ADVANCED APPLIED GEOLOGY
Year:2019 | Volume:9 | Issue:1 (31)|Papers Count:8

Introduction Carbonate reservoirs is a challenge for geologists comparing to sandstone reservoirs due to their variation. These reservoirs are characterized as a combination of limestone and dolomite such as the Bangestan reservoir in Ahvaz oil field located at Dezful embeyment (70 km length and 7 km width). The Bangestan reservoir is divided into 10 zones based on lithological and petrophysical properties. Dolomitized limestones are indicating a higher permeability than pure limestone formation and so can be considered as a potential location of hydrocarbon accumulation (Halley and Schmoker, 1983). Dolomitization is introduced as a main controlling agent (Tucker and Wright, 1991) and different models are cited for dolomitization process (Meyers et al., 1997). The process led to produce several complicated textures in limestone units (Gregg et al., 2001). The present research work is an attempt to study dolomitized horizons in this field involving their identification, and distribution. Diagenetic processes such as dedolomitization and certification and their role in reservoir quality (Lucia, 1999) are also discussed. Methodology To determine of dolomite distribution and textures, 3240 thin sections of selected samples taken from 36 drilled wells were used. Dolomite and calcite were distinguished by thin sections staining with the help of red Alizarin (Meng et al., 2019). Limestone facies were determined using Dunham classification (Dunham, 1962). Borehole logs and petrophysical data were applied to interpret the role of dolomites in reservoir quality. Findings Thin sections petrographic study revealed a sequence of dolomitetion, dedolomitization and certification processes in pores (Fig. 1A, B) attributed to fluidal composition changes. The sequence is started by the formation of dolomite, and followed by dedolomitized crystals (Fig. 1C, D, E, F) and chert (Fig. 1A, B, D) precipitation. Dolomitization process was initiated from the edge of calcite crystals and progressed (Mehmood et al., 2018) (Fig. 1G). Dolomite crystals presence in stylolite (Fig. 1H) and adjacent area are indicating two stages of dolomitization process which is possibly related to hydrothermal fluids (Martin-Martin et al., 2017). The model is also supported by dolomite discrepancy, however, burial model is also a candidate for matrix dolomites due to released fluids by shale (Kazhdumi Formation) compaction (Mattes and Mountjoy, 1980). The presence of abundant stylolite associated with dolomite crystals supports the burial and hydrothermal models. Determined carbonate facies in 2120 m of carbonate column studied are wackestone-mudstone, wackestone-packstone and grainstone – packstone in a decreasing frequency order. Wackeston facies is a suitable unit for dolomitization (Martí n-Martí n et al., 2017) but the presence of super saturated Mg-fluid is necessary. Carbonate facies are varied from NW to SE of the field with involving of paleohigh (Esrafili-Dizaji et al., 2015). According to thin sections petrography, euhedral dolomite crystals frequency decreased from NW towards SE parts. This pattern is the result of combination two factors (A) carbonate facies, and (B) pore type. The facies are mostly grain supported in SE part while mud supported facies in NW part. Thin sections petrography indicated that porosity distribution in dolomitic units affected by facies changes, dolomite crystal forms and dedolomitization and certification diagenetic processes through the field. Well-formed crystals of dolomite is considered as a main factor of porosity increasing and improving reservoir quality. The plot is showing an excellent linear relation between horizontal and vertical permeability and total porosity due to well-formed crystals of dolomite. The study of petrophysical logs (neutron, gamma and density) showed that the porosity was increased by dolomitization process but dedolomitization and chertification decreases it. A BA Dolomite Calcite Chert Dolomite Calcite C Dolomite Calcite E Dolomite Calcite F Dolomite Calcite D Dolomite Calcite Chert G Dolomite Calcite H Stylolite Chert Figure 1. Selected core thin sections microphotographs indicating diagenetic processes in Ahvaz oil field, well#63: (A, B) pore filings by dolomite, calcite and chert (3518m depth); (C, D) localized replacement of euhedral dolomite crystals (3428m depth); (E ) dolomite and dedolomitized crystals (3425m depth); (F, G) partially dedolomitized dolomites (3517m depth; G-sample is from 3520 m depth in well #49) and (H) dolomite crystals and stylolite (3694m depth). Conclusion Evaluation of dolomitization process, in 36 wells in Ahvaz oil field indicated that euhedral dolomite crystals have been more developed in northwestern part than central and southeastern parts. Wackestone and packstone/ grainstone facies have the highest and lowest values of euhedral dolomite crystals, respectively. Iso-thickness and iso-porosity maps of dolomitized horizons indicated that the northwestern production zones are more affected by dolomitization process. In spite of euhedral dolomite in NW part, thin section petrography verified that dedolomitization process decreased the porosity in this part respect to central part of the field. The permeability values of the horizons containing euhedral dolomite crystals indicating higher values than the others. There is a very sharp linear relation between porosity and permeability distribution (high correlation coefficient values) in horizons with more than 25 % euhedral dolomite. The study of petrophysical logs also shows the porosity to be increased by dolomitization but decreases by dedolomitization and certification processes.

