In this paper, an improved ant colony optimization (ICMPACO) algorithm based on the multi-population strategy, co-evolution mechanism, pheromone updating strategy, and pheromone diffusion mechanism is proposed to balance the convergence speed and solution diversity, and improve the optimization performance in solving the large-scale optimization problem. In the proposed ICMPACO algorithm, the optimization problem is divided into several sub-problems and the ants in the population are divided into elite ants and common ants in order to improve the convergence rate, and avoid to fall into the local optimum value. The pheromone updating strategy is used to improve optimization ability. The pheromone diffusion mechanism is used to make the pheromone released by ants at a certain point, which gradually affects a certain range of adjacent regions. The co-evolution mechanism is used to interchange information among different sub-populations in order to implement information sharing. In order to verify the optimization performance of the ICMPACO algorithm, the traveling salesmen problem (TSP) and the actual gate assignment problem are selected here. The experiment results show that the proposed ICMPACO algorithm can effectively obtain the best optimization value in solving TSP and effectively solve the gate assignment problem, obtain better assignment result, and it takes on better optimization ability and stability.

/pdf/an-improved-ant-colony-optimization-algorithm-based-on-1yjw4mm9u3.pdf

An Improved Ant Colony Optimization Algorithm Based on Hybrid Strategies for Scheduling Problem

Heat transfer behavior of nanoparticle enhanced PCM solidification through an enclosure with V shaped fins

Heat transfer simulation of heat storage unit with nanoparticles and fins through a heat exchanger

Heat transfer and turbulent simulation of nanomaterial due to compound turbulator including irreversibility analysis

Heat transfer of nanoparticles employing innovative turbulator considering entropy generation

Steam-assisted-gravity-drainage (SAGD) has been proved effective in heavy oil recovery. Preheating of the wellbore-surrounding reservoir is to circulate steam in the injector and producer so that heat can be conducted into surrounding oil layer. At this stage, the amount of steam injected into the reservoir is neglected. As a result, creating a large temperature difference between wellbore and annuli is key during the preheating process. A model is established for estimating steam properties in the wellbores so that the highest steam temperature in wellbores can be achieved. The model is comprised of mass, energy and momentum balance equations and the model is solved with numerical method. It is found that: (a) rich heat energy reflected in high steam quality does little effect on heat absorption rate of oil layer. The only effective method for temperature increase in oil layer is to increase the steam temperature in wellbores; (b) in order to increase the heat conduction rate to oil layer, a lower steam quality, a higher steam pressure and a lower mass flow rate is recommended.

A numerical model for wet steam circulating in horizontal wellbores during starting stage of the steam-assisted-gravity-drainage process

Geothermal energy has been in rich in both onshore and offshore conditions. Air can be used as working fluid at offshore platforms for geothermal energy development. However, existing models cannot...

The design and analysis of a coaxial casing heat exchanger used in geothermal energy systems


 The identification of oil and water layers (OWL) from well log data is an important task in petroleum exploration and engineering. At present, the commonly used methods for OWL identification are time-consuming, low accuracy or need better experience of researchers. Therefore, some machine learning methods have been developed to identify the lithology and OWL. Based on logging while drilling data, this paper optimizes machine learning methods to identify OWL while drilling.
 Recently, several computational algorithms have been used for OWL identification to improve the prediction accuracy. In this paper, we evaluate three popular machine learning methods, namely the one-against-rest support vector machine, one-against-one support vector machine, and random forest. First, we choose apposite training set data as a sample for model training. Then, GridSearch method was used to find the approximate range of reasonable parameters' value. And then using k-fold cross validation to optimize the final parameters and to avoid overfitting. Finally, choosing apposite test set data to verify the model.
 The method of using machine learning method to identify OWL while drilling has been successfully applied in Weibei oilfield. We select 1934 groups of well logging response data for 31 production wells. Among them, 198 groups of LWD data were selected as the test set data. Natural gamma, shale content, acoustic time difference, and deep-sensing logs were selected as input feature parameters. After GridSearch and 10-fold cross validation, the results suggest that random forest method is the best algorithm for supervised classification of OWL using well log data. The accuracy of the three classifiers after the calculation of the training set is greater than 90%, but their differences are relative large. For the test set, the calculated accuracy of the three classifiers is about 90%, with a small difference. The one-against-rest support vector machine classifier spends much more time than other methods. The one-against-one support vector machine classifier is the classifier which training set accuracy and test set accuracy are the lowest in three methods.
 Although all the calculation results have diffierences in accuracy of OWL identification, their accuracy is relatively high. For different reservoirs, taking into account the time cost and model calculation accuracy, we can use random forest and one-against-one support vector machine models to identify OWL in real time during drilling.

Optimization of Models for Rapid Identification of Oil and Water Layers During Drilling - A Win-Win Strategy Based on Machine Learning


 Very limited studies were done on the superheated steam-assisted-gravity-drainage (SAGD) process. Besides, study of the effect of physical heating of steam at different state on the productivity during the SAGD process has not been reported.
 In this paper, a numerical model is proposed to reveal the effect of physical heating on the productivity of a horizontal well pair during the SAGD process. Results show that: (a) the temperature increase of superheated steam plays a weak influence on the productivity of a horizontal well pair during the SAGD process. (b) the heated area increases with the steam quality, while the increase of the heated area under superheated steam injection is not obvious compared with the heated area when the steam quality is equal to 1.0. (c) as the mobility of oil in the steam chamber increases, the fraction of oil produced from the steam chamber increases, and the pressure near the production well increases. (d) when the steam quality increases or becomes superheated steam, the mobility of the oil in the steam chamber increases, and the supply rate of oil near the production well gradually become larger than the decrease rate of oil in the same region induced by elastic energy. This study provides a
 The paper offers a comprehensive insight into the SAGD process, and helps petroleum engineers for SAGD project design in heavy oil reserves.

Effect of Steam State on the Productivity of a Horizontal Well Pair During the Steam-Assisted-Gravity-Drainage Process: Physical Aspect Analysis

In this paper, we put forward critical future prospects based on the paper that was published on International Journal of Heat and Mass Transfer. At present, engineers are familiar with the advantages and disadvantages of different working fluids in thermal recovery of heavy oil. For all the oil displacement mechanism, they can be summarized into only three aspects: (a) physical heat conduction, (b) chemical reactions, and (c) the differences in hydrodynamic properties between fluids. This is already well known in the existing body of knowledge. However, how much will each of these three mechanisms contribute to the total oil recovery efficiency is quite interesting and important to the industry. Based on the commented paper and some related references, we pointed out the future research directions in this area.

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Fengrui Sun

Papers

A numerical model for wet steam circulating in horizontal wellbores during starting stage of the steam-assisted-gravity-drainage process

The design and analysis of a coaxial casing heat exchanger used in geothermal energy systems

Optimization of Models for Rapid Identification of Oil and Water Layers During Drilling - A Win-Win Strategy Based on Machine Learning

Effect of Steam State on the Productivity of a Horizontal Well Pair During the Steam-Assisted-Gravity-Drainage Process: Physical Aspect Analysis

Slight insights and perspectives of future heavy oil recovery