scholarly journals Quantifying the partial penetration skin factor for evaluating the completion efficiency of vertical oil wells

Author(s):  
Ekhwaiter Abobaker ◽  
Abadelhalim Elsanoose ◽  
Faisal Khan ◽  
Mohammad Azizur Rahman ◽  
Amer Aborig ◽  
...  

AbstractAn oil well's productivity is generally considered the standard measure of the well's performance. However, productivity depends on several factors, including fluid characteristics, formation damage, the reservoir's formation, and the kind of completion the well undergoes. How a partial completion can affect a well's performance will be investigated in detail in this study, as nearly every vertical well is only partially completed as a result of gas cap or water coning issues. Partially penetrated wells typically experience a larger pressure drop of fluid flow caused by restricted regions, thus increasing the skin factor. A major challenge for engineers when developing completion designs or optimizing skin factor variables is devising and testing suitable partial penetration skin and comparing completion options. Several researchers have studied and calculated a partial penetration skin factor, but some of their results tend to be inaccurate and cause excessive errors. The present work proposes experimental work and a numerical simulation model for accurate estimation of the pseudo-skin factor for partially penetrated wells. The work developed a simple correlation for predicting the partial penetration skin factor for perforated vertical wells. The work also compared the results from available models that are widely accepted by the industry as a basis for gauging the accuracy of the new correlation in estimating the skin factor. Compared to other approaches, the novel correlation performs well by providing estimates for the partial penetration skin factor that are relatively close to those obtained by the tested models. This work's main contribution is the presentation of a novel correlation that simplifies the estimation of the partial penetration skin factor in partially completed vertical wells.

2021 ◽  
Vol 13 (9) ◽  
pp. 4648
Author(s):  
Rana Muhammad Adnan ◽  
Kulwinder Singh Parmar ◽  
Salim Heddam ◽  
Shamsuddin Shahid ◽  
Ozgur Kisi

The accurate estimation of suspended sediments (SSs) carries significance in determining the volume of dam storage, river carrying capacity, pollution susceptibility, soil erosion potential, aquatic ecological impacts, and the design and operation of hydraulic structures. The presented study proposes a new method for accurately estimating daily SSs using antecedent discharge and sediment information. The novel method is developed by hybridizing the multivariate adaptive regression spline (MARS) and the Kmeans clustering algorithm (MARS–KM). The proposed method’s efficacy is established by comparing its performance with the adaptive neuro-fuzzy system (ANFIS), MARS, and M5 tree (M5Tree) models in predicting SSs at two stations situated on the Yangtze River of China, according to the three assessment measurements, RMSE, MAE, and NSE. Two modeling scenarios are employed; data are divided into 50–50% for model training and testing in the first scenario, and the training and test data sets are swapped in the second scenario. In Guangyuan Station, the MARS–KM showed a performance improvement compared to ANFIS, MARS, and M5Tree methods in term of RMSE by 39%, 30%, and 18% in the first scenario and by 24%, 22%, and 8% in the second scenario, respectively, while the improvement in RMSE of ANFIS, MARS, and M5Tree was 34%, 26%, and 27% in the first scenario and 7%, 16%, and 6% in the second scenario, respectively, at Beibei Station. Additionally, the MARS–KM models provided much more satisfactory estimates using only discharge values as inputs.


MRS Advances ◽  
2016 ◽  
Vol 1 (30) ◽  
pp. 2199-2206 ◽  
Author(s):  
Monica Michel ◽  
Jay A. Desai ◽  
Alberto Delgado ◽  
Chandan Biswas ◽  
Anupama B. Kaul

ABSTRACT2D materials have shown to be the next step in semiconductor use and device manufacturing that can allow us to reduce the size of most electronics. One of the novel ways to obtain 2D materials is through liquid exfoliation, in which these materials can be obtained by dispersing the smallest possible particles in different solvents. Once obtained, the solutions can be used to manufacture devices via different processes, one of which is inkjet printing. This process relies in selecting “jettable” fluids, which need to have the necessary combination of viscosity and surface energy or “wettability”. In this work we have modified the viscosities and surface energies of five solvents: IPA (Isopropanol), NMP (N-methyl – 2 pyrrolidone), DMA (Dimethylacetamide), DMF (Dimethylformamide) and a mixture of Cyclohexanone / Terpineol 7:3. We have found an avenue to tailor the viscosity of these solvents though the addition of Ethyl Cellulose (EC), where the viscosity has been increased by up to 15 times at an EC concentration of 6%. For inkjet printing, ideally a viscosity of 4 – 10 cP is recommended, which we have been able to achieve with all of the solvents studied. It has been found that the different solvents present different susceptibilities to the EC addition, with DMA and DMF being the least sensitive to the EC addition. We have also studied the change in the drop dynamics and interactions of the 2D solutions with the substrate. Through this analysis we have found solvents that appear to be attractive for inkjet printing of MoS2 and graphite.


2012 ◽  
Vol 27 (02) ◽  
pp. 195-204 ◽  
Author(s):  
Mohammad Tabatabaei ◽  
Ali Ghalambor ◽  
Boyun Guo

2020 ◽  
Vol 10 (19) ◽  
pp. 6637
Author(s):  
Xiaohong Wang ◽  
Wenhui Fan ◽  
Shixiang Li ◽  
Xinjun Li ◽  
Lizhi Wang

Accompanied by the development of new energy resources, lithium-ion batteries have been used widely in various fields. Due to the significant influence of system performance, much attention has been paid to the accurate estimation and prediction about health status of lithium-ion batteries. In a battery pack, the structure connection causes sophisticated interaction between cells, or between the cells and the pack. Therefore, the degradation of any cell is the result of the deterioration of conjoint cells, and a rapid degradation speed for any individual cell can lead to the accelerated degradation of others beyond expectation, which is one of the primary reasons why the State of Health and life cannot be calculated precisely. To solve this problem, a novel method based on integrated state information from cells has been proposed to estimate status of packs, considering about the degradation effect that cells contribute to the corresponding pack. Using this method, the interactive relationship was described in the form of a neural network in order to mine the effect from the inter-degradation between cells. It was proven that the novel method had better performance than a method based only on the degradation indicators from battery packs.


SPE Journal ◽  
2005 ◽  
Vol 10 (04) ◽  
pp. 440-448 ◽  
Author(s):  
Russell T. Johns ◽  
Larry W. Lake ◽  
Rafay Z. Ansari ◽  
Arnaud M. Delliste

Buildings ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 629
Author(s):  
Jinsong Liao ◽  
Panagiotis G. Asteris ◽  
Liborio Cavaleri ◽  
Ahmed Salih Mohammed ◽  
Minas E. Lemonis ◽  
...  

An accurate estimation of the axial compression capacity of the concrete-filled steel tubular (CFST) column is crucial for ensuring the safety of structures containing them and preventing related failures. In this article, two novel hybrid fuzzy systems (FS) were used to create a new framework for estimating the axial compression capacity of circular CCFST columns. In the hybrid models, differential evolution (DE) and firefly algorithm (FFA) techniques are employed in order to obtain the optimal membership functions of the base FS model. To train the models with the new hybrid techniques, i.e., FS-DE and FS-FFA, a substantial library of 410 experimental tests was compiled from openly available literature sources. The new model’s robustness and accuracy was assessed using a variety of statistical criteria both for model development and for model validation. The novel FS-FFA and FS-DE models were able to improve the prediction capacity of the base model by 9.68% and 6.58%, respectively. Furthermore, the proposed models exhibited considerably improved performance compared to existing design code methodologies. These models can be utilized for solving similar problems in structural engineering and concrete technology with an enhanced level of accuracy.


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