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RSS FeedsSustainability, Vol. 11, Pages 6527: New Artificial Neural Networks Model for Predicting Rate of Penetration in Deep Shale Formation (Sustainability)

 
 

20 november 2019 08:00:02

 
Sustainability, Vol. 11, Pages 6527: New Artificial Neural Networks Model for Predicting Rate of Penetration in Deep Shale Formation (Sustainability)
 




Rate of penetration (ROP) means how fast the drilling bit is drilling through the formations. It is known that in the petroleum industry, most of the well cost is taken by the drilling operations. Therefore, it is very crucial to drill carefully and improve drilling processes. Nevertheless, it is challenging to predict the influence of every single parameter because most of the drilling parameters depend on each other and altering an individual parameter will have an impact on the rest. Due to the complexity of the drilling operations, up to the present time, there is no reliable model that can adequately estimate the ROP. Artificial intelligence (AI) might be capable of building a predictive model from a number of input parameters that correlate to the output parameter. A real field dataset, of shale formation, that contains records of both drilling parameters such as, rotation per minute (RPM), weight on bit (WOB), drilling torque (τ), standpipe pressure (SPP) and flow pump (Q) and mud properties such as, mud weight (MW), funnel and plastic viscosities (FV) (PV), solid (%) and yield point (YP) were used to predict ROP using artificial neural network (ANN). A comparison between the developed ANN-ROP model and the number of selected published ROP models were performed. A novel empirical equation of ROP using the above-mentioned parameters was derived based on ANN technique which is able to estimate ROP with excellent precision (correlation coefficient (R) of 0.996 and average absolute percentage error (AAPE) of 5.776%). The novel ANN-based correlation outperformed three published empirical models and it can be used to predict the ROP without the need for artificial intelligence software.


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28 viewsCategory: Ecology
 
Sustainability, Vol. 11, Pages 6528: Efficiency Analysis of Zinc Refining Companies (Sustainability)
Sustainability, Vol. 11, Pages 6526: Resistance to Regulation: Failing Sustainability in Product Lifecycles (Sustainability)
 
 
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