Pipeline is an important part in the oil & gas industry. If a failure occurs in the pipeline, it can harm the company, Both materially and financially. By using a business intelligence application that can perform Spatial Monitoring and pipeline analysis, failure events can be emphasized by doing mitigation in its vulnerable areas. ArcGIS Insight supports this monitoring process.
Surface and Subsurface data usually come from two different types of applications. Sometimes when it is necessary to perform an analysis, different applications can complicate the analysis process. By using a web application that could combine subsurface data and surface data , a more holistic analysis can be carried out.
Extraction of crude oil in the well is done by using a bobbin pump. In this case, a comparison is sought between the number of nods at one time and the amount of crude oil produced. Based on this information, the clustering process is then carried out using the K-Means method in machine learning. The clustering results show 6 types of pumping classes.
The results of clustering in oil wells are further analyzed using business intelligence. By using a box plot, the relationship between the number of nods per minute and pump fillage value for each cluster can be seen. The lower the number of nods per minute accompanied by the higher the pump fillage value, the more it indicates that the well has a high level of productivity.