Uncertainty in Reservoir Parameter Estimation From Attributes

Swan, Herbert¹, Rod K. Nibbe², Michael Faust³, Brian Russell⁴, Dan Hampson⁵ (1) ConocoPhillips Alaska Inc, Anchorage, AK (2) formerly ConocoPhillips Alaska Inc, Anchorage, AK (3) ConocoPhillips Alaska Inc, Anchorage, (4) Hampson-Russell, a Veritas Company, Calgary, AB (5) Hampson-Russell Software Services Ltd, Calgary, AB

Seismic attribute calibration is a commonly used procedure that identifies linear relationships between seismic interval attributes and an averaged reservoir property, e.g. porosity, to predict that property away from the wells. This paper focuses on criteria that may be used to select which attributes should be used, and on a method to quantify the statistical uncertainty of the resulting estimates, assuming the attributes are normally distributed.

The standard criteria used to select an optimal set of attributes are the correlation coefficient between predicted and measured properties, the maximum prediction error at a single well, and the root mean-square error at all the wells. These standard criteria are important and valid but ignore the likelihood that one or more chosen attributes are in fact uncorrelated from the reservoir property, despite an apparent (albeit spurious) correlation with reservoir samples from a limited set of wells. Therefore, the standard criteria must be balanced against the probability of spurious correlation when weighing the various attribute combinations.

The standard criteria also fail to quantify the inherent uncertainties of the predicted properties estimate. This can be accomplished by computing a map of the prediction interval, which displays the ranges of uncertainty of the prediction to a particular level of significance. Combinations of attributes never seen at the wells (e.g., at a gas cap or facies change) result in more uncertainty and correspondingly wider prediction intervals. Attribute combinations that are highly correlated with each other also yield larger prediction intervals.

Examples of the successful application of this technique will be shown from the Alpine field of Alaska and its Nanuq and Fiord satellites.