1 research outputs found
Towards an effective automated interpretation method for modern hydrocarbon borehole geophysical images
Borehole imaging is one of the fastest and most precise methods for collecting subsurface
data that provides high resolution information on layering, texture and dips, permitting a
core-like description of the subsurface. Although the range of information recoverable
from this technology is widely acknowledged, image logs are still used in a strictly
qualitative manner. Interpreting image logs manually is cumbersome, time consuming
and is subjective based on the experience of the interpreter. This thesis outlines new
methods that automate image log interpretation and extract subsurface lithofacies
information in a quantitative manner.
We developed two methodologies based on advanced image analysis techniques
successfully employed in remote sensing and medical imaging. The first one is a pixelbased
pattern recognition technique applying textural analysis to quantify image textural
properties. These properties together with standard logs and core-derived lithofacies
information are used to train a back propagation Neural Network. In principle the trained
and tested Neural Network is applicable for automated borehole image interpretation
from similar geological settings. However, this pixel-based approach fails to make use
explicitly of the spatial characteristics of a high resolution image.
TAT second methodology is introduced which groups identical neighbouring pixels into
objects. The resultant spectrally and spatially consistent objects are then related to
geologically meaningful groups such as lithofacies by employing fuzzy classifiers. This
method showed better results and is applied to outcrop photos, core photos and image
logs, including a ‘difficult’ data set from a deviated well. The latter image log did not
distinguish some of the conductive and resistive regions, as observed from standard logs
and core photos. This is overcome by marking bed boundaries using standard logs. Bed
orientations were estimated using an automated sinusoid fitting algorithm within a formal
uncertainty framework in order to distinguish dipping beds and horizontal stratification.
Integration of these derived logs in the methodology yields a complete automated
lithofacies identification, even from the difficult dataset. The results were validated
through the interpretation of cored intervals by a geologist.
This is a supervised classification method which incorporates the expertise of one or
several geologists, and hence includes human logic, reasoning, and current knowledge of
the field heterogeneity. By including multiple geologists in the training, the results
become less dependent on each individual’s subjectivity and prior experience. The
method is also easily adaptable to other geological settings. In addition, it is applicable to
several kinds of borehole images, for example wireline electrical borehole wall images,
core photographs, and logging-while-drilling (LWD) images. Thus, the theme of this
dissertation is the development of methodologies which makes image log interpretation
simpler, faster, less subjective, and efficient such that it can be applied to large quantities
of data