Board 90: Core Feature Extraction with Computer Vision

Authors:
raw_author_name: Daniel Torres
raw_affiliation_string: California State University, Bakersfield
raw_author_name: Alberto Cruz
raw_affiliation_string: California State University, Bakersfield

Abstract: Abstract In the oil and gas industry, exploration is largely dependent on the study of the subsurface hundreds or thousands of feet below. Most of the data used for this purpose is collected using borehole logging tools. Although sophisticated, these tools are limited as to how precisely they can measure the subsurface in terms of vertical resolution. There is one method of studying the subsurface that provides unlimited vertical resolution – core samples. Although core samples provide scientists the opportunity to generate a full, continuous data set, lab analysis work is normally done at one-foot intervals, as anything more would be prohibitively expensive. This means at best, a representative data set is generated. However, if the subsurface is not homogeneous, it is difficult to generate a representative data set with lab analysis done at one-foot intervals. This is a void that artificial intelligence can fill. More specifically, a properly trained neural network can analyze high-resolution core images continuously from top to bottom and generate a continuous analysis. It is also important to note that geologic interpretation tied to core analysis can introduce human error and subjectivity. Here too, a properly trained neural network can generate results with extreme levels of accuracy and precision. One core analysis expert believes that core analysis done manually is flawed about 70% of the time. This flawed analysis can result from lack of experience and or a lack of knowledge of the geologic formation. We are not the first to attempt to analyze core samples with vision algorithms. A group of Stanford researchers used micro-computed tomography (micro-CT) and Scanning Electron Microscopy (SEM) images of core samples to characterize the porous media. While promising, SEM and micro-CT imaging is expensive, and more importantly it is not a standard practice in the oil and gas industry to collect these types of images, making these images rare. One other work applied convolutional neural networks to a GIS based regional saturation system, but our work is significantly different. It is well known that training a neural network requires abundant data, thankfully with the method of core analysis we are proposing that will not be a problem. Through industrial partnerships we've obtained hundreds to thousands of core images sufficient to train a neural network, as well as core interpretations tied to those images coming from a core analysis expert with over 40 years of experience. We are the first to propose automatic hydrocarbon saturation as well as lithology prediction from core slab images. We propose the use of convolutional neural networks to analyze core samples at a single site. We plan to conduct experiments using a variety of neural networks to determine the best practices, and explore how such a service can be offered to the industry via the software-as-a-service paradigm. In the past, automated analysis through core slab images has not been possible simply because images of the required resolution were not common, but that has changed. If implemented successfully, this proposed method could become the new standard for core evaluation.