ELECTROFACIES is a deterministic or analytical way to perform well logs data partitioning, which shows variation of geologic or reservoir characteristics. In this paper, we used well logs of two cored wells at one of the oil fields in south of Iran. An initial ELECTROFACIES model was developed based on well logs which included of 25 facies. Corresponds facies in this model merged together according to core micro facies analysis that showed 5 sedimentary facies associations. So it optimized by transmuting to new model (5 facies). Results of optimized model showed good conformity with core micro facies (in cored wells). Finally, we propagated it to all wells and simulated sediment facies in all wells of the subjected oil field.

ELECTROFACIES is a deterministic or analytical way to practice the partitioning of well log data, which show a variation of geologic or reservoir characteristics. In this paper, we used three cored wells located in one of the oil fields in the south of Iran. Based on the core data (porosity-permeability), the three reservoir zones were identified to have different characteristics. Based on common well logs in all wells (Rhob, Nphi, Dt, and Rt) and MRGC method, an initial ELECTROFACIES model with 7 facies was developed. By comparing the results with the core data, those facies with the same reservoir quality were merged together. Thus, we obtained a new model with 3 facies. The new optimized model was then applied to 3 cored wells. It successfully separated poor, moderate, and good reservoir zones. Therefore, the above model was propagated into all wells. The results allowed creating a 3Dfacies model of the reservoir in the field. This model properly separated the poor, moderate, and good zones of reservoir.

ELECTROFACIES study is one of the useful methods in the petrophysical analysis of wells without real geological data that can be used in the study of reservoir prop- erties. In this research, on the basis of thin section studies from the Bangestan reservoir in one of the wells within the Mansouri oil field, four sedimentary facies were identified. Based on electrical logs analysis and using clustering method of MRGC, electrical models with 14 ELECTROFACIES were identified. The combination of ELECTROFACIES, based on average values of their CGR, led to an optimized model which was composed of three facies. Because of a good correlation between the optimized model and real geological data in one of the studied wells, this model can be used for the evaluation of other wells in this field. We hope this model can be used in the future studies of this field in the evaluation of reservoir properties and modeling.

The reservoir ELECTROFACIES study is one of important subjects in hydrocarbon reservoirs scope now. Determination of the high reservoir quality zones can play an important role in production view of the hydrocarbon reservoir and their development. ELECTROFACIES is defined on the basis of clustering which is grouping all similar log data in unique set and distinguished it from other sets. In the present research at first using SOM, MRGC and DC methods, primary model of ELECTROFACIES in a number of field’s wells has been determined. ELECTROFACIES have determined by different methods correlated with identified flow unit’s derived Core storage capacity (phie*h) -flow capacity (K*h) data, and SOM method has been chosen among them for clustering which had the highest accordance.9 created Initial ELECTROFACIES reduced to 4 ELECTROFACIES according to the analogy of some parameter; such as, porosity and gamma logs. This ELECTROFACIES have been generalized for entire filed resulting in creation of a model with separation capability of the deferent reservoir zones. This model shows a decrease in reservoir quality from the upper part to the bottom of the reservoir also depicts reservoir quality changes whole the studied field.

Logarithmic Mean of Transverse relaxation time (T2LM) and total porosity of the Combinable Magnetic Resonance tool (TCMR) are the main parameters of the Nuclear Magnetic Resonance (NMR) log which provide very substantial information for reservoir evaluation and characterization.  Reservoir properties, for example, porosity and permeability, free and bound fluid volumes, and clay-bound water, could be calculated through the interpretation of T2LM and TCMR. In this manuscript, an intelligent approach has been used by us to predict NMR log parameters and their corresponding ELECTROFACIES from well log data. We define NMR ELECTROFACIES as classes of NMR log parameters representing reservoir quality are defined by us. For this purpose, NMR logs and petrophysical data are available for two different formations situated in the Ahvaz field. Data from Ilam formation were applied in order to construct the intelligent models, the same as Asmari formation, data were applied for reliability evaluation of the created models.  The outcome results reveal higher performance levels of the Neural Network (NN) technique compared to the neuro-fuzzy (NF) model. The synthetically generated T2LM and TCMR logs are then calculated for the four logged wells from the Ahvaz oilfield using a mathematical function, and they are named Virtual Nuclear Magnetic Resonance (VNMR) logs. Finally, VNMR logs were classified into a set of reservoir ELECTROFACIES by cluster analysis approach.  Correlations between the VNMR ELECTROFACIES and reservoir quality based on porosity and permeability data helped evaluate the reservoir quality quickly, cost-effectively, and accurately.

