Prospectivity maps can only be deciphered after 25 years of geology studies, or by smoking something stronger than a cigarette… something legal in Amsterdam and Colorado.
Fortunately, I found one with a legend to make your life easier. Thank me later.
2. AI for production and maintenance optimization
All right, let’s move on to the second field of application of AI and ML in the oil industry: the optimization of production and maintenance processes.
Yes, because once you have found the oil, you have to extract it as effectively as possible. After seeing the prospectivity maps, did you think it was just a frustrating treasure hunt?
Once again, the predictive power of algorithms and their incredible ability to handle complex data comes to our rescue!
Drilling correctly with machine learning
Collecting geologic data allows oil companies to predict how formations will react to specific drilling techniques.
In fact, combinations of ML algorithms can be fed with this data, create models of the soil, and offer oil engineers the best path through a rock formation, both in terms of drilling direction and rate of penetration.
ML-powered systems will not focus on single variables but take into account a wide range of factors that could potentially shape the drilling strategy. Among them: seismic vibrations, equipment features, seismic vibrations, soil permeability, and thermal gradients.
Oil extraction automation: the role of APCs
Ok, we found the oil and started drilling. Then? Well, time to talk about APCs…
An oil field usually consists of many oil wells. Each of them requires an Advanced Process Control (APC) system that autonomously handles the choke valve, electrical submersible pump (ESP) speed, and all the other components of the facility.
I said “each of them”, nobody excluded. We don’t want to create emotional trauma in some well.
The APC automatic manipulation is necessary in order to achieve target production while operating the ESP efficiently within its operating possibilities.
How to make APCs better with algorithms?
The core of each APC system is an empirical model of the oil well filled with information about the main well variables, such as temperature, pressure, amps, etcetera.
Developing each model separately is quite a challenging job that can be avoided by treating wells with similar characteristics and patterns in groups and using ML algorithms (especially clustering) to combine all this data.
Such a mechanism was implemented, for instance, by the Saudi Arabian national oil company “Saudi Aramco”, enabling an 80% reduction in the engineering effort to develop the wells models.
What about maintenance?
All this techy stuff sounds really cool. Until it breaks.
The best way to reduce maintenance costs is to predict failures before they occur, using an ML-based predictive maintenance approach. This involves observing the components of an oil well and spotting changes in their operation that could be a sign of future failure.
Basically, algorithms examine the various activities of the oil facility, detecting any sign of shifting from the ideal performance data used to train them.
This data is collected by specific sensors and processed through a wide range of ML-based methodologies. In particular, neural networks (NN), support vector machines (SVM), and decision trees are the most commonly used.
Of course, the same can be done to scan the integrity of pipelines and avoid disastrous oil spills.
3. AI to face environmental and personnel risks
Hey, did I just say “disastrous oil spills”? Yep, that’s quite a big deal.
Oil is normally stored in central repositories and then distributed across pipelines. These distribution systems are prone to material degradation and corrosion, due to different temperatures and environmental conditions.
Failure to manage this problem in advance can result in catastrophic damage that disrupts the entire manufacturing process and causes environmental disasters.
This happened in the Macondo Incident of 2010 when 4.9 million barrels of oil were spilled, and 850 million dollars were spent by the US government for cleaning up the mess.
The ML-based predictive maintenance mentioned earlier is a great way to prevent accidents like this from happening. AI technologies can detect signs of corrosion by analyzing various parameters, forecast the likelihood of corrosion, and send an alert.
AI and ML for personnel safety
All this talk of apocalyptic accidents should have given you an idea of the dangers associated with the oil industry. But that’s not all.
Oil plants operate in truly critical environments. This means that the risk of injury is much higher than in many other industrial environments.
Think of the huge temperature range, exposure to toxic fumes, fires and explosions from oil spills or malfunctions, and deadly monsters born from petroleum (see image below).