This project is associated with computational sciences activity group under the collaboration framework between Schlumberger, the largest oilfield services company in the world, and IPT (Petroleum Engineering & Applied Geophysics) and IDI (Computer and Information Science) at NTNU. The aim is to automatically extract fault surfaces from seismic volume with the least amount of human intervention.
Interpreting faults in 3D seismic data is still one of the most time-consuming and tedious aspects of seismic interpretation. Automatic Fault Extraction (AFE) is a process designed to automatically interpret fault surfaces from 3D seismic volume. The AFE workflow is comprised of the following three main steps explicitly or implicitly:
A. Fault attributes: select an appropriate fault-sensitive attribute to highlight the fault location
B. Fault likelihood: transform the attribute volume into a fault likelihood/confidence volume
C. Fault surfaces: generate a localized surface mainly in form of polygons, from the confidence volume
Over the last decade, a multitude of AFEs have been developed, including kinds of fault attributes (e.g. chaos, curvature), various fault likelihood (ant tracking, hough transform, skeletonization, applying geological constrain) and different fault surfaces (e.g. in format of fault sticks, polygon meshes). Although a few of them has been implemented in the commercial software but it is not satisfying the end users.
Exploring new methods to further improve the performance and reduce the human intervention will accelerate the interpretation workflow by orders of magnitude. Thus it continuously attracts lots of researchers from kinds of disciplines and try to treat the AFE from different viewpoints. With our background in computer science, it would be possible to at least investigate two sub-disciplines:
1) Computer vision: A possible solution in mind would be ‘active contour model’, also called ‘snakes’. It is a framework in computer vision for delineating an object outline from a possibly noisy 2D image. The snakes model is popular in computer vision, and snakes are greatly used in applications like object tracking, shape recognition, segmentation, edge detection and stereo matching.
2) Machine learning (Optional): Neural Network has been used for fault extraction in OpendTect. Other choice from machine learning would be support vector machines (SVMs). It is a supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. In the context of fault extraction, the classification is fault or not fault. It is optional depending on the progress in item 1.
The project is ambitious and could lead to a publication. It will be implemented as a Plugin for Petrel, which is a Schlumberger owned E&P software platform that provides an integrated solution from exploration to production. Novel results will also be compared against A3Mark our seismic attributes benchmark for quantitative results.
[Necessary] Good programming skills in Matlab, C#, TDT4195 (Visual Computing Fundamentals)
[Preferably] TDT4230 (Graphics & Visualization), TDT4265 (Computer Vision), Machine Learning.
Dataset to be used: Will be provided by Schlumberger
Advisors: Liyuan Xing (NTNU), Victor Aarree (Schlumberger), Nader Salman (Schlumberger), Theo Theoharis (NTNU).