Predicting water-based drilling fluid filtrate volume in close to real time from routine fluid property measurements

Abstract

Drilling operations depend on precisely controlling drilling fluid filtration volume (FV), which affects formation integrity, costs, and borehole stability. Maintaining optimal FV is essential to prevent well control issues, yet forecasting it is challenging due to process complexity and measurement limitations. This study adapts machine and deep learning (ML/DL) models to predict FV in almost real-time based on more easily measured fluid properties. Radial-basis-function neural network (RBFNN), generalized regression neural network (GRNN), multilayer perceptron (MLP), convolutional neural network (CNN), and Gaussian process regression (GPR) ML models are applied to 1186 records of density, viscosity, and solids content in water-based drilling fluids deployed in fourteen wellbores. CNN outperformed other models with the lowest root mean square error (RMSE) of 0.5381 mL and demonstrated resilience to overfitting and noisy data, unlike RBFNN and GRNN. The proposed method provides reliable near-real-time FV predictions, which could be beneficial in optimizing drilling operations by helping prevent potential drilling-fluid-related issues. Fast and accurate FV forecasting from routine fluid properties represents a crucial advancement for drilling operations, highlighting the need for future dataset expansion to encompass a wider range of conditions and fluid types.

Keywords

Water-based drilling fluid / Near-real-time filtration estimation / Key fluid properties / Predictive machine-learning model / Convolutional neural network (CNN)

CRediT authorship contribution statement

Shadfar Davoodi: Conceptualization, methodology, data curation, investigation, software, visualization, formal analysis, validation, writing-original draft, writing, reviewing and editing. Mohammed Ba Geri: investigation, visualization, formal analysis, reviewing and editing. David A. Wood: Conceptualization, writing, reviewing, editing, formal analysis and validation. Mohammed Al-Shargabi: Writing, reviewing and editing, methodology, visualization. Mohammad Mehrad: Conceptualization, methodology, data curation, investigation, software, visualization, formal analysis, validation, writing-original draft, writing, reviewing and editing. Alireza Soleimanian: Writing, reviewing and editing, formal analysis, validation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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