An Interactive 5-Day Training Course
Quantitative Sedimentology & Facies Statistics
Sedimentology Converted Into Model Numbers
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Duration
5 Days
Price
$ 3,500
Starting From
31 May 2027
Home Geology and Geoscience Quantitative Sedimentology & Facies Statistics
Quantitative Sedimentology & Facies Statistics
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Quantitative Sedimentology & Facies Statistics - GG-QSED-PEA27
| Code | Date | Time | Duration | Location | Currency | Early Bird Fee Per Person |
|---|---|---|---|---|---|---|
| GG-QSED-PEA27 | 31 May - 04 Jun 2027 | 10 AM CST | 4 Hours Per Day |
Online |
USD |
3500 |
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Quantitative Sedimentology & Facies Statistics
Description
Reservoir models require numbers. A static model needs facies proportions, object dimensions, variogram ranges, net to gross and transition probabilities, and every one of these is a sedimentological quantity. In practice they are often supplied from default software values, from a single published analogue or from judgement, when they could be derived from the data available. The consequence is models whose architecture is not based on the sedimentology that was so carefully described.
This training covers the methods that convert sedimentological observation into model input. Grain size and textural statistics are established first, covering measurement methods, distribution parameters, graphical and moment methods, and what the statistics do and do not indicate about process. Bed thickness analysis follows, covering thickness distributions, power law and lognormal behaviour, truncation and censoring effects, and the interpretation of thickness statistics in terms of depositional process. Facies statistics are then developed: proportion estimation and its sampling error, transition count matrices, embedded and fixed interval Markov analysis, and the testing of facies ordering against randomness. Dimensional and connectivity statistics follow, covering measurement, bias correction, width to thickness relationships and percolation behaviour. Spatial statistics are then developed, covering variogram construction from well and outcrop data, range estimation, anisotropy and the common errors in variogram fitting. The training closes with uncertainty quantification and with the delivery of model inputs together with their confidence.
Facies proportions carry sampling error that is almost never stated. A proportion estimated from three wells in a field is a sample statistic with a confidence interval, and with a small number of wells that interval is wide. Since connectivity is strongly non-linear near the percolation threshold, a net to gross estimate of fifty-five per cent with a confidence interval spanning forty-five to sixty-five per cent corresponds to models that behave quite differently. Reporting the proportion without the interval conceals a first-order uncertainty.
Bed thickness distributions are informative and routinely truncated. Many turbidite and other event bed successions show approximately power law thickness distributions, which indicates something about the process generating them. The measured distribution is always truncated at the thin end, because beds below a few centimetres are not recorded in a standard core description, and at the thick end by the limited length of section observed. Fitting a distribution without accounting for both truncations gives wrong parameters, and extrapolating from such a fit gives wrong net sand.
Markov analysis tests whether facies ordering is real. A vertical succession may show apparent cyclicity that is actually random, and the human eye is poor at telling the difference. A transition probability matrix compared against the expectation for randomly ordered facies shows whether particular transitions occur more often than chance, which is the quantitative test for the facies successions that interpretations rely on. Where the ordering is not significant, an interpretation built on the apparent cycles needs revisiting.
Finally, variogram ranges derived from well data are frequently meaningless and are used anyway. A field with ten wells spaced a kilometre apart provides almost no information about spatial correlation below a kilometre, which is the scale that matters for most reservoir architecture. The range used in such a model has come from somewhere else, usually a default or an assumption, and it should be derived from analogue or outcrop data and labelled as such rather than presented as a measurement.
