نشریه زمین شناسی نفت ایران
سال دهم شماره 2 (پیاپی 20، پاییز و زمستان 1399)

The most important reason for studying any oil reservoir is the more efficient use of the production parts of the reservoir and the first step in identifying the reservoir is its zoning. Zoning is determined based on lithological changes by combining production data and petrophysical logs in each reservoir. In order to improve and accelerate the zoning of oil reservoirs, like other branches of science, the use of software has become common in recent years. One of the most powerful of these software's is Cyclolog. The science of using this software is cyclostratigraphy, which can be used to separate reservoir zones based on sedimentary cycles and their knowledge. Cyclolog software with the help of petrophysical logs taken from the wellbore and especially gamma diagram (GR) allows subsurface matching and preparation of matching charts in selected wells. In this study, in the three oil fields studied (Karanj, Paranj and Parsi) using cyclolog software, a total of seven positive timelines (Pb3000, Pb2000, Pb1500, Pb1000, Pb500, Pb400, and Pb300) as well as five negative timelines (Nb4000, Nb3000, Nb2000, Nb1000, and Nb500) were detected. Accordingly, the Pb1500 timeline is the separator and the boundary of the Chattian and Aquitanian peaks, which in the wells of all three studied fields almost cross the boundary of reservoir zones 3 and 4. The Nb4000, Nb3000, and Nb2000 timelines are also Chattian age. The Nb3000 timeline in Karanj oil field has crossed the boundaries of zones 4 and 5 in most of the wells due to calibration with biometric evidence (biostratigraphy) and indicates the top of the formation. The age of the Nb500 timeline is Burdigalian and passes through the middle of their reservoir zone 1 in the study area. The boundary between the Aquitanian and Burdigalian peaks is defined by the Nb1000 timeline. This timeline crosses the boundaries of zones 1 and 2 in all three fields studied.

Permeability is one of the most important petrophysical parameters that play a key role in the discussion of production and development of hydrocarbon fields. In this study, first, the magnetic resonance log in Asmari reservoir was evaluated and permeability was calculated using two conventional methods, free fluid model (Coates) and Schlumberger model or mean T2 (SDR). Then, by constructing a simple model of artificial neural network and also combining it with Imperialist competition optimization (ANN-ICA) and particle swarm (ANN-PSO) algorithms, the permeability was estimated. Finally, the results were compared by comparing the estimated COATES permeability and SDR permeability with the actual value, and the estimation accuracy was compared in terms of total squared error and correlation coefficient. The results of this study showed an increase in the accuracy of permeability estimation using a combination of optimization algorithms with artificial neural network. The results of this method can be used as a powerful method to obtain other petrophysical parameters.

Permeability is one of the important characteristics of oil and gas reservoirs that is difficult to predict. In the present solution, experimental and regression models are used to predict permeability, which includes time and high costs associated with laboratory measurements. Recently, machine learning algorithms have been used to predict permeability due to better predictability. In this study, a new ensemble machine learning model for permeability prediction in oil and gas reservoirs is introduced. In this method, the input data are labeled using the lithology information of the logs and divided into a number of categories and each category was modeled by machine learning algorithm. Unlike previous studies that worked independently on models, here we were able to predict the accuracy of such a square mean error by designing a group model using ETR, DTR, GBR algorithms and petrophysical data. Improve dramatically and predict permeability with 99.82% accuracy. The results showed that group models have a great effect on improving the accuracy of permeability prediction compared to individual models and also the separation of samples based on lithology information was a reason to optimize the Trojan estimate compared to previous studies.

The Qom Formation is located at the Maragh area (20 kilometers southwest of Kashan). The formation with 216 m thickness contains shale and limestones. Volcanic rocks unconformably are covered by the Qom Formation. The upper boundary of the Qom Formation with the Upper Red Formation is also unconformable. Nine microfacies and terrigenous facies were identified based on the main components and sedimentological features. These microfacies and terrigenous facies were deposited on an open-shelf carbonate platform. Three environments were recognized in this carbonate platform. These environments include the inner shelf (restricted and semi-restricted lagoon), middle shelf, and outer shelf. In addition, three third-order and one incomplete depositional sequences were identified based on the vertical distribution of microfacies.

In the oil industry, artificial intelligence is used to identify relationships, optimize, estimate and classify porosity. One of the most important steps in evaluating the petrophysical parameters of the reservoir is to identify the porosity properties. The main purpose of this study is to compare the accuracy and generalizability of three multilayer feed neural networks (MLFNs), radius base function networks (RBFNs) and probabilistic neural networks (PNNs) to estimate porosity using seismic properties. In this regard, geological data of 7 wells were evaluated from an offshore oil field in Hindijan in the northwest of the Persian Gulf basin. Acoustic impedance was estimated using model-based inversion method and then the mentioned neural networks were designed using optimal seismic properties and evaluated by stepwise regression method. Finally, it became clear that the MLFN model did not work well for estimating porosity. PNN has the best performance accuracy in porosity interpolation, but RBFN generalizability is better.

The Qom Formation is the main reservoir and source rock of hydrocarbons in central Iran. Foraminifera are now central to our ability to date, correlate and analyse the sedimentary basins that are currently key to the economic wellbeing of the world. Morphogroup analysis, due to independence of species level taxonomy, as wel as permit to comparison of assemblages of differing ages, is a useful tool for ecological and palaeoecological interpretation. It is independent of species level taxonomy and is thus relatively elementary to translate from one worker to another. Foramniferal study of the Qom Formation in the Bujan (eastern Sirjan; with Rupelin-Chattian in age and 156 m thickness) and Varkan (southwestern Kashan; with Rupelin in age and 190 m thickness) sections resulted in identification of seven morphogroups. The morphogroups were distinguished according to test/shell morphology and architecture (general shape, mode of coiling, and arrangement and number of chambers), inferred life habitat either living on the surface of the sediments or within the sediments (epifaunal and infaunal), and feeding strategy (suspension-feeder, herbivore, etc.). Generaly, epifaunal morphogroups were dominated in both study sections. The morphogroup analyses showed variations in the percentage of the dominant morphotypes, suggesting fluctuations in the paleoecological conditions. In the Bujan section, the Rupelin deposits are dominated by calcareous porcelaneous morphogroups; while the Chattian deposits are dominated by hyaline morphogroups, which indicates the lower and upper parts were deposited in inner ramp (lagoonal environments) and middle ramps, respectively. This significant change through time reffers to gradual increasing of the basin depth, decreasing the light intensity, reducing the salinity and decreacing the nutrient level. De dominance of the hyaline morphogroups throughout of the Varkan section is indicative of the deposition in middle ramp environments with normal salinity under meso-photic to oligo-photic conditions.