A method using Neural networks and directive attributes to detect and display fluid indicators in seismic cubes Paul Meldahl Statoil ASA

Content Introduction Examples of detected fluid indicators The detection method Conclusions

From the Gym Hall to the Virtual Reality “Funn”, Stig Kvendseth 3D seismic interpretation require transparency in the seismic cubes

( ) A section from a seismic cube A section from ”TheChimneyCube” How to increase the transparently of the seismic cubes? Enhance, detect and display selected classes of objects at a time. How to enhance, detect and display selected class of objects at a time ? Use our object detection method founded on supervised neural network and directive attributes Object- detection 3D interpretation require transparent seismic cubes

Chimneys and pock marks indicating focus areas of fluid flow above the top reservoir Example of transparent seismic cubes:

A section from a seismic cube The same section after detection of seismic chimneys Object- detection Fluid indicators may be diffuse and difficult to distinguish How to enhance and detect diffuse objects in the seismic? Enhance, detect and display selected classes of objects at a time. How to enhance, detect and display selected class of objects at a time ? Use our object detection method founded on supervised neural network and directive attributes

Example of transparent seismic cubes: Salt, seismic chimneys, reservoir, shallow reflectors and pock pock marks Object detection

Example of a transparent seismic cube; seismic chimneys on top of reservoir

Example of transparent seismic cube; Seismic chimneys and faults indicate where the faults may leak

Directivity principle in the detection method : The size and orientation of windows to extract a set of attributes follow the objects we wish to detect x Vertical for chimney’s Horizontal for ‘DHI’ s Dipping for faults Dip-steered for flexible bodies and interfaces x x

The detection method: Overview

The detection method: Workflow Read the seismic cube from the conventional seismic interpretation system Choose one class of objects to be detected and the associated set of seismic attributes Pick training points in the seismic cube on objects to be and not to be detected Start the automated process of training neural network Start the automated process of detection Display the transparent cube where the selected class of objects are enhanced Write the transparent cube back to the conventional interpretation system

Prediction of fluid flow by detection of Chimney and Faults may have impact in Exploration Transparent seismic cubes show spatial relations between seismic objects A powerful and flexible method for the Detection of Seismic Objects as: Chimneys, Faults, 4D objects, Reflectors, Salt and DHI’s Is presented The detection is efficient because it’s user friendly and uses the combinations of directive attributes and supervised neural network A challenge is to develop semi automated mapping procedures which take advantages of transparency Conclusions

From real sniffing to “seismic sniffing” by neural networks and directive seismic attributes ? From ‘The Prize’ by Daniel Yergin

Acknowledgement To Statoil and dGB for making object detection a reality