جستجوی مقالات مرتبط با کلیدواژه « well logs » در نشریات گروه « زمین شناسی »

Identification Tiny Wall Effects Has Always Been Important for Oil Engineers, as These Are Useful Tools for Regional Tectonic Studies and Have a Significant Impact on Oil/Gas and Reservoir Quality. In Fractured Zones, Good Information Can Be Obtained Directly and Indirectly Using Seismic Information, Petrophysical Logs, Wellhead Experiments, Drilling Mud History and Core Description. In This Research, Using the FMI Diagram in Geolog Software, Different Types of Open and Closed Fractures, Layering Boundary, Fault and Stylolite and Borehole Breakouts were identified and Their Properties were discussed. 1829 Open Fractures Were Detected by Detecting the FMI Dynamic Image in the Study Well, Most of Which Lack Continuity and Opening for Identification. 69 Closed Fractures were identified. In this study, 430 Boundary layers were read, most of which were detected at high confidence interval. Two Potential Faults were identified at the Study Intervals, Both of Which Caused Dip Changes and Abrupt Texture Changes. The Stylolite Identified in the Study Well Were 12 Total, Most of Them Having Short Amplitude and Parallel Stratification. Based on the interpretations made to determine in situ stresses at different depths of the breakouts wells, 140 fractures were identified. The breakouts, which indicate the direction of least stress (σh), are along the NW-SE and are consistent with the usual Zagros trend. In addition, 128 inductive fractures were detected in the studied well. This type, which indicates maximum horizontal stress (σH), was along the NW-SE as wells.

Asmari carbonate formation in Qale Nar oil field is one of the most important oil reservoirs in Dezful embayment. This reservoir has a lot of heterogeneity due to intensive changes in the sedimentary environment and diagenetic processes, which makes it very difficult to predict the petrophysical properties of the rock in reservoir. Creating a logical connection between sedimentary and electrical facieses, it can be effective in better understanding the reservoir and reducing uncertainties. In this study, using petrographic data and core description in six wells, 7 limy facies associations and one coarse crystalline dolomite were identified. Based on the petrophysical well logs of gamma, neutron, density, acoustic and photoelectric, and using Geolog software by MRGC algorithm, 5 electrofacieses (from 1 to 5) of the reservoir were determined. Electrofacies 1 has the best and electrofacies 5 has the worst reservoir quality. Examination of the frequency of electrofacies shows that good and very good electrofacieses make 30% of the Asmari reservoir of Qale Nar oilfield.

One of the basic steps of oil exploration is to define the hydrocarbon zone. Different methods have been used so far for defining such zones. For a specific dataset, finding the most appropriate method leads to more accurate estimates and predictions of analysis besides improving the speed of calculations. Support Vector Machine (SVM), which is one of the methods for analyzing the data, uses kernel functions. It finds a better relationship between data factors and hydrocarbon zone leading to better estimates and classifications.    In this article, hydrocarbon zone detection has been done using seismic and well data.    The purpose of facies analysis is to obtain important petrophysical parameters of the reservoir and to identify heterogeneous boundaries below the ground. The results of the interpretation of petrophysical parameters are the input of the three-dimensional reservoir modeling process and through these parameters, the reservoir parameters are distributed in three-dimensional space. This model is widely used in various sections such as exploration and drilling of new wells, overdraft from a reservoir, determination of suitable areas for overdraft, reduction of drilling risk and risk, determination of reservoir lithology and identification of key well and its extension to other wells in the region. The most important petrophysical parameters are shale volume, porosity, permeability, reservoir fluid saturation and reservoir lithology.    The study of seismic facies has been started since the 90's and due to its importance and application in reservoir description, it has always been considered by many researchers.    To perform the analysis above, first, the hydrocarbon zones were spotted across the Asmari Formation using well logs and well geology reports. Next, the SVM method was used to detect each hydrocarbon zone using well logs. There was an acceptable agreement between the results of SVM method and well geology reports. Second, hydrocarbon zones detection was done using seismic data by SVM. At this stage, seismic attributes were extracted from the seismic trace in the well location. Then, covariance matrix and cross plots of seismic attributes used to identify the most effective attributes to hydrocarbon zones detection. In order to validate the results, the seismic attributes of another trace near the well location were used for hydrocarbon zone detection. SVM results matched hydrocarbon zones with low error.

