[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Next Issue
Volume 4, September
Previous Issue
Volume 4, July
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
energies-logo

Journal Browser

Journal Browser

Energies, Volume 4, Issue 8 (August 2011) – 9 articles , Pages 1112-1257

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Select all
Export citation of selected articles as:
475 KiB  
Article
Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach
by Karin Kandananond
Energies 2011, 4(8), 1246-1257; https://doi.org/10.3390/en4081246 - 22 Aug 2011
Cited by 141 | Viewed by 12518
Abstract
Demand planning for electricity consumption is a key success factor for the development of any countries. However, this can only be achieved if the demand is forecasted accurately. In this research, different forecasting methods—autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and [...] Read more.
Demand planning for electricity consumption is a key success factor for the development of any countries. However, this can only be achieved if the demand is forecasted accurately. In this research, different forecasting methods—autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and multiple linear regression (MLR)—were utilized to formulate prediction models of the electricity demand in Thailand. The objective was to compare the performance of these three approaches and the empirical data used in this study was the historical data regarding the electricity demand (population, gross domestic product: GDP, stock index, revenue from exporting industrial products and electricity consumption) in Thailand from 1986 to 2010. The results showed that the ANN model reduced the mean absolute percentage error (MAPE) to 0.996%, while those of ARIMA and MLR were 2.80981 and 3.2604527%, respectively. Based on these error measures, the results indicated that the ANN approach outperformed the ARIMA and MLR methods in this scenario. However, the paired test indicated that there was no significant difference among these methods at α = 0.05. According to the principle of parsimony, the ARIMA and MLR models might be preferable to the ANN one because of their simple structure and competitive performance Full article
(This article belongs to the Special Issue Intelligent Energy Demand Forecasting)
Show Figures

Figure 1

Figure 1
<p>Time series plot of electricity demand.</p>
Full article ">Figure 2
<p>Correlogram of electricity demand.</p>
Full article ">Figure 3
<p>Correlogram of the residual.</p>
Full article ">Figure 4
<p>The architecture of a neural network.</p>
Full article ">Figure 5
<p>The ANN analysis results.</p>
Full article ">Figure 6
<p>Residuals <span class="html-italic">vs</span>. fitted plot.</p>
Full article ">Figure 7
<p>The normal probability plot of residuals.</p>
Full article ">
508 KiB  
Article
A General Mathematical Framework for Calculating Systems-Scale Efficiency of Energy Extraction and Conversion: Energy Return on Investment (EROI) and Other Energy Return Ratios
by Adam R. Brandt and Michael Dale
Energies 2011, 4(8), 1211-1245; https://doi.org/10.3390/en4081211 - 19 Aug 2011
Cited by 60 | Viewed by 14154
Abstract
The efficiencies of energy extraction and conversion systems are typically expressed using energy return ratios (ERRs) such as the net energy ratio (NER) or energy return on investment (EROI). A lack of a general mathematical framework prevents inter-comparison of NER/EROI estimates between authors: [...] Read more.
The efficiencies of energy extraction and conversion systems are typically expressed using energy return ratios (ERRs) such as the net energy ratio (NER) or energy return on investment (EROI). A lack of a general mathematical framework prevents inter-comparison of NER/EROI estimates between authors: methods used are not standardized, nor is there a framework for succinctly reporting results in a consistent fashion. In this paper we derive normalized mathematical forms of four ERRs for energy extraction and conversion pathways. A bottom-up (process model) formulation is developed for an n-stage energy harvesting and conversion pathway with various system boundaries. Formations with the broadest system boundaries use insights from life cycle analysis to suggest a hybrid process model/economic input output based framework. These models include indirect energy consumption due to external energy inputs and embodied energy in materials. Illustrative example results are given for simple energy extraction and conversion pathways. Lastly, we discuss the limitations of this approach and the intersection of this methodology with “top-down” economic approaches. Full article
Show Figures

