Work

Modeling of Fluid Content in Vibratory Screening Residual Solids Using Artificial Neural Networks

Year: 2025

Type: article

Abstract: The proper management of drilling waste, particularly the fluid content in residual solids from shale shakers, remains a critical challenge in oil and gas operations. Traditional methods relying on laboratory analysis introduce significant delays, hindering real-time process optimization. This study proposes an artificial neural network (ANN)-based virtual sensor to predict fluid content in vibratory screening residual solids in real time. Experimental data were collected from an industrial shale shaker system under varied operational parameters, including motor speed, feed flow rate, and screen inclination. A multilayer perceptron model was developed using TensorFlow, featuring input normalization, dropout regularization, and optimized training with stochastic gradient descent. The ANN architecture achieved a mean absolute error of 0.03 and a loss of 0.002, demonstrating robust convergence without overfitting. Statistical validation via t-tests confirmed no significant difference between predicted and experimental values (p-values of 0.67 for test data and 0.85 for the full dataset). The model’s accuracy under stable operating conditions enables continuous monitoring without additional hardware, addressing the industry’s reliance on delayed laboratory measurements. Key implications include real-time operational adjustments, reduced waste management costs, and a scalable solution for existing systems. This work bridges a critical gap in Artificial Intelligence (AI) applications for solids control, offering a practical framework for enhancing separation efficiency and sustainability in drilling operations.

Source: The Journal of Engineering and Exact Sciences

DOI: 10.18540/jcecvl11iss1pp21481

Authors: Rodrigo Sislian, Renan Silva Cotta, Rafael Yuri Medeiros Barbosa, Vinícius Pimenta Barbosa, Rubens Gedraite, Matheus Paredes Guedes

Institutions: Federal Institute of São Paulo, Universidade Federal de Uberlândia

Language: English

Topic: Drilling and Well Engineering

Subfield: Ocean Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Responsible consumption and production

Open Access status: gold

Estimated APC paid: $19