Cost-Effective Geochemical Workflows for Reservoir Connectivity and Production Allocation Using Mud Gas & Geochemical Fingerprints: Part I – Mud Gas.

Characterizing vertical drainage in unconventional reservoirs (shale gas, tight oil, tar sands, and coal bed methane) remains a major challenge due to the lack of cost-effective and reliable methods for identifying contributions from individual producing intervals. In addition, static reservoir models often fail to capture the extreme heterogeneity, complex mineralogy, ultra-low permeability, and compartmentalization that characterize these systems.

This study presents an integrated geochemical workflow that combines mud gas isotope data with molecular hydrocarbon compositions extracted from drill cuttings to characterize vertical connectivity, identify reservoir compartments, and improve static reservoir models. Stable carbon (δ¹³C) and hydrogen (δD) isotopes of methane, ethane, and propane were integrated with alkylbenzene and biomarker fingerprints acquired during drilling in vertical and horizontal wells. These high-density datasets were analyzed using exploratory data analysis (EDA) and unsupervised multivariate statistical methods to identify geochemical fingerprints and evaluate reservoir connectivity.

The workflow successfully identified reservoir compartments, allocated production to individual intervals, and detected previously unrecognized flow barriers. Incorporating these geochemical signatures into static models refined compartment boundaries, improved estimates of connected hydrocarbon volumes, and supported optimized well placement and completion design. The methodology was validated in both unconventional and conventional reservoirs, demonstrating a rapid and cost-effective alternative to techniques such as microseismic monitoring, fiber-optic sensing, and chemical tracers. Overall, the workflow provides valuable input for static reservoir modeling and supports early field development by improving reservoir characterization while reducing cost.

The first part of this study focuses on mud gas molecular composition and stable gas isotopes (δ¹³C and δD of methane, ethane, and propane) acquired during drilling. These high-resolution datasets provide continuous geochemical information that enables the identification of reservoir compartments, vertical connectivity, and fluid migration pathways. The results establish the geochemical framework for the integrated workflow presented in this study.