Figure 1

Figure 1
<p>Schematic for flows within a single processing stage.</p>
Full article ">Figure 2
<p>Observed flows in US refining sector, 2008 [<a href="#B45-energies-04-01211" class="html-bibr">45</a>], neglecting indirect (embodied) consumption. Deviation from our simple model occurs due to the inclusion of some energy content from external natural gas inputs into the principal energy stream in the form of H<math display="inline"> <msub> <mrow/> <mn>2</mn> </msub> </math> (called flow <math display="inline"> <mrow> <mi>d</mi> <mi>E</mi> </mrow> </math> in this figure). This flow exists in reality but is not included in our simple model of a processing stage. Flows are rounded independently and may not sum exactly to 100. Flow <math display="inline"> <msub> <mi>X</mi> <mn>11</mn> </msub> </math> is of uncertain magnitude (refined products use within refinery process boundary, e.g., diesel fuel use on refinery site).</p>
Full article ">Figure 3
<p>Energy production process with one pathway and <span class="html-italic">n</span> stages.</p>
Full article ">Figure 4
<p>Electricity production from a PV panel with data from [<a href="#B54-energies-04-01211" class="html-bibr">54</a>,<a href="#B55-energies-04-01211" class="html-bibr">55</a>,<a href="#B56-energies-04-01211" class="html-bibr">56</a>] modeled as an energy extraction and conversion pathway. Collection losses are neglected in this case (see text for discussion). In reality a proportion of the finished product would be fed back into the production process. Unfortunately no data are available on this.</p>
Full article ">Figure 5
<p>Comparison of NER<math display="inline"> <msup> <mrow/> <mi>β</mi> </msup> </math>, NER<math display="inline"> <msup> <mrow/> <mi>γ</mi> </msup> </math> and <math display="inline"> <msub> <mi>F</mi> <mi>s</mi> </msub> </math> as a function of processing stage <span class="html-italic">s</span>.</p>
Full article ">Figure 6
<p>Comparison of final energy output given changes in location of large processing energy input. Note that a processing intensive technology further in the processing chain results in less final energy output.</p>
Full article ">Figure 7
<p>Comparison between EER<math display="inline"> <msup> <mrow/> <mi>γ</mi> </msup> </math> and NER<math display="inline"> <msup> <mrow/> <mi>γ</mi> </msup> </math> with changes in the makeup of consumption between internal (<span class="html-italic">X</span>) and external (<span class="html-italic">E</span>) consumption. In all cases indirect consumption is equal to 0.</p>
Full article ">Figure 8
<p>Energy production process with one pathway and two stages of only internal consumption.</p>
Full article ">Figure 9
<p>Energy production process with one pathway and two stages.</p>
Full article ">Figure 10
<p>Energy production process with one pathway and <span class="html-italic">n</span> stages.</p>
Full article ">Figure 11
<p>Energy production process with one pathway and <span class="html-italic">n</span> stages and external energy inputs from outside the production pathway.</p>
Full article ">Figure 12
<p>Energy production process with one pathway and <span class="html-italic">n</span> stages, including external (<span class="html-italic">E</span>) and indirect (<span class="html-italic">I</span>) energy inputs.</p>
Full article ">
231 KiB  
Article
A Carbon Footprint of an Office Building
by Miimu Airaksinen and Pellervo Matilainen
Energies 2011, 4(8), 1197-1210; https://doi.org/10.3390/en4081197 - 19 Aug 2011
Cited by 49 | Viewed by 10747
Abstract
Current office buildings are becoming more and more energy efficient. In particular the importance of heating is decreasing, but the share of electricity use is increasing. When the CO2 equivalent emissions are considered, the CO2 emissions from embodied energy make up [...] Read more.
Current office buildings are becoming more and more energy efficient. In particular the importance of heating is decreasing, but the share of electricity use is increasing. When the CO2 equivalent emissions are considered, the CO2 emissions from embodied energy make up an important share of the total, indicating that the building materials have a high importance which is often ignored when only the energy efficiency of running the building is considered. This paper studies a new office building in design phase and offers different alternatives to influence building energy consumption, CO2 equivalent emissions from embodied energy from building materials and CO2 equivalent emissions from energy use and how their relationships should be treated. In addition this paper studies how we should weight the primary energy use and the CO2 equivalent emissions of different design options. The results showed that the reduction of energy use reduces both the primary energy use and CO2 equivalent emissions. Especially the reduction of electricity use has a high importance for both primary energy use and CO2 emissions when fossil fuels are used. The lowest CO2 equivalent emissions were achieved when bio-based, renewable energies or nuclear power was used to supply energy for the office building. Evidently then the share of CO2 equivalent emissions from the embodied energy of building materials and products became the dominant source of CO2 equivalent emissions. The lowest primary energy was achieved when bio-based local heating or renewable energies, in addition to district cooling, were used. The highest primary energy was for the nuclear power option. Full article
(This article belongs to the Special Issue Energy Savings in the Domestic and Tertiary Sectors 2011)
Show Figures

