ایمان زحمتکش

Shear wave velocity is one of the important parameters for determining mechanical and petrophysical properties in hydrocarbon reservoirs. Shear waves are usually obtained from dipole sonic imager (DSI) tools or core analysis in the laboratory. However, these methods as common sources for shear wave estimation are time-consuming and costly and thus can only provide information on shear wave in a few drilled wells. To overcome these limitations, different artificial intelligence methods are used to estimate the mentioned parameter through the conventional well logs. In this study, the shear wave velocity was estimated using ensemble learning methods in Asmari Reservoir in the Mansouri oilfield. In this study, shear wave velocity was estimated using ensemble learning methods such as voting, stacking, bagging, and boosting in the Asmari reservoir, and the results were compared with conventional models such as Linear regression (LR), support vector regression (SVR), nearest neighbor algorithm (KNN), decision tree (DT), neural network (ANN) and hybrid methods such as combining neural network with genetic algorithm (ANN-GA) ), particle swarm (ANN-PSO) and fuzzy systems (ANFIS). In order to evaluate and validate the models, correlation coefficient (R2) and root mean square error (RMSE) were used. A comparison of conventional models and hybrid methods with ensemble learning methods showed that ensemble algorithms perform better in shear wave estimation. Considering the testing phase, among the ensemble learning methods, the Catboost model has provided the lowest error (RMSE=0.058) and highest correlation coefficient (R=0.983) can determine shear wave with high accuracy.

With the booming exploration and development of unconventional hydrocarbon resources, the accurate estimation of source rock factors such as residual hydrocarbon potential (S2) from well logs has become increasingly important. Along with organic material properties, changes in lithology within an interval of possible source also induce well log responses. Artificial intelligence techniques may interpret these lithology-induced log responses as a signal for changes in the organic matter content and/or properties, resulting in decreasing their efficiency. In the present research, a new methodology called the litho-based method was proposed based on modeling the relationship between log data and S2 parameter for each type of lithology using Adaptive Neuro Fuzzy Inference System (ANFIS). The performance of the newly developed methodology was compared with those of the traditional ANFIS and hybrid methods for which the training process was carried out using a dataset including different lithologies. Results showed that the litho-based method successfully removed the adverse effects of lithological variations on the course of ANFIS training, resulting in estimating much more reliable S2 values. Among the traditional methods, utilizing particle swarm optimization (PSO) algorithm in conjunction with ANFIS showed higher performance. Nevertheless, the aforementioned hybrid approach is not as efficient as the litho-based method. The applicability of the proposed methodology was approved by applying it over Pabdeh source rocks for a well in SW Iran. Finally, it is recommended to use the litho-based method for estimating other geochemical factors as well as petrophysical parameters through log data.

Limited access to fresh water sources has raised water shortage as a crisis. Therefore, solar desalinations have received special attention today. A stepped solar desalination device which has 28 steps with a height of 30 mm, a width of 110 mm and a length of 840 mm has been designed, built and used in this research. In order to increase the daily production rate, the device has been tested and optimized in 4 different modes: first mode) simple, second mode) with phase change material (PCM), third mode) with Hybrid Nano phase change material (NPCM), fourth mode) with Hybrid NPCM under magnetic field. The phase change material and Hybrid Nano phase change material have been used as a source of thermal energy storage to continue the desalination of water when the sun sets. After conducting the tests, it was found that the fourth mode of the test, i.e. the Hybrid Nano phase change material under the magnetic field, had a 98% increase in the production rate compared to the simple mode.

In this research, the combined displacement inside a closed square enclosure was investigated. The geometry of this chamber is considered as a quadrilateral with the same dimensions, which is tilted and the upper and lower sides are insulated, and the left side is hot and the right side is cold. The effect of changing the angle of the chamber wall in some specific angles (θ), changes in the volume ratio of nanoparticles in Reynolds numbers 10 and 100 and in Richardson numbers 0.1 and 1 in the range of laminar flow are investigated in two dimensions. The base fluid is water and the nanofluid mixture is considered homogeneous. To solve Navirastox and discrete energy equations, the equations were solved numerically. Discretized equations were solved by coding in Fortran software. The results are presented in the form of Nusselt number distribution, temperature contour, flow contour and velocity vector in different solutions. The results showed that increasing the volume fraction of nanoparticles in the base fluid increases the Reynolds number and the dimensionless Nusselt number. An increase in the Richardson number, especially at high Reynolds numbers, leads to a noticeable increase in the Nusselt number. Also, the highest amount of heat transfer is related to the angle of 90 degrees.

