Energy companies are utilizing innovative technology to gather information from remote sites to monitor production, prospect new development, and maintain existing systems. The amount of data that is generated from open hole wireline systems, drill site data, maintenance sensors, and remote sensing data is staggering.
While most companies have a vast wealth of domain experts that know the science of energy exploration and development, current analytical tools require expertise in statistical modeling, coding, and scripting novel algorithms. The Ayasdi Platform provides a new approach to save energy companies millions to billions of dollars in their data discovery efforts and is accessible to domain experts without the need to write a single line of code or perform queries.
Oil spills and platform disasters are detrimental to the environment, and local communities. Their effect can linger for decades, costing billions to clean up. By analyzing a fusion of information collected from real time log data, field reports, and remote sensing, red flags can be revealed to avert potential disasters before they happen.
It’s expensive to find and drill for oil. Analyzing all available information can save billions in lost revenue from failed drilling attempts, especially when combining Logging While Drilling (LWD) and Measurement While Drilling (MWD) data measurements across sites. This can be used to identify prime locations for resource acquisition and successful drilling operations.
Mechanical performance is critical when drilling for oil. Drill bits are equipped with sensors that generate millions of data points per day. By analyzing the sensor data, manufacturers can design more durable drill bits, and can use this information to predict when drill bits need to be replaced, leading to better planning for maintenance downtimes.
Mechanical failures and unexpected obstacles that lead to disasters require mission critical, responsive remediation and compliance with environmental regulations. By having the ability to quickly analyze data which includes data points from machinery, biological samples, and chemicals can lead to faster remediation.
Download the Paper: TDA to Identify Bacteria in Deepwater Horizon Oil Plume
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