نشریه زمین شناسی نفت ایران
سال دوم شماره 1 (پیاپی 3، بهار و تابستان 1391)

The most prominent parameter of seismic studies is seismic wave velocity. This parameter is influenced by different factors such as rock properties (Compaction, age, Lithology, Porosity, Pore Shape and etc), fluid properties (Viscosity, Density, fluid type, Saturation) and environment (Stress history, Depositional environment, production history, temperature, pressure, etc). Therefore, by identification, study and investigation of the relationship between seismic velocity and these parameters, properties of rock, fluid and environment from seismic data can be inferred. The main factors affecting these parameters are the porosity and pore ambiences. Many studies have been conducted to obtain and understand these relations. Most of the theoretical equations haven’t considered changes in seismic properties from pores. Therefore, the seismic inversion, AVO and pore volume calculated based on these equations, include much uncertainties. One of the equations that consider several factors such as porosity, pore type, mineralogy and pore fluid is provided by Kuster and Toksoz. In this study, using this equation and inverse modeling technique, geometry and pore type and percentage of any type of pore shape in 3 wells penetrated one Irainain hydrocarbon reservoir were estimated. In this reservoir, Spherical and Disk shape have the highest percentage.

The Oligo-miocence Asmari Formation in the Cheshmeh Kush Oil Field consists of a mixedcarbonate-siliciclastic succession. The carbonate intervals of the Formation display a highdegree of vertical heterogeneity created by a complex diagenetic history. This study is aimed to investigate the effect of diagenetic events on reservoir quality of carbonate intervals of the Asmari Formation. Core samples and thin sections were studied from sedimentological and diagenetic point of view. Results from cathodoluminesence and scanning electron microscopy were used to investigate diagenetic features in details. Core analysis data (porosity and permeability) and wire-line logs (porosity and oil saturation values) from studied interval were used in order to examine reservoir properties. Diagenetic studies and their comparison with petrophysical data demonstrated that dolomitization, cementation (calcite, anhydrite and celestite cements), compaction and dissolution are the most important diagenetic events affecting porosity and permeability of the reservoir. Based on vertical distribution of diagenetic features and reservoir characteristics, diagenetic zones (DZ) of the carbonate succession were introduced. Medium crystalline dolostones with sparse compaction features and limited anhydrite cement (DZ-23, 27 and 30) comprise the highest value of porosity and permeability. Whereas intense mechanical and chemical compaction and evaporate (anhydrite and celestite) cementation in some dolomitic intervals have thoroughly reduced reservoir quality (DZ-12, 11 and 24). Compaction and calcite cementation (coarse spary, equant and poikilotopic types) in some limestone intervals damaged reservoir properties and created non-reservoir intervals (DZ-3, 20 and 17). In contrast, high value of interparticle and dissolution porosities along with minor compaction and cementation effects has improved reservoir properties of the Asmari limestones (DZ-31 and 32). This study shows that the reservoir characteristics of the Asmari Formation in the studied field are dominantly affected by diagenetic events and therefore diagenetic studies and determination of diagenetic zones in field-scale are the most important part in static reservoir modeling and reservoir quality prediction of the reservoir.

This study is aimed at petrophysical evaluation of the Ghar reservoir using Multimin methodby Geolog software in five wells from the Abouzar oilfield. For this purpose, well log datacomprising of neutron, density, sonic, gamma, resistivity and photoelectric absorption were utilized and their analysis lead to determination of quantitative petrophysical properties such as porosity, volume of shale, water, oil saturation and qualitative parameters including lithology and clay mineral types. The analyses revealed that three zones could be identified in the Ghar reservoir. Meanwhile, there are three shaly interlayers within the Ghar foemation. By application of the cutoff values on oil in place (OIP), petrophysical properties were determined zone by zone and based on Net to Gross ratio (N/G) high reservoir quality zone was identified. Finally by using clustering algorithm, reservoir rock types were identified based upon six properties including density, neutron, gamma ray, volume of shale, water saturation and effective porosity. The facies were introduced on the basis of their priority in reservoir quality so that there is an agreement between petrophysical evaluation results and electrofacies. General lithology of the reservoir in composed of upper loose sands and consolidated sand in the lower part. The lower sands are consolidated by the calcite cement. Overall, the volume of clay minerals in the lower part is less than that of upper part. However, productive zones were separated by a thin shaly layer. The clay minerals type in the shaly layer differs from those present in the reservoir rocks. Total and effective porosity are almost identical which is due to low volume of shale.

