Conversational AI Assistant for Drilling Performance Optimization

The challenge

Drilling engineers work in a data-intensive environment. Optimizing performance depends on getting insights quickly out of large, fragmented datasets: structured sources such as well KPIs and operational metrics, and unstructured sources such as daily drilling reports.

Even with advanced analytics tools in place, several problems remained:

• Fragmented data access: engineers often have to gather data across multiple wells, systems and interfaces.
• Manual navigation and reporting: even with multiwell analytics tools, users have to go through several dashboards to extract insights.
• Unstructured data: daily drilling reports and logs need manual interpretation and are difficult to query.
• Technical barriers: accessing and combining datasets often requires SQL expertise, which limits who can do it.
• Time-consuming analysis: consolidating and interpreting data delays decisions in time-sensitive operations.

Our approach

The client uses the conversational AI assistant built into ID3 Benchmark to streamline drilling performance analysis.

Instead of navigating dashboards or writing SQL, drilling engineers ask questions in natural language and get immediate, data-driven answers. The assistant retrieves information from multiple datasets and also interprets the results.

Example: benchmarking for new well planning

In a typical workflow, an engineer asked the ID3 AI Assistant:

I’m planning a new well with the same drilling phases as WELL-04A (DRILLING 17 1/2”, CASING 13 3/8”, DRILLING 12 1/4”, CASING 9 5/8”, and DRILLING 6”). Considering all wells in the system as offset wells, please perform a benchmark for the drilling KPIs: ROP net, ROP gross, and WTW. Tell me which well has the best performance for these KPIs so I can use those values as targets for my new well.

Within moments, the assistant ran a benchmark across all relevant wells and drilling phases (17 1/2”, 12 1/4” and 6”) and returned a clear summary:

• WELL-03A was the top performer for average net ROP (93.03 m/h) and gross ROP (49.76 m/h).
• WELL-02A had the best average WTW (13.56 minutes).

It also gave a short interpretation and suggested follow-up analysis. It noted that the KPIs were aggregated across the selected phases, and offered to break them down by individual phase or to go down to stand-level data for statistically sounder results.

The example shows the assistant simplifying multi-well benchmarking and supporting new well planning directly: engineers set specific performance targets based on existing wells and make faster, more precise, data-driven decisions.

The outcome

With the assistant embedded in ID3 Benchmark, engineers no longer navigate multiple dashboards or assemble reports by hand, which cuts the time needed to reach actionable insights.

More people can use the data. With natural-language queries, users without SQL expertise can run advanced analyses, so critical information reaches a wider range of team members. KPIs are interpreted in real time, which supports faster, better-informed operational decisions.

Performance optimization has also become more proactive. The assistant helps identify inefficiencies quickly, improves benchmarking across wells and supports continuous improvement, so teams act on insights instead of only reviewing them.

More broadly, it changes how engineers work with data: from static dashboards to a conversation, and from manual analysis to AI-assisted decisions. The assistant acts as a digital engineering copilot that supports their expertise, helping them make faster and more confident decisions.