In this research, the performance of a three-dimensional oscillating heat pipe in different inclination angles from 0 to 90 degrees with distilled water and ferrofluid as operating fluid has been studied and compared experimentally. Fe3O4 ferrofluid was prepared with a mass fraction of 0.1% and tetramethyl ammonium hydroxide surfactant was used for its stability. First Experiments were performed at different input heat input fluxes (30 to 300 watts) and in filling ratios if 50 and 60%, and the optimal filling ratio (50%) was determined. Then with this value, the thermal performance of the oscillating heat pipe was evaluated at different angles without presence and also by applying a magnetic field. The experimental results showed that Fe3O4 nanoparticles could improve the thermal performance of the device. It was also found that the optimal filling ratio for this device is 50%. As the inlet heat flux increases, the thermal resistance of the OHP decreases. In addition, the results showed that the inclination angle of the heat pipe has a significant effect on the performance of the OHP. In horizontal mode, the device did not work at all, but by applying a magnetic field, the device can continue to operate.

As an eternal and widespread energy source, solar energy has a low density while its intensity is changing continuously. Unavailability of the solar energy in nights and the gap between the time of radiation and its consumption are concerned as the main drawbacks of this type of energy. In applications such as domestic hot water (DHW), phase change materials (PCMs) can successfully remove this shortcoming due to their high thermal capacity and constant temperature during the phase change process. However, thermal conductivity of water is relatively low which reduces the performance of vacuum tube solar collectors. This properties can be improved substantially with the utilization of nanofluids. This paper presents a numerical study on nanofluid natural convection in a vacuum tube solar collector with phase change materials. The studied nanoparticles include copper oxide, titanium oxide, iron oxide, aluminum oxide, and graphene oxide. The obtained results show that for all of the current nanoparticles, rise in the nanoparticles volume fraction is accompanied by a decrease in the exit temperature of the collector. It is found that the highest temperature belongs to the graphene oxide nanoparticles.

Assessment of petroleum generation potential of the source rock as a function of total organic carbon content and kerogen type is of great importance in oil and gas exploration studies. The main aim of this research is to compare the performance of artificial neural networks trained by back propagation algorithm (ANN-BP) and metaheuristic methods including genetic algorithm (ANN-GA) and particle swarm optimization (ANN-PSO) for prediction of total organic carbon (TOC) content and remaining petroleum potential (S2) from the wireline data. For this purpose, Pabdeh Formation (Paleocene-Oligocene) in Mansuri oilfield is studied. Based on the results of linear regression on the test data, ANN-PSO method provides more accurate predictions of Rock-Eval derived TOC and S2 parameters with correlation coefficient (R2) values of 0.8548 and 0.9089, respectively. In addition, hydrogen index (HI) is appropriately predicted based on the relationship between TOC and S2 values obtained from the ANN-PSO method with R2 value of 0.6882, from which different types of kerogen can be distinguished with classification accuracy of 74 percent. Geochemical zonation of Pabdeh Formation based on organic richness and kerogen type reveals three distinctive parts, among which the middle part (Brown Shale Unit, BSU) demonstrates the greater petroleum generation potential with having the significant values of total organic carbon and hydrogen index. Therefore, the BSU can play an important role in hydrocarbon charging of the oilfield traps if it attains proper level of thermal maturity. Accordingly, precise determination of petroleum generation characteristics of Pabdeh Formation using the ANN-PSO model proposed in this study will lead to a reduction in uncertainty associated with petroleum system modeling, and therefore will considerably increase the exploration efficiency in the Mansuri oilfield.

