Real-time optimization of offshore oil and gas production may be decomposed into production optimization, maximization of value from the daily production of reservoir fluids, and reservoir management, optimization of injection and reservoir drainage on the time scales of months and years. Each subproblem may consider difierent models which are less complex than an all-purpose model.
Any model used in either reservoir management or production optimization must be fitted to production data, a set of historical measurements, consisting of test separator measurements and possibly others such as measured total rates, pressures, temperatures, and choke openings. If the information content in this set of production data is low, it may cause uncertainty. In recent years, explicit treatment of uncertainty in reservoir models has received much attention, but little attention has been paid to uncertainty in models for production optimization.
In this paper we make model-independent assertions about uncertainty in production optimization through a case study of actual production data from a North Sea oil and gas field. As the information content in production data describing normal day-to-day operations was observed to be low, we propose that an explicit treatment of uncertainty may be as relevant for production optimization as it is for reservoir management.
We highlight three challenges for further research based on our observations that have received little attention in the literature until now. Firstly, the expected losses incurred due to uncertainty should be quantified to assess their significance. Secondly, costs and values of uncertainty mitigation should be estimated to allow proposed actions to be evaluated through structured business cases. Thirdly, strategies for making decisions in day-to-day operations under uncertainty should be investigated.
Production in the context of offshore oil and gas fields can be considered the total output of production wells, a mass flow with components including hydrocarbons, for simplicity often lumped into oil and gas, often in addition to water, CO2, H2S, sand and possibly other components. Production travels from wells through flow lines to a processing facility for separation, as illustrated in Figure 1.
Fig 1- A schematic model of offshore oil and gas production
Production is constrained in several ways, including: On the field level, the capacity of the facilities to separate components of production and the amount of available lift gas is limited by the by compression capacity. The production of groups of wells may travel through shared flow lines or inlet separators which have a limited liquid handling capacity. The production of individual wells may be constrained due to slugging, other flow assurance issues or due to reservoir management constraints.
The aim of production optimization is to determine a setpoint for a set of chosen decision variables which is optimal by some metric. These setpoints are implemented by altering the settings of production equipment. Decision variables may be any measured or computed variables associated with the production system which are influenced by changes in settings, but the number of decision variables is limited by the number of settings. We may for instance determine the settings of a gas lift choke by deciding a target lift gas rate, a target annulus pressure or a target gas lift choke opening. On short timescales the flow from individual wells can be manipulated by production choke settings, by gas lift choke settings and possibly routing settings.
