Introducing a Ten-Step Integrated Petrophysical Rock Type Verification Process that Combines Deterministic Methods, Saturation Height Modeling, Advanced Flow Units and IPSOM
Authors: Gunter Gary , Sahar Mohamed Yasine , Viro Eduardo , Negahban Shahin , Allen David F. 
Published: November 2018

Abstract
This paper uses case studies to introduce a Ten-Step Integrated Petrophysical Rock Type (PRT) Verification Process that Combines Deterministic Methods, Saturation Height Modeling (SHM), Advanced Flow Units and Independent Probability Self-Organizing Mapping (IPSOM) neural networks. This method was tested in carbonates, sandstones and un-conventional shales. The input data to the method is a core-log integrated porosity, permeability and a pore throat radius indicator based on deterministic methods and mercury injection porosimetry capillary pressure.
Challenges remain for investigators and teams in applying PRT techniques in field studies. The integrated verification 10-Step Method combines several processes into a PRT workflow. These results provide confidence that PRT can be successfully applied for populating 3-D grids in non-cored wells and inter-well areas. Results from this 10-Step method reduces uncertainty and provides a step by step workflow process, that starts with determistic PRT and then applies a IPSOM method verifying the solution.
This workflow process combines deterministic techniques with neural networks using the following steps after core-log integration and deterministic petrophysical rock types are determined (Gunter et.al, 2017). The new process and method is shown using 3 case studies (a sandstone reservoir, carbonate system and an un-conventional shale): The number of PRTs (based on cutoffs) is selected using common statistical analysis approaches (such as Cumulative Distribution Functions, histograms or probability plots).The best PRT grouping is determined from the shape of log computed Sw and Swirr compared to the shape of PRT and pore throat radius indicators in depth space.Further validation of PRT includes comparing results to geological facies and mercury injection capillary pressure (MICP or HPMI), special core analysis results and apply a saturation height model (SHM) to verify the definitions of the Deterministic Petrophysical Rock Types (DPRT). Then repeat the SHM process after the probabilistic PRT are determined in Step 10.Core and Log based thickness-weighted averages are computed and compared for each DPRT.Core-Log X-Y cross plots are prepared for each method.Select a limited number of "PRT training points 1-3" for each of the PRTs as determined in steps 1-4.Apply an IPSOM neural network and Heterogeneous Reservoir Analysis (HRA) then compare predicted probabilistic PRT to initial deterministic PRT in depth space and cross-plot space and repeat until the best statistical results are obtained.Repeat IPSOM neural network analysis using "no training points" and evaluate PRT results.Individual wells can be further verified using Multi-Component Advanced Flow Unit Plots and confirm reservoir flow and storage capacities relate to PRT.Completing a final verification of identifying the "best PRT" includes comparing core-log based saturations with SHM model predicted Sw, free water level and honoring the geological column height based on DPRT.
Results of applying this new method are it improves and refines the PRT process, reduces uncertainty and subjective interpretations. Reducing uncertainity is important, especially when petrophysical rock types are the basis to compute the initial fluid saturations at each grid node and assign dynamic properties such as relative permeability curves in reservoir simulations. This new method provides a verification process that uses both deterministic and probabilistic techniques. These final PRTs are coupled with a saturation height model can be extended to fill 3D volumes and fluid distributions.