In the design of air ductworks, a permissible velocity for air in the main branch is taken firstly. Then, according to this velocity and flow rate in the main branch, the diameter of the duct and the corresponding head loss are computed. Here, for the rectangular ducts, the dimensions are found taking into account the limitations in the height of the duct. Thereafter, the diameter of the duct (or the dimensions of the rectangular duct) are calculated in other branches according to the flow rate in each branch as well as the head loss in the main branch. Finally, the fan head is obtained based on the head loss in the branches having the highest losses. It is obvious that change in the conditions in accompanied with the repetition of the computations. To remove this shortcoming, in this work, an alternative method is proposed through coding in the EES software. In this method, the branches are input to the code once. Then, with any changes in the conditions, the code is required to be run once more. To demonstrate the suitability of this method, it is utilized here for the design of a typical ductwork. Thereafter, a parametric analysis is undertaken to examine the effects of the duct height as well as the velocity on the main branch on the area of the consumed plate. This analysis is based on the response surface method.

The use of hydraulic flow units as a method to determine the quality of the reservoir and also the use in the zoning of hydrocarbon reservoirs has a very high role in the development of reservoirs. The most reliable data in the discussion of reservoir study are obtained from the core, which due to the high cost of core retrieval, there is a small amount of this data in each field, however, the data from the core may Can play a decisive role in the study of the reservoir. In the present study, core porosity and permeability data were investigated and 6 hydraulic flow units (HFU) were identified using the regional flow index method, which were separated based on reservoir quality. Then 6 electrofacies were determined using petrophysical logs by self-organized method. The conformity of the electrofacies and the designated hydraulic flow units indicated a good convergence between them and an increase in reservoir quality from unit 1 to unit 6. The results of this study can be used as input for the construction of field facies models in the modeling process and locating new wells.

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.

Online evaluation of potential evaporation can be useful in water resources management. In this research, the use of software for smartphones to estimate the potential evaporation from free water surface and ground surface and also to estimate the possibility of reducing evaporation due to the installation of photovoltaic panels on water bodies is considered. The data used is extracted from the NASA-POWER cloud network database and the software intelligently detects the selected point in relation to two states of dry area or water area and performs the relevant calculations. The proposed relation for the evaluation of potential evaporation was analyzed by considering 10 year average data of Doosti dam (2008–2017) and its error rate and some experimental relation of potential evaporation and evaporation from the free water surface with the rate of evaporation from the evaporation pan located at the site were determined. The results indicated that the proposed relation yielded a smaller error compared with all the other relations. In order to evaluate the efficiency of the proposed relation and the data extracted from the NASA POWER database, the evaluations done by the software were compared against the values from the evaporation pans in the synoptic stations in six cities. This revealed the validity of the software outputs to be within 8–16%.

In this study, heat transfer of nanofluid in a channel with an externally–applied magnetic field and a porous obstacle is simulated and discussed. To simulate the flow field inside the obstacle, the Darcy–Brinkman–Forchheimer model is implemented in the LBM method. Thereafter, effects of influencing parameters on the heat transfer rate are analyzed utilizing the experimental design method. The results show that the nanoparticles type has the greatest effect on the heat transfer with the contribution ratio of 62.79%. The second and the third parameters are the obstacle size and the Reynolds number, with the contribution ratios of 20.71% and 7.58%, respectively. The contribution ratios of the Hartmann number and the nanoparticles fraction are estimated about 3%. However, it is found that in this problem, the influences of the porosity, type, and the Darcy number of the obstacle on the mean Nusselt number are almost negligible.

In this paper, the heat transfer of the forced convection of nanofluid in a annular porous channel on internal and external walls is numerically investigated. The nanofluid is simulated using the dual phase and flow model in the porous region by the Darcy-Brinkman-Forcheimer model. The fluid flow is considered as two-dimensional, laminar, steady, axi-symmetric axial and incompressible. The porous medium is uniform and homogeneous, and the physical properties of the nanofluid and the porous medium are assumed constant. The governing equations have been solved using the finite volume method and SIMPLE algorithm. The effect of parameters such as Darcy number, porosity barrier height and thickness, thermal conductivity of porous region to fluid and volume fraction of nanoparticles on flow field, heat transfer and pressure drop have been investigated. The results show that the use of porous barriers in the flow path leads to significant changes in the characteristics of flow and heat transfer. Reducing the Darcy and Reynolds numbers leads to the formation of a vortex behind the barriers that have a significant impact on heat transfer. By reducing the Darcy number, the heat transfer increases significantly. It will also cause a severe pressure drop in the flow. Increasing the thermal conductivity ratio of the solid-liquid matrix will increase the local Nasslet number of the wall in the area of the barrier, which will be more significant in the high permeability. Increasing the height of the porous barriers reducing the thickness of the boundary layer and increasing the heat transfer.

