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AssigneeExxonMobil Upstream Research Company
InventorChul-Sung Kim
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A method for assessing reliability of a prediction model constructed from N training data attribute vectors and N associated observed values of a specified parameter. Each training data attribute vector includes seismic attributes obtained from seismic data traces located at or near a well and each associated observed value is obtained from well log or core data from the well. A predicted value of the specified parameter is determined for each of the N training data attribute vectors, from the training data attribute vectors and the prediction model. A residual is determined for each of the N training data attribute vectors, as the difference between the associated observed value and the predicted value of the specified parameter for the training data attribute vector. An attribute vector for the designated location is determined. The predicted value of the specified parameter at the designated location is determined, from the attribute vector for the designated location and the prediction model. N basic probability distributions are determined from the N training data attribute vectors, the N associated observed values, the N residuals, and the predicted value. N basic probability assignments are determined for each of three hypotheses that the predicted value is reliable, unreliable, and unpredictable, respectively, from the N basic probability distributions. A reliability value, an unreliability value, and an unpredictability value for the predicted value are determined as combinations of the N basic probability assignments for each of the three hypotheses.
A method for assessing reliability of a predicted value of a specified geological or geophysical parameter at a designated location, the predicted value obtained from a prediction model constructed from N training data attribute vectors and N associated observed values of the specified parameter;
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