Artificial neural network model for predicting drill cuttings settling velocity

• Main Authors: Okorie E. Agwu, Julius U. Akpabio, Adewale Dosunmu
• Format: Article
• Language: English
• Published: KeAi Communications Co., Ltd. 2020-12-01
• Series: Petroleum
• Subjects: Drill cuttings; Artificial neural network; Mud; Cuttings settling velocity
• Online Access: http://www.sciencedirect.com/science/article/pii/S2405656119301142

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 the inputs, hidden and output layers are presented in this work unlike other artificial intelligence-based models in the literature. The range of application for the developed ANN model is 0.0001 < Particle Reynolds's number <100 and 0.471 < cuttings sphericity <1. With the model developed in this work, the cuttings settling velocity can be predicted with minimal errors in a quick, less cumbersome, non-iterative manner and is not limited by cuttings shapes' factor and fluid flow regimes.