Journal of petroleum exploration and production technology, Feb 3, 2024
This study aims to analyze in situ stresses and wellbore stability in one of the Iranian gas rese... more This study aims to analyze in situ stresses and wellbore stability in one of the Iranian gas reservoirs by using well log data, including density, sonic (compressional and shear slowness), porosity, formation micro-image (FMI) logs, modular formation dynamics tester (MDT), and rock mechanical tests. The high burial depth, high pore pressure, and strike-slip stress regime of the field require an optimal design of geomechanical parameters based on an integrated data set consisting of static and dynamic data, which is available for this study. Firstly, poroelastic modulus and vertical stress were calculated. Afterward, the Eaton's equation was used to estimate pore pressure from well logging data. The geomechanical parameters were also calibrated through the interpretation of image data, the use of the modular formation dynamics tester (MDT), and laboratory rock mechanic tests. Employing poroelastic equations, the lowest and highest horizontal stresses were calculated. It was shown that the maximum horizontal stress and minimum horizontal stress correspond to sigma H and sigma h, indicating the strike-slope fault regime. The findings of this research indicated that the equivalent mud weight (EMW) resulted in 10-13 ppg suitable for the Kangan Formation and 11-14 ppg suitable for the Dalan Formation. Additionally, the well azimuth in the NE-SW direction provided the best stability for drilling the encountered formations. Therefore, the results of this study serve as cost-effective tools in planning adjacent wells in carbonate formations of gas field to predict the wellbore stability and safe mud window.
Using NMR Log to Estimate Permeability in One of the Carbonate Formations in South of Iran
Proceedings, 2009
Permeability is an elusive parameter in hydrocarbon reservoirs as it is very difficult, if not im... more Permeability is an elusive parameter in hydrocarbon reservoirs as it is very difficult, if not impossible, to determine precisely and directly from current subsurface logging technologies. In this research, an attempt is made to test some methods for estimating permeability as a function of depth from Nuclear Magnetic Resonance (NMR) logging in one of carbonate reservoirs in south of Iran. For accurate permeability estimation, an Artificial Neural Network (ANN) model with two different inputs is applied. In the first case, NMR porosity has been used as input data but in the second case there is no NMR data as input and core porosity has been used. Also three NMR models such as average-T2, free-fluid and Swanson model, have been used for permeability estimation. The results of all these methods are compared with the core permeability. The trends of permeabilities obtained by NMR models have good compatibility with core permeability, so they can be used for in-situ permeability estimation. The results of ANN model shows that using NMR porosity, beside traditional log data, as input for ANN leads to considerably increase in correlation coefficient relative using core porosity. So it can be used as a reliable method for permeability prediction.
A novel test structure to implement a programmable logic array using split-gate flash memory cells
ABSTRACT We developed a novel configurable logic array test structure using a highly scalable 3rd... more ABSTRACT We developed a novel configurable logic array test structure using a highly scalable 3rd generation split-gate flash memory cell that features low power and fast configuration time. This split-gate SuperFlash® configuration element (SCE) has been demonstrated with a 90nm embedded Flash technology. The resulting SCE eliminates the need for esoteric fabrication process, and sensing, and SRAM circuits and reduces configuration time for programmable arrays (PA) such as FPGAs and CPLDs. Additionally, SCE inherently ports the advantages of SST's split-gate Flash memory technology with compact area, low-voltage read operation, low-power poly-to-poly erase and source-side channel hot electron (SSCHE) injection programming mechanisms, along with superior reliability.
Journal of Petroleum Exploration and Production Technology, 2018
Tight gas sandstone (TGS) reservoirs are one of the most integral parts of the unconventional res... more Tight gas sandstone (TGS) reservoirs are one of the most integral parts of the unconventional reservoirs pyramid. Uncertainty in petrophysical properties of a TGS reservoir will cause great challenges in reservoir characterization and also 3D properties modeling. The main goal of this study is to implement a new workflow based on saturation height modeling (SHM) to reduce this uncertainty in a TGS reservoir by acquiring a global in situ water saturation function and also calculating more accurate permeability values. Capillary pressure curves and well logs from ten different wells in four different giant basins of western US TGS reservoirs are the input data in this study. After grouping the capillary pressure curves based on the corresponding cores sorting, size, and texture, and also applying some initial corrections, five different SHM methods have been applied to each group. Using regression methods, the function of each model has been rewritten based on the cores’ petrophysical...
Prediction of water saturation in a tight gas sandstone reservoir by using four intelligent methods: a comparative study
Neural Computing and Applications
Reservoir water saturation is an important property of tight gas reservoirs. Improper calculation... more Reservoir water saturation is an important property of tight gas reservoirs. Improper calculation of water saturation leads to remarkable errors in following studies for development and production from reservoir. There are conventional methods to determine water saturation, but these methods suffer from poor generalization and cannot be applicable for various conditions of reservoirs. These methods also depend on core measurements. On the other hand, well log data are usually accessible for all the wells and provide continuous information across the well. Customary techniques are not fully capable to prepare meaningful results for predicting petrophysical properties, especially in presence of small data sets. In this regard, soft computing approaches have been used here. In this research, Support Vector Machine, Multilayer Perceptron Neural Network, Decision Tree Forest and Tree Boost methods have been employed to predict water saturation of Mesaverde tight gas sandstones located in Uinta Basin. Tree Boost and Decision Tree Forest are powerful predictors which have been applied in many research fields. Multilayer Perceptron is the most common neural network, and Support Vector Machine has been used in many petrophysical and reservoir studies. In this research, by using a small data set, the ability of these methods in predicting water saturation has been studied. Based on the data from four wells, two data set patterns were designed to evaluate training and generalization capabilities of methods. In each pattern, different combinations of well data were used. Three error indexes including correlation coefficient, average absolute error and root-mean-square error were used to compare the methods results. Results show that Support Vector Machine models perform better than other models across data sets, but there are some exceptions exhibiting better performance of Multilayer Perceptron Neural Network and Decision Tree Forest models. Correlation coefficient values vary from 0.6 to 0.8 for support vector machine, which exhibits better performance in comparison with other methods.