Figure 1

Figure 1
<p>Yearly energy consumption in different cases. Electricity AC represents for electricity for air conditioning systems.</p>
Full article ">Figure 2
<p>The share of different energy use in different cases.</p>
Full article ">Figure 3
<p>The share of each energy consumption and embodied CO<sub>2</sub> in different cases when average district heating, cooling and electricity are used. The heating includes both space heating and domestic hot water heating.</p>
Full article ">Figure 4
<p>The share of each energy consumption and embodied CO<sub>2</sub> equivalent in different cases when district heating, cooling from bio-fuels is used and electricity is from renewable energy sources.</p>
Full article ">Figure 5
<p>Primary energy consumption as a function of the relation between embodied and energy derivated CO<sub>2</sub> equivalent emissions. The CO<sub>2</sub> embodied corresponds to the CO<sub>2</sub> emissions from materials during their life time and CO<sub>2</sub> energy corresponds the CO<sub>2</sub> emissions from energy use in the building (heating, cooling and electricity). The time period used in calculations is 50 years.</p>
Full article ">
1066 KiB  
Article
Model Predictive Control-Based Fast Charging for Vehicular Batteries
by Jingyu Yan, Guoqing Xu, Huihuan Qian, Yangsheng Xu and Zhibin Song
Energies 2011, 4(8), 1178-1196; https://doi.org/10.3390/en4081178 - 17 Aug 2011
Cited by 47 | Viewed by 10503
Abstract
Battery fast charging is one of the most significant and difficult techniques affecting the commercialization of electric vehicles (EVs). In this paper, we propose a fast charge framework based on model predictive control, with the aim of simultaneously reducing the charge duration, which [...] Read more.
Battery fast charging is one of the most significant and difficult techniques affecting the commercialization of electric vehicles (EVs). In this paper, we propose a fast charge framework based on model predictive control, with the aim of simultaneously reducing the charge duration, which represents the out-of-service time of vehicles, and the increase in temperature, which represents safety and energy efficiency during the charge process. The RC model is employed to predict the future State of Charge (SOC). A single mode lumped-parameter thermal model and a neural network trained by real experimental data are also applied to predict the future temperature in simulations and experiments respectively. A genetic algorithm is then applied to find the best charge sequence under a specified fitness function, which consists of two objectives: minimizing the charging duration and minimizing the increase in temperature. Both simulation and experiment demonstrate that the Pareto front of the proposed method dominates that of the most popular constant current constant voltage (CCCV) charge method. Full article
(This article belongs to the Special Issue Electric and Hybrid Vehicles)
Show Figures