The Fahliyan Formation of Khami Group is hosting important hydrocarbon reserves in Iran and also is a main reservoir rock in the Abadan Plain oil fields which is Neocomian in age. In the studied wells its thickness is about 440 meters. In the Abadan Plain, the Fahliyan Formation transitionally overlies the argillaceous limestone of the Garau Formation and its upper boundary changes into marl and argillaceous limestone of the Gadvan Formation. According to thin sections examinations prepared from cuttings and cores plus ELECTROFACIES analysis 11 microfacies and 2 lithofacies are recognized. This formation consists of two carbonate and mixed carbonate-siliciclastic (mixed zone) members. The Lower Fahliyan was deposited in carbonate ramp environment while, the Upper Fahliyan was deposited in a mixed carbonate-siliciclastic environment. To determine ELECTROFACIES, the rock types were modeled with using MRGC method. Best correlation between petrographical and ELECTROFACIES is 12 cluster model (in MRGC method). These results suggest that the ELECTROFACIES model is in agreement with heterogenetic rock type such as mixed carbonate–siliciclastic environment observed in petrography. Also, in homogenous rock type such as carbonate ramp environment electherofacies can’t completely determine geological facies. Based on petrographical and ELECTROFACIES this formation is composed of three third order sequences with type sb2 sequence boundaries. But, the third sequence in mixed carbonate–siliciclastic zone is terminated with sb1 sequence boundary just below the Gadvan Formation.

In this study, ELECTROFACIES analysis has been done using MRGC (Multi-resolution graph-based clustering) method to be used in static modeling. Then, the resulting facies was modeled and compared together by applying different geostatistical stochastic algorithms in Petrel software. Two ELECTROFACIES classes including reservoir and non-reservoir facies were determined which were used for facies modeling. Seismic data was also applied for seismic facies construction and also to construct trend maps for appropriate facies distribution. In order to investigate the effect of five applied different geostatistical algorithms used in facies modeling on porosity distribution, the constructed facies models were used for porosity modeling. According to this study, the uncertainty of ELECTROFACIES modeling without applying seismic data increases which in turn reduces the accuracy of porosity models. In addition, ELECTROFACIES modeling considering the sequential indicator simulation (SIS) algorithm and applying the seismic trend maps, enhance the accuracy of the porosity model. Moreover, construction the of seismic facies is considered the best method for facies modeling to be used for porosity modeling due to the high correlation coefficient between acoustic impedance and porosity.

In this paper, ELECTROFACIES in ASMARI formation in Gachsaran oil field were determined using multi resolution graph-based clustering method (MRGC), This oil field is located in the southwest of Iran. The determination of ELECTROFACIES in this field is performed by using the combination of image logs and other well logs obtained from one of the wells in the field. In order to obtain an exact evaluation, environmental corrections were performed on the logs. The comparison of lithology results, shale volume, porosity, and water saturation with determined facies, using clustering analysis by applying image logs, shows acceptable agreement between the obtained ELECTROFACIES and the corresponding lithological results, and represents a new categorization of the formation. This new categorization has reservoir aspects; the variations of the petrophysical properties in each facies are unique and the variations of these indices are determined in individual facies. Moreover, by considering the span of the identified ELECTROFACIES, the reservoir and non-reservoir layers are distinguished based on the performed zoning.

An ELECTROFACIES in defined by a similar set of log responses that characterize a spe-cific bed and allow it to be distinguished from other beds. ELECTROFACIES character-ization is a simple and cost-effective approach to obtaining permeability estimates in heterogeneous carbonate reservoirs using commonly available well logs. For-mation permeability is often measured directly from core samples in the laboratory or evaluated from the well test data. The first method is very expensive. Moreover, the well test data or core data are not available in every well in a field; however, the majority of wells are logged. We propose a two-step approach to permeability prediction from well logs that uses non parametric regression in conjunction with multivariate statistical analysis. First, we classify the well-log data into electrofa-cies types. This classification does not require any artificial subdivision of the data population and it follows naturally based on the unique characteristics of well-log measurements reflecting minerals and lithofacieswithin the logged interval. A com-bination of principal components analysis (PCA), model-based cluster analysis (MCA), and discriminant analysis is used to characterize and identify ELECTROFACIES types. Second, we apply nonpararnetric regression techniques to predict perme-ability using well logs within each ELECTROFACIES. Three nonparametric approaches are examined, namely alternating conditional expectations (ACE), support vector machine (SVM), and artificial neural networks (ANN), and the relative advantages and disadvantages are explored. For permeability predictions, the ACE model ap-pears to outperform the other non parametric approaches. We applied the proposed technique to a highly heterogeneous carbonate reservoir in the southwest of Iran.

In this study, 3D model of effective porosity in heterogeneous Sarvak reservoir in one oil filed located in Dezful Embayment has been constructed. Therefore, geostatistics method has been applied for porosity propagation along with using trend maps of seismic attributes as well as seismic cube as secondary variable for Collocated co-kriging. Thus, the different seismic attributes including Attenuation, Envelope, RMS amplitude, Sweetness and Acoustic impedance attributes have been provided. The multiple attribute property has been used for preparing the related trend maps to be used in porosity modeling. Moreover, ELECTROFACIES analysis has been done via applying MRGC algorithm in order to control porosity distribution in 3D reservoir model. In this study, we tried to consider the trend maps, ELECTROFACIES, and seismic acoustic impedance cube along with applying geostatistics algorithms for 3D porosity propagation. Based on this study, the lower Sarvak has better reservoir quality than the Upper Sarvak due to developing of good reservoir facies. According to this study, the accuracy of the porosity model increases when it is used simultaneously seismic data and ELECTROFACIES, resulted in decreasing uncertainty of porosity distribution.