Geomechanical hydrocarbon reservoirs are important in oil and gas fields' development. Also one of the most important issues is Oil wells stability. Considering being economical this Issue, Determine mud Pressure for well stability is important. The amount and direction of the main tensions, Mechanical properties with pore pressure are input necessary parameters for study. By choosing stubble failure criteria can doing analysis with Fine precision. This study is about how to determine stress around wellbore and how to measure safe mud weight window at every Slope and Azimuth for well in oilfield in south of Persian Gulf by geomechanical method. For this study, then we chose 4 different depth for typical sampling and calculated bottom hole pressure, direct and azimuth of well that well be stable by using Mohr-Colomb criteria. By identifying the input data, different direct and azimuth of well is calculated. Mud window for 4 different depth respectively is 27-36. 5 & 29-39. 5 & 41-55 & 47. 5-55. In continue maximum and minimum mud drilling density is calculated. In the end Obtained ruptures in different direct and azimuth and some of them have been reported.

In the present work, the ignimbrites of Sabalan and Sahand volcanoes have been studied. The geological, petrographic, and geochemical properties of Sabalan and Sahand ignimbrites presented here. Sabalan ignimbrites contain plagioclase, amphibole, pyroxene, biotite phenocrysts and rock fragments. The rock fragments are mostly pumice and obsidian. Sahand ignimbrites have plagioclase, sanidine, quartz, hornblende and biotite glassy groundmass. Sieve texture in plagioclase crystals and corrosion of the margin of some minerals in Sabalan and Sahand ignimbrites indicate that primary magma is affected by contamination and mixing before eruption. The Sabalan and Sahand ignimbrites have composition range of trachy-dacite to rhyolite and dacite to rhyolite, respectively. In the trace elements normalized diagrams, these rocks are enriched in LREE and LILE relative to HREE and HFSE, respectively. High content of Sr/Y-Y and La/Yb-Yb indicate that these rocks have high SiO2 adakites nature. The Sabalan and Sahand ignimbrites were sourced from the partial melting of lower crust with garnet amphibolite composition. The degree of partial melting in the primary magma of sabalan ignimbrites appear to be higher than that of Sahand ignimbrites.

In this research, GMS 10. 0. 11 software was used to investigate the qualitative changes and modeling of variations in concentration and prediction of TDS in the Varamin aquifer. In this regard, after collecting geological, meteorological, hydrological and hydrogeological data, geophysical experiments and exploratory boreholes, rainfall statistics and groundwater levels in wells, a database in ArcGIS10. 5 software was created and to build the conceptual model, transmitted this information into the GMS software environment. Then based on the conceptual model, the simulation, calibration and verification of groundwater level were done using the Modflow2000 code in GMS software. Calibration results indicate that the fitting is very suitable for observed and calculated head by the model at R2=0. 99. After this stage, the solute transport model was developed to evaluate the changes in TDS concentration by the MT3DMS subsystem. The results of the study showed that if the condition of operation and the entry of sewage to the western and southwestern areas of the aquifer continue to be similar, at the end of the forecast period, then the concentration will even reach 7000 mg/l, and the inadequate condition in these waters areas will be used for drinking and farming purposes.

