A completion engineer designs a perforation plan with even cluster spacing, uniform phasing, and a limited entry pressure calculation that looks correct on paper, and then the well comes online producing from a fraction of the clusters that were actually perforated. The frac treatment went in as planned, the pumping data looked normal, and nothing in the surface record explained why some clusters took most of the fluid and proppant while others took almost none. The answer usually sits in near-wellbore stress variation the original design never accounted for, and it only becomes visible once fiber optic data from the completed well is compared back against the plan. You can see how that comparison gets built into the design process itself by choosing to book a demo with our team.
Half Your Perforation Clusters May Not Be Contributing to Production, and Standard Design Won't Tell You Which Half
Even spacing and uniform phasing assume every cluster along a lateral sees the same stress environment, but near-wellbore stress shadowing and heterogeneous rock properties routinely mean some clusters dominate fluid uptake while others barely open. iFactory uses DTS and DAS fiber optic data from completed wells to measure actual cluster efficiency and feed it back into the next design.
Limited Entry Design Assumes Uniformity the Rock Rarely Provides
Limited entry perforation design calculates the number and size of perforations per cluster needed to force roughly even fluid distribution across all clusters in a stage, based on a target pressure drop across the perforations relative to the fracture propagation pressure. The math behind that calculation is sound, but it depends on an assumption that stress and rock properties are reasonably uniform along the lateral, which is frequently not the case in heterogeneous unconventional reservoirs.
Near-wellbore stress shadowing between adjacent clusters, natural fracture networks, and lithology changes across even a single stage can all cause fluid to preferentially enter a subset of clusters regardless of how carefully the limited entry pressure was calculated. A design that looks perfectly balanced in the completion plan can still produce a stage where three of six clusters absorb most of the treatment, leaving the reservoir volume around the other three essentially unstimulated.
What DTS and DAS Actually Show About Cluster Performance
Distributed temperature sensing and distributed acoustic sensing measure different physical signals but both reveal the same underlying reality: which clusters are actually taking fluid during the treatment and which are effectively bypassed. Reading them correctly, and reading them together rather than in isolation, is what turns raw fiber data into a design correction.
See What Your Fiber Data Is Already Telling You About Cluster Efficiency
iFactory turns raw DTS/DAS traces into cluster-by-cluster efficiency scores you can compare directly against the original design.
Closing the Loop From Completed Well Back to Next Design
Fiber diagnostics have existed for years, but the value most operators actually capture from them stops at a post-job report showing which clusters underperformed on the well that was just completed. The design used on the next well in the pad is rarely adjusted systematically based on that data, because turning a fiber trace into a specific perforation density, phasing, or limited entry pressure change requires a modeling step most completion teams do not have time to run manually on every well.
Uniform Design Versus Fiber-Informed Design
| Design Approach | Uniform Limited Entry Design | Fiber-Informed AI Design |
|---|---|---|
| Cluster Spacing Basis | Even spacing assuming uniform stress environment | Spacing adjusted for measured stress shadowing pattern from offset wells |
| Phasing Selection | Fixed phasing angle applied across all stages | Phasing correlated to cluster efficiency results by lithology zone |
| Limited Entry Pressure | Calculated once from average rock property assumptions | Recalculated per stage against zone-specific rock property estimates |
| Feedback From Completed Wells | Post-job report reviewed manually, rarely changes next design systematically | Cluster efficiency data feeds directly into offset well design parameters |
Where Cluster Efficiency Programs Lose Value
Cluster Efficiency Data Also Informs Refrac Candidate Selection
The same cluster-level contribution data used to correct the next offset well's perforation plan is also one of the more reliable inputs for identifying refrac candidates on older wells in the same field. A well where fiber data or production logs suggest a large fraction of original clusters never contributed meaningfully to flow is a fundamentally different refrac case than a well where all clusters were efficient but the reservoir has simply depleted, and the two cases call for very different treatment designs.
Building a field-level library of cluster efficiency patterns by lithology zone, rather than treating each well's diagnostic data as a one-off report, is what makes this distinction possible at scale. Over time this library becomes as useful for refrac economics as it is for original completion design, since it lets an engineer estimate incremental recovery potential before committing capital to a re-stimulation program.
Cluster Efficiency Insight Crosses Three Roles on the Completions Team
Questions Completion Engineers Ask About Cluster Efficiency Optimization
Stop Designing the Next Well on the Same Uniform Assumptions
iFactory turns fiber diagnostics from a post-job report into a systematic input for every offset well's perforation design.
