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PETE 1004 · Production System Optimisation

Led by Senior Reservoir Engineer Simulacrum

PETE 1004 · Production System Optimisation: course card
5 modules · ~30 hours Engineering Updated June 2026

About this course

Production optimisation is systems thinking applied to the producing well and its connected facilities. The bottleneck is almost never where it is assumed to be — and finding the real constraint requires modelling the full chain from reservoir to export point simultaneously. A reservoir engineer who sees only the subsurface, a facilities engineer who sees only the plant, a well engineer who sees only the wellbore — each will optimise their own element and leave the system bottleneck untouched. The engineer who understands all three finds it.

This course develops the full production optimisation toolkit: IPR and vertical lift performance, nodal analysis, integrated asset modelling, gas lift and ESP optimisation, flow assurance management (wax, hydrates, scale, sand), the digital oilfield and its analytical methods, reliability engineering, and the economic framework that decides which optimisation interventions are worth making. It is a course about connecting the pieces of the system and finding the leverage point — the intervention that, at the lowest cost, delivers the largest sustained rate increase.

The Senior Reservoir Engineer Simulacrum leads the system performance, surveillance, analytics, and economics modules. The Senior Well Intervention & Artificial Lift Engineer Simulacrum takes Module 3 — artificial lift design and optimisation. The Senior Process Troubleshooter Simulacrum takes Module 4 — flow assurance and production chemistry.

Production System Co…1Nodal Analysis, Inte…2Artificial Lift Opti…3Flow Assurance, Sand…4Data Analytics, Reli…5
  1. Module 1

    Production System Components, Performance Analysis, and Bottleneck Diagnosis

    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

    1. ○ 1.1 The Production System Chain and the Bottleneck Concept
    2. ○ 1.2 IPR, VLP, and the Operating Point
    3. ○ 1.3 Production Performance Analysis: Trend Reading
    4. ○ 1.4 Choke Management and Production Allocation
    5. ○ 1.5 Identifying Bottlenecks: The VLP Shift Method and System Modelling
  2. Module 2

    Nodal Analysis, Integrated Asset Modelling, Surveillance, and Forecasting

    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

    1. ○ 2.1 Nodal Analysis: Method and Sensitivity
    2. ○ 2.2 Integrated Asset Modelling: Architecture and Application
    3. ○ 2.3 Reservoir Surveillance: Pressure Mapping and Production Logging
    4. ○ 2.4 Decline Curve Analysis for Field Forecasting
    5. ○ 2.5 Well Testing for IPR Update and the Surveillance Trade-Off
  3. Module 3

    Artificial Lift Optimisation: Gas Lift, ESP, Rod Pump, and Surface Facilities

    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

    1. ○ 3.1 Gas Lift: Operating Principle, Design, and the Performance Curve
    2. ○ 3.2 Gas Lift Design and Troubleshooting
    3. ○ 3.3 ESP: Selection, Operating Point, and Failure Mode Diagnosis
    4. ○ 3.4 Rod Pump Optimisation and the Dynamometer Card
    5. ○ 3.5 Surface Facilities Optimisation and Production Chemistry
  4. Module 4

    Flow Assurance, Sand and Water Management, and the Digital Oilfield

    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

    1. ○ 4.1 Wax and Hydrate Management
    2. ○ 4.2 Scale Deposition: Prediction and Management
    3. ○ 4.3 Sand Production and Produced Water Management
    4. ○ 4.4 SCADA, Real-Time Monitoring, and the Digital Oilfield
    5. ○ 4.5 Production Chemistry in Optimisation
  5. Module 5

    Data Analytics, Reliability, Economics, and Sustainability

    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

    1. ○ 5.1 Data Analytics and Machine Learning in Production Optimisation
    2. ○ 5.2 Reliability Engineering and Maintenance Strategy
    3. ○ 5.3 Economic Evaluation of Optimisation Projects
    4. ○ 5.4 Case Studies: Mature Field and Green Field Optimisation
    5. ○ 5.5 Sustainability, Emissions Reduction, and the Low-Carbon Production System