Fuzzy Unsupervised Artificial Learning Based on Credibilistic Fuzzy C-Means
Abstract
This study proposes an unsupervised artificial learning approach based on the Credibilistic Fuzzy C-Means (CFCM) algorithm to enhance the governance and analysis of oil production data. The research focuses on supporting decision-making in managing oil output from the MOTOBA oil field, operated by PERENCO in Moanda, Democratic Republic of Congo, covering the period from 2018 to 2021. The methodology involves structuring and segmenting production data using the CFCM algorithm, which enables the identification of meaningful production patterns despite the presence of uncertainty and imprecision in the data. The analysis identified three distinct clusters: wells with low production, wells with moderate production, and wells with high production. These clusters offer valuable insights into the variability of well performance and provide a basis for optimizing operational strategies. The credibilistic enhancement of traditional fuzzy clustering allows for more effective handling of data uncertainty, resulting in a robust and interpretable model—particularly beneficial in complex and data-limited environments. This clustering framework supports more refined monitoring, resource allocation, and operational planning, making it well-suited for the dynamic nature of oil field management. Furthermore, the methodology demonstrates potential scalability and applicability to other industrial domains facing similar challenges in data quality and decision-making under uncertainty. Ultimately, this work contributes to the advancement of data-driven governance in natural resource management through a rigorous and adaptable analytical approach.
Keywords: Artificial learning; Clustering; Credibilist; Fuzzy C-means; Fuzzy logic.
Abstrak
Studi ini mengusulkan pendekatan pembelajaran buatan tanpa pengawasan berdasarkan algoritma Credibilistic Fuzzy C-Means (CFCM) untuk meningkatkan tata kelola dan analisis data produksi minyak. Penelitian ini berfokus pada dukungan pengambilan keputusan dalam mengelola produksi minyak dari ladang minyak MOTOBA, yang dioperasikan oleh PERENCO di Moanda, Republik Demokratik Kongo, yang mencakup periode 2018 hingga 2021. Metodologi ini melibatkan penataan dan segmentasi data produksi menggunakan algoritma CFCM, yang memungkinkan identifikasi pola produksi yang bermakna meskipun terdapat ketidakpastian dan ketidaktepatan dalam data. Analisis ini mengidentifikasi tiga klaster yang berbeda: sumur dengan produksi rendah, sumur dengan produksi sedang, dan sumur dengan produksi tinggi. Klaster ini menawarkan wawasan berharga tentang variabilitas kinerja sumur dan menyediakan dasar untuk mengoptimalkan strategi operasional. Peningkatan kredibilistik dari pengelompokan fuzzy tradisional memungkinkan penanganan ketidakpastian data yang lebih efektif, menghasilkan model yang kuat dan dapat ditafsirkan—terutama bermanfaat dalam lingkungan yang kompleks dan terbatas data. Kerangka pengelompokan ini mendukung pemantauan, alokasi sumber daya, dan perencanaan operasional yang lebih baik, sehingga sangat sesuai untuk sifat dinamis pengelolaan ladang minyak. Lebih jauh lagi, metodologi ini menunjukkan potensi skalabilitas dan penerapan pada domain industri lain yang menghadapi tantangan serupa dalam kualitas data dan pengambilan keputusan dalam ketidakpastian. Pada akhirnya, karya ini berkontribusi pada kemajuan tata kelola berbasis data dalam pengelolaan sumber daya alam melalui pendekatan analitis yang ketat dan adaptif.
Kata Kunci: Pembelajaran buatan; Pengelompokan; Kredibilitas; Fuzzy C-means; Logika Fuzzy.
2020MSC: 68T05, 62H30, 90C90.
Keywords
References
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P. E. Handbook, General Engineering, vol. I. 2007. doi: 10.1007/1-84628-080-x_16.
J. Zhou, Q. Wang, C.-C. Hung, and X. Yi, “Credibilistic Clustering: The Model and Algorithms,” Int. J. Uncertainty, Fuzziness Knowledge-Based Syst., vol. 23, no. 04, pp. 545–564, 2015, doi: 10.1142/S0218488515500245.
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C. Paper, M. Sh, I. Technology, and A. N. Academy, “Opportunities and Challenges Big Data in Oil and Gas,” no. August, 2016.
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M. M. Khadidja, “Étude comparative des deux méthodes d’évaluation objective de la qualité d’expérience audiovisuelle par Logique Floue de type 1 ettype 2,” Université Mohamed Sadik Benyahia de Jijel, 2021.
Khezzan/abd arhmane and B. Mouhamed, “Modelisation flout basee sur les méthodes de clustering,” Université 8 mai, 2015.
P. Smets and R. Kennes, “The transferable belief model,” Artif. Intell., vol. 66, no. 2, pp. 191–234, 1994, doi: 10.1016/0004-3702(94)90026-4.
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DOI: 10.15408/inprime.v7i1.44234
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