How to create property volumes OpendTect Master Class June 15, 2014

Outline Introduction From well data From attribute analysis From Neural Network Facies/Litho cube Summary and Conclusions

Introduction In OpendTect, property volumes can be created different ways: From wells : using the well logs From attributes From the Neural Network plugin Using HitCube inversion (see dedicated webinar) The purpose of this presentation is to go this different options and see their use and how they can be used together.

Outline Introduction From well data From attribute analysis From Neural Network Facies/Litho cube Summary and Conclusions

Properties in wells For any well, logs can be loaded or created. How to create a log: At a well location, any 2D/3D attributes and volumes can be extracted and saved as a well log if defined at this location. The Create option in the well manager allows to compute logs using user-defined formulas or from the rock physics library. These saved formulas are organized by property to predict and correspond to the more commonly and often used relationships. Tip : use the convert option to save an existing log with other units.

Property volumes from well logs Individually For a particular well, log cubes can be created from the logs available at this well. The log is repeated on a selected amount of traces around the well location.

Property volumes from well logs Group of wells Well logs can be interpolated between logs using the volume builder. Two options are available : Well-log interpolator (new in 5.0) : interpolation along z-slices HorizonCube-Well Interpolator : interpolation along the events of the HorizonCube. The interpolation is done using either inverse distance or triangulation methods. Logs may be extended vertically if missing by extending parallel to top/base or using the top/base values. –

Property volumes from well logs – usage A LogCube allows accessing well logs by reading a store cube and thus logs in workflows requiring stored cubes as input. Example: To use a log as input to a neural network. LogCubes are also an excellent way to visually QC inversion results, since the log values are projected on the slice used for the display (inline, random line). They allow attributes to be applied to logs. Finally LogCubes allows logs to be exported as traces in SEG-Y format. Only drawback: A LogCube only translates log(s) to a cube. It does not fill the gap between the wells.

Property volumes from well logs – usage The HorizonCube-Well interpolator allows creating 3D models better constrained structurally. It is very commonly used for building models prior to an inversion.

Outline Introduction From well data From attribute analysis From Neural Network Facies/Litho cube Summary and Conclusions

Attribute analysis Lithology probability A property such as the lithology can be defined based on attributes extracted from the seismic, e.g. energy and spectral decomposition. The FingerPrint attribute combines attributes of an interpreted feature. It outputs a probability volume, the similarity between the local attribute response and the attribute response of the interpreted feature. The feature is defined by a single position, or a set of locations stored in a pickset.

Attribute analysis A property such as the lithology can be defined based on attributes extracted from the seismic, e.g. energy and spectral decomposition, or inverted volumes. Relationship between attributes/inverted volumes and the target property logs can be extracted from crossplot analysis, either in the form of an equation, or using Probability Density Functions. A property cube can then be computed by applying the extracted relationship using: Scaling in the Copy Cube option in the Seismic manager if the relationship is linear. Mathematics attribute if the relationship is non linear. Bayesian classification

Attribute analysis - Example Well logs <--> Attributes Crossplot relationship Upscaled Property log Attribute/ Stored volume

Attribute analysis - Example Option 1: Copy cube Manage Seismic data Copy cube

Attribute analysis - Example Option 2: Mathematics attribute 2D/3D attribute set

Outline Introduction From well data From attribute analysis From Neural Network Facies/Litho cube Summary and Conclusions

Neural Networks Neural Networks are used to combine multiple attributes into meta- attributes for object detection such as chimneys or faults or property prediction. There are two types of Neural Networks: Unsupervised: A set of attributes is extracted at one or several set(s) of random locations in the target. It is used to CLASSIFY the data using the response defined by the set of attributes. Supervised: A target value is provided for each training location. The set of attributes is then used to PREDICT the target values on all the samples not used during the training.

Neural Network : Property Prediction In the case of property prediction, two types of input are used: The target property to be predicted is selected among the well logs present in the wells. The other inputs are selected from attributes. A typical input is an inverted impedance cube. The Neural Network uses the logs from the selected wells within a defined interval. It defines where the picks are to be extracted. Two types of property logs can be predicted: ordinary logs and lithologs. Lithologs have the particularity of having discrete values.

Neural Network : Property Prediction Data is extracted along the deviated tracks of selected wells within a defined interval. A user defined percentage of these data is used for testing. The extracted data is presented in a crossplot table before the training starts. This allows reviewing and Qcying of the data: unreasonable values or behaviours may be edited. The Neural Network training starts after the data QC step. The final step before the actual training is the data balancing: this step ensures to have the same amount of data points per bin. the data range to use can be limited (discard unwanted output values for example). Some random noise is added when balancing the data. Demo

Neural Network – Property Prediction example : Porosity prediction

Neural Network – Property Prediction example : Porosity prediction Porosity prediction from Neural Network (displayed along horizon MSF4)

Outline Introduction From well data From attribute analysis From Neural Network Facies/Litho cube Summary and Conclusions

Facies/Litho cube Facies/Litho cubes have the particularity of having only discrete values. They are usually computed based on cut-off values of other properties such as porosity, Vclay, density…etc. Unsupervised Neural Network can allow a unbiased classification based on attributes. The different classes have then to be identified. If litho logs are available in the wells, they can be predicted using Neural Network – Property Prediction and selecting lithology code option. The output will then have only discrete values. Facies/Litho cubes can also be computed using the Mathematics attribute.

Outline Introduction From well data From attribute analysis From Neural Network Facies/Litho cube Summary and Conclusions

Summary Licence Requirements: Property volume in OpendTect From well data LogCube / Attribute log FREE Well log interpolator FREE HorizonCube-Well log interpolator HorizonCube LICENCE Attribute analysis Mathematics attribute FREE Crossplot tool FREE Bayesian classification FREE Neural Network NeuralNetwork LICENCE

Summary Well + Wells + HorizonCube Complexity + Volume(s) + Volume(s) Log Cube + Wells Well Log interpolator + HorizonCube HorizonCube Well Log interpolator + Volume(s) (number of input, computation time, algorithm...) Complexity Crossplot analysis (mathematics + copy cube) + Volume(s) Bayesian classification Volume(s) Neural Network – pattern recognition + Well Neural Network – Property prediction

Conclusions Property volumes can be created from well logs and/or attributes depending on the objective, nature of the target and available data. A property prediction can be achieved using different approaches with different level of complexity in their methodologies. They can be used together to QC the results and refined the prediction. One should keep in mind that the output volume can always be converted back to a log at the well locations, if appropriate. The attribute logs can then be compared with existing logs, to measure the prediction error qualitatively and quantitatively.