Why well performance is an engineering decision, not just a number
Every well tells a story through its data - and the story changes constantly. Flow rate drifts, pressure declines, water cut creeps up, the gas-oil ratio shifts. Read in isolation, each of these is just a reading. Read together, against the well's expected behaviour and the reservoir it draws from, they tell the production engineer whether the well is healthy, whether it is declining faster than it should, and whether an intervention would pay for itself. The difficulty is that this data lives in different systems - SCADA, well-test databases, downhole gauges, intervention logs - and assembling the full picture for even one well is slow, let alone across a field.
Good well performance reporting brings these sources into one well-health view, runs the decline analysis automatically, and ranks wells by the gap between actual and expected performance - so engineering attention lands where it will recover the most.
The metrics that belong on a well performance dashboard
- Flow rate - oil, gas and liquid rates per well, against the expected curve
- Pressure - wellhead and, where available, downhole pressure trend
- Water cut - the share of water in production, with trend and breakthrough flags
- Gas-oil ratio (GOR) - a leading indicator of reservoir and well behaviour
- Decline rate - actual decline against the type-curve, well by well
- Intervention history - the events and their measured effect on subsequent performance
Identifying wells that need intervention
The hardest part of intervention planning is not deciding how to fix a well - it is deciding which wells to look at, and in what order. A useful well performance dashboard makes that triage automatic: it surfaces wells that have fallen below their decline curve, wells with rising water cut or GOR, and wells whose recent intervention has not delivered the expected uplift. Each pattern points to a different candidate intervention, and seeing them ranked by recoverable volume turns a long candidate list into a prioritised work programme.
Decline-curve analysis and linking well behaviour to the reservoir
A well is a window into the reservoir. When several wells in the same area show the same shift - a step change in GOR, an acceleration in decline, a sudden water breakthrough - the signal is often reservoir-wide rather than well-specific. Well performance reporting that places individual well behaviour in its reservoir context helps engineers tell the difference between a mechanical problem in one well and a reservoir-management issue affecting many, which is the difference between a workover and a change in field strategy.
Comparing wells across fields and basins fairly
Comparing wells is only useful if the comparison is fair. A well in a tight, high-pressure zone and a well in a mature, watered-out zone cannot be judged on the same raw numbers. The dashboards we build normalise well performance for the conditions each well operates in, so cross-well and cross-field comparison highlights genuine under- and over-performance rather than just the underlying geology.
Spreadsheet well tracking vs unified well performance reporting
| Aspect | Spreadsheet tracking | Unified well performance reporting |
|---|---|---|
| Decline-curve analysis | Run occasionally, well by well | Run continuously across every well |
| Intervention targeting | Loudest or most recent well | Ranked by deviation and recoverable volume |
| Reservoir context | Held separately by reservoir team | Well behaviour read in reservoir context |
| Cross-well comparison | Raw numbers, often unfair | Normalised for conditions |
Well performance reporting across operating contexts
Conventional wells
Long-lived wells where gradual decline and water management dominate. Reporting that tracks decline against curve and flags rising water cut early is what extends economic life.
Unconventional and shale wells
Steep early decline and large well counts. Reporting that compares each well against its type-curve, pad by pad, is essential to separating normal early decline from genuine underperformance.
Artificial-lift wells
ESP, gas-lift and rod-pump wells where equipment performance and well performance are intertwined. Reporting that ties lift-system data to well output is critical to distinguishing a reservoir problem from an equipment one.
The Power BI architecture behind well performance reporting
On a typical SolveBI deployment we land SCADA and downhole-gauge data, well-test results, artificial-lift telemetry and intervention records into Microsoft Fabric, then expose a single well-performance model through Power BI. Production engineers see the decline-curve and intervention-candidate view; reservoir engineers see well behaviour in reservoir context; operations sees the live well-health view - all from one Power BI dataset, with consistent type-curve logic applied across the field.
Common mistakes in well performance reporting
- Tracking rate, not decline. Raw output hides the well that has fallen furthest below its curve.
- Ignoring water cut and GOR trends. They are the leading indicators that explain where oil rate is going.
- Wells in isolation. Without reservoir context, a field-wide signal looks like a string of unrelated well problems.
- Unfair cross-well comparison. Different conditions demand normalisation, or the comparison just measures geology.
- No intervention feedback loop. If the measured effect of past interventions is not tracked, the next ones are guesses.
From scattered well data to a ranked intervention programme.
Book a free 30-minute consultation with a Microsoft-certified SolveBI consultant. We'll map your SCADA, well-test and intervention data, agree the right well-performance metrics, and quote a phased Power BI deployment you can budget against.
