A North Sea oil and gas operator had recorded three major subsurface instability events over a six-month period and wanted to better understand whether operational data could provide earlier visibility of developing conditions associated with potential sand production.
Sand production can create both production and asset-integrity risks. Changes occurring subsea may ultimately affect topside processing equipment, including the slug catcher, but identifying the relationships between these conditions can be difficult using individual process measurements alone.
VROC was asked to conduct a historical-data study to determine whether AI models could identify relationships between wellhead pressure and temperature, valve configurations and topside equipment behavior associated with the recorded instability events.
VROC trained AI models using historical operational data from the subsea system, including pressure, temperature and valve-configuration data, together with measurements from the slug catcher, including level, pressure and temperature.
Because the slug catcher receives production flow from subsea flowlines and risers, its behavior provided an additional topside indicator that could be assessed alongside subsurface conditions.
The analysis examined relationships across these datasets to determine whether abnormal operating patterns could be associated with the previously recorded instability and potential sand-production events.
Importantly, the models were evaluated retrospectively against historical events; they were not put into live production as part of this study.
The combined analysis of subsurface and slug-catcher data showed that the models were able to identify patterns associated with the historical undesirable operating events.
Within the study dataset, VROC demonstrated a strong capability to monitor relationships between subsurface conditions and topside equipment behavior associated with potential sand events.
The results indicated that, if operationalized against live data, this type of modelling could potentially provide operators with earlier visibility of developing instability and additional time to investigate or take preventative action.
The operator had experienced significant production and equipment exposure from subsurface instability and sand-related events. Client-provided estimates indicated:
Production deferral exposure
Multiple-well shutdowns associated with these events were reported to have resulted in accumulated production deferral valued at approximately USD $2.5M.
Topside equipment exposure
Sand flow can contribute to erosion and damage to topside equipment such as the slug catcher, creating potentially significant repair or replacement costs in addition to production losses.
Production disruption exposure
The client estimated accumulated production losses associated with sand-related disruption to topside activities at approximately USD $11.5M.
These values represent client-estimated exposure associated with historical sand-related events. They are not realized savings or cost avoidance attributable to VROC, as the models developed in this study were not operationalized.
VROC can analyze historical operational data to identify relationships, abnormal behavior and potential predictive use cases before models are deployed into live operations.
Industry: Oil & Gas
Application: Subsurface Instability & Sand Production
System: Subsea Wells / Slug Catcher
Deployment Status: Study only — not operationalized
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