Looking at one month of production can tell me what happened during that period.
It doesn’t always tell me whether what happened was normal.
That’s something I learned after spending more time reviewing oil and gas information in EnergyLink. A number can look surprisingly high or low until I compare it with the property’s history.
Now, when something catches my attention, I zoom out before deciding what it means.
I Start With One Property
I don’t try to understand production trends by mixing several properties together.
Every property can have its own history.
I choose one and compare it with itself over time.
That gives me a much cleaner picture.
I Make Sure the Periods Are Comparable
Before looking for a trend, I verify that I’m actually comparing the correct production periods.
I don’t want document dates or later adjustments to make two unrelated periods look like a direct comparison.
My timeline follows production activity.
I Look at Several Months
Two months can show a difference.
Six months can show a direction.
If something looks unusual, I usually want more context than:
Last month: 100
This month: 82
Instead, I look at something like:
March: 103
April: 101
May: 100
June: 98
July: 97
August: 82
Now August clearly deserves more attention.
I Separate a Trend From a Sudden Change
These are two very different situations.
Gradual Movement
Production changes slowly across several periods.
Sudden Movement
Production stays relatively consistent and then changes sharply.
I don’t approach those patterns the same way.
The shape of the history is often more informative than one percentage difference.
I Don’t Expect a Perfectly Straight Line
Real production history isn’t always smooth.
One month may move higher.
Another may move lower.
That doesn’t automatically create a meaningful trend.
I’m looking for a pattern that persists, not every small fluctuation.
I Compare Product Types Separately
If the available information includes different products, I don’t automatically combine everything into one trend.
Oil may show one pattern while gas shows another.
Looking at the details separately can reveal something hidden by the overall result.
I Mark Unusual Months
When a particular production period clearly stands out, I make a short note.
For example:
June noticeably below surrounding months.
That gives me a point to investigate without cluttering every month with commentary.
I Check for Adjustments Before Calling Something a Trend
Historical information can be revised.
If an earlier period was adjusted, I want to know that before using it as part of my comparison.
Otherwise, I may build a trend around numbers that don’t represent the same version of the history.
I Keep Adjusted Months in Context
When I know a period was revised, I don’t automatically exclude it.
I simply remember that it has additional context.
My note might say:
April revised on later statement.
That is usually enough.
I Look at Pricing Separately From Production
A lower statement amount doesn’t necessarily mean production fell.
That’s why I don’t use the final amount as a shortcut for production history.
If I’m investigating production, I look at production.
Then I can review pricing and other details separately.
Keeping those questions separate prevents me from mixing two different trends.
I Do the Same With Deductions
A change in deductions can affect the overall result without telling me anything about actual production movement.
So when I’m studying the property’s production history, deductions remain a separate layer.
Later, I can compare how both changed over the same periods.
I Use Percentage Changes Carefully
Percentages can be useful, especially when the difference isn’t obvious.
But I don’t need to calculate a percentage for every month.
I mainly use it when I want to understand the scale of a change.
For example:
A move from 100 to 98 is very different from 100 to 60.
The raw numbers often tell me enough.
I Compare Similar Time Windows
If I have enough history, I may look beyond the previous few months.
Sometimes comparing a broader period helps me understand whether something is genuinely unusual.
The more history I have, the easier it becomes to recognize the property’s normal range.
I Don’t Assume I Know the Cause
Seeing a trend and explaining a trend are different things.
If production has declined across several periods, my first conclusion is:
Production has declined across several periods.
Not:
I know exactly why this happened.
I keep observations separate from explanations until I have enough information.
I Keep My Trend Notes Short
I don’t need a detailed report every month.
My notes might look like:
March–May: Relatively stable.
June: Noticeable decrease.
July: Similar to June.
August: Lower again.
That tells me far more later than a folder full of unexplained numbers.
Patterns I Pay More Attention To
Some changes naturally catch my attention more than others:
✅ A sharp change after several stable periods.
✅ Several consecutive periods moving in the same direction.
✅ One product changing while another stays stable.
✅ A month that doesn’t resemble the surrounding history.
✅ Previously reported periods being adjusted.
✅ A new pattern continuing for multiple months.
Production History Mistakes I Avoid
❌ Comparing different properties as though they should behave alike.
❌ Building a trend from only two months.
❌ Using final statement amounts as a substitute for production data.
❌ Ignoring adjustments.
❌ Treating every small fluctuation as significant.
❌ Assuming a visible trend automatically explains its cause.
❌ Mixing production, pricing, and deductions into one number.
My EnergyLink Trend Checklist
When reviewing a property’s history, I ask:
Am I comparing the same property?
Are the production periods correct?
What do the last several periods look like?
Is the movement gradual or sudden?
Is one product driving the change?
Were any periods adjusted later?
Is this a one-month event or a continuing pattern?
What do I actually know versus what am I assuming?
History Gives the Current Month Meaning
One EnergyLink production period is a snapshot.
The history turns that snapshot into context.
A lower month may be completely normal for the property.
Or it may be the first major change after a long stable period.
I can’t tell the difference by looking at one number.
That’s why I keep the property consistent, compare several production periods, account for adjustments, and separate actual production from pricing and deductions.
I don’t need a complicated forecasting model.
Sometimes simply looking backward carefully is enough to understand why the newest month deserves a closer look.