A workflow with the objective of quantifying the added value of seismic-based information to improved prediction of oil production in the Wolfcamp section of the Midland Basin is described. A variety of multivariate statistical algorithms are employed in the workflow: unsupervised and supervised classification, categorical-based nonlinear regression, and spatial regression for well log estimation. Porosity is estimated from elastic inversion volumes within the context of lithotype classification originating with well logs. Porosity feet becomes the primary driver in production prediction. Two parallel analyses are performed. In the first, only well logs are used to predict porosity along all vertical wellbores. As a proxy for the impact of well spacing, different minimum search distances are investigated for porosity estimation error and associated error in prediction of cumulative oil production at six months. The second analysis is similar except, along each wellbore, porosity is extracted from the seismic porosity volume and then used as soft data. These two analysis flows are then applied to horizontal wellbores, which have no original porosity logs, to compare how well each predicts known cumulative production. The incorporation of seismic-based information does show significant error reduction.

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