Contact Sensitivity to Cutoffs and Petrophysical Assumptions: A Reproducible Method to Stress-Test STOIIP in Marginal Fields

Abstract

In marginal fields, STOIIP (Stock Tank Oil Initially In Place) uncertainty often concentrates around two linked issues: where the hydrocarbon contact sits and how pay is defined through cutoffs and petrophysical assumptions. Small shifts in water saturation cutoff, net-to-gross criteria, or assumed contact depth can move STOIIP enough to change development decisions, yet many evaluations report a single contact and a single cutoff set without quantifying sensitivity. This paper presents a reproducible workflow to stress-test STOIIP against cutoff and petrophysical assumption uncertainty while keeping the analysis transparent and auditable. The method begins by defining a controlled "assumption set library, including alternative cutoff sets such as shale volume, porosity, and water saturation thresholds, alternative saturation models where applicable, and alternative contact interpretations based on available evidence (pressure gradients, test data, resistivity trends, and completion outcomes). Each assumption set is treated as a named scenario with documented rationale, rather than an informal adjustment. For each scenario, the workflow computes net reservoir, net pay, average properties, and STOIIP using the same calculation engine and data inputs, ensuring changes in outcomes trace directly to the assumption changes. Sensitivity is quantified in three ways: (1) one-at-a-time cutoff sweeps to expose non-linear behavior, (2) paired sweeps that reflect real coupling (e.g., Sw cutoff with porosity cutoff), and (3) Monte Carlo runs where the contact depth and key petrophysical parameters vary within constrained ranges. Outputs include STOIIP response surfaces, tornado ranking of the dominant assumption drivers, and a compact decision risk table showing which assumptions can flip economic viability. A case study demonstrates how the workflow highlights hidden fragility in a base-case STOIIP estimate and guides targeted data acquisition decisions, such as which pressure points, logs, or tests would most reduce uncertainty per unit cost. The paper concludes with recommended reporting standards for marginal-field evaluations, including minimum sensitivity checks that should accompany any STOIIP number presented for investment decisions.