An approach for total organic carbon prediction using convolutional neural networks optimized by differential evolution

Silva, Rodrigo Oliveira; Saporetti, Camila Martins; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher; Pereira, Egberto; Goliatt, Leonardo

doi:10.1007/s00521-023-08865-7

Cited by 6 publications

(1 citation statement)

References 52 publications

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“…For this unknown reason, the fluid density must be in the constant test. However, while we tend to monitor and evaluate the drilling fluid profiles in constant check, artificial and deep neural networks are by far employed to reduce time-cost [23][24][25] effects on the drilling and production of hydrocarbons in the petroleum and civil engineering industries. Its application to solve complex and technical problems is derived from biological neuron concepts.…”

Section: Rheologymentioning

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: Rheologymentioning

confidence: 99%