By the end of this training, participants will be able to:
- Calculate and interpret grain size and textural statistics
- Analyse bed thickness distributions and account for truncation effects
- Estimate facies proportions with their sampling uncertainty
- Construct transition probability matrices and apply Markov chain analysis
- Test facies ordering and cyclicity against randomness
- Measure sand body dimensions and correct for known biases
- Apply connectivity and percolation relationships quantitatively
- Construct and interpret variograms from well and outcrop data
- Quantify and propagate uncertainty in sedimentological model inputs
- Deliver statistically defensible inputs to reservoir modelling
Organisations sending participants to this training will:
- Base reservoir model inputs on data rather than defaults
- State and propagate uncertainty in sedimentological inputs
- Avoid connectivity errors from unstated proportion uncertainty
- Derive variogram parameters from appropriate data sources
- Improve the statistical defensibility of static models
- Improve communication between sedimentologists and modellers
Participants will:
- Convert sedimentological observation into model-ready numbers
- Quantify uncertainty in facies proportions and dimensions
- Test facies ordering rather than assuming it
- Construct variograms correctly and recognise when data is inadequate
- Deliver model inputs with stated confidence
- Work credibly with reservoir modellers and engineers
- Sedimentologists and reservoir geologists supplying model inputs
- Reservoir modellers building static models
- Geostatisticians working with geological data
- Petrophysicists working on net to gross and proportions
- Technical staff reviewing static model parameters
- Geoscientists working on outcrop analogue studies
- Experienced geoscientists requiring quantitative methods
Module 1 - Quantitative Sedimentology and Model Inputs
- Which model inputs are sedimentological quantities
- Facies proportions, dimensions, ranges and transitions
- Where default values come from and why they persist
- Consequences of unsupported model parameters
- Data types available and what each constrains
- Sampling design for quantitative work
- Measurement consistency and its importance
- Statistical thinking in a geological context
- Uncertainty as a deliverable rather than a weakness
- Building a quantitative workflow
Module 2 - Grain Size and Textural Statistics
- Grain size measurement methods and comparability
- Sieve, laser and image analysis methods
- Phi scale and its use
- Graphical statistics: mean, sorting, skewness, kurtosis
- Method of moments statistics
- Grain size distributions and their shapes
- Polymodal distributions and their interpretation
- Grain size and process interpretation
- Limits of grain size as a process indicator
- Grain size and permeability relationships
- Textural maturity indices
- Presenting grain size data usefully
Module 3 - Bed Thickness Analysis
- Bed definition and measurement consistency
- Thickness distributions and their forms
- Power law thickness distributions
- Lognormal and exponential distributions
- Process interpretation of thickness statistics
- Truncation at the thin end and its causes
- Censoring at the thick end
- Correcting for truncation in distribution fitting
- Amalgamation and apparent thickness
- Thickness trends through a succession
- Thickness statistics in net sand estimation
- Using thickness data predictively
Module 4 - Facies Proportions and Transitions
- Facies proportion estimation from wells
- Sampling error and confidence intervals
- Well bias and preferential sampling
- Proportion variation across a field
- Vertical proportion curves and their construction
- Transition count matrices
- Transition probability calculation
- Embedded against fixed interval Markov analysis
- Testing against random ordering
- Identifying significant facies transitions
- Cyclicity testing and spectral methods
- Interpreting significant and non-significant ordering
Module 5 - Dimensional and Connectivity Statistics
- Sand body dimension measurement
- Apparent against true width corrections
- Truncation bias where bodies exceed exposure
- Width to thickness distributions
- Aspect ratio statistics by environment
- Published dimensional compilations and their use
- Net to gross definition and consistency
- Percolation theory basics
- Connectivity against net to gross relationships
- Body shape effects on the percolation threshold
- Amalgamation ratio measurement
- Connectivity measurement in models
Module 6 - Spatial Statistics and Variograms
- Spatial correlation concepts
- Variogram definition and construction
- Experimental variogram calculation
- Lag selection and pairing
- Variogram models: spherical, exponential, Gaussian
- Range, sill and nugget interpretation
- Anisotropy and directional variograms
- Vertical against horizontal ranges
- Variograms from sparse well data and their limits
- Deriving ranges from outcrop and analogue data
- Common variogram fitting errors
- Documenting the source of a range
Module 7 - Uncertainty Quantification
- Sources of uncertainty in sedimentological inputs
- Measurement, sampling and interpretation uncertainty
- Confidence intervals for proportions and dimensions
- Bootstrap and resampling methods
- Distribution selection for model parameters
- Correlation and dependency between parameters
- Propagating uncertainty into model outcomes
- Scenario against parameter uncertainty
- Interpretation uncertainty and alternative models
- Communicating uncertainty to decision makers
- Avoiding false precision
- Documenting assumptions and their basis
Module 8 - Delivering Model Inputs
- Specifying inputs for object-based modelling
- Specifying inputs for variogram-based modelling
- Training images for multi-point statistics
- Trend and proportion conditioning
- Rules and relationships between facies
- Delivering inputs with their uncertainty
- Reviewing a static model against its sedimentological basis
- Checking model output statistics against inputs
- Testing model realism against analogue statistics
- Working effectively with modellers
- Documenting a quantitative sedimentological study
- Maintaining a corporate analogue statistics database
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.
