No abstract

The friction factor is a crucial parameter in calculating frictional pressure losses. However, it is a decisive challenge to estimate, especially for turbulent flow of non-Newtonian fluids in pipes. The objective of this paper is to examine the validity of friction factor correlations adopting a new informative-based approach, the Akaike information criterion (AIC) along with the coefficient of determination (R 2 ). Over a wide range of measured data, the results show that each model is accurate when it is examined against a specific dataset while the El-Emam et al. (Oil Gas J 101:74-83, 2003) model proves its superiority. In addition to its simple and explicit form, it covers a wide range of flow behavior indices and generalized Reynolds numbers. It is also shown that the traditional belief that a high R 2 means a better model may be misleading. AIC overcomes the shortcomings of R 2 as a trade between the complexity of the model and its accuracy not only to find a best approximating model but also to develop statistical inference based on the data. The authors present AIC to initiate an innovative strategy to help alleviate several challenges faced by the professionals in the oil and gas industry. Finally, a detailed discussion and models' ranking according to AIC and R 2 is presented showing the numerous advantages of AIC.

Hydraulic fracturing has increased immensely in recent years. An accurate prediction of frictional pressure losses of fracturing slurries is crucial for successful treatment and avoiding premature screen-out or even treatment failure. Scarce data and lack of theoretical basis of slurry flow, especially in coiled tubing, has led to very limited number of correlations that are available to predict slurry frictional pressure losses. Yet, the accuracy of the available correlations is still questionable. The current paper presents a statistical comparative analysis of the available frictional pressure losses correlations for slurry flow in straight and coiled tubing employing the recently introduced math modeling technique giving weight for the models known as AIC (Akaike information criterion). With the help of AIC, the authors evaluated the available correlations to examine their accuracy. The results show that none of the available correlations can accurately predict friction pressure losses of slurries. The correlations show some reasonable accuracy within a very limited data range. However, they failed outside this range indicative of their poor applicability. AIC shows how much information is lost when using these correlations which can lead to erroneous results, and even job failure. This fact keeps the gates widely opened for more in-depth experimental, analytical, and theoretical analysis for better understanding of flow behavior with fracturing slurries aiming at developing a more realistic correlation to predict their frictional pressure losses. This paper represents the authors' first step toward developing such correlation, with the application of information theory and AIC.

No abstract

Hydraulic fracturing has increased immensely in recent years. An accurate prediction of frictional pressure losses of fracturing slurries is crucial for successful treatment and to avoid premature screen-out or even treatment failure. Scarce data and lack of theoretical basis of slurry flow, especially in coiled tubing has led to very limited number of correlations that are available to predict slurry frictional pressure losses. Yet, the accuracy of the available correlations is still questionable. The current paper presents a statistical comparative analysis of the available frictional pressure losses correlations for slurry flow in straight and coiled tubing employing the recently introduced math modeling technique that gives weight for the models known as Akaike information criterion, AIC. With the help of AIC, the authors evaluated the available correlations to examine their accuracy. The results show that none of the available correlations can accurately predict friction pressure losses of slurries. The correlations show some reasonable accuracy within a very limited data range. However, they fail outside this range indicative of their poor applicability. AIC shows how much information is lost when using these correlations which can lead to erroneous results, and even job failure. This fact keeps the gates widely opened for more in-depth experimental, analytical, and theoretical analysis for better understanding of flow behavior of fracturing slurries aiming at developing a more realistic correlation to predict their frictional pressure losses. This paper represents the authors' first step toward developing such correlation, with the application of information theory and AIC.