Petrophysical evaluation of Ilam formation and verification the results with acoustic impedance section
Petrophysical evaluation is one of the most important factor in management, production, developme... more Petrophysical evaluation is one of the most important factor in management, production, development and oil field reservoir estimation. In this research, petrophysical properties such as lithology, saturation, porosity and shale content calculated in Ilam formation in two zones by using well log data. The calculations made by the Multimine probabilistic method in Geolog7 software in the exploration well A. On the basis of NPHI-RHOB cross plot the lithofacies of Ilam formation consists of more calcite and less dolomite and shale. The final evaluation based on this research showed that there is ideal porosity (13%) in the whole Ilam and the oil is present at a depth from 2964 to 3000 meters. For more investigation, the results compared with the acoustic impedance attribute section which obtained from seismic inversion by using ناریا کیسیفُئژ سوارفىک هیمٌدسواش 2 Hampson-Russell software. It is observed a good consistence between the results of petrophysical evaluation and acoustic impe...
Journal of Natural Gas Science and Engineering, 2014
Permeability is the most important petrophysical property in tight gas reservoirs. Many researche... more Permeability is the most important petrophysical property in tight gas reservoirs. Many researchers have worked on permeability measurement methods, but there is no universal method yet which can predict permeability in the whole field and in all intervals of the wells. So artificial intelligence methods have been used to predict permeability by using well log data in all field areas. In this research, Multilayer Perceptron Neural Network, Co-Active Neuro-Fuzzy Inference System and Support Vector Machine techniques have been employed to predict permeability of Mesaverde tight gas sandstones located in Washakie basin in USA. Multilayer Perceptrons are the most used neural networks in regression tasks. Co-Active Neuro-Fuzzy Inference System is a method which combines fuzzy model and neural network in a manner to produce accurate results. Support Vector Machine is a relatively new intelligence method with great capabilities in regression and classification tasks. Each method has advantages and disadvantage and here their capability in predicting permeability has been evaluated. In this study, data from three wells were used and two different dataset patterns were constructed to evaluate performances of the models in predicting permeability by using either previously seen data or unseen data. The most important aspect of this research is investigation of capability of these methods to generalize the training patterns to previously unseen data. Results showed that all methods have acceptable performance in predicting permeability but Co-Active Neuro-Fuzzy Inference System and Support Vector Machine performs so better than Multilayer Perceptron and predict permeability more accurate.
Journal of Petroleum Exploration and Production Technology
This study outstretches a new method to specify the heterogeneity of the studied reservoir by com... more This study outstretches a new method to specify the heterogeneity of the studied reservoir by combining BHI (Borehole Image Log) and conventional log data for 4 existing wells. The first step to achieve this goal was utilizing borehole electrical images to assess the quantity of vugs/modals fraction and porosity distribution. Spectrum porosity was calculated through probabilistic analysis based on core and FMI data which has specified two general types of porosity in studied field as primary porosity (microporosities and intercrystalline) and secondary porosity (vugs). Due to irregular dispersion, noncategorization, and scattering of the porosity-permeability graph obtained from laboratory core data, next step was using petrophysical image facies prediction for generating an electrical facies to incorporate the different reservoir quality. This lithology (litho types) is produced by BHI analysis which reflects geological and petrophysical properties of the field. Then a porosity-permeability cross-plot has been made based on core data and produced facies codes which build in the previous step to check the validity of BHI Facies code extraction. Finally, heterogeneity analysis has been done through an innovative step-by-step workflow to determine spectrum porosity log which is divided into 6 categories/portions as Resistive, Matrix, Isolated, Connected, Bed boundaries and Fractures porosity.
Journal of Natural Gas Science and Engineering, 2014
Permeability is the most important petrophysical property in tight gas reservoirs. Many researche... more Permeability is the most important petrophysical property in tight gas reservoirs. Many researchers have worked on permeability measurement methods, but there is no universal method yet which can predict permeability in the whole field and in all intervals of the wells. So artificial intelligence methods have been used to predict permeability by using well log data in all field areas. In this research, Multilayer Perceptron Neural Network, Co-Active Neuro-Fuzzy Inference System and Support Vector Machine techniques have been employed to predict permeability of Mesaverde tight gas sandstones located in Washakie basin in USA. Multilayer Perceptrons are the most used neural networks in regression tasks. Co-Active Neuro-Fuzzy Inference System is a method which combines fuzzy model and neural network in a manner to produce accurate results. Support Vector Machine is a relatively new intelligence method with great capabilities in regression and classification tasks. Each method has advantages and disadvantage and here their capability in predicting permeability has been evaluated. In this study, data from three wells were used and two different dataset patterns were constructed to evaluate performances of the models in predicting permeability by using either previously seen data or unseen data. The most important aspect of this research is investigation of capability of these methods to generalize the training patterns to previously unseen data. Results showed that all methods have acceptable performance in predicting permeability but Co-Active Neuro-Fuzzy Inference System and Support Vector Machine performs so better than Multilayer Perceptron and predict permeability more accurate.
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Papers by Mehdi Tadayoni