Stochastic time-depth conversion of seismic horizons by geostatistical tools to produce probabilistic models of gross rock

Paul Gibb, Petrosys | Interica; Pedram Masoudi, Geovariances (a Datamine company, France); Ryan Mooney, Petrosys | Interica

ABSTRACT

Exploration of frontier petroleum systems requires robust estimation of subsurface geometry and associated uncertainties. Traditional deterministic workflows, interpretation, velocity modelling, time-depth conversion, and volumetrics, provide a single best estimate outcome, but do not fully characterise uncertainty propagation from seismic interpretation and sparse well control into prospect risk. We present an integrated stochastic workflow using geostatistical methods (Bayesian kriging with external drift and multiple-realization simulation) to produce probabilistic depth horizons and probabilistic gross rock volume (GRV) estimates. This quantifies the range of plausible outcomes and highlights where new data could most effectively reduce uncertainty.

Keywords:

stochastic workflow ,probabilistic depth horizons, probabilistic gross rock volume (GRV),probabilistic volumetric analysis, spill-point