Introduction Rare earth elements are important strategic elements and have a wide range of technological applications. Sangan iron ore deposit is located in 308 km southeast of Mashhad and 18 km north-east of Sangan city and is part of Khaf-Kashmar-Bardsan volcanic-belt (Karimpour, 2003). The study site lies on the southern C anomaly, which is an anomaly of western part of Sangan iron mine associated with magnetite skarn mineralization. The purpose of this study is to determine nature and origin of REE mineralization fluids, physicochemical conditions of REE concentration and the pattern of rare earth element distributions in this zone. Samples of skarn-ore and host rocks were collected from this zone for petrographic and geochemical studies. The samples show LREE enrichment relative to HREE in the iron ore and skarn zones. The obtained results suggesting that REE mineralization was associated with emplacement of intrusive rocks with simultaneous iron ore deposition. Methodology Mineralogy of Skarns The skarn in this zone is in terms of mineralogical, including amphibole skarns (actinolites) and garnet skarns. The broadest skarn zone in the southern C anomaly is the amphibole skarn zone. The main mineral that forms this skarne unit is amphibole with the combination of ferroactinolite. The expansion of the garnet skarn zone in the low area is garnet of the andradit-grosolar type. Geochemistry To determine the pH of the formation setting, the La/Y parameter can be used such that values less than 1 of this ratio represent the acidic setting and more than one of them represents the alkaline setting (Crinci and Jurkowi, 1990). Rare earth elements are typically deposited in alkaline conditions (Patino et al., 2003). The separation degree of LREE relative to HREE can be calculated from the ratio (Gd/Yb)N and (La/Yb)N. These ratios are used to determine the degree of separation of LREE from HREE in geochemical processes (Aubert et al., 2001; Yosoff et al., 2013). To calculate the Ce/Ce* ratio, the following equation is used: Ce/Ce*={(2Ce)sp/Ce(chon)}/{(La)sp/La(chon)}+{(Pr)sp/Pr(chon)} This equation is a measure of Ce anomalies, so that values greater than 1 represent positive anomalies and values less than 1 represent a negative anomie of Ce. Determine the origin of the mineralized fluid In the Magmatic origin of skarn deposits, the values of Eu/Eu*, Ce/Ce* and (Pr/Yb)N indices increase with increasing amounts of rare earth elements, reflecting the characteristics of rare earth elements in magmatic fluid, while in atmospheric waters the amount of rare earth elements is very low (Kato, 1999). Hence, in skarn deposits with atmospheric waters origin, (Pr/Yb)N ratios decrease with decreasing amounts of rare earth elements, but significant changes in Ce/Ce* values are not observed with decreasing rare earth elements (Kato, 1993). Results and discussion In the skarn and iron ore zone, the frequency of LREE varies between 59. 56 to 305. 91 and 54. 95 to 147. 1ppm and HREE ranges from 5. 79 to 45. 86 and 40 to 22. 89 ppm, indicating the LREE is enriched to HREE in this zone. The ratio of La/Y in skarn and iron ore studied is about 0. 44 to 5. 95 and 0. 5 to 2. 4, respectively, which indicates the alkaline environment for most of the samples, leading to the deposited of rare earth elements from fluids in this is the environment. The calculated values for skarn and iron ore for Ce/Ce* are between 0. 13 to 0. 84 and 0. 02 to 0. 15, respectively, which indicates a negative anomaly. In the diagrams (Pr/Yb)N and Ce/Ce* (Fig. 1), the skarn and iron ore zones are located near the magmatic waters and tend to be very low in the atmospheric waters. Figure 1. Chondrite normalized diagrams, (a) (Pr/Yb) N and (b) Ce/Ce*, versus the total REE, are used to distinguish magmatic fluids from the atmosphere that can play a role in mineralization (Kato, 1999). Conclusions Due to the nature of the low pH of the primary fluids, REEs are likely to form the complex with ligands and are removed from the environment by washing. Given that the La/Y mean can be used to determine the pH of the formation setting, this ratio can be indicative of the alkaline environment governing the formation of the skarn zone. Therefore, rare earth elements appear to be deposited and enriched in the Skarn zone due to system temperature degradation and the rise of the pH of the fluids. From the Ce/Ce* and (Pr/Yb)N parameters it can be concluded that the effective fluids in mineralization in the southern C anomaly deposit were a mixture of magmatic and atmospheric waters, which made atmospheric fluids a very small part of this mixed fluid. In parallel with injection, deposition and crystallization of deep intrusive masses, a significant amount of REE fluid through the shear zone of the region into the carbonate rocks of the injection area and eventually leads to the formation of iron ore. It can be said that the originator of the ore has a reproductive relationship with the intrusive mass in the region. Mineralogy and geochemical studies of rare earth elements indicate that factors such as changing the physico-chemical conditions of the environment (pH, Eh and temperature), alterations, ligand activity and the presence of sub-mineral phases play an important role in the concentration of rare earth elements in skarn samples.

