Efficient production optimization strategies using transient measurements Dinesh

Efficient production optimization strategies using transient measurements Dinesh Krishnamoorthy, Bjarne Foss, Sigurd Skogestad Norwegian University of Science and Technology Trondheim, Norway VII Brazil – Norway Production Optimization workshop, Rio de Janeiro © 2018 SUBPRO/NTNU. Distribution, copying and publishing only with written consent from NTNU

Outline • Introduction to SUBPRO • Daily Production Optimization • Case study • Conclusion 2 SUBPRO

SUBPRO SUBSEA PRODUCTION AND PROCESSING The primary objective of the center is to become a global leader for research based innovation within subsea production and processing NTNU: Research Innovations Industry partners: Commercial utilization

Partners SUBPRO

Field architecture RAMS Separation – Fluid characterization Separation Process Concepts System Control Field development concepts Reliability and availability in design Influence of chemicals on produced water quality Membranes for gas dehydration (testing) Modelling for control of subsea processes Multiphase boosting models Condition and prognostic maintenance Prevention of wax deposition Combined H 2 S and hydrate control Adaptive control of subsea processes Optimizing condition monitoring Sequential separation Fluid particle breakup Process control algorithms Modeling of coalescence Characterization of particle breakup Estimation of unmeasured variables Compact separation Enhanced virtual flow metering Research Areas Control for extending component life Production optimization under uncertainty

Daily Production Optimization SUBPRO Downstream processing facilities Upstream oil & gas production © Photo: Ormen Lange subsea field with onshore gas processing facility at Nyhamna. Source: Norwegian Petroleum Directorate

Daily Production Optimization Real-time production data SUBPRO Real-time decision making © Photo: Ormen Lange subsea field with onshore gas processing facility at Nyhamna. Source: Norwegian Petroleum Directorate

Steady-state optimization (Static RTO) Steady-state optimization Update static Model Data processing SUBPRO

Main Challenge: Transient Measurements SUBPRO Câmara MM, Quelhas AD, Pinto JC. Performance Evaluation of Real Industrial RTO Systems. Processes. 2016, 4(4).

Dynamic RTO • Not commonly used • Computationally expensive • Numerical issues Campos MC, Teixeira H, Liporace F, Gomes M. “Challenges and problems with advanced control and optimization technologies”. IFAC ADCHEM 2009 SUBPRO

Hybrid RTO SUBPRO Dynamic Estimation + Static Optimization 11

Simulation Case Gas processing capacity Maximize Total oil

GOR variation 13 SUBPRO

GOR estimation – using static Models 14 SUBPRO

GOR estimation – using Dynamic Models SUBPRO

Results SUBPRO

Results SUBPRO Computation Time [s] 4 3. 3631 3. 5 3 2. 5 2 1. 5 0. 9025 1 0. 5 0 0. 0223 0. 0184 SRTO 0. 0282 0. 0199 HRTO avg. time 17 max time DRTO

Results SUBPRO Integrated Profit Production rates 12 3 10. 93 10 2. 5 8 2 6 1. 5 4 2. 9802 2. 9695 2. 25 2. 9809 2 2. 7019 2. 7509 HRTO DRTO 1. 8256 1 1. 77 0 0. 5 0 SRTO HRTO Total Oil Flared Gas DRTO SRTO

Results SUBPRO Integrated Profit Computation Time [s] 4 3. 3631 3. 5 3 2. 5 2 2 1. 5 0. 9025 1 0 2. 7509 HRTO DRTO 2. 5 3 0. 5 2. 7019 0. 0184 0. 0223 SRTO avg. time max time 1 0. 5 0. 0199 0. 0282 HRTO 1. 8256 DRTO 0 SRTO

Data-driven Production Optimization • Time scale separation – System dynamics – Perturbation (online testing) – Convergence • Very slow convergence SUBPRO

Data driven Production Optimization SUBPRO

SUBPRO SUBSEA PRODUCTION AND PROCESSING Thank you ! www. ntnu. edu/subpro