OIL AND GAS
Recognizing an opportunity to reduce drilling time in the presence of large volumes of historical streaming data is difficult. Integrating machine learning algorithms and big data visualization tools enables the engineers to determine the optimal landing zone, rpm and Weight On Bit (WOB) thus achieving a higher Rate Of Penetration (ROP) while maintaining steerability.
The value of an unconventional asset is determined by the number of drilling locations, spacing, stacking and their individual performances. Machine Learning and modern optimization algorithms will maximize ROI by recommending completions designs and spacing. After identifying production drivers, our intelligent design of experimentation process recommends how to proceed and learn through fewer experiments. Learning faster, smarter, we improve the economics of a larger set of wells.
Drilling and completions costs vary over time. To accelerate the capture of current cost data a Machine Learning assisted mobile app can be developed to process field tickets and updates current spend closer to real-time. This new cost data is then injected into forecasting tools to guide expected spend through to year-end.
Operators face increasing pressure to maintain capital spend within guidance. Machine Learning systems can interrogate historic spend, scheduling and operations data to forecast expected expenditures. By providing accurate visualizations of expected and actual costs, planning teams have the flexibility to scale operations up or down in response to available capital, thereby avoiding shutting down operations too soon or not investing all available capital.
POWER AND UTILITIES
Dispatching power plant generation optimally enables wholesale generators to maximize their revenue. Machine Learning based models for power plant performance and startup will optimize plant offers in deregulated energy markets.
Forecasting power generation from renewable assets including wind, solar and storage can get complicated. We can help renewable operators optimize their asset dispatch by building Deep Learning and Deep Reinforcement Learning algorithms that outperform traditional methods.
Using OSIsoft operational data greatly improves outage planning. Plant managers can use machine learning applications to better forecast asset outage spend and duration. Engine degradation and equipment events can be predicted and monitored by using supervised and non-supervised machine learning techniques.
We advise on, and develop machine learning applications that use business and plant data delivering real-time forecasts for key process and control variables aligned with business objectives.
We are here to help, whether you are looking to boost your bottom line, or simply create the tools you need to do the job, we are here to help every bit of the way. Our detailed and industry knowledgeable staff are more than capable.