Adaptive Target Setting in Completion Operations: A Multi-Method Data-Driven Framework for Fleet Performance Optimization

Abstract Digital transformation in well completions has created an opportunity to reframe how fleet performance is evaluated and improved. Traditional methods often rely on static benchmarks or subjective assessments, which do not adequately reflect the operational variability or evolving field conditions. This study develops a modular framework for standardized fleet-level performance evaluation, leveraging monthly scorecards to ensure consistent metric tracking across operations. It also introduces an adaptive target-setting methodology that dynamically aligns performance goals with each fleet's operational capacity and execution profile, independent of geographic region, geological setting, or stimulation design. The framework integrates historical operational data with a set of algorithmic techniques to define dynamic, context-aware performance benchmarks. A selected suite of Key Performance Indicators (KPIs) is used to assess performance across operational execution, equipment reliability, personnel consistency, power usage, and commercial efficiency. For each KPI, the system evaluates temporal trends, cross-fleet variability, and signal stability using statistical summaries and diagnostic tools. Rather than adopting a one-size-fits-all approach, the framework applies to multiple modeling strategies based on metric behavior. These include rolling statistics, trend modeling, smoothing techniques, and clustering-based methods. Metrics are analyzed using attributes such as coefficient variation and interquartile distribution to determine whether universal or fleet-specific targets are more appropriate. A classification layer helps assign the right target-setting logic, pairing peer benchmarking with localized historical analysis where needed. Gaussian Mixture Models are used to segment KPI distributions into distinct performance bands, which provide structure to how targets are anchored and allow performance to be interpreted in terms of realistic expectations. The framework is designed to update monthly, ensuring that targets remain aligned with evolving operational baselines without requiring manual recalibration. While deployment is underway, retrospective analysis across 45 fleets and over a dozen KPIs demonstrates the potential impact of this approach. Historical application of the methodology suggests that adaptive targeting could significantly improve target attainment while reducing the risk of misaligned expectations. Preliminary results also show promise in segmenting fleets into meaningful performance groups to better direct managerial attention and resource allocation. Importantly, the framework supports transparency in how targets are set and enables more consistent evaluation across diverse operational settings. This approach advances performance management by embedding adaptive logic into a field-ready system. By combining statistical insight with operational context, the system enables data-informed decision-making without requiring end users to interpret complex models. More importantly, it supports organizational alignment by grounding performance conversations in transparent, data-backed targets. The framework also promotes sustainable change management by fostering fairness, clarity, and continuous improvement. As companies scale digital adoption in completions, this model offers a repeatable path for turning operational data into targeted, actionable strategies.