Detecting Invisible Lost Time from Drilling Activity Data

Invisible lost time is the repeatable gap between actual and achievable routine work. It is different from visible NPT: a few minutes spread across hundreds of connections, transitions, or drilling intervals can exceed one obvious event. The analysis must segment like processes, use a defensible benchmark, and rank total opportunity rather than isolated records.

Separate ILT from NPT

Nonproductive time is usually associated with a recognizable event:

• equipment failure,
• stuck pipe,
• lost circulation,
• waiting on materials,
• fishing,
• well-control problems.

The operation has departed from the planned workflow. Invisible lost time is more subtle. The rig may still be performing the intended operation. It is simply performing it less efficiently than it reasonably could. Examples might include:

• slow drilling connections,
• delayed transitions back to bottom,
• excessive time preparing for a connection,
• inconsistent tripping connections,
• unnecessarily long pump-start procedures,
• repeated short delays between otherwise normal states.

The distinction matters because the improvement method is different. For NPT, the question may be:

How do we prevent this failure from happening again?

For ILT, the question is more often:

Why does this routine operation consistently take longer than our demonstrated best performance?

That is a benchmarking problem.

Define the event before timing it

Before a connection can be timed automatically, the system has to identify when it begins and ends. Operational-state logic turns drilling → come off bottom → connection activity → return toward bottom → drilling resumes into timestamped transitions.

From those timestamps, different pieces of the workflow can be measured. For example:

• Pre-connection transition: How long does it take from leaving the drilling condition to beginning the actual connection activity?
• Connection activity: How long does the pipe-handling / slips portion take?
• Post-connection transition: How long does it take after the connection is completed to return to drilling?
• Complete cycle: How much time passes between the end of one drilling interval and the beginning of the next?

The exact KPI names are less important than the principle:

Do not reduce the workflow to one number if the objective is to improve the workflow.

Segmenting a routine connection into operational phases identifies where the additional time actually occurs.

Use a repeatable benchmark

A 7.5-minute drilling connection is not automatically bad. It may include:

• a required operational check,
• an unusual pressure response,
• crew coordination,
• a temporary equipment constraint.

One observation rarely tells us much. Across 180 connections, the distribution might be:

• P10: 4.2 min
• P25: 4.6 min
• median: 5.1 min
• P75: 5.9 min
• P90: 7.3 min

Now the question becomes more interesting. The rig has repeatedly demonstrated that the operation can be completed in roughly 4–5 minutes. But a substantial fraction of events take much longer. That creates an identifiable performance opportunity. This is why ILT analysis is fundamentally statistical. The objective should not be to find the single fastest event and declare every other event inefficient. The objective is to understand the distribution. It is tempting to say:

Our fastest connection was 3.8 minutes, so every connection should take 3.8 minutes.

That is usually too aggressive. The fastest observation may have benefited from:

• favorable sequencing,
• unusually simple conditions,
• measurement noise,
• incomplete event boundaries,
• or circumstances that are not repeatable.

A percentile-based benchmark is often more useful. P25 connection time represents performance demonstrated regularly rather than once. If a rig's P25 is 4.6 minutes and its median is 5.1 minutes, moving the median toward the lower quartile is a realistic initial target grounded in operational capability. Define event-level invisible lost time as:

$$ILT_i = T_{actual,i} - T_{benchmark,i}$$

for event i, when:

$$T_{actual,i} > T_{benchmark,i}$$

A 6.0-minute connection against a 4.8-minute benchmark contains 1.2 minutes of opportunity. Repeated 150 times, that is 180 minutes, or three hours.

This simple calculation illustrates why individually small improvements matter. But the benchmark has to be defensible. It should ideally account for differences in:

• operation type,
• rig,
• hole section,
• depth,
• equipment configuration,
• and other relevant context.

Comparing unlike operations can manufacture ILT that does not actually exist.

Localize total opportunity

Time distributions tell us how much operations vary. Depth tells us where the variation occurs. SPE/IADC-184743 describes a high-resolution days-versus-depth approach using real-time data rather than one daily point. The purpose was to expose flat spots that conventional daily plots could hide and allow engineers to investigate what occurred at those depths.[1] That concept is extremely useful for ILT. Imagine a depth plot where the well progresses steadily for several thousand feet. Then around 11,800–12,300 ft: the curve becomes noticeably flatter. There may be no formal NPT event.

But that interval consumed more clock time per foot. The next question becomes:

Which rig states occupied the additional time?

Perhaps:

• more reaming,
• slower connections,
• additional circulation,
• repeated downlinks,
• extra surveys,
• more slide drilling,
• reduced ROP.

The days-versus-depth plot identifies the location. Rig-state analysis identifies the cause.

DrillingMetrics narrows a well-level time gap to a 31-minute connection and then to its one-second traces, preserving the path from opportunity to cause.

Two workflow opportunities illustrate why frequency matters:

Operation	Opportunity per event	Occurrences	Total opportunity
A	30 seconds	250	125 minutes
B	5 minutes	10	50 minutes

Operation B looks much worse when individual events are inspected. Operation A has the larger effect on the well. This suggests a useful prioritization quantity:

$$Total\ Opportunity = Opportunity\ per\ Event \times Event\ Frequency$$

The largest ILT opportunities are often not the worst-looking individual events. They are the routine inefficiencies repeated most often. This is why connections receive so much attention in drilling-performance analysis. They happen hundreds of times.

Inspect the contributing event

For a hypothetical well with 200 drilling connections, the cycle distribution is:

• P25 = 4.5 min
• median = 5.2 min
• P75 = 6.0 min
• P90 = 7.4 min

Rather than using the single fastest connection, management chooses 4.7 minutes as a realistic benchmark based on repeatedly demonstrated performance. An average excess of 0.8 minutes across 200 connections yields 160 minutes, or about 2.7 hours, of opportunity.

Now segment the slow events. The additional time comes from:

• pre-connection: 15%
• central connection activity: 20%
• return-to-drilling transition: 65%

That changes the operational recommendation completely. The main opportunity is not: make connections faster. It is reduce the delay between completion of the connection and resumption of productive drilling. That is what actionable ILT analysis should do.

Report decisions, not just durations

A report containing 40 charts can technically contain more information than a report containing five. It may also be less useful. SPE/IADC-184743 specifically notes that ILT reports can become unwieldy when they contain too many plots and emphasizes customizable reporting while retaining standardized comparisons.[1] A useful ILT report might therefore answer only a few questions:

1. Where was the largest total time opportunity?

2. Which routine operation generated it?

3. Was the issue slower typical performance or high variability?

4. At which depths, crews, or shifts was it concentrated?

5. What benchmark has already been demonstrated repeatedly?

Everything else can remain available for investigation. The summary should direct attention. The detailed data should provide evidence.

Ranking total opportunity rather than individual-event severity focuses improvement on the workflows that matter most to overall well time.

Related technical resources

• The Average Trap: Comparing Drilling Performance With Distributions
• Why Rig-State Classification Is Foundational to Real-Time Drilling Analytics
• Practical Considerations in Offset-Well Benchmarking

References

1. Behounek, M., Thetford, T., Yang, L., Hofer, E., White, M., Ashok, P., Ambrus, A., and Ramos, D. Human Factors Engineering in the Design and Deployment of a Novel Data Aggregation and Distribution System for Drilling Operations. SPE/IADC-184743-MS, SPE/IADC Drilling Conference and Exhibition, The Hague, Netherlands, 2017.