Figure 1

Figure 1
<p>Fast charging control framework based on model predictive control.</p>
Full article ">Figure 2
<p>The battery RC Model.</p>
Full article ">Figure 3
<p>Increase in temperature under different charging rates.</p>
Full article ">Figure 4
<p>Neural network for predicting battery temperature.</p>
Full article ">Figure 5
<p>Scheme of the standard GA.</p>
Full article ">Figure 6
<p>Initialization of one special individual by introducing the best control sequence optimized in previous step into the present step.</p>
Full article ">Figure 7
<p>Battery time-variant properties. Taking OCV and Ro as examples. Data source: Advisor.</p>
Full article ">Figure 8
<p>Pareto fronts of CCCV and MPC charging methods in simulation. The expected trajectories of MPC are modified from CCCV by multiplying 1.05.</p>
Full article ">Figure 9
<p>Curves during CCCV and MPC charging processes using modified 3 C profile.</p>
Full article ">Figure 10
<p>Pareto fronts of CCCV and MPC charging methods in experiments. The expected trajectories of MPC are modified from CCCV by multiplying by 1.10.</p>
Full article ">Figure 11
<p>Experimental curves during the CCCV and MPC charging processes using a modified 2 C profile.</p>
Full article ">
892 KiB  
Article
Removal and Conversion of Tar in Syngas from Woody Biomass Gasification for Power Utilization Using Catalytic Hydrocracking
by Jiu Huang, Klaus Gerhard Schmidt and Zhengfu Bian
Energies 2011, 4(8), 1163-1177; https://doi.org/10.3390/en4081163 - 12 Aug 2011
Cited by 39 | Viewed by 9533
Abstract
Biomass gasification has yet to obtain industrial acceptance. The high residual tar concentrations in syngas prevent any ambitious utilization. In this paper a novel gas purification technology based on catalytic hydrocracking is introduced, whereby most of the tarry components can be converted and [...] Read more.
Biomass gasification has yet to obtain industrial acceptance. The high residual tar concentrations in syngas prevent any ambitious utilization. In this paper a novel gas purification technology based on catalytic hydrocracking is introduced, whereby most of the tarry components can be converted and removed. Pilot scale experiments were carried out with an updraft gasifier. The hydrocracking catalyst was palladium (Pd). The results show the dominant role of temperature and flow rate. At a constant flow rate of 20 Nm3/h and temperatures of 500 °C, 600 °C and 700 °C the tar conversion rates reached 44.9%, 78.1% and 92.3%, respectively. These results could be increased up to 98.6% and 99.3% by using an operating temperature of 700 °C and lower flow rates of 15 Nm3/h and 10 Nm3/h. The syngas quality after the purification process at 700 °C/10 Nm3/h is acceptable for inner combustion (IC) gas engine utilization. Full article
(This article belongs to the Special Issue Biomass and Biofuels)
Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) Biomass fuel for gasification; and (<b>b</b>) Updraft gasifier.</p>
Full article ">Figure 2
<p>Flow chart of the updraft gasifier.</p>
Full article ">Figure 3
<p>(<b>a</b>) Catalyst pieces; (<b>b</b>) Deactivated catalyst; and (<b>c</b>) Reactor for catalytic hydrocracking.</p>
Full article ">Figure 4
<p>Sampling processes of syngas before and after hydrocracking.</p>
Full article ">Figure 5
<p>Temperatures in catalytic reactor under different flow rates: (<b>a</b>) Syngas flow rate 20 Nm<sup>3</sup>/h; (<b>b</b>) Syngas flow rate 15 Nm<sup>3</sup>/h; and (<b>c</b>) Syngas flow rate 10 Nm<sup>3</sup>/h.</p>
Full article ">Figure 5 Cont.
<p>Temperatures in catalytic reactor under different flow rates: (<b>a</b>) Syngas flow rate 20 Nm<sup>3</sup>/h; (<b>b</b>) Syngas flow rate 15 Nm<sup>3</sup>/h; and (<b>c</b>) Syngas flow rate 10 Nm<sup>3</sup>/h.</p>
Full article ">
1789 KiB  
Article
Analysis of Wind Generator Operations under Unbalanced Voltage Dips in the Light of the Spanish Grid Code
by Vicente León-Martínez and Joaquín Montañana-Romeu
Energies 2011, 4(8), 1148-1162; https://doi.org/10.3390/en4081148 - 8 Aug 2011
Cited by 6 | Viewed by 7209
Abstract
Operation of doubly fed induction generators subjected to transient unbalanced voltage dips is analyzed in this article to verify the fulfillment of the Spanish grid code. Akagi’s p-q theory is not used for this study, because control of the electronic converter is not [...] Read more.
Operation of doubly fed induction generators subjected to transient unbalanced voltage dips is analyzed in this article to verify the fulfillment of the Spanish grid code. Akagi’s p-q theory is not used for this study, because control of the electronic converter is not the main goal of the paper, but rather to know the physical phenomena involved in the wind turbine when voltage dips occur. Hence, the magnetizing reactive power of the induction generators and their components, which are related with the magnetic fields and determine operation of these machines, are expressed through the reactive power formulations established in the technical literature by three well-known approaches: the delayed voltage (DV) method, Czarnecki’s Current’s Physical Components (CPC) theory and Emanuel’s approach. Non-fundamental and negative-sequence components of the magnetizing reactive power are respectively established to define the effects of the distortion and voltage imbalances on the magnetic fields and electromagnetic torques. Also, fundamental-frequency positive-sequence and negative-sequence reactive powers are decomposed into two components: due to the reactive loads and caused by the imbalances. This decomposition provides additional information about the effects of the imbalances on the main magnetic field and electromagnetic torque of the induction generator. All the above mentioned reactive powers are finally applied to one actual wind turbine subjected to a two-phase voltage dip in order to explain its operation under such transient conditions. Full article
(This article belongs to the Special Issue Wind Energy 2011)
Show Figures