The geomechanical and petrophysical parameters of the reservoir such as shear wave velocity, porosity and permeability are regarded as the most important elements in estimating reserves, reservoir simulation, and overall field exploitation strategies. Recently, several methods of artificial intelligence techniques have been used to predict this parameter by using well log data. However, predicting the characteristics of heterogeneous reservoirs always has been facing many problems and an appropriate response is rarely achieved. In this paper, a new methodology is presented for reservoir parameters (shear wave velocity, porosity and permeability) estimation by combining artificial neural network and Particle Swarm optimization (PSO) in Asmari formation of mansuri oilfield. Performance of proposed hybrid scheme was evaluated by comparing the results with the Neural Network and Nero-Fuzzy methods as well as hybrid genetic algorithm–neural network strategy (GA–NN). Comparison of the results shows that PSO-ANN outperforms all the other methods and it can be considered as a powerful tool for reservoir parameters estimation, especially in cases where a precise estimation criterion is crucial.

This paper deals with the injection of twin oblique nanofluid jets into a channel with water cross-flow. In this regard, the effects of different geometric and physical parameters including the velocity, distance, and angles of the jets as well as the nanoparticles volume fraction therein are studied. The Eulerian-Eulerian two-phase model is employed to analyze the present problem. By solving separate equation sets for water and the nanoparticles, this approach provides the possibility of behavior prediction for each of the phases inside the flow field, separately. The accuracy of the current simulations is confirmed by comparing the obtained results with available experimental data. The results show that replacement of a single jet with twin jets increases the heat exchange from the target surface and makes its distribution more uniform along the surface. In addition, it is found that rise in the velocity and distance of the jets leads to heat transfer improvement. However, the effect of the nanoparticles volume fraction in the injected nanofluid on the heat transfer rate of the target surface is strongly dependent to the nanoparticles penetration into the water cross-flow. Closer scrutiny of the results reveals that the injection angles of the twin jets play an important role in the nanoparticles penetration as well as their distribution pattern inside the flow field and thereby, by adjusting these angles, the heat exchange from the target surface can be improved.

This paper deals with heat transfer in jet impingement of nanofluids. Attention is focused to compare single-phase and two-phase nanofluid models and to study separate behaviors of the base fluid and the nanoparticles through the Eulerian-Eulerian two-phase model. For this purpose, jet impingement of Al2O3/water nanofluid in different conditions is simulated adopting the single-phase, two-phase mixture and Eulerian-Eulerian models and the corresponding results are discussed. For the solution of the governing equations of the three models, the control-volume approach is used. The accuracy of the current simulations is demonstrated by comparing the obtained results with those of open literature. The results indicate that in all of the approaches, increase in the Reynolds number as well as nanoparticle fraction leads to heat transfer improvement. During the current computations, the two-phase models predict higher heat transfer as compared to the single-phase model. Closer scrutiny of the two-phase approaches indicates that heat transfer of the Eulerian-Eulerian model is higher than the mixture model. However, it is found that with increase in the Reynolds number and decrease in the nanoparticle fraction, results of the two approaches become closer. Finally, the Eulerian-Eulerian model demonstrates that temperature distribution in the base fluid and the nanoparticles are similar but the corresponding velocity distributions are distinct.

