Identifying RESERVOIR rock types and their most significant vertical and horizontal heterogeneities is an essential component of RESERVOIR CHARACTERIZATION process, which are among the key input parameters in to a three-dimensional geological and flow simulation models. A RESERVOIR classification and rock typing study were carried out on the Asmari formation of a mixed siliciclastic and carbonate RESERVOIR in Iran. Detailed core analysis data including capillary pressure, core porosity, core permeability and core description supplemented by well logs revealed a complete vertical sequence of seven distinct clastic and carbonate RESERVOIR rock types. Identification of the RESERVOIR intervals and pay zones was carried out by means of the above results. Core based RESERVOIR rock types were examined for each cored wells and log based RESERVOIR rock types were selected and assigned in the uncured wells. The above data were applied as input parameters in a method based on Fuzzy Logic inference. The Fuzzy Logic technique was calibrated in 4 cored wells and blind tested in the other cored wells to determine the RESERVOIR rock types. After the secondary calibration of the Fuzzy Logic against the core data, this technique was applied on 28 wells without any core data. The results reveal a very good match between the core data analyses and the Fuzzy Logic determination of the RESERVOIR rock types. This technique can be applied to reduce the uncertainty of determination of the rock typing or as a very good predictor in uncured wells.

CHARACTERIZATION of large RESERVOIR models with a great number of uncertain parameters is frequently carried out by ensemble-based assimilation methods, due to their computational efficiency, ease of implementation, versatility, and non-necessity of adjoint code. In this study, multiple ensemble-based assimilation techniques are utilized to characterize the well-known PUNQ-S3 model. Accordingly, actual measurements are employed to determine porosity, horizontal and vertical permeabilities, and their associated uncertainties. In consequence, the uncertain parameters of the model will gradually be adapted toward the true values during the assimilation of actual measurements, including bottomhole pressure and production rates of the RESERVOIR. Monotonic reduction of root-mean-squared error and capturing the key points of the maps (such as direction of anisotropy and porosity/permeability contrasts) verify successful estimation of the geostatistical properties of the PUNQ-S3 model during history matching. At the end of the assimilation process, the RMSE values for Deterministic Ensemble Kalman Filter, Ensemble Kalman Filter, Ensemble Kalman Filter with Bootstrap Regularization, Ensemble Transform Kalman Filter Symmetric Solution, Ensemble Transform Kalman Filter Random Rotation, and Singular Evolutive Interpolated Kalman filter are 1.120, 1.153, 1.132, 1.132, 1.129, and 1.113, respectively. In addition to RMSE, the quality of history match as well as prediction of future performance are looked into in order to assess the performance of the assimilation process. Obviously, the results of the ensemble-based assimilation methods closely match the true results both in the history match section and in the future prediction section. Besides, the uncertainty of future predictions is quantified using multiple history-matched realizations. This is due to the fact that Kalman-based filters use a Bayesian framework in the assimilation step. Accordingly, the updated ensemble members are samples of the posterior distribution through which the uncertainty of future performance is assessed.

In the recent studies of the Lame parameters (lp and l/m) for RESERVOIR CHARACTERIZATION, the main mathematical tool for investigating complex relationships between variations in Lame parameters and changes in the lithology and rock properties of RESERVOIRs is regression analysis. The heterogeneity of the RESERVOIR under consideration and few available wellsin the area pushed the authors to use soft computing methods instead of conventional mathematical tools. The self-organizing map is an excellent tool in exploratory phase of data mining. It projects input space on prototypes of a low-dimensional regular grid that can be effectively utilized to visualize and explore properties of the data. An improved identification of RESERVOIR properties such as pore fluids and lithology variations can be obtained by analyzing changes in rigiditym, incompressibilityl, and density p. The reliability of results depends on extraction of useful petrophysical parameters (e.g. density, porosity and Vp/Vs ratio) from seismic data and linking them with lithologyor rock properties. In this study self-organizing maps and artificial neural networks are used to categorize the data into lithologically and petro physically meaningful classes and to model the rock parameters and lithology variations by exploiting AVO inversion (LMR) method.

The history of the well production rate is a useful source of data not only for the well production potential in the future but also for the RESERVOIR CHARACTERIZATION. The RESERVOIR average permeability, reserve, drainage area and well skin factor can be estimated using decline curve analysis technique which is based on the Fetkowich method. The decline curve can be generated by analytical solution of flow equations in porous media. In order to use the Fetkowich method, several assumptions should be made such as constant bottom- hole well pressure, no acidization, no aquifer effect in the RESERVOIR and the RESERVOIR should be homogeneous. Since fluid flow equations in the fractured RESERVOIRs are different than homogeneous RESERVOIR, we need to know if the Fetkowich method can be applied in the fractured RESERVOIRs as well.First, a hypothetical RESERVOIR with real data was constructed having several production wells with constant bottom- hole well pressure and the decline curves were generated for the RESERVOIR. Then, this decline curve was compared with the Fetkowich decline curve. In the present work, it was shown that a good estimation for the fracture permeability could be obtained by using the Fetkowich method. Finally, using a real heterogeneous RESERVOIR data (Cheshmeh Khosh oil field), the RESERVOIR permeability was determined with the Fetkowich method. Cheshmeh Khosh oil field is one of the Iranian carbonate fractured RESERVOIRs in south-west Iran. Although this RESERVOIR does not have the same conditions for using decline curve analysis, using some periods that the production rate is declining, a good estimation for some RESERVOIR properties University College of Engineering, University of Tehran 3 can be made. Therefore, we need to determine the time decline period for each well and the data should be smooth. Since there was a good agreement between the calculated permeability and the permeability from well test data, it was concluded that the Fetkowich method can be applied in the fractured RESERVOIR as well. However, these results are not trustable for skin factor determination.

