Pengaruh Temperatur dan Ion Klorida terhadap Laju Korosi Baja Karbon pada Perpipaan Crude Oil Onshore: Tinjauan Literatur

Ferdy Azlan Hanafi; Aris Herdiana; Puji Astuti Ibrahim

doi:10.31004/riggs.v5i2.11439

Authors

Ferdy Azlan Hanafi Institut Teknologi Petroleum Balongan
Aris Herdiana Institut Teknologi Petroleum Balongan
Puji Astuti Ibrahim Institut Teknologi Petroleum Balongan

DOI:

https://doi.org/10.31004/riggs.v5i2.11439

Keywords:

Korosi Internal, Baja Karbon, Crude Oil Onshore, Temperatur, Ion Klorida

Abstract

Korosi pada pipa minyak darat merupakan penyebab utama kerusakan infrastruktur industri minyak dan gas yang berdampak pada penurunan keandalan operasi, kerugian ekonomi, serta risiko pencemaran lingkungan. Laju korosi pada pipa baja karbon dipengaruhi oleh berbagai faktor, terutama suhu operasi dan konsentrasi ion klorida dalam air formasi. Penelitian ini bertujuan mengkaji pengaruh kedua faktor tersebut terhadap mekanisme dan laju korosi melalui tinjauan literatur. Kajian dilakukan terhadap 29 artikel ilmiah yang diterbitkan pada periode 2021–2026, dengan dua jurnal utama sebagai referensi. [5][2] menggunakan pendekatan eksperimental berbasis pengujian elektrokimia untuk mengevaluasi perilaku korosi baja karbon pada berbagai kondisi lingkungan. Hasil kajian menunjukkan tiga temuan utama. Pertama, pengaruh suhu terhadap laju korosi berlangsung dalam dua fase dengan tiga rentang karakteristik, yaitu di bawah 60°C, antara 60–80°C, dan di atas 80°C, yang berkaitan dengan perubahan pembentukan lapisan pelindung. Kedua, hubungan antara konsentrasi ion klorida dan laju korosi bersifat nonlinier, dengan laju korosi tertinggi pada larutan NaCl 3,5% berat. Ketiga, interaksi suhu dan ion klorida menghasilkan efek protektif pada suhu tinggi dengan konsentrasi ion klorida sedang, tetapi mempercepat korosi pada suhu sedang dengan konsentrasi ion klorida tinggi. Kajian ini juga mengidentifikasi lima celah penelitian, terutama keterbatasan data lapangan di Indonesia dan belum tersedianya model prediksi berbasis machine learning yang tervalidasi untuk kondisi lokal. Temuan ini diharapkan menjadi dasar pengembangan strategi mitigasi korosi yang lebih efektif.

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