An Interactive 5-Day Training Course
Discrete Fracture Network (DFN) Modelling
Building Fracture Models That Flow
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Duration
5 Days
Price
$ 3,500
Starting From
19 Jul 2027
Home Geology and Geoscience Discrete Fracture Network (DFN) Modelling
Discrete Fracture Network (DFN) Modelling
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Discrete Fracture Network (DFN) Modelling - GG-DFN-PEA27
| Code | Date | Time | Duration | Location | Currency | Early Bird Fee Per Person |
|---|---|---|---|---|---|---|
| GG-DFN-PEA27 | 19 - 23 Jul 2027 | 10 AM CST | 4 Hours Per Day |
Online |
USD |
3500 |
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Discrete Fracture Network (DFN) Modelling
This training covers discrete fracture network modelling from data to flow simulation. It works through fracture data collection and bias correction, fracture set definition and statistical description, geomechanical and geological conditioning, DFN construction, connectivity analysis, upscaling to equivalent flow properties, calibration against well tests and production, and the use of DFN models in development decisions.
Description
A discrete fracture network model represents individual fractures explicitly rather than averaging them into a permeability value, which is the only way to capture the connectivity behaviour that controls flow in fractured reservoirs. The difficulty is that the model must be built from data that samples a tiny fraction of the rock: a few wells, each seeing fractures over a borehole diameter, from which the orientation, size, intensity and aperture distribution of a field-wide network must be inferred.
This training develops the full workflow. Data collection comes first, covering fracture observation from core, image logs, outcrop analogues and seismic, with the sampling bias of each and the corrections required. Fracture set definition follows, covering orientation clustering, set separation, and the statistical distributions used for orientation, length, intensity and aperture. Conditioning is then developed: geological conditioning to structural position, lithology and mechanical stratigraphy, and geomechanical conditioning to the stress field so that only plausibly open fractures carry flow. DFN construction follows, covering stochastic generation, deterministic fracture insertion, fracture corridors and faults, and multiple realisation generation. Connectivity analysis is then addressed, including percolation behaviour and the identification of connected clusters. Upscaling follows, covering the calculation of equivalent permeability tensors and sigma factors for dual porosity simulation. The training closes with calibration against well test and production data and with the application of DFN results to development decisions.
Fracture length is the least constrained input and the most influential on connectivity. Boreholes give orientation and intensity reasonably well, but they give almost no information about how far a fracture extends, because a borehole intersects a fracture at a point. Yet length determines whether fractures connect: a network of short fractures at a given intensity may be entirely disconnected while the same intensity of long fractures forms a percolating cluster. Length distributions therefore come from outcrop analogues, from seismic-scale structures and from calibration against dynamic data, and the uncertainty should be carried through multiple realisations rather than fixed at one value.
Geomechanical conditioning separates a plausible model from an arbitrary one. A DFN built from all observed fractures, each assigned the same aperture, treats a mineralised Palaeozoic fracture set the same as an open set favourably oriented in the current stress field. Conditioning aperture and transmissivity to the normal stress acting on each fracture plane, and to the observed mineralisation state, produces a network in which the conductive subset is a small and specific part of the total. That subset is what the well test sees.
Calibration is what makes a DFN useful rather than decorative. An uncalibrated network produces an equivalent permeability that may be wrong by orders of magnitude. The practical approach generates multiple realisations across the uncertain parameters, upscales each, and retains those whose equivalent permeability and anisotropy are consistent with well test results and whose connectivity reproduces observed pressure communication. The retained set then defines a realistic range rather than a single answer.
Finally, the output that matters is usually not the DFN itself but what it implies for development. Equivalent permeability tensors and transfer functions for the simulator, well placement guidance from connected cluster geometry, expected breakthrough behaviour, and the sensitivity of recovery to the fracture uncertainty are the deliverables. A DFN study that ends with a picture of a fracture network has not answered the question it was commissioned for.
By the end of this training, participants will be able to:
- Collect and quality control fracture data from core, image logs, outcrop and seismic
- Correct fracture intensity for wellbore sampling bias
- Define fracture sets and fit orientation, length, intensity and aperture distributions
- Condition fracture models geologically to structure, lithology and mechanical stratigraphy
- Condition fracture apertures geomechanically to the in situ stress field
- Construct stochastic and deterministic discrete fracture networks
- Analyse network connectivity and percolation behaviour
- Upscale a DFN to equivalent permeability tensors and transfer functions
- Calibrate DFN realisations against well test and production data
- Apply DFN results to well placement and development decisions
The training is a full modelling workflow worked on a fractured field data set. Participants process core and image log fracture data with bias correction, fit distributions, build conditioned networks and generate multiple realisations across the uncertain parameters. Networks are upscaled and the resulting permeability compared against well test values, with realisations retained or rejected accordingly. Connectivity analysis is used to propose well locations, and the recovery sensitivity to fracture uncertainty is quantified.