Introduction Most software produces models based on a pre-constructed grid. A grid network in 3-D modeling is an assemblage of voxels or cells of equal size. The number of cells producing is related to the spacing of the available data. The smaller the differences in the real data, the lower the dimensions of the cell unit, thus increasing the number of cells. The intersection of the lines formed by this network is called the node of the network and is calculated using the given data. These values are used for calculating each cell quantity and evaluating different cell values in the three-dimensional spaces. If one part is deleted based on operator order, the percent of remaining volume can easily be calculated by dividing the residual volume to total volume of the model. This study shows how to model spatial form and determine the quantity of mineral in the rock through the application of a small-scale high precision calculation. By paying attention to the used modeling method and using a high amount of data, the result will be as close as possible to the real distribution of minerals in the rock. In this research, to introduce the method and due to the full range of studies, the area 4 of metamorphism (Figure 1) Skarn Hassan Abad was selected for this research and was evaluated (mineralogy diversity is one of the essential reasons for this choice). Fig 1. Geological map of the studied area located in the south west of Yazd City. Methodology In this study, serial section images are used for the modeling of the rocks. Five minerals (Clintonite, Diopside, Vesuvianite, garnet, and epidote) were selected and examined. The sampling is to cover all parts of the metamorphosed area and have a uniform distribution in the area, as well as all samples, should be safe and identifiable and not be used for displaced specimens. Thirty-three samples were prepared according to the principles from different skarn ranges. At first, all specimens were cut in a few centimeters and then thin sections were prepared, and a fixed camera took a digital image of the surface on a polarizing microscope. A layer with one-centimeter thickness was removed, and the above procedure was repeated. Figure 2 shows images of serial sections of one rock samples. For total calculations and producing the network of image coordination, MATLAB software was used, and for final modeling, RockWorks software was used. Closest point method was used for modeling. In this method, each unknown point gets the value of the sharp boundaries (Wylie, 2005). Fig 2. Serial thin section of one studied samples (Xpl, Vesu: Vesuvianite). Results and Discussion Consequently, each image is identified by an m×n×3, matrix where m and n are length and width of the image respectively and dependent on the number of pixels per image. Numerical three is indicative of the red, green and blue colour bands. Matrixes of successive images are arranged as a column, and the Z value of each image in successive sections and the fourth value related to the numerical value of the green color was added. The final matrix with four columns X, Y, Z, and G is stored in a standard ASCII file. Finally, 33 models were prepared for 33 rock samples. The obtained models show a complete distribution of color band value for each mineral with the corresponding color scale. After calculating the statistical parameters, for each of the five minerals desired in all models, by the filtering, the amount of each mineral was determined. Other parts other than the minerals in question should be removed from the model, to do this. For this purpose, the normal distribution curve of the prepared models was used. According to the desired tables, depending on the increase or decrease of selected minerals in all profiles, it is possible to determine the zoning of minerals around the intrusive rock accurately. For other minerals, these values are calculated and listed separately in the tables, and due to the amounts obtained from this mineral and the presence or absence of specific skarner minerals, the conditions of temperature and pressure governing the metamorphic region with a higher probability of Measurement. The normal distribution curve for each reaction zone (skarn zone) is shown on average from all four zones in Fig. 3. According to the volumetric tables of 5 minerals selected, the total amount of garnet, diopside, vesuvianite, clintonite and epidote minerals is 58. 