Determination of petrophysical properties is the most important part of hydrocarbon reservoirs characterization. Accordingly, by obtaining total and effective porosity, the volume of shale, and the saturation of the fluids, the reservoir regions are determined. Identification of fluid saturation as a petrophysical characteristic has an important effect in hydrocarbon reservoir studies. The exact calculation of this factor depends on the cementation exponent (m) which is commonly considered as a constant number. Due to the heterogeneous structure of carbonate rocks, the use of constant values can lead to a large error in the fluid saturation calculations.
In this study, in order to improve the cementation exponent and to accurately calculate the saturation of the fractured Asmari reservoir in one of the wells south of Dezful Embayment oilfields, petrophysical, core and image log data (FMI) were used.

Material & Methods:

Petrophysical evaluation was performed once with a conventional method, taking into account the constant value for m and again with the variable m value specified by image logs. In this regard, firstly, the petrophysical interpretation of full set logs of the studied well were made, using the Multimin model in Geolog software. Then, the FMI image logs of the well were used to extract various structural phenomena, especially fractures. Finally, the variable cementation exponent was calculated by taking into account the density of fractures of different zones and the petrophysical evaluation was again considered with consideration of its effect.

Based on image log analysis in studied reservoir, three major fractures, including open and semi-open fractures, filled fractures and breakout fractures -due to borehole collapse- have been identified. These fractures are more commonly observed in the lower part of the Asmari carbonate reservoir in the studied well. Image log analysis showed that with increasing the open and semi-open fractures’ density, variation of cementation exponent also increased. So, the effect of this exponent on petrophysical evaluation is important. Also, the results of petrophysical evaluation showed that the amount and pattern of fractures are important factors controlling the cementation exponent of the studied reservoir. As the fractures increase, the cementation exponent decreases and as a result, the water saturation decreases.

Comparison of petrophysical evaluation with conventional and image log methods and correlation with the core data indicate that despite the changes in cementation exponent, porosities of Asmari reservoir did not change significantly in the studied oil field. On the contrary, small changes in the cementation exponent, can lead to large variations in the saturation factor in this reservoir.
Therefore, the correct estimation of the cementation exponent in each zone is necessary for an accurate estimation of the saturation factor in the reservoirs. Therefore, using image logs to evaluate petrophysical properties in order to improve the evaluation of reservoir quality can be a precise and relatively rapid method in reservoir studies.

The Oligo-Miocene Asmari Formation consists of carbonate facies with lesser amount of evaporites. The evaporite ‎unit deposited at the base of the Asmari Formation is called Basal Anhydrite. Depositional model was investigated ‎using field data, geophysical logs and the Basal Anhydrite facies in the Dezful Embayment (DE). Seven microfacies ‎were recognized in seven outcrops located in the Bangestan, Sefid, Asmari and Anaran anticlines. They were ‎deposited in tidal flat and lagoon environments of inner ramp. Anhydrite facies is a distinct and pure evaporite unit ‎with no carbonate components. Two main textures, fine crystalline (alabastrine) and coarse crystalline ‎‎(porphyroblast), were distinguished in samples indicating replacement of anhydrite by gypsum. Gamma ray, neutron, ‎sonic and density logs of 20 wells in 15 oilfields in the DE indicate that evaporite facies, present at the base of the ‎Asmari Formation, is composed of anhydrite and halite. Anhydrite is present in the most of the DE wells and halite ‎and small amount of anhydrite are present in three central wells. The results of geophysical logs and microfacies ‎studies of the Asmari Formation reveal shallow water-shallow basin environment for the Basal Anhydrite.‎

