The Bottleneck That Wasn't

The Upgrade Everyone Agreed On

Every operations team at the terminal pointed at the same thing. Storage filled, ships waited at berth, and cleanup kept eating availability. The fix was obvious. Upgrade the terminal.

They were right about the symptom and wrong about the cause, and the difference was worth hundreds of millions.

A sour gas operator was about to absorb a major rise in upstream production. More gas meant proportionally more sulfur, every day, through a logistics chain already running close to capacity. If the sulfur could not move, the gas could not be produced. The terminal upgrade was on the table, a third train was on the table, and the capex committee needed to know which combination would actually deliver the throughput and which would absorb capital without solving the problem.

Spreadsheets could not answer that. The system ran on too many clocks that never lined up: train cycles, ship loading, equipment that failed on its own schedule, and stockpiles at three sites that moved against each other. Averages hid the interactions. The interactions were the whole problem.

A National Operator Decided to Find Out Before Committing the Capital

The operator's own team brought the list: fourteen specific upset events they had seen, or come close enough to that they could not be dismissed. Equipment failures, maintenance windows, a berth lost to collision. The question was not whether each could happen. It was which combinations the system could ride through, which needed pre-positioned mitigation, and which had no acceptable mitigation at all.

So SimWell built a model of how the chain actually behaved, then ran the operator's fears against it.

What the Model Found, and What the Operator Now Owns

The case study covers the engagement end to end. Inside it:

• The finding that reframed the entire capex decision: the bottleneck the model traced sat two stations upstream from where every operations team had been pointing, and a terminal upgrade alone would not have delivered the target.
• Why two trains could not hit the lower throughput target under any combination the model ran, and why a third train became a precondition for everything else.
• The fourteen upset scenarios sorted into four severity bands, including the combinations that quietly drove the system to a gas plant shutdown and the time-to-shutdown the model put on each.
• The recommendation set spanning four operating systems, sequenced as a capex plan the committee could approve, defer, or reject item by item.

Fill out the form for immediate access.