This paper deals with water flow in a backward-facing step with blowing of different nanofluids. The objective is to evaluate the effect of nanofluid blowing on the heat transfer rate. For this purpose, the Eulerian-Eulerian two-phase model is employed. The accuracy of the current simulations is demonstrated by comparing the obtained results with those of open literature. The results show that increasing the nanofluid blowing as well as nanoparticles fraction therein improve heat exchange from different surfaces of the channel. Comparing the results of different nanofluids leads one to conclude that the bottom wall heat transfer attains its maximum value when the blowed nanofluid contains nanoparticles with the highest thermal conductivity. However, it is found that maximum heat transfer in the top wall is achieved during blowing of a nanofluid with the highest nanoparticle penetration into the channel flow. Moreover, it is observed that discrepancies appearing between the results of different nanofluids become more remarkable as one increases the nanofluid blowing or nanoparticles fraction therein. Finally, the Eulerian-Eulerian model demonstrates that among the interphase forces, the effects of virtual mass and particle-particle interaction forces are negligible in such a way that they can be ignored.

To study of petroleum reservoirs which their productions are function of their fractured system, fractures assessment is necessary and important in oil production optimization and field development. The purpose of this research is recognition of a quick method for identification of fractured zones using petrophysical logs which are handy in all wells, and their effects on porosity and permeability of Asmari reservoir of Balarood oil field. The result shows that although some factors such as lithology and reservoir fluids effects on petrophysical logs, requiring minor corrections to logs by means of mathematical methods such as derivation can be used to determine fractured zones on logs, which show great consistency with image logs and velocity deviation log. Results show that zones of abundant bearing fractures can be easily recognized on first stage of derivation on petrophysical logs. Derivation process of any log is defined as a function for that log. Tectonic information and geological prospective of oil field will have an apparent contribution in identification of fractured zones. In addition, fractures and porous zones have had effective impact on the reservoir rock properties. The results indicated that the production in the Asmari reservoir of this field is a combination of fractures and rock matrix and fracture analysis can be done by using petrophysical logs in old drilled wells without image logs as well. As six logs RHOB, NPHI, GR, DT, PEF, CAL have vital role in petrophysical studies, these six tools are the best logs to diagnosis of fractured zones due to their responses against the fracture along with doing some corrections in these logs.

Fractures are considered as one of the important structures in fractured reservoirs due to their effect on fluid currents and reservoir parameters such as porosity and permeability. Fracture parameters can only be directly calculated with core and image logs. Cores have serious limitations, so image logs are the best method. The aim of this study is the systematic fractures analysis of the Asmari Formation in the Marun field as one of the giant oilfields in world. The main objectives of image logs were evaluating structural dip, characterizing natural fractures and field structure heterogeneity, and finally correlating the results with complimentary methods such as Velocity Deviation Log (VDL), Repeat Formation Test (RFT), mud lost data, and isodip map in the carbonate Asmari Formation. Generally, electric and ultrasonic imaging tools record vast amounts of high-resolution data. This enables geoscientists to describe in detail the structural fracture networks. The results indicate that the highest fracture density is in the zones 1, 20, and 30 of the Asmari reservoir that show high correlation with VDL and mud lost data. Image logs also show a range of bedding dips from 20˚ in the northern limb to 30˚ in the southern limb with strikes ranging from 10˚ to 270˚N. Regarding the general pattern of fractures, it is evident that they are related to the folding and are classified mainly as longitudinal, transverse, and oblique. The longitudinal pattern is dominant and often forms open fractures. They are characterized by N50W-S50E and mainly observed in the upper Asmari zones. Moreover, to find the vertical relation of the layer and fractures, RFT data were used. The findings revealed the presence of a vertical relation in the upper horizons of the reservoir, especially in the eastern section due to the high fracture density.

Entropy generation of nanofluids during natural convection in rectangular porous enclosures is analyzed in this paper. The objective is to find optimum circumstances from the standpoints of the First Law and the Second Law of thermodynamics. For this purpose، the mass، momentum، and energy conservation equations are solved numerically. Thereafter، the generation of entropy is calculated and discussed. Computations are undertaken for Cu، Al2O3، and TiO2 nanoparticles in a base fluid of water and the corresponding results are compared. Moreover، the influences of volume fraction of the nanoparticles، Rayleigh number، enclosure aspect ratio، radiation exchange، and non-Darcy effects on heat transfer and entropy generation in the porous enclosure are analyzed. Inspection of the presented results demonstrates that radiation exchange and non-Darcy effects possess prominent consequences on heat transfer and entropy generation inside the enclosure. Among the current nanofluids، the highest heat transfer and entropy generation appears in the Cu-water nanofluid.