List of Articles petrophysical log

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  1 - Reservoir fluid type identification from petrophysical logs using pattern recognition methods
  امیر ملا جان حسین معماریان بهزاد تخم چی
  20.1001.1.22518738.1390.1.2.2.2
  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 ( More

  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. Manuscript profile
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  2 - Cyclostratigraphy study of Asmari reservoir in Karanj, Paranj and Parsi oil fields
  Ardavan Khalili Hosyen Vaziri moghadam Mehran Arian
  20.1001.1.22518738.1399.10.20.5.4
  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 More

  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 Manuscript profile
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  3 - Designing an Ensemble model for estimating the permeability of a hydrocarbon reservoir by petrophysical lithology Labeling
  abbas salahshoor Ahmad Gaeini Alireza shahin mossayeb kamari
  20.1001.1.22518738.1399.10.20.6.5
  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 measu More

  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. Manuscript profile
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  4 - Identification of gas in carbonate rock using wavelet transform
  Hassan Omrani Hashem omrani
  Gas can be diagnosed in clean sand rock by petrophysical log. It is not easy to determine the gas in carbonate rock by petrophysical log. The R.F.T. tool is used to determine the gas in carbonate rock. The fluid density in the rock is determined by calculating the press More

  Gas can be diagnosed in clean sand rock by petrophysical log. It is not easy to determine the gas in carbonate rock by petrophysical log. The R.F.T. tool is used to determine the gas in carbonate rock. The fluid density in the rock is determined by calculating the pressure difference related to depth. The R.F.T. tool has some disadvantages, such as being expensive, taking much time to run, and rock having a neutron porosity of about 15%, and sometimes the R.F.T. tool is stuck in well. This study applies the wavelet transformation, a recent advance in signal analysis technique, to detect reservoir rock fluid. The porosity and water saturation are denoised using the "demy" mother wavelet. At last, the pore hydrocarbon saturation, porosity denoise by the "demy" wavelet, pore volume plot and R.F.T. tool are plotted together in one figure to identify the kind of fluid in sand and carbonate rocks. Manuscript profile