Decide What to Simulate, How Much Model to Build, and When to Believe the Result
Reservoir Simulation Strategies
Study scoping and design, model selection and gridding strategy, data preparation, history matching approach, prediction cases, uncertainty handling and the critical review of simulation results.
Reservoir Simulation Strategies
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Reservoir Simulation Strategies - RE-RSA-PEA27
| Code | Date | Time | Duration | Location | Currency | Early Bird Fee Per Person |
|---|---|---|---|---|---|---|
| RE-RSA-PEA27 | 28 Jun - 02 Jul 2027 | 10 AM CST | 4 Hours Per Day |
Online |
USD |
4000 |
Boost your team's skills and your budget! Enjoy group discounts for collaborative learning. Send an inquiry to info@peassociations.com.
Reservoir Simulation Strategies
A course on how to plan, run and use reservoir simulation studies rather than on how to operate simulation software. It covers study scoping and objectives, model type and grid design decisions, data preparation and initialisation, history matching strategy and its pitfalls, prediction case construction, uncertainty and multiple realisation approaches, run management, and the critical review of simulation results by the people who must act on them.
Description
Reservoir simulation is capable of representing almost any reservoir behaviour, which is precisely why it is so often misused. A model with several hundred adjustable parameters can be matched to almost any history, and a match achieved by adjusting parameters that were never measured tells nothing about the future. Studies routinely take longer than planned, deliver results after the decision has been made, and answer questions nobody asked. The technical skill of building and running a model is widely taught. The judgement of what to model, how much detail is justified, when a match is meaningful and how far a prediction can be trusted is not.
This course addresses that judgement. It covers study scoping and the definition of decisions the study must support, model type selection from tank models through streamline to full field compositional, grid design and resolution decisions, upscaling and the loss of information it entails, data preparation and quality control, initialisation and equilibration, relative permeability and pseudo-function treatment, well and completion representation, history matching strategy including parameter selection, objective functions, manual versus assisted approaches and the diagnosis of a match that is fitting the wrong thing, prediction case construction and constraint handling, uncertainty and multiple realisation workflows, run management and quality control, and the critical review of simulation output. It is written for engineers who commission, supervise, review or use simulation studies as much as for those who build them.
A simulation study is an expensive commitment. It absorbs months of specialist time, requires data that may not exist, and produces an output whose credibility is difficult for a non-specialist to assess. The decision to run one should therefore begin with the decision it is intended to inform. If that decision would be the same under any plausible simulation result, the study should not be run. If the decision hinges on something a simulation cannot resolve, a simulation will not help. A surprising proportion of studies fail one of these tests and are commissioned anyway.
Where a study is justified, the design determines its value. Model resolution should be set by the physics that matters for the question, not by the resolution of the geological model. A coning study needs vertical resolution near the well and can be a sector. A gas injection study may need compositional treatment. A field-wide voidage and pressure question may be answered better by a multi-tank material balance in a fraction of the time. Choosing the smallest model that answers the question is a technical decision that saves months.
History matching is where most credibility is won or lost. A match is a demonstration that the model can reproduce the past, which is a necessary condition for trusting its predictions and nowhere near a sufficient one. Matches obtained by adjusting parameters far from measured values, or by tuning well-by-well multipliers that absorb the error without explaining it, produce models that fit history perfectly and predict badly. Recognising the difference requires knowing which parameters are genuinely uncertain, which are constrained by measurement, and what physical mechanism each adjustment is standing in for. This course builds that judgement.
By the end of this training, participants will be able to:
- Determine whether a simulation study is justified and define the decisions it must support
- Scope a study with realistic objectives, data requirements, schedule and resources
- Select model type and complexity appropriate to the question being answered
- Design grid resolution, orientation and layering for the physics that matters
- Prepare and quality control the static, dynamic, fluid and rock-fluid data a model requires
- Initialise and equilibrate a model and verify the initial fluids in place
- Develop a history matching strategy with justified parameter selection and objective functions
- Diagnose a match that reproduces history for the wrong reasons
- Construct prediction cases with realistic constraints and represent uncertainty in the forecast
- Critically review simulation results and communicate their reliability to decision makers
The course is delivered as a strategy and judgement programme rather than a software training course. Each subject is developed through the decisions the study team must make and the consequences of making them badly. Case material follows complete studies from commissioning to result, including studies that answered the wrong question, matched history through unjustified parameter adjustment, or delivered after the decision was taken. Participants work through scoping, design and review exercises, and examine simulation output critically to determine what it does and does not establish.
