An Interactive 5-Day Training Course
Integrated Carbonate Reservoir Characterisation
Running the Full Carbonate Study
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Duration
5 Days
Price
$ 3,500
Starting From
19 Apr 2027
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Integrated Carbonate Reservoir Characterisation
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Integrated Carbonate Reservoir Characterisation - GG-ICARB-PEA27
| Code | Date | Time | Duration | Location | Currency | Early Bird Fee Per Person |
|---|---|---|---|---|---|---|
| GG-ICARB-PEA27 | 19 - 23 Apr 2027 | 10 AM CST | 4 Hours Per Day |
Online |
USD |
3500 |
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Integrated Carbonate Reservoir Characterisation
Description
Carbonate characterisation fails more often through poor integration than through poor technique. Individual disciplines produce competent results that do not reconcile: a geological model with facies that the petrophysics does not see, a permeability model that the well tests contradict, a seismic inversion that disagrees with the well data, a simulation that history matches by adjusting parameters the geology does not support. The technical work is adequate; the integration is not, and the characterisation does not support the decision it was commissioned for.
This training addresses the study as a whole rather than the methods within it. Study definition is established first: what decision the study supports, what questions must be answered, what precision is required and what the study should therefore include and exclude. Data audit follows, covering the assessment of available data, its quality and its gaps, and the acquisition decisions that follow. Workflow design is then developed, covering the sequence of work across disciplines, the dependencies between them, the points at which results must be reconciled, and the practical management of a multidisciplinary team. Integration is then treated discipline by discipline: geological and petrophysical reconciliation, seismic calibration and its limits, well test and production constraints on the static model, and the treatment of disagreement between data types. Model construction and validation follow, with attention to validating before simulation rather than after. Uncertainty management and scenario definition are then developed. The training closes with delivery, including the communication of results and uncertainty to decision makers.
The study should be designed backwards from the decision. A characterisation commissioned to support an infill drilling programme needs to resolve remaining oil distribution and connectivity, and can be comparatively relaxed about absolute in-place volume. One commissioned to support a reserves booking needs defensible volumes and recovery factors, and can be relaxed about short-range heterogeneity. A study that attempts to answer every question to the same standard takes longer, costs more and frequently fails to resolve the one question that mattered. Scoping against the decision is the step that most determines whether the study succeeds.
Disagreement between data types is information, not an inconvenience. When well test permeability exceeds log-derived permeability by a large factor, fractures or high permeability layers are contributing and the static model does not contain them. When material balance gives a smaller connected volume than the static model, part of the mapped volume is not connected. When seismic inversion indicates porosity where the wells do not, either the inversion is wrong or the wells have missed something. Each disagreement localises a problem, and resolving them in sequence is how a characterisation improves. Averaging across them conceals the information.
Validation before simulation saves most of the time that history matching consumes. A static model can be tested before it ever reaches a simulator: does it reproduce the in-place volume calculated independently, does its connectivity match the pressure data, do its permeability distributions reproduce well test results, does it honour the production evidence for barriers. Models that fail these checks will not history match except by adjusting parameters to compensate for the geology, and the resulting model predicts poorly. Checking first is cheaper than matching afterwards.
Finally, the deliverable is a decision input, not a model. A study that concludes with a static and dynamic model, a set of maps and a technical report has produced the materials but not the answer. What a development team needs is a statement of what the reservoir is likely to do, what the main uncertainties are, which of them would change the decision, and what could be done to reduce them. Framing the delivery that way changes how the study is run as well as how it is reported.