Porosity parameter is an important reservoir property that can be obtained by studying the well core. However, all wells in a field do not have a core. Additionally, in some wells such as horizontal wells, measuring the well core is practically impossible. However, for almost all wells, log data is available. Usually these logs are used to estimate porosity. The porosity value obtained from this method is influenced by factors such as temperature, pressure, fluid type, and amount of hydrocarbons in shale formations. Thus it is slightly different from the exact value of porosity. Thus, estimates are prone to error and uncertainty. One of the best and yet most practical ways to reduce the amount of uncertainty in measurement is using various sources and data fusion techniques. The main benefit of these techniques is that they increase confidence and reduce risk and error in decision making. In this paper, in order to determine porosity values, data from four wells located in Azadegan oil field are used. First, multilayer neural network and multiple linear regressions are used to estimate the values and then the results of these techniques are compared with a data fusion method (Bayesian theory). To check if it would be possible to generalize these three methods on other data, the porosity parameter of another independent well in this field is also estimated by using thesetechniques. Number of input variables to estimate porosity in both the neural network and the multiple linear regressions methods is 7, and in the data fusion technique, a maximum of 7 input variables is used. Finally, by comparing the results of the three methods, it is concluded that the data fusion technique (Bayesian theory) is a considerably more accurate technique than multilayer neural network, and multiple linear regression, when it comes to porosity value estimation; Such that the results are correlated with the ground truth greater than 90%.

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.

Deep oil reservoirs with high heterogeneity need thorough management to maximizeproduction and recovery along with minimizing OPEX and CAPEX. This management isintegration between technology, human resource and processes. Smart Well technology helps oil companies to meet aforementioned goals. Since smart well technology imposes high initial expenditure it is a risky and costly decision for oil companies to apply it for all companies. Indeed, this fact dictates prioritization of oil fields based on several parameters to decide where this technology should be implemented first. In this paper we present a novel screening technique under Analytical Hierarchy Process (AHP) engine. This technique needs criteria and sub-criteria affecting smart well potential of fields such as Geological, Geographical, Environmental and Economical parameters. In this study, the main components of the four main mentioned parameters have been extracted. All of them weighted according to our objective function. The result of this research would be impact weight of each parameter with respect to each other that can be used an engineering box for making decision among several fields for implementing smart well technology.

Darquain anticline is located at 5 km northeast of city of Abadan. The axis trend of thisoilfield is north through south. In this study، Kazhdumi، Gadvan، Garu and SargeluFormations in Darquain Oilfield، in Abadan Plain، were analyzed by Rock-Eval 6 instrument. S2 vs. TOC plot revealed that kerogen type in this oilfield predominantly is of mixed of types II & III. Significant S2 Adsorption by matrix of Kazhdumi Formation in well numbers 1 and 2 (5. 33-14. 06 mg HC/gr rock) and Gadvan Formation in well numbers 2 and 3 (3. 1-3. 2 mg HC/gr rock) is due to low thermal maturity as well as low Gas-Oil Ratio factor. In Garu and Sargelu Formations amounts of adsorbed S2 by matrix are respectedly 0. 82 and 0. 84 mg HC/gr rock، that represent a medium thermal maturation and medium to high Gas-Oil Ratio factor. Quantity of TOClive in the Kazhdumi، Gadvan، Garu and Sargelu formations estimated to be in the range of 0. 6-1. 6، 0. 2-1. 9، 1. 53 and 8. 38 by weight percent respectively. This represents potential for the studied formations fair to excellent petroleum generation. Also the studied wells were modeled، by which the Ro of the source rocks were calculated according to their depth. Also transformation ratio of organic matter and the initial TOC is estimated. Transformation Ratio of Kerogen in studied formations ranges from 0. 12 to 0. 66. This is in accordance with estimated Easy Ro by PBM software (0. 5-0. 8). It can then be concluded that Kazhdumi Formation is in early oil window and already started to generate hydrocarbon. This is also verified by Tmax data. The Gas-Oil Ratio of Kazhdumi and Gadvan Formations is 0-1 indicating variable hydrocarbon generation. Also this factor for Garu and Sargelu is 0. 58-1 indicates that they have more gas generation potential rather than oil generation potential. The inferences drawn from It can be inferenced from iso TOCoil and TOCgas maps led to theconclution that، in west and southwestern parts of the basin، the depth during deposition of mentioned formations was more in compare to other parts of Darquain.

The Early Triassic Kangan Formation is the main reservoir in the Persian Gulf. In this studyreservoir rock types were recognized according to lithology, rock fabric, geometry andamount of porosity. Therefore, 7 reservoir rock types were determined: - Anhydrite without reservoir quality, - limy– dolomite with mud dominated fabric without reservoir quality, - limy– dolomite with mud dominated fabric and an average reservoir quality, -limy– dolomite with mud dominated fabric and good reservoir quality, - dolomite with crystalline fabric and low reservoir quality, - limestone with grain dominated fabric with an average reservoir quality and - dolomite with crystaline fabric with a good reservoir quality.Based on petrophysical logs(Gamma ray, sonic, neutron & density), 5 reservoir units and 6non – reservoir units were identified. Reservoir units are mainly formed of porous grain dominated limestone, crystalline dolomite and mud dominated fabric dolomite, and non –reservoir units include anhydrite and limy dolomite without porosity. Petrophysical and petrographical studies indicate that moldic, intercrystaline and interparticle porosities are the most effective porosities in the reservoir units of this formation, whereas others like vuggy, fracture and intraparticle porosities have minor role in reservoir quality.