Synthetic Base Drilling Fluid Applications Under Extreme Pressure and Temperature Conditions in Gulf of Mexico

Hernandez, Monserrat; Quissak, Felipe; Cordova, M.; Torres, Jorge

doi:10.2118/198969-ms

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…In previous literature from [41,42], the suspension of solid particles was quantified, and segments [43] of the flow patterns were presented in 2-D imagery; these techniques were informative to the drilling management and to the current study. However, the data for testing, training, validation, image processing, and objective analysis [44][45][46] are exponentially revealed in the preceding sections.…”

Section: Image Processingmentioning

confidence: 99%

Filter Cake Neural-Objective Data Modelling and Image Optimization

Wayo,

Irawan,

Satyanaga

et al. 2024

Preprint

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Neural-objective and image optimization approaches for drilling fluid rheology automation are crucial for drilling engineering optimization. A myriad of intelligent computational models are employed to predict and monitor the parameters of mud rheology and filter cake permeability posture using an artificial neural network feedforward (ANN-FF) function, a non-ANN-FF function, an image processing tool, and a model optimization tool. 498 datasets of synthetic-based mud (SBM) flat rheology from a drilling mud laboratory at Nazarbayev University were imported into a user-friendly MATLAB application using image processing and nftool. Apart from the Google TensorFlow Sequential API DNN architecture used, a Levenberg-Marquardt training algorithm with input sigmoid hidden neurons 10, 12, and 18 coupled with a linear output layer was also used to predict the SBM flow index. OBM and SBM filter cakes were processed for void spaces; however, a model study was extrapolated to maximize filtration volume Vf. The study’s findings show that the ANN-FF model employed for rheological property monitoring and prediction had higher steady and exponential levels of accuracy and a correlation coefficient of 0.96-0.99. More so, SBM and OBM image processing presented an area of void spaces of 1790M2 and 1739M2, respectively. The porosity and permeability postures of both SBM and OBM resulted in a significant void space capable of maximizing the flow index. In addition, the single-objective modelling based on a genetic algorithm did validate the experimental rheological data for flow index or filtration volume Vf optimization; the study resulted in the finding that the constrained physics-informed objective function hindered maximizing oil recovery and instead predicted possible formation damage. It is empirical to note that automating flow predictions with neural, objective, and image functions has presented an alternative novel method for non-programmers using MATLAB and Google Colabs that is capable of enhancing mud rheological parameters, drilling efficiency, and hydrocarbon recovery.

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Section: Image Processingmentioning

confidence: 99%

Filter Cake Neural-Objective Data Modelling and Image Optimization

Wayo,

Irawan,

Satyanaga

et al. 2024

Preprint

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Filter Cake Neural-Objective Data Modeling and Image Optimization

Wayo,

Irawan,

Satyanaga

et al. 2024

Symmetry

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Designing drilling mud rheology is a complex task, particularly when it comes to preventing filter cakes from obstructing formation pores and making sure they can be easily decomposed using breakers. Incorporating both multiphysics and data-driven numerical simulations into the design of mud rheology experiments creates an additional challenge due to their symmetrical integration. In this computational intelligence study, we introduced numerical validation techniques using 498 available datasets from mud rheology and images from filter cakes. The goal was to symmetrically predict flow, maximize filtration volume, monitor void spaces, and evaluate formation damage occurrences. A neural-objective and image optimization approach to drilling mud rheology automation was employed using an artificial neural network feedforward (ANN-FF) function, a non-ANN-FF function, an image processing tool, and an objective optimization tool. These methods utilized the Google TensorFlow Sequential API-DNN architecture, MATLAB-nftool, the MATLAB-image processing tool, and a single-objective optimization algorithm. However, the analysis emanating from the ANN-FF and non-ANN-FF (with neurons of 10, 12, and 18) indicated that, unlike non-ANN-FF, ANN-FF obtained the highest correlation coefficient of 0.96–0.99. Also, the analysis of SBM and OBM image processing revealed a total void area of 1790 M µm2 and 1739 M µm2, respectively. Both SBM and OBM exhibited notable porosity and permeability that contributed to the enhancement of the flow index. Nonetheless, this study did reveal that the experimental-informed single objective analysis impeded the filtration volume; hence, it demonstrated potential formation damage. It is, therefore, consistent to note that automating flow predictions from mud rheology and filter cakes present an alternative intelligence method for non-programmers to optimize drilling productive time.

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Synthetic Base Drilling Fluid Applications Under Extreme Pressure and Temperature Conditions in Gulf of Mexico

Cited by 2 publications

References 9 publications

Filter Cake Neural-Objective Data Modelling and Image Optimization

Filter Cake Neural-Objective Data Modelling and Image Optimization

Filter Cake Neural-Objective Data Modeling and Image Optimization

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