Hasan A. Nooruddin

Using soft computing techniques to predict corrected air permeability using Thomeer parameters, air porosity and grain density

Soft computing techniques are recently becoming very popular in the oil industry. A number of computational intelligence-based predictive methods have been widely applied in the industry with high prediction capabilities. Some of the popular methods include feed-forward neural networks, radial basis function network, generalized regression neural network, functional networks, support vector regression and adaptive network fuzzy inference system. A comparative study among most popular soft computing techniques is presented using a large dataset published in literature describing multimodal pore systems in the Arab D formation. The inputs to the models are air porosity, grain density, and Thomeer parameters obtained using mercury injection capillary pressure profiles. Corrected air permeability is the target variable. Applying developed permeability models in recent reservoir characterization workflow ensures consistency between micro and macro scale information represented mainly by Thomeer parameters and absolute permeability. The dataset was divided into two parts with 80% of data used for training and 20% for testing. The target permeability variable was transformed to the logarithmic scale as a pre-processing step and to show better correlations with the input variables. Statistical and graphical analysis of the results including permeability cross-plots and detailed error measures were created. In general, the comparative study showed very close results among the developed models. The feed-forward neural network permeability model showed the lowest average relative error, average absolute relative error, standard deviations of error and root means squares making it the best model for such problems. Adaptive network fuzzy inference system also showed very good results.

Field Application of a Modified Kozeny-Carmen Correlation to Characterize Hydraulic Flow Units

Hydraulic Flow Unit (HU) has been used extensively as a technique in permeability modeling and rock typing. Amaefule et al. (1993) introduced for the first time the concept of Reservoir Quality Index (RQI) and Flow Zone Indicator (FZI) by using the Kozeny-Carmen (K-C) model to characterize HU and predict permeability in uncored wells and intervals. This technique has helped in enhancing the capability to capture the various reservoir flow behavior based on its respective characters. Yet, there are challenges in using the original correlation due to its inherent limitations and over simplified assumptions that prevent accurate HU definitions. This study highlights some of those shortcomings and proposes a modified K-C correlation that enhances the HU characterization. It is found that the conventional K-C model ignores the inherent nonlinear behavior between the tortuosity and porosity. Hence, handling the tortuosity term in a more representative manner demonstrates a more rigorous correlation that extends the applicability of this powerful technique into more heterogeneous rocks such as those found in carbonate reservoirs. This paper presents a reservoir simulation case study that is conducted to validate the applicability of the proposed model as a rock typing technique in a heterogeneous carbonate reservoir in the Middle East region. Relative permeability curves, Leverett J-Function curves and initial water saturation distribution show good agreement within each HU generated using the proposed model. It is recognized that modified Kozeny-Carmen technique give better matching of initial water saturation model than the conventional technique when compared to open-hole logs which, in turn; adds confidence to initial hydrocarbon-in-place calculations and reservoir behavior predictions. This result will ultimately enhance the prediction of reservoir performance under various scenarios in reservoir simulation. Introduction Several types of porosity-permeability transforms are available to determine permeability from well log-derived porosity in uncored oil and gas wells. Typically these transforms put emphasis on lithology or facies during reservoir characterization. In addition, several pore system dependent techniques are also published. Shenawi et al. (2009) have described commonly used porosity-permeability transform types, such as: i) transforms by facies, ii) by Winland technique, iii) by pore geometry, and iv) by geologic zones. Transforms by hydraulic unit approach provide way better results than those typical transforms. Rock typing by hydraulic units can be defined as units of rock that have unique porosity-permeability relationship, capillary pressure profiles and relative permeability curves. It has many applications in reservoir characterization and simulation studies. Once the rock typing is done properly, it can lead to a reliable estimation of the permeability in the uncored wells, accurate generation of initial water saturation profiles and consequently, reliable reservoir simulation studies. (Davies et al., 1996; Guo et al., 2005; Shenawi et al., 2007) 2 [SPE 149047 /SAS 1074] Amaefule et al. (1993) HU Characterization Technique In 1993, Amaefule et al. introduced for the first time the concept of reservoir quality index (RQI) and flow zone indicator (FZI) to identify HU based on the K-C model. In this regard, Amaefule’s technique is recognized as a very simple, practical, and widely used established technique. This well-known approach classifies rock types using the original K-C model. The popular form of the original K-C model is given by: