Elasticdocs As An Automated Information Retrieval Platform for Unstructured Reservoir Data Utilizing A Sequence Of Smart Machine Learning Methods Within A Hybrid Cloud Container

There is a tremendous amount of information available and stored in digital geoscientific documents and published reports in the energy industry. These documents contain a distillation of reservoir information from diverse discipline of geologists, geophysicists, petrophysicists and drillers, that are stored in unstructured format, which find further use in succeeding reservoir modeling stages. In particular, national data management repositories and oil companies hosts these huge amounts of historical well reports containing information such as lithology, hydrocarbon shows, and other reservoir data. Due to the large volume, vintage variety, and non-standardized formats, extraction of valuable information that are used as inputs for interpretation, is an arduous, very time-consuming task. Our solution is to develop ElasticDocs a machine learning-enabled platform in a hybrid cloud container that automatically reads and understand hundreds or thousand of technical documents with little human supervision through a smart combination of machine learning algorithms including optical character recognition (OCR), elatic search, natural language processing (NLP), clustering and deep convolutional neural network. The platform uses a hybrid, 2-tier data service architecture leveraging on the strength of both the strength of local servers and cloud to enhance data security, integrity, and accessibility.