In the capital-intensive oil and gas industry, businesses rely heavily on expensive assets that are deployed in harsh environmental conditions. From a drilling point in the sea to an intermediate station in the desert, the dynamic environmental conditions at each point along the long line affect the performance of the assets deployed along the line. The systems that are used to support these mission-critical assets must also be highly reliable, responsive and secure.
One company that operated a long-distance gas pipeline encountered numerous challenges with the overall efficiency of its pipeline, ranging from sub-optimal usage to wastage of natural resources. Even with the optimal equipment and setup, the wide array of variables in operating conditions combined with the sheer distance covered by the pipeline made running the business difficult.
In this case, there were 22 injection stations along the length of the pipeline, operating under very disparate conditions with different efficiencies. This made it difficult to identify the interdependent effectiveness of these injection stations, despite having a large data set on various parameters at each injection substation. Even a single instance of failure could cost the company hundreds of thousands of dollars in lost revenue as well as any additional costs for repairs that had to be made.
The company was spending $5 million per mile of pipeline annually in corrective maintenance. Along with this, the loss of revenue due to the undelivered material was estimated at $250 million. With energy prices dropping, the loss in revenue directly reduced the bottom line of the company. With the clock ticking and revenue dipping, building a perfect efficiency improvement model became a top priority.
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