empirical correlation
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Fuel ◽  
2022 ◽  
Vol 307 ◽  
pp. 121880
Author(s):  
N. Sekularac ◽  
X.H. Fang ◽  
V. Shankar ◽  
S.J. Baker ◽  
F.C.P. Leach ◽  
...  

2021 ◽  
Vol 17 (6) ◽  
pp. 742-751
Author(s):  
Sazmin Sufi Suliman ◽  
Norasikin Othman ◽  
Norul Fatiha Mohamed Noah ◽  
Norela Jusoh ◽  
Raja Norimie Raja Sulaiman

In this study, determination of droplets in the presence of blended mixture of surfactants (Span 80 and Tween 80) and nanoparticles, iron (III) oxide (Fe2O3) were investigated using a single stage mixer-settler extractor with 4-pitched flat blade impeller on one shaft employment. Additionally, the influence of Fe2O3 and blended surfactant mixture of Span 80 and Tween 80 on the dispersion of emulsion in terms of Sauter diameter (D32) measurement was compared with new correlations. Results indicate that the presence of Fe2O3 in the blended mixture of surfactant simultaneously decreased in D32 by 79 % and the stability of the emulsion system was enhanced. Overall, empirical correlation for droplet size at different conditions are obtained, and the modified correlation for D32 is presented. The correlation found is D32/DI =0.02265(3.419Φi−1)We-0.6. The calculated average absolute relative deviation (%AARD) is 2.69 %, thus indicating a good accuracy and acceptability between the presented correlation and experimental data.


2021 ◽  
Vol 44 (4) ◽  
pp. 408-416
Author(s):  
E. V. Shakirova ◽  
A. A. Aleksandrov ◽  
M. V. Semykin

It is known that oil in reservoir conditions is characterized by the content of a certain amount of dissolved gas. As reservoir pressure decreases this gas is released from oil significantly changing its physical properties, primarily its density and viscosity. In addition, the oil volume also reduces, sometimes by 50–60 %. In this regard, when calculating reserves, it is necessary to justify the reduction amount of the reservoir oil volume when oil is extracted to the surface. For this purpose, the concept of formation volume factor of reservoir oil has been introduced. The formation volume factor of oil is considered one of the main characterizing parameters of crude oil. It is also required for modeling and predicting the characteristics of an oil reservoir. The purpose of the present work is to develop a new empirical correlation for predicting the formation volume factor of reservoir oil using artificial intelligence methods based on MATLAB software, such as: an artificial neural network, an adaptive neuro-fuzzy inference system, and a support vector machine. The article presents a new empirical correlation extracted from the artificial neural network based on 503 experimental data points for oils from the Eastern Siberia field, which was able to predict the formation volume factor of oil with the correlation coefficient of 0.969 and average absolute error of less than 1 %. The conducted study shows that the prediction accuracy of the desired parameter in the developed artificial intelligence model exceeds the accuracy of study results obtained by conventional statistical methods. Moreover, the model can be useful in the prospect of process optimization in field planning and development.


2021 ◽  
Vol 50 (3) ◽  
pp. 23-27
Author(s):  
Boriana Tchakalova ◽  
Boyko Berov

The liquid limit is one of the most commonly used index properties of soils. The paper compares liquid limit values determined by the Vasiliev cone penetrometer method and by the Casagrande cup method, based on 45 natural clay samples collected from the Kozloduy Town area (North Bulgaria). An empirical correlation based on these liquid limit results has been derived.


2021 ◽  
Author(s):  
Thitaree Lertliangchai ◽  
Birol Dindoruk ◽  
Ligang Lu ◽  
Xi Yang

Abstract Dew point pressure (DPP) is a key variable that may be needed to predict the condensate to gas ratio behavior of a reservoir along with some production/completion related issues and calibrate/constrain the EOS models for integrated modeling. However, DPP is a challenging property in terms of its predictability. Recognizing the complexities, we present a state-of-the-art method for DPP prediction using advanced machine learning (ML) techniques. We compare the outcomes of our methodology with that of published empirical correlation-based approaches on two datasets with small sizes and different inputs. Our ML method noticeably outperforms the correlation-based predictors while also showing its flexibility and robustness even with small training datasets provided various classes of fluids are represented within the datasets. We have collected the condensate PVT data from public domain resources and GeoMark RFDBASE containing dew point pressure (the target variable), and the compositional data (mole percentage of each component), temperature, molecular weight (MW), MW and specific gravity (SG) of heptane plus as input variables. Using domain knowledge, before embarking the study, we have extensively checked the measurement quality and the outcomes using statistical techniques. We then apply advanced ML techniques to train predictive models with cross-validation to avoid overfitting the models to the small datasets. We compare our models against the best published DDP predictors with empirical correlation-based techniques. For fair comparisons, the correlation-based predictors are also trained using the underlying datasets. In order to improve the outcomes and using the generalized input data, pseudo-critical properties and artificial proxy features are also employed.


2021 ◽  
Vol 44 (2) ◽  
pp. 141-152
Author(s):  
Kamal Hamzah ◽  
Amega Yasutra ◽  
Dedy Irawan

Hydraulic fracturing has been established as one of production enhancement methods in the petroleum industry. This method is proven to increase productivity and reserves in low permeability reservoirs, while in medium permeability, it accelerates production without affecting well reserves. However, production result looks scattered and appears to have no direct correlation to individual parameters. It also tend to have a decreasing trend, hence the success ratio needs to be increased. Hydraulic fracturing in the South Sumatra area has been implemented since 2002 and there is plenty of data that can be analyzed to resolve the relationship between actual production with reservoir parameters and fracturing treatment. Empirical correlation approach and machine learning (ML) methods are both used to evaluate this relationship. Concept of Darcy's equation is utilized as basis for the empirical correlation on the actual data. The ML method is then applied to provide better predictions both for production rate and water cut. This method has also been developed to solve data limitations so that the prediction method can be used for all wells. Empirical correlation can gives an R2 of 0.67, while ML can gives a better R2 that is close to 0.80. Furthermore, this prediction method can be used for well candidate selection means.


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