Organisations sending participants to this training will:
- Build fracture models calibrated to dynamic data rather than uncalibrated ones
- Represent fracture connectivity realistically in reservoir simulation
- Improve well placement in fractured reservoirs
- Quantify the recovery impact of fracture uncertainty
- Specify fracture data acquisition that supports modelling
- Build in-house DFN capability for fractured assets
Participants will:
- Build discrete fracture network models from end to end
- Correct fracture data for sampling bias
- Condition networks geologically and geomechanically
- Upscale networks for simulation use
- Calibrate models against well test and production evidence
- Deliver DFN results as development guidance
- Reservoir geologists and modellers working on fractured reservoirs
- Structural geologists supporting fracture characterisation
- Petrophysicists interpreting image logs for fracture data
- Reservoir engineers running dual porosity simulations
- Geomechanics specialists working on fracture conductivity
- Technical staff reviewing fracture models
- Geoscientists responsible for fractured asset characterisation
Module 1 - Fracture Data and Its Limitations
- Data sources for fracture modelling
- Core fracture description and its value
- Natural against induced fracture discrimination
- Borehole image log interpretation
- Oriented core and orientation determination
- Outcrop analogue data collection
- Seismic scale fracture and fault data
- Mud loss and drilling evidence
- Production logging and dynamic evidence
- What each source constrains and what it does not
- Data quality control before modelling
- Specifying acquisition for DFN purposes
Module 2 - Sampling Bias and Intensity Measures
- Wellbore sampling of three-dimensional networks
- Orientation bias and the Terzaghi correction
- Deviated and horizontal well sampling
- Scanline and window sampling in outcrop
- Censoring and truncation effects
- Intensity measures: P10, P21, P32 and their relationships
- Converting between intensity measures
- Intensity variation with structural position
- Combining data from multiple wells
- Estimating field-wide intensity from sparse data
- Intensity uncertainty and its propagation
Module 3 - Fracture Set Definition and Statistics
- Orientation data presentation and stereonets
- Clustering and fracture set identification
- Fisher and Bingham orientation distributions
- Dispersion parameters and their fitting
- Fracture length distributions: power law, lognormal, exponential
- Length data sources and their limitations
- Aperture distributions and measurement
- Aperture against length relationships
- Transmissivity assignment
- Fracture shape assumptions
- Spatial distribution models
- Fitting and testing distributions
Module 4 - Geological Conditioning
- Structural control on fracture distribution
- Fracture intensity near faults
- Damage zone width and intensity scaling
- Fold related fracture distribution
- Curvature attributes as intensity drivers
- Lithological control on fracturing
- Mechanical stratigraphy and fracture confinement
- Bed thickness and spacing relationships
- Diagenetic control on fracture preservation
- Fracture corridors and swarms
- Building an intensity model
- Conditioning networks to the intensity model
Module 5 - Geomechanical Conditioning
- In situ stress determination for DFN work
- Normal and shear stress on fracture planes
- Slip tendency and dilation tendency
- Critically stressed fracture identification
- Stress dependent aperture models
- Mineralisation and its effect on conductivity
- Conditioning transmissivity to stress
- Separating conductive and non-conductive sets
- Stress change during depletion
- Fracture closure and its production effect
- Coupled geomechanical DFN approaches
Module 6 - Network Construction
- Stochastic fracture generation methods
- Deterministic fracture insertion
- Faults and fracture corridors in the network
- Hierarchy and multiple scales in one model
- Model domain and boundary effects
- Honouring well observations
- Conditioning to seismic attributes
- Realisation generation and parameter sampling
- Number of realisations required
- Computational limits and model size
- Quality control of generated networks
- Visualising and reviewing a network
Module 7 - Connectivity and Upscaling
- Connectivity analysis methods
- Connected cluster identification
- Percolation threshold in fracture networks
- Connectivity against intensity and length
- Flow based upscaling methods
- Equivalent permeability tensor calculation
- Permeability anisotropy from the network
- Grid block size and upscaling validity
- Sigma factor and matrix fracture transfer
- Dual porosity and dual permeability parameters
- Discrete fracture simulation against upscaled models
- Preserving connectivity through upscaling
Module 8 - Calibration and Application
- Well test data as a calibration target
- Permeability thickness comparison
- Flow regime signatures and their reproduction
- Interference test and connectivity calibration
- Production and breakthrough history calibration
- Tracer data and flow path validation
- Rejecting and retaining realisations
- Defining a calibrated uncertainty range
- Well placement guidance from connectivity
- Horizontal well orientation and fracture intersection
- Recovery sensitivity to fracture uncertainty
- Reporting a DFN study for development use
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.
