Filter Cake Neural-Objective Data Modeling and Image Optimization
Wayo D.D.K. Irawan S. Satyanaga A. Kim J. Bin Mohamad Noor M.Z. Rasouli V.
August 2024Multidisciplinary Digital Publishing Institute (MDPI)
Symmetry
2024#16Issue 8
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.
filter cakes , image , mud rheology , neural network , objective optimization
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Department of Petroleum Engineering, School of Mining and Geosciences, Nazarbayev University, Astana, 010000, Kazakhstan
Faculty of Chemical and Process Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, Kuantan, 26300, Malaysia
Department of Civil and Environmental Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Astana, 010000, Kazakhstan
Energy and Petroleum Engineering Department, University of Wyoming, Laramie, 82071, WY, United States
Department of Petroleum Engineering
Faculty of Chemical and Process Engineering Technology
Department of Civil and Environmental Engineering
Energy and Petroleum Engineering Department
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