جستجوی مقالات مرتبط با کلیدواژه « Particle Swarm Optimization » در نشریات گروه « مهندسی شیمی، نفت و پلیمر »

Optimizing the placement of wells is a crucial step in field development as it directly impacts production and cost. Inappropriate well placement can lead to decreased production and higher costs due to the expensive drilling process. Vertical well placement focuses on optimizing wellhead coordinates (x and y parameters), while horizontal and deviated wells require considering the depth of the wells (z parameter) along with operational limitations. This research presents an automatic framework that utilizes the particle swarm optimization algorithm to optimize the location of horizontal wells, taking into account drilling limitations. The objective function used is the net present value (NPV). This framework defines the number of particle swarm optimization variables based on operational constraints and well geometry. The algorithm randomly selects parameter values and applies the optimization procedure while considering specific constraints until the stop criteria are met. The framework successfully optimized the x, y, z, LP (Landing point), and KOP (Kick of Point) parameters in two heterogeneous synthetic models and a benchmark model (PUNQ_S3). On average, the net present value increased by 22% in all models, with greater heterogeneity resulting in a higher increase compared to homogeneous models.

Fractured network modeling is the main prerequisite for fluid flow simulation in many applications such as groundwater resource management, oil and gas reservoir simulation, geothermal energy resource modeling and etc. The aim of this study is to develop an iterative object-based algorithm for fractured network modeling that considers both statistical parameters and spatial connectivity of fractures. The presented algorithm uses the functions of fracture neighborhood matrices in image processing to make clear the fracture continuity and to determine their spatial distribution using the matrix of interconnected fracture cell detection, Sobel matrix, Prewitt matrix and Laplacian matrix. The objective function defines the differences between the features of the reference fracture network and the stochastic generated fracture network using a norm of two. To solve this objective function, optimization algorithms of Gravitational search, particle swarm and hybrid of these two metaheuristic algorithms have been used. In this paper, the metaheuristic algorithm of particle swarm optimization enjoys more validity in fracture network modelling. Therefore, among the metaheuristic algorithms, PSO algorithm regenerates the reference fracture network with the accuracy of 98.89%.

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.

   Sulfide removal from sour water is essential, before reuse or release of sour water into the environment. Regarding the high costs of traditional methods, biological removal can be used as a reliable alternative. Biological sulfide removal from sour water was investigated in a batch reactor using Thiobacillus sp. as a dominant species of a mixed culture. A conceptual model was developed to describe the process of H2S removal from sour water in the batch reactor. The model considers H2S and O2 transfer between liquid and gas phases, biological oxidation of H2S to sulfate and elemental sulfur, and chemical oxidation of H2S to thiosulfate in the liquid phase. The governing equations were derived using the principles of mass conservation and biochemical reactions. Several batch runs were performed to obtain experimental data on the variation of sulfide, sulfate, thiosulfate, and oxygen concentrations in the system as a function of time, and an algorithm was devised to use the method of Particle Swarm Optimization together with the numerical solution of the model equations to estimate biokinetic parameters. Additional batch runs under different conditions were performed to verify the accuracy of the model. These results indicated reasonable accuracy of the model to predict the performance of a batch reactor for the removal of H2S from sour water. The novelty of this model is considering different pathways for sulfide oxidation which includes product selectivity.
Main The maxim specific oxygen uptake rate (SOUR=OUR/X) is one of the most important parameters in the evaluation of the biological activity of the microorganisms. The calculated value for this parameter was almost constant (16 mg DO g-1 VSS min-1) during all sulfide oxidation tests indicating that the maximum specific oxidation capacity of the biomass is independent of substrate and biomass concentration. Results exhibited bacteria prefer to partially oxidized sulfide to elemental sulfur, however this preference is a function of dissolved oxygen and substrate availability.

Single-criterion techniques in which just a single objective is considered cannot offer the perfect solution because they cannot take into account the trade-off between conflicting technical and economic conditions. In this study, a multi-criteria algorithm was developed based on experimental design methods, particle swarm optimization, and fuzzy logic. It was able to solve the optimization problem via considering different objectives simultaneously, finding the optimum values of effective factors. To evaluate the efficiency of the workflow, a case study was done in which influential parameters (water flooding duration, polymer concentration, duration of polymer injection, and polymer adsorption) for the design of an enhanced oil recovery operation of polymer flooding in a sandstone reservoir were optimized considering technical (cumulative oil production) and economic (net present value) objectives. The results were compared to the results of the base-case scenario as well as a single objective algorithm (particle swarm optimization). Compared to the base-case scenario, cumulative oil production increased more than 58% and net present value rised from $ 6.9 to 13.1 MM as well. Although the optimum scenario proposed by single-criterion optimization algorithm based on technical objective produced more oil compared to the best solution of the multi-purpose algorithm, a severe reduction was observed in the economic objective simultaneously. Finally, the results of this study demonstrate that multi-objective algorithms are more applicable to precise and realistic decision-making.

In this paper, an algorithm is presented to optimize a PFHE through minimizing three different objective functions: the number of entropy generation unit, total annual cost (TAC) and combined entropy generation and TAC. First, Minimization of total number of entropy generation units for specific heat duty requirement under given space restrictions and minimization of TAC are considered as objective functions using Particle swarm optimization (PSO) and are treated individually. Heat exchanger length, fin frequency, numbers of fin layers, lance length of fin, fin height and fin thickness or different flow length of the heat exchanger are considered for optimization. The result show that if TAC is selected as an objective function, the rate of convergence and optimization will be improved, then sum of entropy generation and cost was selected as the best candidate for objective function.