Pipeline throughput
Bowline Midstream
Scored from your answers. Not a comparison with other companies.
Your answers show few gaps in how this number is defined, owned and built.
Your throughput data is already in a state an AI model can use, and your anomaly model is running on it. The gap is at the edges: new field instruments only appear after their tags are mapped by hand, so for about a week a model cannot see the newest assets. Any alert it raises in that window is based on an incomplete picture of the pipeline.
The data is not what is holding the anomaly model back.
Your answers on definition, ownership, assembly and change records are all at the top of the scale, and the model is in production. You told us control room staff are slow to use it. With the data in this state, the question to look at is how the alerts fit into their work, not what sits underneath them.
Count how many tags were added in the last three months and how long each took to show up in throughput.
Pull a week of anomaly alerts and mark which ones control room staff acted on, ignored or did not see.
For the ignored alerts, ask two operators why, and write down their exact words.
What would a control room operator need to see in an alert to act on it?
Who maps new tags today, and what do they need that the instrument register does not give them?
Which number should get the same treatment as throughput next?
This page looks at one number and twelve questions about how it gets made. A Structured Review looks across all five readiness areas (strategy, data, systems, governance and change) with your leadership team in half a day.
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