Figure 1

Figure 1
<p>Low voltage ride through (LVRT) as specified in Spanish grid code: (<b>a</b>) three-phase faults; (<b>b</b>) single- and two-phase faults.</p>
Full article ">Figure 2
<p>Reactive power limitations established by Spanish grid code: (<b>a</b>) three-phase faults; (<b>b</b>) single- and two-phase faults.</p>
Full article ">Figure 3
<p>Wind generator and load at net interconnection point.</p>
Full article ">Figure 4
<p>Doubly fed induction generator and crowbar system.</p>
Full article ">Figure 5
<p>Connection of the Fluke 1760 three-phase power quality recorder.</p>
Full article ">Figure 6
<p>Two-phase voltage dip, RMS voltages (p.u.).</p>
Full article ">Figure 7
<p>Reactive powers: (<b>a</b>) Delayed Voltage, (<b>b</b>) Czarnecki, (<b>c</b>) FPRP.</p>
Full article ">Figure 8
<p>Non-fundamental reactive power.</p>
Full article ">Figure 9
<p>Czarnecki’s CPC fundamental reactive powers: (<b>a</b>) total, (<b>b</b>) due to reactive loads, (<b>c</b>) caused by imbalances.</p>
Full article ">Figure 10
<p>Negative-sequence reactive powers: (<b>a</b>) total, (<b>b</b>) due to reactive loads, (<b>c</b>) caused by imbalances.</p>
Full article ">Figure 11
<p>Positive-sequence reactive powers: (<b>a</b>) total, (<b>b</b>) due to reactive loads, (<b>c</b>) caused by unbalances.</p>
Full article ">
315 KiB  
Article
Entropy-Based Bagging for Fault Prediction of Transformers Using Oil-Dissolved Gas Data
by Yuanbing Zheng, Caixin Sun, Jian Li, Qing Yang and Weigen Chen
Energies 2011, 4(8), 1138-1147; https://doi.org/10.3390/en4081138 - 4 Aug 2011
Cited by 18 | Viewed by 6433
Abstract
The development of the smart grid has resulted in new requirements for fault prediction of power transformers. This paper presents an entropy-based Bagging (E-Bagging) method for prediction of characteristic parameters related to power transformers faults. A parameter of comprehensive information entropy of sample [...] Read more.
The development of the smart grid has resulted in new requirements for fault prediction of power transformers. This paper presents an entropy-based Bagging (E-Bagging) method for prediction of characteristic parameters related to power transformers faults. A parameter of comprehensive information entropy of sample data is brought forward to improve the resampling process of the E-Bagging method. The generalization ability of the E-Bagging is enhanced significantly by the comprehensive information entropy. A total of sets of 1200 oil-dissolved gas data of transformers are used as examples of fault prediction. The comparisons between the E-Bagging and the traditional Bagging and individual prediction approaches are presented. The results show that the E-Bagging possesses higher accuracy and greater stability of prediction than the traditional Bagging and individual prediction approaches. Full article
(This article belongs to the Special Issue Future Grid)
Show Figures

Figure 1

Figure 1
<p>Basic process of Bagging for an ensemble of prediction functions.</p>
Full article ">Figure 2
<p>Error rate of CF by different ensemble methods.</p>
Full article ">
328 KiB  
Article
Water Transfer Characteristics during Methane Hydrate Formation Processes in Layered Media
by Peng Zhang, Qingbai Wu, Yibin Pu and Yousheng Deng
Energies 2011, 4(8), 1129-1137; https://doi.org/10.3390/en4081129 - 2 Aug 2011
Cited by 5 | Viewed by 6509
Abstract
Gas hydrate formation processes in porous media are always accompanied by water transfer. To study the transfer characteristics comprehensively, two kinds of layered media consisting of coarse sand and loess were used to form methane hydrate in them. An apparatus with three PF-meter [...] Read more.
Gas hydrate formation processes in porous media are always accompanied by water transfer. To study the transfer characteristics comprehensively, two kinds of layered media consisting of coarse sand and loess were used to form methane hydrate in them. An apparatus with three PF-meter sensors detecting water content and temperature changes in media during the formation processes was applied to study the water transfer characteristics. It was experimentally observed that the hydrate formation configurations in different layered media were similar; however, the water transfer characteristics and water conversion ratios were different. Full article
(This article belongs to the Special Issue Natural Gas Hydrate 2011)
Show Figures