Image logs of 14 wells in Rag sefid anticline are showing 6 main fracture sets treanding N45, EW, N35, N100, N150 and N162 respectively. Development of fractures in the eastern part of the Rag sefid anticline, especially in the frontal edge, are in effect of the propagation fault related folding so that the most open longitudinal and cross axial fractures show N100 and N10 trends. Due to fold axis rotation 30 degrees to the north In the western part of the anticline the orientation of the fractures have changed and major fractures are longitudinal type and have N160 trend. In the middle and curved part of anticline NE-SW trend fracture sets are more developed by reactivation of hendijan basement fault. Convergence of stress axises as a result of the fault triple interaction In effect of Rag sefid thrust dipping to the north east and dextral shears dut to reactivation of hendijan and southern part of Izef basement faults caused the Restraining bend and dextral shear zone In the western part of the Rag sefid anticline. Creation of this shear zone caused clockwise rotation of anticline axis, rising more in northen west culmination on asmari top formation than southern east culmination, change in fractures orientation and also development and increase the density of fractures in the curved part of Rag sefid anticline.

Based on the extensive studies, undoubtedly, the shear wave velocity (Vs) plays a fundamental role in hydrocarbon reservoir evaluation. Using Vs often allows us to identify the seismic signatures of lithology, pore fluid type and pore pressure, efficiently. Unfortunately Vs data is not available in all reservoirs and it is necessary to predict it. To date, intelligent systems have been utilized as powerful and routine tools for this purpose. In this study, PSO algorithm that is one of the artificial intelligence system used for Vs estimation. The algorithm is utilized in linear and nonlinear ways by intelligently derived Equations. This study have a total 3190 data points from Asmari reservoir from two wells in one of the oilfields, SW Iran. All data points have porosity log and measurement Vs by Dipole Shear Sonic Imager. These data are divided into two parts, one part included 2090 data points are used for constructing model and the other part included 1100 data points are used for testing and validation model. Shear wave velocity is predicted by PSO algorithm and results compared with real Vs measurements by DSI. Regression between predicted Vs by Linear PSO algorithm and measured Vs is about 0.92, whereas it is about 0.95 by using nonlinear PSO algorithm. Results of this study and comparison with other studies confirm that particle swarm optimization is suitable for predicting Vs in other oilfields.

In the oil buildings to investigate fractures in the reservoir rock fractures in stages of production and field development is very important and necessary. Now application of software repository in the realization of this issue will help to petroleum with the geologists.. Marun oil field is one of the largest oil fields in the South West Iran of the city of Ahvaz in the Northeast and the North embayment Dezful of geology in the eastern. Marun field is the most important Asmari reservoir. The aim of this study was to investigate systematically Asmari reservoir fractures and the development of fractures in the reservoir. For this purpose, using image logs, best and most complete method interpretation and results of 11 wells in fracture studying and 16 wells in in-situ stress direction studying. According to frequency of fractures (based on image logs interpretation results) in the southern and north-eastern limb of the field, The theory of tectonic folding and bending events later caused the possible activities during the north-south strike-slip faults affect the compression force has been created to strengthen. Most of image logs data in Marun field are fractures with extensions; N155E, N130E, N95E, N60E, N30E.

Electro-facies is a unit of the vertical sediments sequence on the wire-line logs that can be differentiated from the upper and lower units. For detection of the electro-facies interval in Kangan and upper Dalan formations in south Pars Gas Field، wire-line log and core data analysis (wells A، B، C and D) were used. Note، only core data analysis in well A were available. First، thin-Sections study core plugs from this well resulted in the identification of 12 micro-facies and 7 sedimentary environment. For recognition of electro-fasies based on these data، clustering method and core data were used to categories their homogenization of data. In this study، Rock-Types (R-T) that have similar reservoir characteristics were identified using porosity/permeability cross plot from core data; therefore based on these data، 6 Rock-Type were identified. After classification of Rock-Types and micro-facies from the core data، MRGC models of these facies were identified from the wire-line logs in well A. Optimized model in well A were applied in the wells B، C and D to recognized electro-fasies in these wells with no-core data. Average petro-physical properties of each facies in different wells were compared for quality controls of predicted facies. Also facies of 4 wells were correlated to see if they are comparable. As a result، 6 electro-facies were identified with different petro-physical characteristics and the poorest reservoir quality belong to the facies (1) with dominant anhydrite lithology and the best quality reservoir belong to the facies (6) with the dominant dolomite and limetone with grainstone textue. Due to the high accuracy of the results، the suggested model can be used in other wells in this field.

