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A novel predictive model of drag coefficient and settling velocity of drill cuttings in non-Newtonian drilling fluids

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Petroleum Science 2021年第6期18卷 1729-1738页

作者： Tie Yan Jing-Yu Qu Xiao-Feng Sun Wei Li Ye Chen Qiao-Bo Hu College of Petroleum Engineering Northeast Petroleum UniversityDaqing163318HeilongjiangPeople's Republic of China Sinopec Shale Gas Research Institute of Petro China Southwest Oil and Gas Field Chengdu610051SichuanPeople's Republic of China

In oil and gas well drilling operations,it is of great significance to accurately predict the drag coefficient and settling velocity of drill cuttings in non-Newtonian drilling fluids.In this paper,the free-falling of 172 groups of spheres and 522 groups of irregular-shaped sand particles in Newtonian/non-Newtonian fluids were investigated experimentally.It was found that the drag coefficient calculated based on Newtonian correlations can result in a significant error when the particle settles in the non-Newtonian fluid.Therefore,predictive models of drag coefficient were established respectively for different types of fluids.The validity of the proposed drag coefficient model of spheres was verified by comparing it with the previous works.On this basis,the drag coefficient model of irregular-shaped sand particles was established by introducing a shape factor.The models do not use the shape factor that requires detailed threedimensional shape and size information.Instead,two-dimensional geometric information(circularity)is obtained via image analysis techniques.The present new models predict the settling velocity of sand particles in the power-law fluid and Herschel-Bulkley fluid accurately with a mean relative error of5.03%and 6.74%,respectively,which verifies the accuracy of the model.

关键词： Drag coefficient Settling velocity Non-Newtonian fluid drill cuttings Hole cleaning

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Artificial neural network model for predicting drill cuttings settling velocity

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Petroleum 2020年第4期6卷 340-352页

作者： Okorie E.Agwu Julius U.Akpabio Adewale Dosunmu Department of Chemical and Petroleum Engineering University of UyoAkwa Ibom StateNigeria Department of Petroleum and Gas Engineering University of PortHarcourtRivers StateNigeria

The traditional method of using the coefficient of drag–Reynolds number relationship to predict cuttings settling velocity involves an implicit procedure that requires repeated,time-consuming and tedious iterations using Newtonian or mostly non-Newtonian correlations.Usually,these correlations are limited to certain fluid flow regimes.Besides,most of the explicit and direct cuttings settling velocity models that exist are based on the assumption that the cuttings are spherical particles.However,in the field,the cuttings are a mixture of various shapes and are hardly spherical,hence these models when applied to field conditions come off with huge errors.The objective of this work was to use a nature-inspired algorithm(artificial neural network-ANN)to develop a model for estimating cuttings settling velocity that would be robust and useful in the field that would take into account the shape of the cuttings.The data used for this work was obtained from research experiments in the literature.The model was then evaluated using four performance metrics namely:mean squared error(MSE),root mean square error(RMSE),sum of squares error(SSE)and goodness of fit(R2).It was found that the model's predictions obtained in this work agreed with experimental evidence.Furthermore,the developed model possesses the capacity to generalize across new input datasets and can be applied to particles of any shape,hence,defining the novelty of this research and bridging the gap between theory and practice.When compared with state-of-the-art models,the developed models show a high degree of robustness,as the ANN model performed reasonably well with an MSE of 7.5×10−4,an R2 of 0.978,RMSE of 0.0274 and SSE of 0.25.To generalize the results across new input datasets,the developed model was cross-validated with new data that was not part of the training process.It was found that the ANN model had an MSE value 0.00807,RMSE of 0.0898,MAE of 0.065,SSE of 2.74 and MAPE of 0.675%.To ensure the replicability of the ANN model,the weights and biases for

关键词： drill cuttings Artificial neural network Mud cuttings settling velocity

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