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Quantifying Karst-filling Previous HitAnhydriteNext Hit Distributions from Pore to Log Scales; San Andres Formation, Permian Basin Part 2: Petrophysical Deterministic and Statistical Prediction of Previous HitAnhydriteNext Hit

H. F. El-Sobky, A. É. Csoma, R. M. Phelps, and R. M. Ostermeier
ConocoPhillips, Houston, TX, USA

Accurate quantification and modeling of Previous HitanhydriteNext Hit distribution is crucial for proper hydrocarbon flow simulation and production. Depositional Previous HitanhydriteNext Hit and Previous HitanhydriteNext Hit cements may represent persistent flow barriers to significant accumulations of hydrocarbons, which might be the case in many parts of the Permian Basin. Petrophysical determination of the Previous HitanhydriteNext Hit volume is not an easy task especially when the well log resolution is greater than the Previous HitanhydriteNext Hit variation. Two main integrated techniques are investigated to quantify and estimate Previous HitanhydriteNext Hit distribution; a petrophysical deterministic technique and an advanced statistical technique.

Routine core analysis XRD data is used to build a deterministic model for Previous HitanhydriteNext Hit volumetric estimations. Four-component petrophysical model is found to be appropriate for defining the petrophysical endpoints, where total porosity, volumes of dolomite, clastics, and Previous HitanhydriteNext Hit are the four major components that have been selected. Three main petrophysical measurements are used to solve for these components, which are density, cross-sectional area and thermal neutron response. The validity of the 4-component petrophysical model is largely verified through reconstruction of the thermal neutron porosity log utilizing that model.

Core Image segmentation (CIS) analysis is used to detect the Previous HitanhydriteNext Hit abundance based on an object-oriented classification techniques for three cored wells. A number of advanced statistical techniques such as multivariate alternating conditional expectation (ACE) and modular neural network (MNNT), are used to estimate Previous HitanhydriteNext Hit from well logs and results are compared to the CIS output. Both techniques are considered as iterative nonlinear schemes for data pattern recognition. The results from these analyses clearly show that MNNT is superior to both ACE and deterministic petrophysical techniques to estimate Previous HitanhydriteTop abundance in this Permian Basin.

 

AAPG Search and Discovery Article #120034©2012 AAPG Hedberg Conference Fundamental Controls on Flow in Carbonates, Saint-Cyr Sur Mer, Provence, France, July 8-13, 2012