Nowadays, wavelet analysis has many applications to identify and analyze the transient boundaries and waves, such as image borders and earthquake waves. Detection of discontinuities in the sedimentary sequences and identifying the boundaries of sequence stratigraphic surfaces are one of those applications. In this study, wavelet analysis was used to analyze wirline logs data, such as GR, NPHI, RHOB, RHOZ, THOR / URAN and multimin data such as anhydrite percentage volume, in which the data vary in sequence boundaries. Different types of sequence boundaries impact on different types of logs. The wireline logs are affected by structural and lithological changes; thus, we cannot identify those boundaries using a kind of correlation coefficients. In this study, a combination of coefficients for the wireline logs is used. This combination offers a good way to automatically determine the borders fairly accurate. The best results are obtained from the coefficients combination of GR, NPHI, RHOB (RHOZ) logs and the anhydrite percentage volume multimin, and also, the coefficients combination of GR, NPHI, RHOB (RHOZ) logs and thorium to uranium ratio (THOR / URAN) graphs. Comparison between the results of this study in Kangan and Dalan formations of South Pars gas field with the other proved methods show the capability of this technique in combination of data.

Located in the Persian Gulf, the gas field studied in this research is one of the largest gas fields in the world. Its gas-in-place is estimated to be about 14.2 trillion cubic meters while amount of its condensate-in-place might be around 18 billion barrels. This gas field has also an oil layer containing about 6 billion barrels of oil-in-place. In this study, Kangan and Dalan formations of this field were considered. Kangan formation has three main facies: clean carbonate facies, basic clay and shale facies, and evaporate carbonate facies. Dalan formation contains four facies: shore restricted carbonates, shore organic carbonates, carbonates of the open sea, in-shore carbonate-clastic. These two formations have gas & condensate fluids. Since this field is a heterogeneous carbonate system, lithofacies characterization is the best solution for overcoming the problem of heterogeneity in determining the petrophysical properties of the reservoir rock, reservoir modeling and identifying producing zones. However, coring as the most robust method of lithofacies identification is very expensive, time consuming and limited to a few number of wells. Therefore, this study is focused on determining the lithofacies of the study formations from available well logs. For this purpose, multi-resolution graph-based clustering (MRGC) technique which is a dot-pattern recognition method based on non-parametric K-nearest neighbor and graph data representation was applied to sonic, density, neutron porosity and gamma ray logs to define electrofacies similar to core-derived facies determined as eight distinct facies.The cluster of the MRGC method is defined from a model with a specific character associated with the group of lithofacies. Then, Kernel representative index was used to calculate the optimal number of clusters. Small facies groups were formed based on utilizing the neighboring index to determine a K-nearest neighbor attraction for each point. At last, final clusters were constructed by combining the small clusters which lead to identifying eight facies of this gas field from well logs of high accuracy. When electrofacies of one of the wells is built basd on its lithofacies, its cross-plots will be plotted and the certainty of electrofacies with respect to lithofacies will be checked. If the model is acceptable, it is applied to the data from two other wells and their electrofacies will be obtained. For testing facies, the cross-plots of these two wells were also drawn and painted based on facies. If there are similar petrophysical properties for each facies, the model created in the wells without cores is confirmed. MRGC is a fast method that allows the geologist or petrophysist to analyze and test different combinations of data in a short amount of time. It is also not limited by the dimensions of the data and number of the clusters. The method used in this study has obviated the need for extensive coring in this field which caused saving large amounts of money and time; and it can help to optimize the determination of new well locations and optimum pay zones.

