Fracturing Bottomhole Pressure Comparative Study: Automated Data-Driven Algorithm vs. Downhole Gauge vs. Manual Calibration

Accurate bottomhole pressure (BHP) estimation is pivotal for the success of hydraulic fracturing treatments, affecting both operational safety and stimulation effectiveness. In the absence of downhole gauges, engineers typically rely on surface pressure readings combined with frictional and hydrostatic components. However, traditional workflows that use lab-derived fluid friction tables and generalized proppant multipliers fail to capture the well-specific dynamic behavior during treatment. In this study, we benchmark three BHP estimation approaches: (1) a default method using static lookup tables from the flow loop tests, (2) a manually calibrated method using downhole gauge data and engineering judgment, and (3) an automated, data-driven methodology based on real-time ISIP detection and machine learning-predicted friction components. The evaluation spans three fluid systems, crosslinked gel, linear gel, and slickwater. The dataset consists of vertical wells instrumented with pressure gauges. Results show that the automated approach outperforms both traditional methods in accuracy and consistency. For crosslinked fluids, it reduced RMSE and MAPE errors by 35–55% compared to manual and default workflows. In slickwater treatments, the performance of all methods was comparable; however, the automated system demonstrated better capture of near-wellbore complexity without human intervention. The study also explores abnormal transient pressure phenomena post shut-in and highlights the critical dependence of ISIP quality on accurate water-hammer signal analysis. Overall, the automated approach enables scalable, objective, and field-calibrated BHP estimation that eliminates the need for extensive human subjectivity, manual labor, which at best averages the errors from multiple datasets through interpolation, and at worst, is erroneous due to faulty methods and assumptions. The automated approach treats the well for its individual observations which can be unique.