Smart Tech, Machine Learning and invest(able) Digital Assets — Optimizing energy production
Ever wondered and went into ‘deep’ thought about the existence of innumerable precious metals and liquids beneath the earth‘s surface? If not, let me dive deeper to explain through the eyes of data science and artificial intelligence.
The emergence of the “digital oil field” helps produce cost-effective energy while addressing safety and environmental concerns.
Everyone needs it, few know how we get it, and many feel compelled to slow down efforts to finding and producing oil. One of the primary assets of successful, thriving societies is a low-cost energy source.
What drives low cost? Supply greater than demand!
What drives supply? Finding supplies in sufficient quantities so producing oil and gas is economically viable. Finding and producing hydrocarbons is technically challenging and economically risky. The process generates a large amount of data, and the industry needs new technologies and approaches to integrate and interpret this data to drive faster and more accurate decisions. Doing so will lead to safely finding new resources, increasing recovery rates and reducing environmental impacts.
Oil and gas industry was one of the earliest adopters of data science. However it wasn’t known as data science back then. Exploration of oil was and is heavily based on Bayesian Statistics, which is more or less a pillar of data science. Every O&G company is adopting data science for optimizing drilling operations, equipment selection, crew selection, production optimization, storage optimization…
O&G is an old industry but it is a very complicated one. One that has many technologies involved. It has massive amount of data generated every single day. These companies may seem like old dinosaurs but they are moving with the new technology.
For oil and gas businesses operating at the highest levels of efficiency while keeping costs in control and increasing productivity is a challenging task. To limit downtime and minimize risks, oil and gas companies are leveraging data science and machine learning. This helps in predictive maintenance execution consequently empowering people to act before equipment failure occurs. Following are 4 use cases of data science and machine learning in oil and gas industry:
a. Reactive Maintenance: This is the most basic approach which involves letting an asset run until failure. It is suitable for non-critical assets that have little to no immediate impact on safety and have minimal repair or replacement costs so that they do not warrant an investment in advanced technology.
b. Preventative Maintenance (PM): This approach is implemented in hopes that an asset will not reach the point of failure. The preventative maintenance strategy can be formulated on a: fixed time schedule or operational statistics and manufacturer/ industry recommendations of good practice.
c. Condition-Based Maintenance (CBM): CBM is a proactive approach that focuses on the physical condition of equipment and how it is operating. CBM is ideal when measurable parameters are good indicators of impending problems.
d. Predictive Maintenance (PdM): Predictive maintenance is implemented for more complex and critical assets. It relies on the continuous monitoring of asset performance through sensor data and prediction engines to provide advanced warning of equipment problems and failures.
e. Risk-Based Maintenance (RBM): RBM enables comprehensive decision making to plant operations and maintenance personnel using PdM, CBM and PM outcomes.
2. Predictive Analytics
Predictive analytics together with PdM can lead to the identification of issues that may not have been found otherwise. Predictive analytics software keeps a track of historical operational signatures of each asset and compares it to real-time operating data to detect even the precise changes in equipment behavior.
3. Health and Performance Optimization
With Predictive asset analytics software solutions, oil and gas organizations get early warning notifications of equipment issues and potential failures which help them to take corrective measures and improve overall performance. Read how Predictive Asset Analytics software solutions work?
4. Smarter Operations
With the help of predictive analytics, they can ascertain and comprehend actual and expected performance for an asset’s current ambient, loading and operating conditions.
This information helps enterprises in:
- measuring the impact of performance deficiencies on current and future operations;
- assessing the risk and potential consequences associated with each monitored asset; and
- Prioritizing capital and operational expenditures.
These strides in Big Data will not only improve operational efficiency and help solve critical business problems, but has the potential to help the energy industry:
- Discover new energy sources
- Save money on oil drilling and exploration
- Increase efficiency and productivity
- Predict and prevent accidents before they happen
- Avoid power outages
- Gauge consumption patterns
- Match supply to demand
- Plan for better maintenance and repairs
- Minimize impact on environment
- Improve profit margins and long-term viability
With the PermianChain’s Platform-as-a-Service (PaaS) and distributed ledger network we believe that blockchain as a business-model enabler can unify, safeguard, trace and make permanent the data generated from the above mentioned O&G technologies being implemented in today’s digital oilfields — to truly digitize the value of these fields and potentially make marginal oilfields profitable.
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