We know that machine learning can make significant impact on helping oil and gas companies improve key metrics across the value chain.We help companies embrace Machine Learning in their day-to-day operations. We are tuned and highly oriented towards Machine Learning methodologies to turn those large volumes of data into actionable insights like you have never seen before.
If you are in knee deep stages of implementation, or in the process exploring how to get started, and including for those who have established on- going projects. At any stage, we understand how we can help you maximize the value of Machine Learning applications in their operations and how they can embrace the future of Machine Learning
Integrate and harness multi-sensor data to classify operating states, discover new states, and identify anomalous behavior in real-time.
A stitch in time saves nine. But what if you could predict where to stitch! Deploy our models to predict failures in critical assets ahead of time, avoiding unanticipated costly downtimes.
Data in the O&G sector is high-dimensional, heterogeneous, and multivariate. Use our custom frameworks to identify and capture complex interdependencies in such data sets.
Establish a scalable, process efficient data platform to facilitate data science and analytics
Embrace self service reporting, dashboards, visualization for decision support
Enhance the process of decision making through predictive and prescriptive analytics
Operational states for continuous monitoring
New patterns or modes as they occur
Detect deviant behavior
Interpretable intelligence machine learning models are adept at digesting well-featured streaming data to classify labeled states, or even discovering new unknown states. Deploy such models to monitor ongoing operations or flag anomalous operation. We strive to develop models that are not opaque or “blackbox” but those that lend better interpretability.
Adopt the best posture
To be better prepared
Take the right action
Anticipatory intelligence-can we move beyond regression models for risk prediction? We believe we can judiciously combine feature-rich data with structurally rich neural architectures to address challenges traditional regression-based methods struggle with. Deploy these models to anticipate potential failures, assuming a proactive stance.
Infer the ones that matter most
Right-size for best outcomes.
Deep focus -machine learning models don't magically produce miracles with volumes of data. Quality matters. But how do we cope with the 3V’s of big data? Add to that sensor noise. Welcome to feature engineering. Our frameworks use the state-of-the-art algorithms for dimensionality reduction to obtain low-dimensional latent representation of your datasets to drive the models.