77%. As a result, the remaining volumetric percentages are (according to microscopic examination and XRD tests, these minerals have been confirmed in the region): apatite, quartz, low thermolite, wollastonite, significant calcite, and metal ores. These turning points coincide with the “ average ± standard deviation” on the normal distribution curve. In this study, all models were filtered based on the two numbers average ± standard deviation. Since the rock matrix volume is always more extensive than that of the mineral, distribution values around the average ± standard deviation of the data reflect the values related to the rock. Mineral amounts can be obtained by subtracting these amounts from the total population of the model (Fig. 4). The relative amount of minerals in the rock can be obtained by dividing volume of the mineral in the rock to the volume of the rock. Equation (1) shows the percentage of mineral in the rock. (1) Where FMV is the volume of filtered model (volume of minerals) and MV is total volume of model. Fig 3. The mean distribution curve for the normal distribution is in the range of 105 meters (1); 105-222 meters (2); 222-320 meters (3); 320-450 meters (4). Fig 4. Filtered models of the minerals. Conclusions Calculating mineral quantities and the determination of the three-dimensional distribution of minerals is necessary for the petrological and economic geology investigations. The total volumes of 5 selected minerals are 58. 77%. As a result, the remaining volumetric percentages are Apatite, quartz, thermolite in a small amount; Wollastonite and calcite significantly and metallic minerals. By extensive sampling of ores in different areas, it is possible to use this method in ore body volume determination. Also, this method has many applications such as studying of the fluid inclusions, calculation of the type, and amount of porosity in oil reservoirs and studying the tectonic of the selected area.

In this study, the estimation of the results of the standard penetration test with probabilistic methods and artificial neural network using the physical and plasticity properties of clay layers with case study of clayey soils of Tabriz city have been performed. In this research, two machine boreholes were drilled at depths of up to 8 meters and standard penetration tests and other tests were carried out to determine all of the plasticity and physical properties on the prepared specimens. By using the results of experiments as well as available data, a database of 112 series of clay layer properties were prepared. The probabilistic ranges for each of the research variables including activity, moisture content, consistency index, passage percentage sieve 200 and plasticity index were proposed for estimating the corrected standard penetration by using the three sigma probabilistic analyses. The best correlation with the standard penetration based on the consistency index with the correlation slope is 1. 015. Also, the artificial neural networks were studied in different states and with the different number of hidden neurons with all physical and plasticity properties including 11 input variables. The best results of the artificial neural network related to the hidden double layer with 10 and 5 hidden neurons that the determination coefficient and the root mean square error were equal to 0. 805 and 0. 063 at the test stage, respectively. The results of the neural network method have been statistically compared and more appropriate than the probabilistic method.

The aim of this study is to investigate heavy metal concentrations in the Paddy farms of Ahwaz and Bavi counties and determine anthropogenic or natural sources of heavy metals, using statistical methods. 14 farms were chosen in Ahwaz and Bavi counties. After preparing samples, ICP-MS and ICP-OES devices were used to determine the concentration of heavy metals in soil and rice samples, respectively. Average concentration of Ag, As, Cd and Sb is higher than that of standard crust. Based on average environmental risk assessment indices, the highest contamination was related to Ag, As, Cd and Sb, which indicates the anthropogenic input to agricultural soil such as using pesticides and chemical fertilizers in the study area. Cr, Fe, Pb, Ni, and V have natural source; Sb and Cd anthropogenic source; As and Mo both anthropogenic and natural sources; and Ag anthropogenic source. Health risk due to As and Pb is probable through rice consumption. The highest mean concentration of metals in the rice samples was measured for Pb and the lowest for V. Pb concentration in the rice samples was higher than, and As concentration is close to the WHO standard. Non-carcinogenic risk for adults and children due to As and Pb is medium.