Data
Live streams, daily reports, surveys, BHAs, mud data, and historical records.
Drilling intelligence platform
Real-time and historical drilling intelligence, grounded in physics and field data.
DrillingMetrics links live streams, daily reports, surveys, BHAs, engineering models, and offset history in one operational view. Field and office teams work from the same evidence.
- Physics-based models
- Real-time and post-well
- Desktop and mobile
One-second dataWITSML streams processed with operational context.
1,000+ wellsExperience across onshore and offshore operations.
Live + historicalThe same analytical methods across the well lifecycle.
Engineering firstMeasured, modeled, and inferred evidence kept distinct.
One connected workflow
Trace every result from source data to decision.
Each KPI, event, and model result retains the time, depth, wellbore, run, rig state, units, quality, and provenance needed to interpret it.
Context
Time, depth, wellbore, run, rig state, units, quality, and provenance.
Models
KPIs, torque and drag, hydraulics, dysfunction beliefs, and comparisons.
Decisions
Operational views, alerts, reports, and explanations that teams can verify.
Platform
One system for live operations, engineering models, offsets, and review.
Performance and offsets
Compare eligible intervals in operational context.
Align relevant footage by depth, exclude off-bottom states, and keep section, run, BHA, and formation context with the result.
- Build consistent baselines across wells, runs, rigs, and crews.
- Review distributions and event patterns instead of relying on one average.
- Use the same definitions in live dashboards and post-well analysis.
Engineering models
Keep deterministic models beside field response.
Compare pickup, slack-off, torque, and field RPM with depth-aligned torque-and-drag and BHA critical-speed models. Inputs, units, and assumptions remain visible.
- Generate torque-and-drag models and track actual pickup and slack-off response.
- Compare field RPM with modeled torsional, axial, and lateral critical speeds.
- Use model deviation or critical-speed proximity as evidence for earlier investigation.
Directional response
Quantify BHA response in formation and trajectory context.
Combine trajectory and tortuosity with rotation-only build and walk tendencies to identify repeatable assembly behavior without treating association as causation.
- Compare trajectories, spacing, and tortuosity across wells.
- Quantify build and walk tendencies by BHA, stand, and interval.
- Review formation and operating context before attributing the response.
Field case study
A model deviation helped identify a drill-string washout.
Over three hours in the Midland Basin, actual pump pressure separated from the modeled response. The sustained residual and supporting signals helped field personnel distinguish a downhole washout from a surface pump problem.
The diagnosis remained tied to the measured pressure trend, model residual, and operating context.
Read the case studyAIDE + DM MCP Server
Give AI clients controlled access to drilling evidence.
The DM MCP Server exposes authorized DrillingMetrics tools to MCP-compatible clients such as ChatGPT, Claude, or custom applications. Ask for current KPIs, historical comparisons, dashboard links, or a scheduled daily status update, then inspect the supporting evidence.
- Connect conversational and programmatic AI workflows.
- Keep data access, routing, and authorization inside DrillingMetrics.
- Separate measured facts, calculated results, detected events, and interpretation.
Mobile operations
Carry current well context beyond the desktop.
Mobile views keep current status, selected trends, engineering models, and notifications accessible without reproducing the full desktop workspace.
Current status
See the active well, current operation, depth, and essential performance context.
Trends and models
Review selected time traces and engineering views from the field or away from the office.
Notifications
Receive relevant updates and return to the operational evidence behind them.
Technical resources
The engineering behind the analytics.
Concise explanations of the drilling, data, statistical, and software choices behind each method.
From drilling teams
What teams use the platform to do.
“We utilize the real time offset analysis tools on a daily basis (especially in the vertical intermediate section). We’ve found the real time road map is the best way to achieve reliable and repeatable results.”
“DrillingMetrics is a great tool to monitor down hole drilling dynamics in real time to troubleshoot dysfunctions and optimize parameters. It’s also very helpful comparing offset well performance to manage expectations and dial in drilling road maps thru challenging sections.”
“On our New Mexico well, DrillingMetrics gave us extensive data at the rig floor while drilling. Every curve dogleg stayed below the operator-defined limit, the RSS with mud motor BHA tripped through the curve on elevators, and we completed the 10,000-ft lateral in one run.”
Questions
Practical questions.
Does DrillingMetrics work only with real-time data?
No. The platform supports live operations and historical-well processing. Teams can establish an offset baseline before a live deployment or use historical analysis as a standalone project.
What data sources can be used?
Typical inputs include WITSML streams, daily drilling reports, directional surveys, BHA records, mud reports, and existing historical databases. The exact integration depends on the source and authorization model.
Does AIDE make engineering decisions?
No. AIDE retrieves and explains available drilling evidence. Engineering calculations remain deterministic where applicable, and operational decisions remain with the responsible team.
Can service companies use the platform?
Yes. Operators and oilfield service companies have separate entry points, and historical batch-processing workflows can support service-company analysis and reporting.