Petroleum Engineering
Meysam Dabiri-Atashbeyk; Mehdi Koolivand-salooki; Morteza Esfandyari; Mohsen Koulivand
Abstract
Reservoir characterization and asset management require comprehensive information about formation fluids. In fact, it is not possible to find accurate solutions to many petroleum engineering problems without having accurate pressure-volume-temperature (PVT) data. Traditionally, fluid information has ...
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Reservoir characterization and asset management require comprehensive information about formation fluids. In fact, it is not possible to find accurate solutions to many petroleum engineering problems without having accurate pressure-volume-temperature (PVT) data. Traditionally, fluid information has been obtained by capturing samples and then by measuring the PVT properties in a laboratory. In recent years, neural network has been applied to a large number of petroleum engineering problems. In this paper, a multi-layer perception neural network and radial basis function network (both optimized by a genetic algorithm) were used to evaluate the dead oil viscosity of crude oil, and it was found out that the estimated dead oil viscosity by the multi-layer perception neural network was more accurate than the one obtained by radial basis function network.
Mojtaba Ghaedi; Zoltán E. Heinemann; Mohsen Masihi; Mohammad Hossein Ghazanfari
Abstract
In this paper, a very efficient method, called single matrix block analyzer (SMBA), has been developed to determine relative permeability and capillary pressure curves from spontaneous imbibition (SI) data. SMBA mimics realistically the SI tests by appropriate boundary conditions modeling. In the proposed ...
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In this paper, a very efficient method, called single matrix block analyzer (SMBA), has been developed to determine relative permeability and capillary pressure curves from spontaneous imbibition (SI) data. SMBA mimics realistically the SI tests by appropriate boundary conditions modeling. In the proposed method, a cuboid with an identical core plug height is considered. The equal dimensions of the cuboid in x and y directions are set such that the cylindrical core plug and the cuboid have the same shape factor. Thus, by avoiding the difficulties of the cylindrical coordinates, a representative model for the core plug is established. Appropriate grid numbers in x-y and z directions are specified to the model. Furthermore, the rock and fluid properties of SI test are set in the SMBA. By supposing forms of the oil-water capillary pressure and relative permeability and comparing the oil recovery curves of SMBA and SI data, capillary pressure and relative permeability can be determined. The SMBA is demonstrated using three experimental data with different aging times. Suitable equations are employed to represent the capillary pressure and relative permeability curves. The genetic algorithm is used as the optimization tool. The obtained results, especially for capillary pressure, are in good agreement with the experimental data. Moreover, the Bayesian credible interval (P10 and P90) evaluated by the Neighborhood Bayes Algorithm (NAB) is quite satisfactory.
Saeed Mahmoodpour; Mohsen Masihi; Sajjad Gholinejhad
Abstract
The mathematical modeling of fracture networks is critical for the exploration and development of natural resources. Fractures can help the production of petroleum, water, and geothermal energy. They also greatly influence the drainage and production of methane gas from coal beds. Orientation and spatial ...
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The mathematical modeling of fracture networks is critical for the exploration and development of natural resources. Fractures can help the production of petroleum, water, and geothermal energy. They also greatly influence the drainage and production of methane gas from coal beds. Orientation and spatial distribution of fractures in rocks are important factors in controlling fluid flow. The objective function recently developed by Masihi et al. 2007 was used herein to generate fracture models that incorporate field observations. To extend this method, simulated annealing, genetic, and tabu search algorithms were employed in the modeling of fracture networks. The effectiveness of each algorithm was compared and the applicability of the methodology was assessed through a case study. It is concluded that the fracture model generated by simulated annealing is better compared to those generated by genetic and tabu search algorithms.
Mohammad Reza Mahdiani; Ehsan Khamehchi
Abstract
One of the problems that sometimes occur in gas allocation optimization is instability phenomenon. This phenomenon reduces the oil production and damages downhole and surface facilities. Different works have studied the stability and suggested some solutions to override it, but most of them (such as ...
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One of the problems that sometimes occur in gas allocation optimization is instability phenomenon. This phenomenon reduces the oil production and damages downhole and surface facilities. Different works have studied the stability and suggested some solutions to override it, but most of them (such as making the well intelligent) are very expensive and thus they are not applicable to many cases. In this paper, as a new approach, the stability has been studied in gas allocation optimization problems. To prevent the instability, instability has been assumed as a constraint for the optimizer and then the optimizer has been run. For the optimization, first a genetic algorithm and then a hybrid of genetic algorithm and Newton-Quasi have been used, and their results are compared to ensure the good performance of the optimizer; afterwards, the effect of adding the instability constraint to the problem on production reduction have been discussed. The results show that the production loss with adding this constraint to the system is very small and this method does not need any additional and expensive facilities for preventing the instability. Therefore, the new method is applicable to different problems.
Javid Haddad; Reza Mosayebi Behbahani; Mohammadreza Shishesaz
Abstract
Arguably, the natural gas transmission pipeline infrastructure in Iran represents one of the largest and most complex mechanical systems in the world. The optimization of large gas trunk lines known as IGAT results in reduced fuel consumption or higher capability and improves pipeline operation. In the ...
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Arguably, the natural gas transmission pipeline infrastructure in Iran represents one of the largest and most complex mechanical systems in the world. The optimization of large gas trunk lines known as IGAT results in reduced fuel consumption or higher capability and improves pipeline operation. In the current study, a single-objective optimization was conducted for Khormoj compressor station on the Iranian gas trunk line V (IGAT5). The system consists of over 504 kilometers of 56-inch pipeline from South Pars to Aghajari. This system passes through a tortuous terrain with changes in elevation which makes the optimization process even more challenging. Genetic algorithm (GA) was used in this optimization along with detailed models of the performance characteristics of compressors. The results show that in stations having the same compressor in parallel the minimum power (energy) consumption is reached when split flow in all the compressors is the same.
Hamid Heydari; Jamshid Moghadasi; Reza Motafakkerfard
Abstract
Cementation factor is a critical parameter, which affects water saturation calculation. In carbonate rocks, due to the sensitivity of this parameter to pore type, water saturation estimation has associated with high inaccuracy. Hence developing a reliable mathematical strategy to determine these properties ...
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Cementation factor is a critical parameter, which affects water saturation calculation. In carbonate rocks, due to the sensitivity of this parameter to pore type, water saturation estimation has associated with high inaccuracy. Hence developing a reliable mathematical strategy to determine these properties accurately is of crucial importance. To this end, genetic algorithm pattern search is employed to find accurate cementation factor by using formation resistivity factor and the porosity obtained from laboratory core analyses with considering the assumption that tortuosity factor is not unity. Subsequently, particle swarm optimization (PSO) fuzzy inference system (FIS) was used for the classification of cementation factor according to the predominated rock pore type by using the input variables such as cementation factor, porosity, and permeability to classify the core samples in three groups, namely fractured, interparticle, and vuggy pore system. Then, the experimental data which was collected from Sarvak formation located in one of the Iran southwestern oil fields was applied to the proposed model. Next, for each class, a cementation factor-porosity correlation was created and the results were used to calculate cementation factor and water saturation profile for the studied well. The results showed that the constructed model could predict cementation factor with high accuracy. The comparison between the model presented herein and the conventional method demonstrated that the proposed model provided a more accurate result with a mean square error (MSE) of around 0.024 and led to an R2 value of 0.603 in calculating the water saturation.