It's common across industries to confuse these two terms. The key difference to understand is that machine learning (ML) is a subset of artificial intelligence (AI). ML involves self-learning from existing data without having to be manually programmed. For instance, if you want deeper insights into your rig operations, machine learning algorithms can be trained to detect patterns in your data. This enables data to be standardized, grouped and sorted as needed for meaningful reports.
An AI project would have a broader scope than ML, focusing on intelligent solutions that can autonomously perform a number of tasks traditionally done manually by humans. For instance, this would involve not only analyzing data with ML but performing actions based on that data. This could be to enable automated drilling, optimizing operational performance and safety.
Bottom line: Look for an ML solution when you want to support human decision-making with data-driven reports and AI when you want to automate certain parts of decision-making.
In rig operations, data is collected from various onsite sensors, telemetry in the machinery used and other sources. These include third-party, directional drilling, downhole tools, well intervention and mudlogging data. Collecting all this data is a necessary first step but the data will be stuck in silos unless it is aggregated. Data aggregation means bringing together data from multiple sources and presenting it in a summary format. For instance, this could involve compiling different types of data in a common format. WITSML is the standard for data exchange in well operations.
In drilling and well operations, it's common to work with different service providers at different locations, or to switch service providers when you find a better option for a given project. Keep in mind that each service provider tends to use a different data format, yet all data must generally be available in the same format for successful data aggregation. Data standardization is the process of converting disparate data formats into a standardized single format. Also, data standardization is key to prevent vendor lock-in in the form of lost comparability between old and new data when changing service providers.
It's difficult to aggregate data and make it accessible to everyone who could benefit from it across your global oil and gas operations without a single centralized cloud-based data repository. But you can't just throw your data into any old cloud and expect a seamless experience. Transferring large amounts of data requires high performance and continuous optimization of operating conditions. A cloud solution managed by a trusted partner and built on a major cloud vendor with large-scale infrastructure like Azure is more likely to meet these requirements.
Bottom line: High performance, availability and accessibility requirements demand the large-scale cloud infrastructure and continuous optimization of a managed cloud solution.
In the past, some oil and gas companies may have preferred to stick with on-prem cloud solutions for security reasons. However, today security is less about creating walled garden and more about time and vast expertise, which are difficult to find in-house IT teams. A managed cloud solution eliminates this problem by allowing your IT partner's cloud security experts to ensure security for you while you do what you do best. This way, the latest automated security measures built into major cloud providers like Azure can also be continuously configured and monitored.
Bottom line: With a trustworthy and expert partner, a managed cloud solution is more secure than trying to do it yourself.
SiteCom has been leading the digitalization of well operations for over 20 years. With robust ML-driven data aggregation and standardization, SiteCom helps you consolidate and centrally store all your data. One of the latest improvements is that SiteCom is now provided as a high-performance and secure managed cloud solution. We're also in the process of giving the frontend and mobile apps a fresh look. Read more about SiteCom.
