Predictive Analytic of Corrosion Build Up Rate by Utilizing Artificial Neural Network

Oil and gas industry for a few years has spent a lot of money in handling in technical
operation problems such as inefficiencies and failures of pipeline and instrument that
occur when the it is not operated optimally due to external and internal corrosion issue.
Corrosion is a metal's deterioration due to chemical reactions between it and the
environment around it. Corrosion in pipeline and instrument can result in material loss,
thickness reduction, and ultimate failure at times. The part will be completely broken
down at one point and will have to be replaced during production is stopped. Corrosion
must be detected at an early stage in order to prevent a high cost-effective catastrophe for
the business and even the lives of humans. According to Prabha et al. (2014), “corrosion
affects all aspect of exploration and production”. In placing more emphasis, Asmara and
Kurniawan (2018) stated that corrosion predictions are important for corrosion and
materials engineering to pick suitable materials, plan testing, management work and
estimate future cost analysis repairs. The contribution of certain operating parameters
(temperature, salt, chloride and pH) to the oil and gas condenser corrosion rate was
focused in this paper. Condenser is used particularly in refinery operations in the oil and
gas industries. In refineries, corrosion occurances is usually caused by inorganic factors
such as water, H2S, CO2, sulphuric acid and sodium chloride, and not by the organics
(Prabha et al., 2014). Many efforts have been made with advanced technology today to
tackle this problem. This creates a demand for the use of machine learning to predict
corrosion build-up. Historical field data from RAPID Pengerang Petronas are analyzed
using the Artificial Neural Network method to determine the dependence of the corrosion
rate on the operating parameters. This paper main purpose is to build a machine learning
model on corrosion build-up at the condenser and at the same time developing a predictive
corrosion analysis dashboard for monitoring purposes.