Areas of Interest: Deep Learning, Text Analytics, Time Series Analysis, Data Integration, Event Modeling
Deep learning has taken over the machine learning community in the past few years. It has surpassed other machine learning models in many applications including image recognition, speech recognition, and bioinformatics. We are developing a deep neural network framework for oilfield applications. Our vision is to automate decision processes that are currently carried out by human efforts. Specifically, we are working on the problems of steam job prediction and slippage detection. We develop models which capture the correlations between highly interdependent multidimensional sensor data collected from operating oilfields in California and the target decisions we are predicting. We are building a deep convolutional autoencoder which takes multi-dimensional time series as input and outputs encoded features. We input these features into a fully connected deep neural network which is trained for the task at hand.
Smart Oil Field Safety Net (SOSNet)
The goal of the SOSNet project Is to Integrate multiple heterogeneous data streams, apply complex analytics to Identify patterns for facilitating decision making for asset integrity management. We are given multiple heterogeneous data sources and the objective is to find correlations, patterns and perform predictive analytics to achieve the big picture goal of making asset integrity related decision-making effective and robust, which is only possible if we provide a complete view of the environment. To achieve this, the first step is to integrate multiple data streams. We use Ontologies to model and annotate our raw data sources, which facilitates the automatic integration of the data streams. This gives us the web of knowledge or SOSNet facts. Using analytical and information extraction techniques, information (new data, symbols, and classifications) extracted from raw data (e.g. drawing, images etc.) also become part of this web of facts. This integrated repository drives applications such as predictive modeling, alarms and alerts, interactive visualizations and smart search applications.