4D Model Validation

The first step in exploring for hard-to-find deposits is to build and validate a geological model using constrained geometric techniques. Kinematic restoration and forward modelling should then be implemented to test and validate the model; reducing risk and uncertainty in subsequent analysis and therefore maximising the likelihood of finding connections between timing and location for major new ore deposits. Multiple 2D and 3D restoration and validation techniques make it possible to cross-examine the available data, refine and expand models, and to test multiple scenarios. Example of 3D forward model of a complex fold and thrust belt model through time, Bolivian Andes (a - d).

Geological Fracture Prediction and Analysis using 4D Modelling ELLIS, J. F. & VAUGHAN, A. P. M. Midland Valley Exploration Ltd, 2 West Regent Street, Glasgow G2 1RW. E-mail: mve@mve.com

Geological Fracture Prediction

Introduction

Future major ore discoveries are likely to involve a combination of covered, deep or blind deposits. Exploration of these deposits requires a systematic understanding of regional and local geology as well as the deformation history that controlled ore formation and localisation. Fourth-dimension modelling can be used in multiple ways to guide the prediction of fractures more confidently. These models can be used to pin-point structural controls on mineralisation at exploration stage and for risk analysis of stability, or predict fracture patterns for block caving when a deposit goes into production. This poster focuses on the prediction and analysis of subsurface fracture models, using structural modelling as a framework for defining input attributes, static analysis and dynamic fourth-dimensional (kinematic) structural modelling, in Midland Valley’s MoveTM software.

E1E3 is the strain ratio of E1:E3 = (1+e1)/(1+e3), E1E2 is the strain ratio of E1:E2 = (1+e1)/(1+e2).

Simplified and representative example of a fracture recipe. The recipe contains information on the fracture sets expected, the likely deformation style and the input attributes which are the best approximations for each fracture set. Ellis et al. (2014).

Example workflow for the structural approach to fracture modelling in Move. Move provides a unique workflow that makes it possible to track and quantify geometry, stress and strain through time in relation to different stages of deformation.

Conclusion

The modelling approach presented here has three key advantages over other fracture modelling workflows (e.g. a statistical approach, which is often based on limited direct observations); 1. Reduced risk and uncertainty. 2. Modelling fractures in areas without direct measurements. 3. Improved understanding of the geological history and structural evolution.

A: uniform fracture set

B: fold-related fractures

C: fault-related fractures

Fracture Calibration & Validation

Validation can be used to define the uncertainty associated with each assumption made during the modelling process thereby determining if the model is fit for purpose. Each fracture scenario should be tested against available data into order to rank and to fine-tune entry parameters (a - c). Ranking and fine-tuning of fracture sets is an iterative process.

Fracture Prediction

It is possible to build an understanding of the geological processes into theoretical concept models that define a fracture recipe (a), specifying which attributes are used to generate the fracture sets orientation and intensity (b - d).

4D Attribute Capture

Attributes related to the structural geology can be defined by using static attributes, such as curvature (Simple or Gaussian) or for example (a) dip, and dynamic strain attributes captured from the restoration of three-dimensional models with Move’s (b) kinematic unfolding and fault restoration (c) Geomechanical restoration and (d) Fault Response Modelling.

Analysis

Analysis of fracture data allows, for example (a) orientations and (b) stability to be assessed as well as (c) different attributes to be calculated, (including for example block size, porosity and permeability) and connectivity across a region or between wells to be quantified. Analysis can be made for any geological time step i.e. at time of mineralisation or in the present day.

References Ellis, J. F., Johnson, G. & Vaughan, A. P. M. 2014. How well do we understand fracture populations in the subsurface? Workflows for validating fracture models. DFNE – In Press. Micklethwaite, S. 2011. Fault-induced damage controlling the formation of Carlin-type ore deposits. DEStech Publications, Inc. Wightman, R. & Bond, C. 2011 Predicting sub-surface fracture networks from modelled stresses and strain – how well can it replicate natural fractures? Anderson conference poster Shackleton, J.R., Cooke, M.L., Vergés, J. and Simó. T. 2011, Temporal constraints on fracturing associated with fault-related folding at Sant Corneli Anticline, Spanish Pyrenees: Journal of Structural Geology, 33, pp5- 19.

Acknowledgments Midland Valley is acknowledged for the use of their resources in writing this paper and for the use of Move software.

Porosity

Permeability

Fault Response Modelling allows users to calculate and visualize displacement, strain, stress and Coulomb stress changes following slip on single or multiple faults. Vertical Displacement vectors displayed on observation grids and surfaces with elevation. Carlin Data Set from Micklethwaite (2011).

Horizon colour-mapped for elevation with interpreted faults (transparent). The black dashed line represents approximate trend of fold axis. Grid squares are displayed at 2000 m Ellis et al. (2014).

Increments of strain captured in a forward modelled sense in relation to deformation over a fault plane. Strain is defined as a change in length (1D) or shape (2D/3D) due to deformation. The strain attributes are captured during a kinematic restoration or forward model.

The Geomechanical modelling in module in Move utilises elastic mechanical properties and physical laws of motion (Mass Spring theory) to realistically model 3D rock deformation.

Coulomb Stress Analysis can be applied to generate optimal oriented fracture planes for observation grids or topography surfaces. Carlin Data Set from Micklethwaite (2011).

Multiple fracture sets have been generated utilising two increments of strain captured during Geomechanical restoration of faulting and folding of the horizon surface. Ellis et al. (2014).

Different concepts, associated mechanism and attributes can be proposed for the fracture sets. Each fracture scenario should be tested against available data into order to rank them and to fine-tune entry parameters. Ranking and fine-tuning of fracture sets is an iterative process.

The advantage of drill track data is that it is the only direct measure of fractures in the subsurface, however as data is laterally limited it may produce a bias in the fracture sets recorded.

Microseismicity can be recorded and mapped in the subsurface and compared with predicted fractures.

Validation is carried out by comparing known fracture data (or proxy thereof) with the population of the generated fracture model.

Field data provides a useful constraint on attributes which are difficult to define from subsurface data. Shackleton et al. (2012).

Measured Modelled

Understanding the geological evolution of the deposit has the added benefit of helping to reveal uncertainty and reduce risk with all further modelling. Knowledge of the geological model can be used to guide fracture prediction.

Rose plot analysis of fracture sets to assess orientation.

Effective normal stress (MPa)

Shea

r st

ress

(M

Pa)

Stress analysis can be used to determine fracture stability and slip stability.

Analysis of parameters that can be used for further study such geotechnical and hydrological studies.

Block size

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