The Hidden Cost of Manual Type-Curve Compilation
Two to three weeks of completions engineering time per pad is a significant drag when your team is running multiple pads in parallel. The work itself isn't complex — it's data aggregation: pulling production histories from OSIsoft PI, running analogue queries in IHS Markit Harmony, and building the stage-count performance correlations in Excel or Spotfire. But it's time-consuming enough that design review packages frequently arrive too late to inform meaningful parameter debate, and the default becomes last cycle's design rather than a data-optimized one.
How an AI Agent Handles the Analogue Assembly
An AI Labor Company agent integrates with your existing Harmony and OSIsoft PI environments to mine historical completions design packages and production type-curve data. A Gemini-powered agent then assembles analogue well comparisons, stage-count performance correlations, and fluid volume benchmarks for each new pad — automatically, 30 days before design freeze. The package routes to the VP of Completions Engineering for parameter review through an approval workflow that keeps human judgment squarely in the decision seat. Teams running this workflow typically reduce the manual compilation burden by 50–70%, and the agent is typically live and producing design packages within about 8 weeks of deployment.
The Business Case: Better Designs, Not Just Saved Hours
The efficiency gain is real — 50–70% reduction in the time completions engineers spend on data assembly frees significant capacity across a multi-pad development program. But the more important outcome is analytical: when design packages arrive 30 days before freeze rather than 3 days before, engineers have time to challenge parameters, run sensitivity cases, and make stage-count decisions based on analogue performance rather than schedule pressure. In scenarios where a single stage-count optimization on one pad yields measurable production uplift, the design quality improvement can dwarf the labor savings. This is a capital efficiency story as much as an engineering productivity story.
Does the agent make completions design recommendations, or does it just assemble the data package?
The agent assembles and structures the analogue comparison, type curves, and stage-count correlations — it does not make design decisions. Every parameter recommendation requires VP of Completions Engineering sign-off before it advances. The agent's role is to get the right data in front of your engineers earlier, not to replace engineering judgment.
We have a complex Harmony and PI environment with years of legacy data. How does the agent handle data quality issues?
The agent mines historical completions packages to understand your specific analogue selection criteria and data conventions before going live. Where data quality flags arise — inconsistent perforation intervals, missing PI tags — it surfaces those gaps in the review package rather than silently filling them in, so your engineers know exactly what they're working with.
How long does it take to go from contract to receiving the first automated design package?
Typical deployment runs about 8 weeks, covering data environment integration, analogue selection logic calibration, and workflow routing configuration. The first automated package for a real pad is usually produced during the calibration phase so you can validate it against a manually assembled package before full handoff.