Figure 1

Figure 1
<p>Schematic diagram of the experimental apparatus. 1. Gas cylinder; 2. Gas valve; 3. Pressure gauge; 4. Gas valve; 5. Gas line; 6. Reaction cell; 7. PF-meter sensors, length = 7 cm, diameter = 2 cm; 8. Coolant temperature sensor; 9. Low-temperature batch; 10. Data-logging system of pressure value and coolant temperature; 11. Data-logging system of PF value and temperature inside cell.</p>
Full article ">Figure 2
<p>Photographs of hydrate configuration in different layered media (water conversion ratio in the left is 23.1% and that in the right is 16.2%).</p>
Full article ">Figure 3
<p>Parameter changes during methane hydrate formation process in the layered medium: top coarse sand layer, middle loess layer and bottom coarse sand layer: (<b>a</b>) PF value and pressure changes during the whole experiment process; (<b>b</b>) Temperature and PF value changes during cooling process.</p>
Full article ">Figure 4
<p>Parameter changes during methane hydrate formation process in the layered medium: top loess layer, middle coarse sand layer and bottom loess layer (<b>a</b>) PF value and pressure changes during the whole experiment process; (<b>b</b>) Temperature and PF value changes during cooling process.</p>
Full article ">
378 KiB  
Article
Performance of a Polymer Flood with Shear-Thinning Fluid in Heterogeneous Layered Systems with Crossflow
by Kun Sang Lee
Energies 2011, 4(8), 1112-1128; https://doi.org/10.3390/en4081112 - 2 Aug 2011
Cited by 41 | Viewed by 8803
Abstract
Assessment of the potential of a polymer flood for mobility control requires an accurate model on the viscosities of displacement fluids involved in the process. Because most polymers used in EOR exhibit shear-thinning behavior, the effective viscosity of a polymer solution is a [...] Read more.
Assessment of the potential of a polymer flood for mobility control requires an accurate model on the viscosities of displacement fluids involved in the process. Because most polymers used in EOR exhibit shear-thinning behavior, the effective viscosity of a polymer solution is a highly nonlinear function of shear rate. A reservoir simulator including the model for the shear-rate dependence of viscosity was used to investigate shear-thinning effects of polymer solution on the performance of the layered reservoir in a five-spot pattern operating under polymer flood followed by waterflood. The model can be used as a quantitative tool to evaluate the comparative studies of different polymer flooding scenarios with respect to shear-rate dependence of fluids’ viscosities. Results of cumulative oil recovery and water-oil ratio are presented for parameters of shear-rate dependencies, permeability heterogeneity, and crossflow. The results of this work have proven the importance of taking non-Newtonian behavior of polymer solution into account for the successful evaluation of polymer flood processes. Horizontal and vertical permeabilities of each layer are shown to impact the predicted performance substantially. In reservoirs with a severe permeability contrast between horizontal layers, decrease in oil recovery and sudden increase in WOR are obtained by the low sweep efficiency and early water breakthrough through highly permeable layer, especially for shear-thinning fluids. An increase in the degree of crossflow resulting from sufficient vertical permeability is responsible for the enhanced sweep of the low permeability layers, which results in increased oil recovery. It was observed that a thinning fluid coefficient would increase injectivity significantly from simulations with various injection rates. A thorough understanding of polymer rheology in the reservoir and accurate numerical modeling are of fundamental importance for the exact estimation on the performance of polymer flood. Full article
(This article belongs to the Special Issue Advances in Petroleum Engineering)
Show Figures