Organisations sending participants to this training will:
- Commission simulation studies that support real decisions and deliver in time to influence them
- Reduce study cost and duration by building the smallest model that answers the question
- Improve the credibility of simulation-based forecasts and reserves
- Strengthen technical review of internal and contractor simulation work
- Reduce the risk of decisions taken on models matched for the wrong reasons
- Improve the interface between geological modelling, reservoir engineering and simulation
Participants will:
- Judge when simulation is the right tool and when a simpler method is better
- Scope and design studies that deliver usable answers
- Build history matching strategies that produce predictive rather than merely matched models
- Recognise the signs of an over-tuned or physically unjustified model
- Review simulation results critically regardless of who produced them
- Communicate simulation output and its uncertainty honestly to decision makers
- Reservoir engineers who commission, supervise or use simulation studies
- Simulation engineers seeking stronger study design and matching judgement
- Senior reservoir engineers and technical authorities reviewing simulation work
- Field development planners relying on simulation forecasts
- Reserves evaluators assessing simulation-based volumes
- Geoscientists supplying static models to simulation teams
- Subsurface managers accountable for simulation study outcomes
Module 1 — Deciding Whether and What to Simulate
- Identifying the decision the study must support
- Questions simulation answers well and questions it does not
- Alternatives: material balance, analytical methods and streamline approaches
- Value of the study against its cost and duration
- Scoping: objectives, deliverables, schedule and resources
- Data availability assessment before committing to a study
- Setting realistic expectations with the study sponsor
- Study governance, review points and acceptance criteria
Module 2 — Simulation Fundamentals and Model Types
- Governing equations and numerical solution schemes
- Black oil, compositional and thermal formulations and when each is required
- Streamline simulation and its appropriate applications
- Dual porosity and dual permeability options
- Full field, sector, single well and pattern models
- Numerical dispersion, grid orientation effects and stability
- Solver behaviour, timestep control and convergence
- Understanding what the simulator is actually solving
Module 3 — Grid Design and Upscaling Strategy
- Setting resolution by the physics that matters for the question
- Areal and vertical resolution decisions
- Local grid refinement and near-well representation
- Corner point, unstructured and hybrid grid options
- Upscaling from the geological model and the information lost
- Permeability upscaling methods and directional properties
- Verifying that the coarse model preserves flow behaviour
- Model size, run time and the practical limits on iteration
Module 4 — Data Preparation, Initialisation and Model Build
- Static property transfer and quality control
- Fluid model preparation and equation of state handling
- Relative permeability, capillary pressure and rock typing input
- Pseudo-functions and their justification
- Aquifer representation: numerical and analytical options
- Initialisation, equilibration and contact definition
- Verification of initial fluids in place against volumetrics
- Well and completion representation, well indices and inflow modelling
- Production and injection history data preparation and allocation quality
Module 5 — History Matching Strategy
- What a history match does and does not demonstrate
- Selecting parameters for adjustment and justifying each one
- Parameters constrained by measurement and those genuinely uncertain
- Objective function definition, weighting and data conditioning
- Global then local matching sequence
- Manual matching workflow and diagnostic reasoning
- Assisted history matching and optimisation approaches
- Multiplier abuse and matching for the wrong reasons
- Recognising when the model or the data is at fault
- Match quality criteria and when to stop
Module 6 — Diagnosing and Improving a Match
- Field level, well level and phase level match diagnosis
- Pressure match versus rate match priorities
- Water breakthrough timing and its usual causes
- Gas-oil ratio behaviour and its diagnostic value
- Compartmentalisation and connectivity evidence from mismatch
- Reconciling simulation with material balance and well test results
- Using mismatch to improve the reservoir description
- Documenting the match and the reasoning behind adjustments
Module 7 — Prediction Cases and Forecasting
- Prediction case design and the decisions being tested
- Well and field constraint handling: rates, pressures and facilities
- Drilling schedule and well placement representation
- Group control, network coupling and facility limits
- Reliability of long-range prediction from a matched model
- Sensitivity of forecast to parameters not constrained by history
- Comparing development options on a consistent basis
- Forecast validation against analytical expectation
Module 8 — Uncertainty and Multiple Realisations
- Why a single matched model understates uncertainty
- Multiple history matched realisations and their generation
- Experimental design and response surface approaches
- Proxy models and their appropriate use
- Ensemble methods and assisted workflows
- Ranking realisations and selecting representative cases
- Producing a forecast range rather than a single profile
- Communicating a probabilistic forecast from simulation
Module 9 — Run Management, Review and Delivery
- Version control of models, decks and results
- Run management, batch execution and result archiving
- Quality control checks on every run
- Technical review of a simulation study and what to examine
- Questions a reviewer should ask of any matched model
- Reporting simulation results honestly including limitations
- Handing over a model for future use and updating
- Keeping a model alive as new production data arrives
- Integrating simulation output into reservoir management practice
Upon successful completion of this training course, delegates will be awarded an official Certificate of Completion issued by the Petroleum Engineers Association (PEA), an ISO 9001:2015 certified training organization. The certificate carries 10 Credits and formally records the total learning hours completed.
Each certificate is signed by the Course Facilitator and the CEO of the Petroleum Engineers Association, and serves as verifiable proof of professional training that delegates can present to employers and professional bodies worldwide.
Frequently Asked Questions
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PEA reserves the right to make reasonable adjustments to course content, trainers, or schedules where necessary, without entitling delegates to a refund. Comprehensive details of each course — including objectives, target audience, and content — are clearly outlined before enrolment, and it is the responsibility of the delegate to ensure the course's suitability prior to booking.
For any inquiries related to cancellations or bookings, please contact our support team, who will be happy to assist you.