By the end of this training, participants will be able to:
- Scope a characterisation study against the decision it supports
- Audit available data, assess its quality and identify critical gaps
- Design a multidisciplinary workflow with its dependencies and reconciliation points
- Reconcile geological facies with petrophysical rock types
- Integrate seismic data and assess what it genuinely constrains
- Apply well test, pressure and production data as constraints on the static model
- Resolve disagreement between data types and extract information from it
- Validate a static model before simulation
- Define uncertainty scenarios that span the decision-relevant range
- Deliver characterisation results in a form that supports a development decision
Organisations sending participants to this training will:
- Run characterisation studies that answer the question asked
- Reduce wasted effort on work that does not affect the decision
- Improve integration between subsurface disciplines
- Reduce history matching effort through earlier validation
- Define uncertainty scenarios that are decision relevant
- Improve the quality of subsurface input to development decisions
Participants will:
- Scope and plan an integrated characterisation study
- Work effectively across disciplines on carbonate assets
- Resolve conflicting evidence rather than averaging it
- Validate models before committing to simulation
- Communicate results and uncertainty to decision makers
- Build capability to lead integrated subsurface studies
- Subsurface team leaders on carbonate assets
- Reservoir geologists working in integrated teams
- Petrophysicists and geophysicists on carbonate studies
- Reservoir engineers involved in characterisation work
- Reservoir modellers delivering integrated models
- Technical staff reviewing characterisation studies
- Experienced geoscientists moving into study leadership
Module 1 - Study Scoping and Objectives
- Identifying the decision the study supports
- Translating a decision into technical questions
- Required precision and its implications
- Scope definition: what to include and exclude
- Timeline, resource and deliverable definition
- Stakeholder identification and expectations
- Study types: exploration, appraisal, development, mature field
- Value of information thinking in study design
- Common scoping failures
- Writing a study scope document
Module 2 - Data Audit and Acquisition
- Inventory of available data
- Data quality assessment by type
- Core and core analysis data audit
- Log data audit and normalisation needs
- Seismic data quality and vintage issues
- Well test and production data review
- Pressure data compilation and quality
- Legacy data and its reliability
- Identifying critical data gaps
- Acquisition decisions and their justification
- Data management and database setup
- Cost and benefit of additional acquisition
Module 3 - Workflow Design and Team Working
- Workflow sequence and dependencies
- Parallel and serial work streams
- Reconciliation points in the workflow
- Discipline responsibilities and handovers
- Shared assumptions and their documentation
- Avoiding duplicated and orphaned work
- Review gates and their purpose
- Managing a multidisciplinary team
- Communication within the study team
- Software and platform considerations
- Version control and traceability
- Keeping the study aligned to its scope
Module 4 - Geological and Petrophysical Integration
- Stratigraphic framework construction
- Core description and facies scheme
- Petrophysical rock typing
- Reconciling geological facies with rock types
- Carbonate log evaluation and its uncertainties
- Microporosity and saturation reconciliation
- Permeability prediction and its calibration
- Core to log depth matching and calibration
- Net pay definition and its basis
- Resolving geologist and petrophysicist disagreement
- Delivering a consistent rock property description
- Documenting the basis of each property
Module 5 - Seismic Integration
- Seismic data in carbonate reservoirs and its limits
- Well tie and synthetic generation
- Structural interpretation and depth conversion
- Depth uncertainty and its propagation to volume
- Seismic facies and its geological calibration
- Attribute analysis and its carbonate application
- Seismic inversion and rock physics calibration
- What inversion constrains and what it does not
- Fracture indication from seismic
- Reconciling seismic with well evidence
- Using seismic as a conditioning constraint
- Avoiding overinterpretation of seismic in carbonates
Module 6 - Dynamic Data Integration
- Well test interpretation as a model constraint
- Permeability thickness reconciliation across scales
- Boundary and compartment evidence
- Interference and pressure communication data
- Material balance and connected volume
- Production logging and flow profile evidence
- Water and gas breakthrough as architectural evidence
- Tracer data and flow path identification
- Fracture contribution assessment from dynamic data
- Reconciling static and dynamic evidence
- Using disagreement to localise model problems
- Dynamic data as the strongest available constraint
Module 7 - Model Construction and Validation
- Model framework and layering decisions
- Grid design for carbonate reservoirs
- Rock type and property population
- Fracture and dual porosity representation
- Upscaling and its validation
- Volume reconciliation against independent calculation
- Connectivity testing before simulation
- Permeability validation against well tests
- Barrier representation against pressure evidence
- Model quality control checklist
- Identifying models that will not history match
- Revising the static model rather than tuning the dynamic one
Module 8 - Uncertainty, Delivery and Decision Support
- Identifying the uncertainties that matter
- Separating parameter and scenario uncertainty
- Defining decision-relevant scenarios
- Low, mid and high case construction
- Sensitivity analysis and its interpretation
- Value of information for further data acquisition
- Presenting results to a development team
- Communicating uncertainty without losing the message
- Stating what would change the recommendation
- Study documentation and handover
- Keeping the characterisation current through field life
- Reviewing a completed study critically
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
All course bookings made through PEA are strictly non-refundable. By registering for a course, you acknowledge and accept that all fees are payable in full and are not subject to refund under any circumstances, including changes in personal or professional commitments or partial attendance.
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.
