Abhishek: And I will ask a question back to Holly and Larry. From your observations, how do you see machine learning inter-playing with human interactivity with data and analysis? Do you see the machines taking over and answering all the questions, or do you see a important human element in this is well.
Larry: No I was going to say you know machines need to be trained to provide great answers to questions that humans ask. So that's where things are. You need human power to figure out what are the problems worth solving, and you need machines to provide computation power, to provide the repeatability, but I don't think they we're at a stage where machines can run BI by themselves. You still need that human hand, and you still need software to provide those expiration capabilities. Holly, please?
Holly: Think about what machine learning is for. It is to answer questions, and tools like InetSoft's are really used to create better questions, and so there's actually an input side to the benefits of machine learning for BI. There is an output.
| #1 Ranking: Read how InetSoft was rated #1 for user adoption in G2's user survey-based index |
|
Read More |
Like I said machine learning creates data, but when I talk to data science teams and our customers who love InetSoft because it puts the human inside the loop of the machine learning workflow, it helps them validate that the algorithms and the whole process is working. So BI and analytics and especially digital analytics can help at all stages of the machine learning life cycle.
Abhishek: Yeah, I completely agree. I mean I think this is such a complementary technology to the human element of data exploration or visual exploration of data, but I have a hard time imagining a world where the machine is autonomous. Machine learning is being used ways now to pre-build dashboards and the start of a data connections and technologies.
But at no point can it completely augment or replace the human. It can augment, but it certainly can't replace the curiosity of people. Ultimately why are these investments happening around the entire world? It is our curiosity, and we are trying to answer the question.
Holly: It inspires the humans to do more, to do higher value analytics.
Abhishek: That's right. They are complementary. Our next trend, and this is the one we tried to fit as many industry buzzwords in one trend as possible, that the convergence of IOT, Cloud, and Big Data is here. So we have the trifecta. This trifecta has created new opportunities for self-service analytics.
Larry, why don't you start us off with some thoughts on this. Larry: There is an obvious trend, and there is not so obvious trend here. So the obvious trend is obviously the IOT is not only the biggest buzzword on the planet right now, but we are actually seeing a lot of that actually getting realized from healthcare to consumer packaged goods.
I have seen use cases that actually have been realized and bringing value to customers. It is generating massive volumes of structured and unstructured data, and an increasing share of which is actually being deployed on cloud services. What it means for the analytics market is a couple things.
We have seen there has been tremendous innovation. In terms of figuring out how to capture data from sensors and from remote platforms, it could be an oil rig 20,000 feet under the ocean or the blades of an airplane, but we still need a lot to happen for that to make sense to the end-user.
What Data Do Sensors on Oil Rigs Capture?
Sensors on oil rigs capture a wide range of data to monitor and optimize the drilling, extraction, and maintenance processes. They play a vital role in ensuring safe and efficient operations. Here's a breakdown of the key types of data collected by these sensors:
1. Drilling and Well Monitoring Data
- Pressure Data: Monitors downhole and surface pressures to help ensure well control and detect issues like kicks or blowouts. Managing pressure is crucial for balancing the wellbore to prevent formation fluids from entering.
- Temperature: Measures the temperature in various parts of the wellbore, casing, and surrounding formation. This is essential for monitoring the thermal state of the well and for thermal recovery operations.
- Vibration and Strain: Detects any unusual vibrations or stress that might indicate problems with drill bits, casings, or other downhole components.
- Mud Flow and Density: Measures the flow rate, density, and composition of drilling mud. Mud flow data is critical for maintaining hydrostatic pressure, cooling and lubricating the drill bit, and removing cuttings from the borehole.
- Rate of Penetration (ROP): Measures how fast the drill bit progresses through the formation. ROP data can indicate drill bit efficiency and help identify changes in rock formation.
- Torque and Rotation Speed: Provides data on the rotational force and speed applied to the drill string. Abnormal values can indicate issues with drilling or potential failure points in equipment.
2. Reservoir and Production Monitoring
- Fluid Flow Rate: Monitors the rate at which oil, gas, or water flows from the well, which is essential for production optimization and reservoir management.
- Gas-to-Oil and Water-to-Oil Ratios: Helps operators monitor the composition of the fluids extracted, a critical factor in understanding reservoir characteristics and predicting future production.
- Downhole Pressure and Temperature: Provides real-time information on conditions within the reservoir, allowing for better reservoir modeling and management.
- Water Cut: Monitors the amount of water produced along with oil or gas, which is important for understanding reservoir depletion and planning water disposal.
3. Structural and Environmental Data
- Structural Integrity and Corrosion: Sensors measure the integrity of the rig's structure, especially in high-corrosion areas, to ensure safety and longevity of the infrastructure.
- Vibration and Load on Platform: Detects stress and loads on the platform and equipment to prevent structural failures, especially in adverse weather conditions.
- Weather and Sea Conditions: Includes wind speed, wave height, and water temperature, critical for operations planning, especially in offshore drilling environments.
- Emissions Monitoring: Some rigs monitor the amount of greenhouse gases (such as CO₂ and methane) released during operations, which helps in tracking and managing environmental impact.
4. Electrical and Mechanical Systems Monitoring
- Power Generation and Consumption: Oil rigs require vast amounts of power, and monitoring power systems is essential for operational efficiency.
- Motor and Generator Health: Sensors monitor the health of motors and generators, including temperature, vibration, and load, to prevent failures and reduce downtime.
- Battery and Backup Systems: Tracks battery levels and performance of backup systems in case of power outages.
5. Safety and Security Monitoring
- Gas Detection: Monitors the presence of flammable gases (like hydrogen sulfide and methane) to prevent fires, explosions, and worker exposure to toxic gases.
- Fire and Heat Detection: Uses flame and heat sensors to detect potential fires, an essential safety feature given the flammable nature of oil and gas.
- Personnel Monitoring: Tracks the location of crew members for safety, particularly during evacuations or emergencies.
- Intrusion Detection: Offshore rigs in particular may monitor for unauthorized access or intrusion, both for asset protection and crew safety.
6. Data Processing and Remote Monitoring
- Many oil rigs now have edge computing capabilities to process this data in real-time and send relevant information to onshore control centers. Real-time data allows for remote monitoring and intervention, which can be critical for quickly addressing any developing issues. With the advent of IoT (Internet of Things), data from these sensors can be integrated and analyzed in real-time, helping optimize the operation, reduce risks, and increase productivity.
The Value of Sensor Data on Oil Rigs
The data captured by these sensors collectively enables comprehensive monitoring and control of operations on oil rigs, significantly contributing to:
- Enhanced safety: By providing real-time insights into pressure, gas detection, and structural integrity, helping avert catastrophic failures.
- Operational efficiency: Optimizing drilling speed, energy use, and well flow rates for cost efficiency.
- Predictive maintenance: Through vibration, torque, and other sensor data, operators can anticipate and address equipment failures before they cause costly downtime.
- Environmental compliance: Emissions and water cut data help maintain environmental standards, reducing the rig's ecological footprint.
By continuously monitoring and analyzing data, oil rigs can ensure safer, more efficient, and environmentally responsible operations, all while maximizing productivity.