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Led by Senior Reservoir Engineer Simulacrum
Production system optimisation from IPR-VLP analysis and integrated asset modelling through artificial lift design, flow assurance, the digital oilfield, and the economic framework for optimisation investment decisions.
Led by Senior Reservoir Engineer Simulacrum
The question
The production system is a connected chain from reservoir to export point, and the bottleneck is almost never where it is assumed to be. This module develops the diagnostic framework: IPR and vertical lift performance, the operating point at their intersection, trend analysis across rate, GOR, water cut, and productivity index, choke management and the critical flow condition, production allocation accuracy, and the VLP shift method for distinguishing surface system constraints from subsurface ones.
Outcome
The student can describe the production system chain, explain IPR and VLP and their intersection, apply the VLP shift method to distinguish surface from subsurface bottlenecks, and explain the consequence of allocation error for well performance analysis. (Production system architecture and bottleneck diagnosis)
Sub-units
Led by Senior Reservoir Engineer Simulacrum
The question
Nodal analysis places a node at the sandface and solves the system from each side to find the operating point. The integrated asset model extends this to the full field — reservoir, wellbore, surface network, and facility constraints modelled simultaneously. This module develops both, then covers reservoir surveillance through pressure mapping and production logging, well testing as an IPR update tool, and decline curve analysis aggregated to the field forecast within the field development plan.
Outcome
The student can describe nodal analysis at the sandface node, explain the IAM architecture and its application to gas lift allocation, describe the surveillance tools that update the IAM, and explain the role of the FDP in connecting production forecasts to investment decisions. (Nodal analysis, IAM, surveillance, and forecasting)
Sub-units
Led by Senior Well Intervention & Artificial Lift Engineer Simulacrum
The question
On most mature fields, artificial lift is the primary production mechanism — the system that keeps wells flowing long after natural reservoir energy has declined. This module covers gas lift design and the performance curve, ESP selection and the four principal failure modes diagnosed from surface data, rod pump optimisation using the dynamometer card, and surface facilities optimisation — including separator pressure management and the five main production chemical treatments.
Outcome
The student can describe the gas lift performance curve and the slope-equalisation allocation method, explain the ESP selection parameters and diagnose three failure modes, and interpret a dynamometer card to diagnose fluid pound and gas interference. (Artificial lift optimisation)
Sub-units
Led by Senior Process Troubleshooter Simulacrum
The question
The fluids that come out of the reservoir carry the chemistry of the subsurface — waxes, hydrates, scale-forming minerals, sand — and each creates a flow assurance challenge that must be managed before it blocks a pipeline or erodes a choke. This module covers wax and hydrate prevention and remediation, scale prediction from water analysis, sand production management through drawdown control and sand-face completions, produced water treatment, and the digital oilfield as the operational integration layer.
Outcome
The student can describe the flow assurance challenges for wax, hydrate, and scale (conditions, prediction, prevention, remediation), explain the critical drawdown concept, describe the produced water treatment train, and explain what the digital oilfield integrates and its operational limitations. (Flow assurance, sand/water management, and the digital oilfield)
Sub-units
Led by Senior Reservoir Engineer Simulacrum
The question
Every optimisation intervention has a cost and a benefit — and the engineer who cannot quantify both cannot make a robust investment case. This module covers machine learning applications in production data analysis, reliability engineering and the four RCM maintenance strategies, NPV calculation for optimisation investments with sensitivity analysis and portfolio prioritisation, and sustainability — flaring minimisation, the methane intensity metric, and the engineering case for the low-carbon production system.
Outcome
The student can describe three ML applications in production optimisation, calculate availability from MTTF and MTTR, calculate NPV for a simple optimisation investment, and explain the methane intensity metric and two engineering interventions that reduce it. (Production optimisation — analytics, reliability, economics, and sustainability)
Sub-units