ImageLogViewer: An open-source solution for exploring images from micro-resistivity and ultrasonic boreholes

• Rodrigo Piva Wikki Soluções em Engenharia
• Matheus A. Cruz Wikki Soluções em Engenharia
• Paola Braga Wikki Soluções em Engenharia
• Rodrigo Dias Wikki Soluções em Engenharia
• Paulo Siqueira Wikki Soluções em Engenharia / Universidade Federal Fluminense (UFF)
• Willian A. Trevizan Petróleo Brasileiro S.A.
• Candida M. de Jesus Petróleo Brasileiro S.A.
• André M. Souza Wikki Soluções em Engenharia / Universidade de São Paulo (USP) https://orcid.org/0000-0002-0207-8034
• Rodrigo Salvador Universidade Federal Fluminense (UFF) https://orcid.org/0009-0001-1202-5585
• Leandro Fernandes Universidade Federal Fluminense (UFF) https://orcid.org/0000-0001-8491-793X
• Flávia C. Bernardini Universidade Federal Fluminense (UFF) https://orcid.org/0000-0001-8801-827X
• Elaine P. M. de Sousa Universidade de São Paulo (USP)
• Daniel de Oliveira Universidade Federal Fluminense (UFF)
• Marcos Bedo Universidade Federal Fluminense (UFF)

DOI: https://doi.org/10.5753/sbbd_estendido.2024.241021

Resumo

Borehole image logs are critical for characterizing reservoirs in the petroleum industry. Exploring this data is challenging because image logs are extensive in size and demand multiple measurements to be juxtaposed side-by-side, such as resistivity and acoustic data. Despite its importance, no open-source applications exist to analyze such data. In this demonstration, we present ImageLogViewer, a Python-based tool for visualizing image logs. It can simultaneously display acoustic and dynamic/static micro-resistivity data with multiple display modes and custom windowing to facilitate the analysis of structures, e.g., fractures and cavities. It also integrates image processing, machine, and deep learning models for classifying and segmenting regions of interest.

Palavras-chave: Well Logging, Image Analysis, Machine Learning, Data Visualization, Open Source Software

Referências

Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., and Süsstrunk, S. (2012). SLIC Superpixels Compared to State-of-the-Art Superpixel Methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(11):2274–2282.

Brekke, H., MacEachern, J. A., Roenitz, T., and Dashtgard, S. E. (2017). The use of microresistivity image logs for facies interpretations: An example in point-bar deposits of the McMurray Formation, Alberta, Canada. AAPG Bulletin, 101(5):655–682.

Gonzalez, R. and Wintz, P. (1977). Digital Image Processing. Advanced book program: Addison-Wesley. Addison-Wesley Publishing Company, Advanced Book Program.

Kamalian, S., Lev, M. H., and Gupta, R. (2016). Chapter 1 - Computed tomography imaging and angiography – principles. In Masdeu, J. C. and González, R. G., editors, Handbook of Clinical Neurology, volume 135 of Neuroimaging Part I, pages 3–20. Elsevier.

Lai, J., Wang, G., Wang, S., Cao, J., Li, M., Pang, X., Han, C., Fan, X., Yang, L., He, Z., and Qin, Z. (2018). A review on the applications of image logs in structural analysis and sedimentary characterization. Marine and Petroleum Geology, 95:139–166.

Nian, T., Wang, G., and Song, H. (2017). Open tensile fractures at depth in anticlines: A case study in the Tarim basin, NW China. Terra Nova, 29(3):183–190.

Perlin, K. (1985). An image synthesizer. In Proceedings of the 12th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH ’85, page 287–296, New York, NY, USA. Association for Computing Machinery.

Schubert, E., Sander, J., Ester, M., Kriegel, H. P., and Xu, X. (2017). DBSCAN Revisited, Revisited: Why and How You Should (Still) Use DBSCAN. ACM Transactions on Database Systems, 42(3):19:1–19:21.

Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.