1540 Blondelle Using machine learning to read composite logs
Channel: Force Seminars
Date: 2019-10-06

2019 Force Symposium machine learning and subsurface data
Presentations https://www.npd.no/en/force/archive/hackathon-2019/

"When reading a composite or a formation evaluation log (FEL), a geologist is able to “understand” the drilled section by integrating together all the text and graphic information. To achieve this goal, the geoscientist’s cognitive system has developed several capabilities to: • Read and make sense of a geological column by decoding the graphic representation of the lithology almost without the use of the lithology legend. For example, a pattern of dark points on a yellow or white background is immediately perceived as a sandstone pattern.
 • Detect the oil and gas show symbols and to align them with the available logs, lithology and geological descriptions.
 • Read the geological description frequently written on the composite logs • Read the depth marks and to combine all the previous observations together into geological sequences of intervals located in measured depth (MD).
 We have based the development of a prototype to automate the capture of composite information based on the same 4 main points to evaluate the feasibility of developing an industrial solution to automate the extraction of information form legacy composite and FEL that are presently saved as scanned images.
 Method
 Having already an experience applying natural language processing (NLP) techniques on well related documents, we have derived the prototype workflow from our existing iQC platform where several type of text analyses can be performed. • A text searchable paged PDF used for text analysis and display by a user interface
 • A graphic view of each PDF page saved as jpeg files on which the computer vision can be run.
 In the training process, the user has the possibility to “tag” the key words as well as to tag some graphic patterns like the lithological intervals or the O&G shows to produce or improve a training set of objects.
 On demand, the tags are used to generate one model per target metadata. Various algorithms may be used for modelling the information to be detected:
 • Semantic analyser based on a SVM (Support Vector Machine) for text classification.
 • Semantic analyser of the context around metadata using a random forest algorithm.
 • Detection and classification of graphical pattern using a deep learning approach.
 Once the models are built, they can be used to automatically detect text or graphics from new documents in a batch mode.
 For computer vision, the prototype has been based on the darknet library YOLO (You Only Look Once).
 YOLO is a convolutional neural network (CNN) based approach, and is one of the latest published opensource computer vision algorithms and offers good performance on a limited IT setup (ref 1,3) "