The Cretaceous carbonates of Sarvak formation formed large hydrocarbon reservoirs in the South-west region of Iran. The studied field is a tight carbonate reservoir in which several exploration wells have been drilled, and is in the process of development. Since, only few wells have core data, therefore it was decided to integrate the available core and log data using new methods that describe the carbonates heterogeneity more precise. 3D modeling of permeability is an essential part of building robust dynamic model for proper reservoir management and making reliable predictions. A good definition of reservoir rock types (RRT) could relate somehow better geological modes to dynamic models. Rock typing by flow-zone-index (FZI) and rock-quality-index (RQI) values proved to be an effective technique to develop porosity-permeability transforms for RRTs in a reservoir model. RRTs were defined based on the core derived FZI through some mathematical and statistical approaches. Permeability estimation using artificial neural network approach (ANN) was then made through a two-step process. In the first step, FZI log was estimated from a trained neural network using the standard suite of logs as input (Gamma ray, Sonic, Density, Neutron porosity) and FZI-core as output in a subset of cored wells (Key wells). In the second step, individual trained neural networks implemented porosity-log and FZI-log from the first step to predict permeability-log for each RRT. Validation of the predictive capability of the method in two cored wells (Blind-test wells) that are located in the field proved the estimation technique to be robust and was found to be valuable to supplement core data in the prediction of log-permeability in the entire reservoir wells. For the sake of comparison between the result of this work and the work which was based on the integration of sedimentological, petrographical, and diagenetic study, the results were found to be in good agreement for most of the log interval. However, the predictions of the ANN approach in the regions where core data are not available are better and it follows the log property variation logically. Introduction Rock typing is a process for the classification of reservoir rocks into distinct units. These units are deposited under similar geological conditions and undergoes through similar diagenetic alterations. If the rocks are properly classified and defined, a given rock type is imprinted by a unique porosity/permeability correlation, capillary pressure profile, and set of relative permeability curves. In addition, the true dynamic characteristics of the reservoir will be captured in the reservoir simulation model as a more reliable permeability model is used1–3. On the classical plot, the relationship between permeability and porosity is not causal. Whereas porosity is generally independent of grain size, permeability is strongly dependent on grain size. Hence, traditional plot cannot be used reliably to estimate accurate permeability from porosity. Several investigators1–4 have noted the inadequacy of classical approach and have proposed alternative models for relating porosity to permeability specifically for using in carbonates reservoir. From the classical approach it can be concluded that for any given rock type, the different porosity/permeability relationships are evidence of the existence of different hydraulic units. In fact, several investigators5–7 had come to similar conclusions about porosity/ permeability relationships.

TX 75083-3836, U.S.A., fax +1-972-952-9435. AbstractPermeability is one of the crucial parameters in dynamic reservoir modeling and simulation. Direct measurement of permeability through coring and wireline formation testing is expensive and sometimes hard to achieve. In addition, the interval of coring is always limited. In this study available core and wireline log data of a heterogeneous carbonate reservoir located in sarvak formation of Iran are used to predict permeability not only for the cored but un-cored wells too. To reach this purpose, the concept of rock typing has been taken into consideration to overcome the problem of heterogeneity. Flow zone index (FZI) approach is selected to determine the rock types of the reservoir understudy. Mathematical manipulation is then used to transform the continuous FZI values to discrete ones known as discrete rock types (DRT). Wireline log data corresponde to each DRT are individualized and subjected to statistical analysis to find their influence on the process of permeability prediction. Gamma ray, sonic, density and neutron porosity logs have been chosen as input parameters for building artificial neural network (ANN) models for permeability prediction. An individual ANN model is constructed for the process of estimating the permeability for each DRT. The result of permeability prediction using this technique is highly satisfactory but dependent on the successful prediction of FZI in uncored intervals/wells. Fuzzy logic is the approach that was used for estimating the FZI by wireline logs data. Applying fuzzy logic provided accurate predicted FZI logs for uncored wells. By deriving DRTs from the FZI log, relevant built ANN models for each DRT might be used for predicting permeability. Validation of the predictive capability of the method in two cored wells (Blind-test wells) proved the estimation technique to be robust. For the sake of comparison, permeability-effective porosity transform and multilinear regression are applied for permeability prediction of the reservoir understudy. Results of applying these methods are considerably less than the results achived in this work.

