14FORCE Maver How fast is fast Metrics of Machine Learning Enabled Processing of High Volume Well Re
Channel: Force Seminars
Date: 02/10/2018

Title: How fast is fast? Metrics of Machine Learning Enabled-Processing of High Volume Well Reports for Effective Data Search and Class Aggregation in Elastic Docs


National data repositories hosts a huge amount of historical well reports which contain troves of information including but not limited to metadata, images, texts, and processed information including identification of relevant geology, shows, etc. Due to the large volume, vintage variety, and non-standardized formats, extraction of valuable information that are used as inputs for further interpretation, is an arduous task, if not altogether skipped because of the time-consuming nature of manual data mining. We present a workflow for automatically extracting information from these digital documents to improve data aggregation. The first step of the process is a set of heuristic algorithms to identify blocks/segments within a well report, after which, supervised machine learning is used to classify the document segments as either text or non-text. Optical Character Recognition (OCR) is then applied to the text segments to convert them into editable text. Named-entity Recognition (NER) and Pattern Based Recognition (PBR) techniques are then applied to the OCR results in order to extract metadata from the well reports, such as well name, kelly bushing, spud dates, contractors, etc. Text data also are fed to elastic search systems to enable easy search of concepts and geological terminologies. On a separate data pipeline, the non-text components such as images and tables are tagged, and using convolutional neural network, the machine learns to autoclassify at least six image types, including seismic images, stratigraphic charts, maps, cores, and tables so that these data can be aggregated per data type, and information across wells can be compared in terms of the nearest neighbors, and easily searched and displayed so it can be fed into further recommender systems. Here, we present the step-by-step metrics of ML techniques including processing times, precision, and recall, so we can quantify how fast is fast, and decide if fast is also effective. Ultimately, our aim is that the efficiency gains introduced at each of these machine learning steps become greater than the sum of its parts.

Nina Marie Hernandez


Iraya Energies