Calibration of Stimulated Reservoir Volume Estimation Using Continuous Wavelet Transform and Advanced Deep Learning with Rate Transient Analysis: A Case Study from the Marcellus Shale

Summary Stimulated reservoir volume (SRV) remains a critical yet difficult-to-measure parameter in unconventional reservoir development, particularly when microseismic monitoring is unavailable. With this study, we present a data-driven workflow that integrates normalized continuous wavelet transform (CWT) analysis of treating-pressure data, a pretrained deep learning (DL) model, and production-based rate transient analysis (RTA) to estimate and calibrate SRV using stimulation-time measurements only. The workflow builds upon prior work that established a CWT-DL mapping between normalized pressure scalograms and relative microseismic displacement vectors and extends it through a true blind, pad-level validation and production-consistent SRV calibration. The proposed methodology is evaluated using publicly available Marcellus Shale Energy and Environment Laboratory (MSEEL) data. A DL model trained exclusively on the MIP-3H and MIP-5H wells is applied, without retraining, to six Boggess pad wells that were excluded from model development. Predicted event clouds are converted into volumetric SRV proxies using geometric estimators, and the results are independently evaluated against SRV derived from a complete RTA workflow, including flow-regime diagnostics and numerical history matching. Across the Boggess pad, pressure-derived SRV estimates show strong agreement with RTA-derived SRV, with well-level differences ranging from approximately 2% to 11%. To enable meaningful interpretation of these differences, coordinate-level prediction errors from the validation data set were quantified and evaluated at the event-cloud geometry level. The observed discrepancies are therefore interpreted in the context of event-cloud prediction uncertainty, geometric sensitivity in cloud-to-volume conversion, and RTA nonuniqueness, rather than from point estimates alone or as a deterministic truth comparison. The results demonstrate that, within basin-consistent geomechanical conditions and comparable completion practices, treating-pressure data can be used to estimate flow-effective SRV in a production-consistent manner. The proposed workflow provides a practical, pressure-only alternative for SRV calibration when recorded microseismic data are unavailable, while explicitly documenting uncertainty sources, limitations, and requirements for data quality and transferability.