Accurate distribution of geological and petrophysical properties such as facies, porosity and water saturation in carbonate reservoir is an essential part of building robust static and dynamic models for proper reservoir management and making reliable decisions. An integration based approach is applied for the prediction of the essential reservoir properties using well logs and 3D seismic data. The method is based on the derivation of electrofacies classes using Artificial Neural Networks (ANN) method and Geostatisticsl Conditional Simulation (GCS) Approaches. The electrofacies of the carbonate reservoir of late cretaceous understudy, which is located in the south west of Iran, were classified using the unsupervised ANN method. Based on the verification of cross plots of input logs (GR, DT, RHOB, NPHI, and LLD) versus electrofacies classes, seven rock types including one class as shale rock and others sex classes as argillaceous, dense, poor, moderate, good and best limestone rocks were identified. The unsupervised approach has provided unbiased classes of electrofacies that cover the vertical variations of the well logs very well and also provided a very fine correlation for the entire reservoir. Based on the 3D seismic data, nine seismic volume attributes were calculated. The most correlative attributes for prediction each reservoir properties were selected based on the correlation coefficient investigation. At this stage, the ANN was used to create 3D model of electrofacies, effective porosity and water saturation based on the associated 3D seismic derived attributes. These seismic derived reservoir properties were used as secondary variable in collocated cokriging equation through the GCS. The 3D simulation of electrofacies represented similar seismic attribute responses, which was interpreted geologically, resulted in good consistency with sedimentary directions and conditions of the understudy area. In conclusion, the seismic based 3D simulations of electrofacies, effective porosity and water saturation generated using ANN's and GCS have provided geologically more meaningful information about the lateral facies variations and reliable properties distributions in this carbonate reservoir. Introduction Multi-disciplinary geostatisticsl techniques for integrated reservoir characterization in various types of reservoir depositional environments have received a great interest in the industry 1–4. Advances are made in better utilization of seismic data for generating interwell data and reservoir information where well data is non-existent 5–7. Although seismic does not have the vertical resolution of well logs, its aerial sampling coverage is dense and regular, providing details of reservoir unreachable by wells 8. In this study, an attempt has been made to integrate 3D seismic attributes with well logs to make reliable 3D models of petrophysical properties. The understudy field was discovered in 1975 and it is in the process of first stage production. The field, with the area of 17 km in Northwest-Southeast and 14 km in Northeast-Southwest, is an under-saturated oil reservoir. The master development plan study for this field has been started in 2007. Six wells have been drilled and few wells are currently under operation. At First, geological and petrophysical meaningful electrofacies classes were identified through unsupervised Artificial Neural Networks (ANN). Then 3D seismic attributes extracted from the high quality post stacked 3D seismic cube were calibrated with interested petrophysical properties. 3D seismic derived petrophysical properties were modeled by using the most correlative attributes through ANN method. Finally, high resolution 3D models of electrofacies, effective porosity and water saturation were produced using well logs data and associated 3D seismic derived information as a secondary variable through the Goestatistical Conditional Simulation (GCS). These 3D simulations of static properties of this field will be used as input to the dynamic modeling of the fluid flow.

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The studied field, located offshore Sabah, Malaysia is composed of a succession of thick-bedded sand lobes, thin-bedded heterolithics and shale dominated mass transport deposits (MTDs). While the thinly bedded units contain significant hydrocarbon reserves, the distribution and continuity of sand bodies within these units cannot generally be derived from well data alone. Modern analogues suggest that these thin beds are likely composed of small scale sandy lobes embedded into a shale background. In order to better understand the vertical and lateral connectivity of these turbiditic elements, a numerical model integrating well data and seismic inversion results was built, constrained by a conceptual depositional model based on analogue data. Multi-point geostatistics (MPS) were used as a platform to combine geological knowledge - in the form of a realistic training image, well measurements and geophysical trends. In order to reproduce observed facies proportions and architecture, the training images were generated using a novel user-guided, semi-automated workflow based on object-distance simulation. Reproducing the vertical heterogeneity of the model observed in thinly bedded units while retaining the lateral variability evidenced by seismic inversion was achieved by creating first set of facies probability cubes from the seismic inversion results using supervised neural-network estimation, then refining these probabilities by calibrating them to vertical proportion curves extracted from high-resolution facies logs. This technique enabled reproducing complex depositional patterns such as compensational stacking and hierarchical distribution of facies. It also allowed sedimentary bodies directly visible on the seismic inversion results to be integrated explicitly into the facies model. The simulated facies distribution was then used to constrain petrophysical property population, yielding detailed and realistic dynamic models. While the presented approach is specifically tailored to the modeling of thinly bedded deep water environments, the innovative techniques proposed for generating a "photorealistic" training image and for integrating seismic-scale results with high resolution well data could also be used for representing a varied set of depositional settings.