Analyzing and Controlling Factors Impacting Offshore Condensate Optimization with Digital Tools
Suradech Kongkiatpaiboon, Kanchana Prangsawan, Sakka Trithepapirak, Apipol Soponpalarom, Worasak Charungrattanapong
Offshore Technology Conference Asia (OTC Asia), Kuala Lumpur, Malaysia, 2024 · OTC-34684-MS
Getting the most value out of offshore condensate means balancing heat input, vapor-pressure specification and how much of the C4–C5 fraction ends up in gas versus liquid. This work combined exchanger redesign, large simulation sensitivity studies and a machine-learning RVP predictor into one real-time optimization dashboard with automated alerts for a condensate stabilization system. It shows how a mix of equipment fixes and digital tools can replace trial-and-error tuning and raise condensate yield while keeping product safe to transport.
Key results
- The existing parallel layout had needed bundle change-outs only every 4–5 months, which justified re-piping the exchangers to run in series
- Adaptive boosting regressors predicted RVP most accurately, linear statistical models were close behind, and the Korsten first-principles model showed high bias with low variance
- Qualitative outcomes reported: better operating efficiency, tighter RVP prediction and control, less manual investigation, and higher condensate yield
Approach
- Simulated alternative series/parallel piping layouts for the feed/bottoms heat exchangers in HTRI and trialled finned (fancy) tubes on one bundle
- Built an exchanger performance monitor using cold-side duty and flow, with a linear-regression forecast over a rolling 7–14 day window that looks 60 days ahead to plan cleaning, plus alarm/shutdown thresholds and auto e-mail alerts
- Ran hundreds of HYSYS sensitivity cases, converted results to monetary value and mapped them in 3D to locate the global optimum operating envelope
- Compared machine-learning, statistical and first-principles (Korsten) RVP estimators using time-based cross-validation and RMSE/MAE/MAPE
- Deployed a central optimization dashboard in the PI historian with alerts for model drift, exchanger fouling and condensate uplift opportunities
The problem
Condensate stabilization was limited by frequently fouling feed/bottoms exchangers, an unreliable online RVP analyzer, and no integrated view of the trade-offs between heat input, RVP and middle-chain hydrocarbon recovery.
Tools & methods
Summary written for this site from the published paper. For figures, data and the full method, read the paper on OnePetro.