The aim of this study is to characterize and find the location of geological boundaries in different wells across a RESERVOIR. Automatic detection of the geological boundaries can facilitate the matching of the stratigraphic layers in a RESERVOIR and finally can lead to a correct RESERVOIR rock CHARACTERIZATION. Nowadays, the well-to-well correlation with the aim of finding the geological layers in different wells is usually done manually. For a rather moderate-size field with a large number of wells (e. g., 150 wells), the construction of such a correlation by hand is a quite complex, labor-intensive, and time-consuming. In this research, the wavelet transform as well as the fractal analysis, with the aid of the pattern recognition techniques, are used to find the geological boundaries automatically. In this study, we manage to use the wavelet transforms approach to calculate the fractal dimension of different geological layers. In this process, two main features, the statistical characteristics as well as the fractal dimensions of a moving window, are calculated to find a specific geological boundary from a witness well through different observation wells. To validate the proposed technique, it is implemented in seven wells of one of the Iranian onshore fields in the south-west of Iran. The results show the capability of the introduced automatic method in detection of the geological boundaries in well-to-well correlations.

This research presents an applied workflow and results of an integrated study which included contributions from Petrophysical, Geological, Geophysical and RESERVOIR Engineering aspects. We provide input data for a high resolution Geo-statistical Inversion with Porosity Co-Simulation study of gas saturated, Lower Cretaceous Sandstones of Kopet-Dagh basin on GONBADLI structure in North Eastern of IRAN. The study formed part of a project for NIOC-EXP Directorate to deliver an initial deterministic inversion of a 3D- Seismic dataset acquired over the GONBADLI field followed by a Geo- Statistical Inversion and with Porosity Co-Simulation. Petrophysical data of nine wells to be incorporated into the study. The final result are three porosity RESERVOIR models in depth that tie the well data and take advantage of the 3D-Seismic data acquired in the area. The advantages of this technique ( by using two complicated software -RMS & JASON ) over traditional approaches to building RESERVOIR models is that the high vertical resolution of the well data can be powerfully combined with the high lateral resolution of the seismic data. The result is a detailed and accurate RESERVOIR model that makes use of all available data.Up-scaling of the 3D property models has been performed and provided an Eclips-ready set of models for flow simulation and risk analysis under development processes. Quantification of uncertainties in geophysical measurements such as Impedance and Porosity is necessary for complete risk analysis. This is an important consideration in both exploration and RESERVOIR CHARACTERIZATION. We have shown that the methods of Stochastic (SGS and SGCCS) can be used to define limits to the expected values of porosity in Lower Cretaceous-gas-saturated sandstone. Essential and unique to the proposed method to reduce uncertainty is that the 3D output result fully honors the input well Logs, geologic model and seismic-data. The method generates multiple realization (PIS, PSO, P8S), allowing a full 3D analysis of uncertainty of the output results from the Geo-statistical Inversion process. This method at GONBADLI field (DI zone) has provided an improved view of the RESERVOIR connectivity which was not possible using just the well or seismic data. This technique has added confidence to the future production drilling program. Better estimation of gas volume in sandstone layer of Shurijeh formation (Upper and Lower DI member) and detection of permeability barrier between two parts of this interval have improved just by above integrated workflow.

Porosity is one of the most important petrophysical parameters, studied in the subject of RESERVOIR CHARACTERIZATION. Determining porosity and how it changes in hydrocarbon RESERVOIRs is an important issue that has been addressed in various researches. In this research, Poro-Acoustic Impedance (PAI) is introduced as an extended form of Acoustic Impedance (AI). The difference between PAI and AI is related porosity that is directly involved in the PAI. The inclusion of porosity data in the PAI formula made porosity effective in forward modeling and inversion of seismic data. The use of PAI in the forward modeling of synthetic models increases the contrast between the subsurface layers, and the contrast increases twice as compared to the AI. Band Limited Recursive Inversion (BLRI) algorithm is used for inversion of synthetic seismograms and model-based algorithm is used for real seismic data inversion. For real data, due to the existence of well data, seismic horizons and geological information, using the basic model method for inversion is more accurate. The main difference between inversion using PAI and AI is that changes in porosity can be seen directly in the results of PAI inversion. The correlation of porosity with PAI and AI is -0.93 and -0.85, respectively, which shows that porosity has a stronger relationship with PAI. The use of PAI can be a quick and simple solution to understand porosity changes in hydrocarbon RESERVOIRs and increase the accuracy of porosity determination in RESERVOIRs to a great extent.