Figure 1

Figure 1
<p>History of polymer concentration in the injecting water.</p>
Full article ">Figure 2
<p>Oil phase saturation of layers 1, 3, and 5 at 1,283 days for <math display="inline"> <semantics> <mrow> <msub> <mi>k</mi> <mi>v</mi> </msub> <mo stretchy="false">/</mo> <msub> <mi>k</mi> <mi>h</mi> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>σ</mi> <mrow> <mo>(</mo> <mrow> <mi>ln</mi> <mi>k</mi> </mrow> <mo>)</mo> </mrow> <mo>=</mo> <mn>1.5697</mn> </mrow> </semantics> </math> with different <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>α</mi> </msub> </mrow> </semantics> </math>: (<b>a</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>α</mi> </msub> <mo>=</mo> <mn>1.0</mn> </mrow> </semantics> </math>; (<b>b</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>α</mi> </msub> <mo>=</mo> <mn>2.6</mn> </mrow> </semantics> </math>.</p>
Full article ">Figure 2 Cont.
<p>Oil phase saturation of layers 1, 3, and 5 at 1,283 days for <math display="inline"> <semantics> <mrow> <msub> <mi>k</mi> <mi>v</mi> </msub> <mo stretchy="false">/</mo> <msub> <mi>k</mi> <mi>h</mi> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>σ</mi> <mrow> <mo>(</mo> <mrow> <mi>ln</mi> <mi>k</mi> </mrow> <mo>)</mo> </mrow> <mo>=</mo> <mn>1.5697</mn> </mrow> </semantics> </math> with different <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>α</mi> </msub> </mrow> </semantics> </math>: (<b>a</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>α</mi> </msub> <mo>=</mo> <mn>1.0</mn> </mrow> </semantics> </math>; (<b>b</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>α</mi> </msub> <mo>=</mo> <mn>2.6</mn> </mrow> </semantics> </math>.</p>
Full article ">Figure 3
<p>Water phase viscosity of layers 1, 3, and 5 at 1,283 days for <math display="inline"> <semantics> <mrow> <msub> <mi>k</mi> <mi>v</mi> </msub> <mo stretchy="false">/</mo> <msub> <mi>k</mi> <mi>h</mi> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>σ</mi> <mrow> <mo>(</mo> <mrow> <mi>ln</mi> <mi>k</mi> </mrow> <mo>)</mo> </mrow> <mo>=</mo> <mn>1.5697</mn> </mrow> </semantics> </math> with different <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>α</mi> </msub> </mrow> </semantics> </math>. (<b>a</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>α</mi> </msub> <mo>=</mo> <mn>1.0</mn> </mrow> </semantics> </math>; (<b>b</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>α</mi> </msub> <mo>=</mo> <mn>2.6</mn> </mrow> </semantics> </math>.</p>
Full article ">Figure 3 Cont.
<p>Water phase viscosity of layers 1, 3, and 5 at 1,283 days for <math display="inline"> <semantics> <mrow> <msub> <mi>k</mi> <mi>v</mi> </msub> <mo stretchy="false">/</mo> <msub> <mi>k</mi> <mi>h</mi> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>σ</mi> <mrow> <mo>(</mo> <mrow> <mi>ln</mi> <mi>k</mi> </mrow> <mo>)</mo> </mrow> <mo>=</mo> <mn>1.5697</mn> </mrow> </semantics> </math> with different <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>α</mi> </msub> </mrow> </semantics> </math>. (<b>a</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>α</mi> </msub> <mo>=</mo> <mn>1.0</mn> </mrow> </semantics> </math>; (<b>b</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>α</mi> </msub> <mo>=</mo> <mn>2.6</mn> </mrow> </semantics> </math>.</p>
Full article ">Figure 4
<p>Polymer concentration in water phase of layer 5 at 1,283 days for <math display="inline"> <semantics> <mrow> <msub> <mi>k</mi> <mi>v</mi> </msub> <mo stretchy="false">/</mo> <msub> <mi>k</mi> <mi>h</mi> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>σ</mi> <mrow> <mo>(</mo> <mrow> <mi>ln</mi> <mi>k</mi> </mrow> <mo>)</mo> </mrow> <mo>=</mo> <mn>1.5697</mn> </mrow> </semantics> </math> with different <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>α</mi> </msub> </mrow> </semantics> </math>. (<b>a</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>α</mi> </msub> <mo>=</mo> <mn>1.0</mn> </mrow> </semantics> </math>. (<b>b</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>α</mi> </msub> <mo>=</mo> <mn>2.6</mn> </mrow> </semantics> </math>.</p>
Full article ">Figure 5
