Filter cake neural-objective data modeling and image optimization
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 rheolog...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/44101/ http://umpir.ump.edu.my/id/eprint/44101/1/Filter%20cake%20neural-objective%20data%20modeling%20and%20image.pdf |
| _version_ | 1848827032220205056 |
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| author | Wayo, Dennis Delali Kwesi Irawan, Sonny Satyanaga, Alfrendo Kim, Jong Mohd Zulkifli, Mohamad Noor Rasouli, Vamegh |
| author_facet | Wayo, Dennis Delali Kwesi Irawan, Sonny Satyanaga, Alfrendo Kim, Jong Mohd Zulkifli, Mohamad Noor Rasouli, Vamegh |
| author_sort | Wayo, Dennis Delali Kwesi |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T03:54:16Z |
| format | Article |
| id | ump-44101 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:54:16Z |
| publishDate | 2024 |
| publisher | MDPI AG |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-441012025-03-14T03:34:27Z http://umpir.ump.edu.my/id/eprint/44101/ Filter cake neural-objective data modeling and image optimization Wayo, Dennis Delali Kwesi Irawan, Sonny Satyanaga, Alfrendo Kim, Jong Mohd Zulkifli, Mohamad Noor Rasouli, Vamegh QA75 Electronic computers. Computer science TP Chemical technology 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. MDPI AG 2024-08-19 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/44101/1/Filter%20cake%20neural-objective%20data%20modeling%20and%20image.pdf Wayo, Dennis Delali Kwesi and Irawan, Sonny and Satyanaga, Alfrendo and Kim, Jong and Mohd Zulkifli, Mohamad Noor and Rasouli, Vamegh (2024) Filter cake neural-objective data modeling and image optimization. Symmetry, 16 (8). pp. 1-17. ISSN 2073-8994. (Published) https://doi.org/10.3390/sym16081072 https://doi.org/10.3390/sym16081072 |
| spellingShingle | QA75 Electronic computers. Computer science TP Chemical technology Wayo, Dennis Delali Kwesi Irawan, Sonny Satyanaga, Alfrendo Kim, Jong Mohd Zulkifli, Mohamad Noor Rasouli, Vamegh Filter cake neural-objective data modeling and image optimization |
| title | Filter cake neural-objective data modeling and image optimization |
| title_full | Filter cake neural-objective data modeling and image optimization |
| title_fullStr | Filter cake neural-objective data modeling and image optimization |
| title_full_unstemmed | Filter cake neural-objective data modeling and image optimization |
| title_short | Filter cake neural-objective data modeling and image optimization |
| title_sort | filter cake neural-objective data modeling and image optimization |
| topic | QA75 Electronic computers. Computer science TP Chemical technology |
| url | http://umpir.ump.edu.my/id/eprint/44101/ http://umpir.ump.edu.my/id/eprint/44101/ http://umpir.ump.edu.my/id/eprint/44101/ http://umpir.ump.edu.my/id/eprint/44101/1/Filter%20cake%20neural-objective%20data%20modeling%20and%20image.pdf |