Permeability estimation using core data and petrophysical logs is a conventional approachwhich bears high uncertainty especially in carbonate reservoir characterization. In essence,the problem consists not only due to coring expenses rate, but also ambiguity in findingproper explicit log correlation to core data. Moreover, utilizing the correlated formula inwells without core data can pose errors. In this research the permeability was estimated from conventional petrophysical logs and it was calibrated with permeability obtained from core lab experiments. Applied intelligent systems are the matter of this research for permeability values estimation. To construct permeability estimation model, three techniques have been applied including conventional ANN, the Gonzalez, and Hambalek fuzzy logic techniques. These methods were applied in two wells drilled in Surmeh reservoir in Balal field to establish ANN and to derive a relation between core and well. The models were applied in control well in order to check the reliability and capability of models to estimate representative permeability value. The result showed however three foresaid techniques for permeability estimation were successful the secondary porosity distributed the correlation due to its reduction effect on permeability so that they were not interconnected. Therefore this effect was omitted using secondary porosity index in which the permeability estimation were improved and were estimated close to core value.

In order to obtain more accurate results from application of the method of artificial neural networks, instead of selection of the best network determined by trial and error process, we suitably combine the results of several networks that is called committee machine, to reduce the error, and thus, increasing the accuracy of the output results. In this research, ensemble combination of single artificial neural networks has been used in order to estimate the effective porosity of Kangan gas reservoir rock in South Pars hydrocarbon field. To achieve this goal, well logging data of 4 wells in the area at the depth interval corresponding to Kangan formation were used. Acoustic, density, gamma ray and neutron porosity well log data were assigned as the input of the networks while the effective porosity data were considered as the output of the networks. Back- propagation single neural networks having different structures were trained using regularization method and their results were assessed. Then, the networks with the best results, i.e. contained minimum mean of squares of errors in the test step, were selected for making ensemble combinations. To determine the weighting coefficients of the networks in the linear ensemble combinations, we applied three methods of simple averaging, Hashem’s optimal linear combination and non-analytical optimal linear combination employing genetic algorithm, and their results were compared. The best ensemble combination, in which we had the maximum reduction in mean of squares of errors of the test step compared to the best single neural network, was an optimal linear four-network combination obtained by using genetic algorithm optimization method. This best ensemble combination, compared to the best single neural network, reduced the mean of squares of errors in the training and test steps 3.6% and 11.2%, respectively.

Identifying the type and distribution of reservoir fluids is one of the main things on well logging logs and well testing. Several methods have been proposed to identify the type of reservoir fluids that in general، it can be divided into two groups of methods; direct (e. g. well testing) and indirect methods (e. g. seismic and log interpretation). Petrophysical logs due to their high resolution and more conformity are more frequently used than seismic data. This study aims to identify reservoir fluid types in PLs، based on 3 classes of oil، oil-water and water، in carbon reservoir. Suggested method applies wavelet decomposition as well as classification and was applied to PLs in five wells of an oil field in southwestern Iran. Eventually، obtained results have been evaluated by well testing responses.

Analysis of earth science data using different transform methods such as Fourier and wavelet transform, are useful for the extraction of hidden information imbedded in the data. Fortunately, the uses of such transforms have been expanding as reported in recent studies. Wavelets and wavelet transform in signal processing, offer important advantages that are highly useful in transient data analysis. These include multiresolution analysis of data for extraction of different information that reside in different frequency bands as well as extraction of localized features, a distinct property of wavelet transform as compared with other transforms. Furthermore, in wavelet transform, availability of alternative mother wavelets each with its own distinct featuremakes wavelets highly suitable for their usage in different application environment for maximal information extraction. Therefore selection of appropriate mother wavelet for maximal information extraction in a given data analysis, is considered to be an important and necessary step when working with wavelets.Areview of literature in earth science on the subject of optimal wavelet selection reveals little work has been done on the subject. In this paper, we have attempted to overcome this shortcoming in which we have investigated various approaches and considerations for the selection of optimum mother wavelet. In this investigation the results of a case study for selection of optimum mother wavelet for decomposition of set of petrophysical logs gathered from one of the Iranian south oil elds are presented. The signicance of optimum mother wavelet selection in this oil eld is shown.