Alberta Operator Comparison Matrix

How to Use This Matrix

This comparison matrix gives you a repeatable way to evaluate multiple Alberta operators side by side. Instead of relying on intuition or scattered notes, you fill in each column using verified AER data and your own assessment criteria. The result is a ranked list that tells you which operators to pursue first, which to monitor, and which to deprioritize.

Use this alongside the account qualification framework for a deeper scoring methodology, and the well research checklist to gather the data that feeds each column.

Matrix Structure

The comparison matrix has eight columns. Fill in each column for every operator you are evaluating. The columns are designed to capture both quantitative data from public records and qualitative assessments from your research.

Scoring Methodology

Assign a score from 1 to 5 for each of the following factors. Add them together for a composite priority score out of 25. Higher scores indicate operators worth pursuing first.

Score each factor as 1, 2, 3, 4, or 5. Values of 2 and 4 fall between the thresholds above. Add the five scores for a composite out of 25.

Priority Tiers

Use the composite score to group operators into tiers:

• Tier 1 (20–25 points): Pursue immediately. These operators have strong activity, fit your service profile, and are within your reach. Assign your best rep and prepare a custom proposal.
• Tier 2 (14–19 points): Worth active outreach. These operators show solid potential but may have gaps in one factor. Build a relationship and monitor for improving signals.
• Tier 3 (8–13 points): Monitor periodically. These operators may become relevant as conditions change. Include in your watchlist but do not prioritize sales effort.
• Tier 4 (5–7 points): Deprioritize. These operators are a poor fit or have insufficient activity. Remove from active lists and revisit if data changes.

Example: Completed Matrix Entry

Below is a sample entry for a hypothetical operator evaluated by a frac service company based in Grande Prairie:

Example: Side-by-Side Comparison

When comparing multiple operators, the matrix reveals clear differences:

Common Mistakes to Avoid

• Scoring without data: Fill in each column using verified AER records before assigning scores. Guessing defeats the purpose of a structured comparison.
• Treating all columns equally: Service fit and regional fit often matter more than raw well count. Weight the factors that matter most to your business when calculating the composite score.
• Never updating the matrix: Operator activity changes. Re-run the comparison quarterly or when you observe significant data shifts.
• Ignoring accessibility: A perfect-score operator who cannot be reached is not a realistic prospect. The accessibility column keeps expectations grounded.
• Comparing across different service types: A matrix comparing operators for frac services will look different from one comparing operators for environmental remediation. Build separate matrices for distinct service lines.

Reading the Matrix Along the Well Lifecycle

The matrix is not just a static snapshot — it maps onto the well lifecycle. Operators weighted toward active, producing wells need production services, artificial lift, and workover support. Operators weighted toward inactive and suspended wells need recompletion or decommissioning work. Operators with large abandoned inventories need reclamation and P&A. By splitting the well count by status (active, inactive, suspended, abandoned) in the matrix, you see which service line each operator actually feeds, rather than chasing a single blended number.

Weighting for Your Service Line

A frac company should weight the Service Fit and Recent Activity columns most heavily, because completion demand comes from drilling and new licences. An environmental contractor should weight the abandoned and suspended counts, because reclamation demand follows end-of-life wells. A wireline or coiled tubing provider should weight active ratio and workover signals. Use the account qualification framework to apply consistent weights, and the well status interpretation guide to decode each status code before you score.

Combining the Matrix With Territory Planning

Once you have ranked operators, overlay them on a map. The territory planning worksheet shows operator density per region. An operator that scores Tier 1 but sits four hours from your yard may rank below a Tier 2 operator next door. Fold drive time and well density into the final ranking so the matrix reflects real-world economics, not just well counts.

Where to Find the Data

Most columns in the matrix draw from AER ST37 well records, which include operator names, well counts, status classifications, location fields, and spud dates. Production data from Petrinex adds context about operator activity trends. Accessibility ratings come from field intelligence, industry referrals, and direct research.

FracturingHub organizes AER data into searchable operator profiles, making it faster to fill in the well count, status ratio, regional presence, and activity columns without manual spreadsheet work. See how it works for an overview, or check pricing and plans for export and research features.

For more resources, visit the resources page.