Cost-Effective Geochemical Workflows for Reservoir Connectivity & Production Allocation: Part II – Cuttings-Based Geochemical Fingerprinting

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

This study presents an integrated geochemical workflow that combines continuous mud gas analysis with high-resolution molecular fingerprinting of hydrocarbons extracted from drill cuttings to characterize reservoir connectivity, identify reservoir compartments, quantify production contributions, and improve static reservoir models.

The first part of the workflow utilizes mud gas molecular composition together with stable carbon (δ¹³C) and hydrogen (δD) isotopes of methane, ethane, and propane acquired during drilling in vertical and horizontal wells. These continuous, high-density datasets provide detailed information on vertical reservoir variability, fluid migration pathways, and compartmentalization. Exploratory data analysis and unsupervised multivariate statistical methods (PCA, HCA) were applied to identify distinct geochemical domains and establish the reservoir connectivity framework.

The second part focuses on molecular geochemical fingerprinting of solvent-extracted hydrocarbons from drill cuttings. Alkylbenzene distributions and biomarker compositions were used to establish interval-specific geochemical fingerprints that serve as reservoir end members (EMs). Unlike conventional production allocation approaches that require produced oils from isolated reservoir zones, this workflow generates reservoir-specific end members directly from cuttings acquired while drilling. These cuttings-derived fingerprints were subsequently compared with produced oils from surrounding wells using multivariate statistical analysis and Geolog’s geochemical unmixing algorithms to quantify the contribution of individual reservoir intervals to commingled production, identify dominant producing zones, and detect vertical drainage and cross-flow between stacked reservoirs.

The integrated workflow successfully identified reservoir compartments and quantified interval-specific production contributions using cuttings-derived end members (EMs). The approach provides a rapid, cost-effective reservoir characterization solution validated in both unconventional and conventional reservoirs.