Quantifying Petrophysical Uncertainties

Abstract Typical petrophysical deliverables for volumetric and modeling purposes are net reservoir, porosity, permeability, water saturation and contact locations. These data are usually provided without quantitative determination of their uncertainties. Current computing power renders it now feasible to use Monte-Carlo simulation to determine the uncertainty in petrophysical deliverables. Unfortunately, quantitative uncertainty definition is more than just using Monte-Carlo simulation to vary the inputs in your interpretation model. The largest source of uncertainty may be the interpretation model itself. This paper will use a variety of porosity interpretation models to illustrate how the impact of each input on the uncertainty varies with the combination of input values used in any given model. It will show that use of the incorrect model through oil and gas zones may give porosity estimates with Monte-Carlo derived uncertainty ranges that exclude the actual porosity. Core data provides the best means of quantifying actual uncertainty in the petrophysical deliverables. Methodologies for deriving uncertainties quantitatively by comparison with core data will be presented. In the absence of core data, interpretation models should have been tested against core data through the same or similar formations nearby. Monte-Carlo simulation can then be used as an effective means of quantifying petrophysical uncertainty. Comparisons between the core comparison and Monte-Carlo techniques will be made, showing that similar results are achieved with the appropriate interpretation models. The methodologies described in this paper are straightforward to implement and enable petrophysical deliverables to be treated appropriately in volumetric and modeling studies. In addition, quantification of petrophysical uncertainty assists in operational decision-making by letting users know how reliable the numbers produced actually are, and what range of properties is physically realistic. Such work also allows the key contributions to uncertainty to be defined and targeted if overall volumetric uncertainty must be reduced.