Seals for blowout preventers (BOPs) and wellhead connectors operate under some of the most demanding conditions in hydraulic sealing. Because these components sit inside critical-function equipment for high-profile, high-consequence applications, high reliability is a non-negotiable design requirement. Functionally, the seals must perform for extended periods as a static seal while retaining the mechanical and geometric properties to act as a dynamic seal the moment they are called on, possibly during an emergency well-control event. Standard hydraulic seal designs are not built to meet that dual demand.
System Seals addresses this class of problem with OPTIM, a preference-driven optimization method developed to design seals for exactly the situations where "good enough on paper" is not good enough in practice. The BOP and wellhead connector application below shows how OPTIM was used to move a subsea seal design from an initial concept to a validated, optimized geometry.
The Limits of Standard Design Methods
Traditional seal design methods rely on manual calculations, experimental trial and error, and finite element analysis (FEA). FEA is the gold standard in seal simulation because it can model the nonlinear, hyperelastic behavior of elastomers, behavior that manual methods cannot easily capture. But running a full FEA simulation for every candidate design is costly and slow, and subsea seal geometries generate a large number of candidates to evaluate.
A Faster Path: Machine Learning-Assisted Prediction
To reduce that cost, OPTIM incorporates machine learning models trained on historical design data and on FEA simulations run across a targeted set of experiments. Rather than replacing FEA, the ML layer builds on it: models are trained either to predict seal performance directly, or to work coupled with physics-based constraints so predictions stay grounded in real mechanical behavior.
Design Is Not the Same as Optimization
Designing a functional seal is only the first step. Designing the best seal, the one that best balances everything the end user cares about, is a separate problem, and it has traditionally been approached in one of two ways.
Traditional optimization methods search a design surface for a local maximum or minimum of a single target metric.
Utility optimization, the approach behind OPTIM, instead relies on the decision maker’s own preference curves and tradeoff functions across several performance attributes at once, producing a design that balances all of them rather than maximizing one.
The distinction matters. In conventional design, geometry is both the end goal and the performance metric: a seal is designed to hit geometric rules. In OPTIM, geometry is only a means to an end: the goal is mechanical performance, as computed by the machine learning model, and geometry is simply the variable that gets adjusted to reach it.
Case Study: A Double-Acting Seal for BOP / Wellhead Service
OPTIM’s process runs in four steps: (1) define the initial design conditions for the seal, (2) estimate performance with the machine learning model, (3) simulate tens of thousands of alternative designs through an iterative search algorithm, and (4) apply the decision maker’s utility optimization rules to select the best candidate.
Applied to System Seals type 232 seal for a BOP / wellhead connector, the process started from an initial cross-section design:
After running the OPTIM process against the seal’s target performance attributes, the resulting optimized geometry showed a redistributed, more balanced stress profile across the seal cross-section:
This case is a useful proof point precisely because BOP and wellhead seals cannot be optimized for a single attribute in isolation. The design had to satisfy both static (outer-diameter) stability during long dormant periods and dynamic sealing performance when actuated, a combination that conventional, single-metric design software struggles to solve for. Using OPTIM’s multi-attribute utility functions, System Seals eliminated design instability while maximizing sealing performance across both operating modes.
Why It Matters for Offshore and Marine
Subsea BOP and wellhead sealing is an extreme case of a general problem: real seals have to satisfy several competing requirements (reliability, dynamic adaptability, resistance to uncertainty) at once, not just one geometric or material target. OPTIM was built for that reality. By accounting for risk and uncertainty alongside physical performance, it produces seals that are engineered to a customer’s actual performance goals, not just to a spec sheet, and that stay dependable as conditions evolve over the life of the well.
System Seals uses OPTIM across its custom sealing work, and the BOP / wellhead case above reflects the kind of high-consequence, high-reliability application where the method’s advantages are most visible.
1. Figures 1–6: System Seals internal engineering analysis (FEA simulation, machine learning model architecture, and optimization results for a 232 seal design), prepared for this publication.
