading provider of technology for reservoir characterization, drilling, production, and processing to the oil and gas industry achieve digital transformation in their organization.

 

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