<p>History of production wells obtained from simulations for <math display="inline"> <semantics> <mrow> <msub> <mi>k</mi> <mi>v</mi> </msub> <mo stretchy="false">/</mo> <msub> <mi>k</mi> <mi>h</mi> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics> </math> with different <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>α</mi> </msub> </mrow> </semantics> </math> and permeability heterogeneity. (<b>a</b>) <math display="inline"> <semantics> <mrow> <mi>σ</mi> <mrow> <mo>(</mo> <mrow> <mi>ln</mi> <mi>k</mi> </mrow> <mo>)</mo> </mrow> <mo>=</mo> <mn>0</mn> </mrow> </semantics> </math> ; (<b>b</b>) <math display="inline"> <semantics> <mrow> <mi>σ</mi> <mrow> <mo>(</mo> <mrow> <mi>ln</mi> <mi>k</mi> </mrow> <mo>)</mo> </mrow> <mo>=</mo> <mn>0.3156</mn> </mrow> </semantics> </math> ; (<b>c</b>) <math display="inline"> <semantics> <mrow> <mi>σ</mi> <mrow> <mo>(</mo> <mrow> <mi>ln</mi> <mi>k</mi> </mrow> <mo>)</mo> </mrow> <mo>=</mo> <mn>0.7882</mn> </mrow> </semantics> </math>; (<b>d</b>) <math display="inline"> <semantics> <mrow> <mi>σ</mi> <mrow> <mo>(</mo> <mrow> <mi>ln</mi> <mi>k</mi> </mrow> <mo>)</mo> </mrow> <mo>=</mo> <mn>1.5697</mn> </mrow> </semantics> </math>.</p>
Full article ">Figure 5 Cont.
<p>History of production wells obtained from simulations for <math display="inline"> <semantics> <mrow> <msub> <mi>k</mi> <mi>v</mi> </msub> <mo stretchy="false">/</mo> <msub> <mi>k</mi> <mi>h</mi> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics> </math> with different <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>α</mi> </msub> </mrow> </semantics> </math> and permeability heterogeneity. (<b>a</b>) <math display="inline"> <semantics> <mrow> <mi>σ</mi> <mrow> <mo>(</mo> <mrow> <mi>ln</mi> <mi>k</mi> </mrow> <mo>)</mo> </mrow> <mo>=</mo> <mn>0</mn> </mrow> </semantics> </math> ; (<b>b</b>) <math display="inline"> <semantics> <mrow> <mi>σ</mi> <mrow> <mo>(</mo> <mrow> <mi>ln</mi> <mi>k</mi> </mrow> <mo>)</mo> </mrow> <mo>=</mo> <mn>0.3156</mn> </mrow> </semantics> </math> ; (<b>c</b>) <math display="inline"> <semantics> <mrow> <mi>σ</mi> <mrow> <mo>(</mo> <mrow> <mi>ln</mi> <mi>k</mi> </mrow> <mo>)</mo> </mrow> <mo>=</mo> <mn>0.7882</mn> </mrow> </semantics> </math>; (<b>d</b>) <math display="inline"> <semantics> <mrow> <mi>σ</mi> <mrow> <mo>(</mo> <mrow> <mi>ln</mi> <mi>k</mi> </mrow> <mo>)</mo> </mrow> <mo>=</mo> <mn>1.5697</mn> </mrow> </semantics> </math>.</p>
Full article ">Figure 6
<p>History of production wells obtained from simulations for heterogeneous reservoir of <math display="inline"> <semantics> <mrow> <mi>σ</mi> <mrow> <mo>(</mo> <mrow> <mi>ln</mi> <mi>k</mi> </mrow> <mo>)</mo> </mrow> <mo>=</mo> <mn>1.5697</mn> </mrow> </semantics> </math> with different <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>α</mi> </msub> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msub> <mi>k</mi> <mi>v</mi> </msub> <mo stretchy="false">/</mo> <msub> <mi>k</mi> <mi>h</mi> </msub> </mrow> </semantics> </math> . (<b>a</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>α</mi> </msub> <mo>=</mo> <mn>1.0</mn> </mrow> </semantics> </math> (Newtonian fluid); (<b>b</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>α</mi> </msub> <mo>=</mo> <mn>2.6</mn> </mrow> </semantics> </math>.</p>
Full article ">Figure 7
<p>History of injection and production wells obtained from simulations for homogeneous reservoir with different <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>α</mi> </msub> </mrow> </semantics> </math>. (<b>a</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>α</mi> </msub> <mo>=</mo> <mn>1.5</mn> </mrow> </semantics> </math>; (<b>b</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>α</mi> </msub> <mo>=</mo> <mn>2.6</mn> </mrow> </semantics> </math>.</p>
Full article ">Figure 7 Cont.
<p>History of injection and production wells obtained from simulations for homogeneous reservoir with different <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>α</mi> </msub> </mrow> </semantics> </math>. (<b>a</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>α</mi> </msub> <mo>=</mo> <mn>1.5</mn> </mrow> </semantics> </math>; (<b>b</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>α</mi> </msub> <mo>=</mo> <mn>2.6</mn> </mrow> </semantics> </math>.</p>
Full article ">
Previous Issue
Next Issue
Back to TopTop