An Interactive 5-Day Training Course
Geomechanics Uncertainty & Sensitivity Analysis
Testing Uncertainty in Geomechanical Models
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Duration
5 Days
Price
$ 3,500
Starting From
22 Feb 2027
Home Geology and Geoscience Geomechanics Uncertainty & Sensitivity Analysis
Geomechanics Uncertainty & Sensitivity Analysis
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Geomechanics Uncertainty & Sensitivity Analysis - GG-GUNC-PEA27
| Code | Date | Time | Duration | Location | Currency | Early Bird Fee Per Person |
|---|---|---|---|---|---|---|
| GG-GUNC-PEA27 | 22 - 26 Feb 2027 | 10 AM CST | 4 Hours Per Day |
Online |
USD |
3500 |
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Geomechanics Uncertainty & Sensitivity Analysis
This training covers uncertainty and sensitivity analysis in geomechanics. It works through uncertainty sources in mechanical earth models, stress magnitude and orientation uncertainty, property and strength uncertainty, pore pressure uncertainty, correlation between inputs, probabilistic stability and failure analysis, sensitivity analysis methods, calibration and model updating, value of information for geomechanical data and the communication of geomechanical risk.
Description
A mechanical earth model is built from estimated stresses, correlated strengths and inferred pore pressures, and every one of those inputs carries substantial uncertainty. A single deterministic model therefore produces a single mud weight window, a single critical drawdown or a single fracture gradient that is presented with a confidence the inputs do not justify. Quantifying the uncertainty converts these into ranges, identifies which input is responsible for the width of the range, and makes it possible to decide what data would narrow it.
This training develops the subject practically. Uncertainty sources in mechanical earth models are established first and ranked, covering stress, properties, pressure and model form. Stress magnitude and orientation uncertainty follow, covering the particular problem that maximum horizontal stress is inferred rather than measured and the width of the resulting range. Property and strength uncertainty are then developed, covering log correlation scatter, static to dynamic conversion uncertainty and the small sample sizes behind most strength models. Pore pressure uncertainty follows. Correlation between inputs is then addressed, since several geomechanical inputs are physically linked and treating them as independent produces unrealistic combinations. Probabilistic stability and failure analysis follow. Sensitivity methods are then developed. Calibration and model updating follow. Value of information is addressed. The training closes with communication.
The maximum horizontal stress is usually the dominant uncertainty and it is frequently reported as a single number. Because it cannot be measured, it is constrained by inverting wellbore failure observations, and the resulting range is often wide: in many fields a factor of one and a half between the low and high estimate. Wellbore stability, fault reactivation pressure and fracture containment all depend on it. A stability prediction quoted to the nearest tenth of a specific gravity from a model containing this uncertainty is reporting a precision that does not exist.
Independent sampling of correlated inputs generates impossible rocks. Young's modulus, unconfined compressive strength and friction angle are all related to porosity and to each other, so sampling them independently in a Monte Carlo analysis will generate combinations such as a high modulus with a very low strength, which do not occur. These unphysical samples widen the output distribution and bias it. Specifying the correlations, even as rank correlations estimated from the core data, gives a narrower and more credible range.
Sensitivity analysis changes where the effort goes. Running a screening design over the model inputs identifies which ones move the output, and in geomechanics the result is often that one or two inputs dominate completely. A wellbore stability calculation may be almost entirely controlled by the rock strength and the maximum horizontal stress, with the elastic properties and the failure criterion choice contributing little. That result directs the data acquisition and the study effort, and it is cheap to obtain.
Finally, the model should be updated against observed behaviour, and the observations are plentiful. Every well drilled provides evidence: where it was stable and where it was not, at what mud weight, where losses occurred, where breakouts formed and how wide they were. Each of these observations constrains the model inputs, and a model calibrated against the drilling history of ten wells is far better constrained than one built from first principles. Formalising this updating, rather than adjusting the model by hand until it looks right, keeps the uncertainty quantified through the process.
By the end of this training, participants will be able to:
- Identify and rank uncertainty sources in mechanical earth models
- Quantify stress magnitude and orientation uncertainty
- Quantify property and strength uncertainty including correlation scatter
- Assess pore pressure uncertainty and its propagation
- Specify correlations between geomechanical inputs
- Run probabilistic wellbore stability and failure analysis
- Apply sensitivity analysis to identify dominant inputs
- Calibrate models against drilling and production observations
- Apply value of information to geomechanical data decisions
- Communicate geomechanical risk as ranges and probabilities
The training is analysis practice on field geomechanical data sets. Participants build deterministic and probabilistic models from the same data and compare the outputs. Sensitivity designs are run to identify dominant inputs. Models are calibrated against supplied drilling histories and the uncertainty reduction measured. Value of information calculations are run on proposed data acquisition.
Organisations sending participants to this training will:
- Report geomechanical predictions as ranges rather than single values
- Identify the inputs that drive geomechanical predictions
- Direct data acquisition at the dominant uncertainties
- Calibrate models against observed well behaviour
- Avoid false precision in geomechanical advice
- Improve the credibility of geomechanical input to decisions
Participants will:
- Quantify uncertainty in geomechanical inputs
- Run probabilistic stability analysis
- Identify dominant inputs by sensitivity analysis
- Specify correlations correctly
- Calibrate models against field observations
- Present geomechanical risk credibly
- Geomechanics engineers and geoscientists
- Drilling engineers using stability predictions
- Reservoir engineers using geomechanical outputs
- Technical staff building mechanical earth models
- Well planners assessing geomechanical risk
- Assurance staff reviewing geomechanical studies
- Technical staff specifying geomechanical data acquisition
Module 1 - Uncertainty Sources
- Mechanical earth model inputs
- Stress magnitude uncertainty
- Stress orientation uncertainty
- Elastic property uncertainty
- Strength parameter uncertainty
- Pore pressure uncertainty
- Failure criterion and model form uncertainty
- Spatial variability against measurement error
- Reducible and irreducible uncertainty
- Ranking sources by likely impact
- Building an uncertainty inventory
Module 2 - Stress Uncertainty
- Vertical stress uncertainty from density gaps
- Minimum stress uncertainty from test interpretation
- Leak-off test interpretation ambiguity
- Closure pressure identification uncertainty
- Maximum stress inversion and its range
- Stress polygon width
- Dependence on strength in the inversion
- Orientation scatter in observations
- Stress rotation and local variation
- Depth variation uncertainty
- Representing stress uncertainty as distributions
- Consequences of stress uncertainty
Module 3 - Property and Strength Uncertainty
- Log correlation scatter
- Static to dynamic conversion uncertainty
- Small sample size in strength testing
- Scatter in triaxial results
- Sample damage effects on measured values
- Scale effects between core and formation
- Anisotropy parameter uncertainty
- Lithology dependence of correlations
- Spatial variability of properties
- Property distribution construction
- Upscaling uncertainty
- Documenting property uncertainty
Module 4 - Correlation Between Inputs
- Physical links between geomechanical inputs
- Porosity as a common control
- Modulus and strength correlation
- Strength and friction angle relationships
- Stress and property correlation
- Independent sampling and its errors
- Unphysical input combinations
- Rank correlation specification
- Deriving correlations from core data
- Effect of correlation on output range
- Testing correlation assumptions
- Constraining samples physically
Module 5 - Probabilistic Analysis
- Monte Carlo in geomechanics
- Probabilistic wellbore stability
- Probability of failure at a given mud weight
- Mud weight window as a distribution
- Probabilistic fracture gradient
- Probabilistic sand production onset
- Probabilistic fault reactivation pressure
- Probability of compaction exceeding a threshold
- Number of realisations required
- Computational efficiency
- Interpreting probabilistic outputs
- Setting operating limits from distributions
Module 6 - Sensitivity Analysis
- Screening design construction
- One at a time against designed experiments
- Factorial and Plackett-Burman designs
- Response surface construction
- Tornado plot interpretation
- Identifying dominant inputs
- Interaction effects between inputs
- Sensitivity by application
- Stability against fracture design sensitivity
- Fixing non-influential inputs
- Directing effort from sensitivity results
- Reporting sensitivity findings
Module 7 - Calibration and Updating
- Observations that constrain the model
- Breakout occurrence and width
- Tensile fracture occurrence
- Mud weight and stability history
- Loss events and fracture pressure
- Sanding onset observations
- Treatment pressure data
- Inverting observations for inputs
- Bayesian updating approaches
- Formal against informal calibration
- Uncertainty reduction through calibration
- Updating with each new well
Module 8 - Value of Information and Communication
- Value of information in geomechanics
- Which measurement reduces which uncertainty
- Cost of stress tests against their value
- Core programme value assessment
- Image log value assessment
- Decision dependence of data value
- When no further data is justified
- Communicating ranges to drilling teams
- Operating envelopes rather than single values
- Avoiding false precision in advice
- Documenting assumptions and their effects
- Reviewing a geomechanical study for uncertainty treatment
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.
