?Hello guys! I'd like to wish everyone a happy end to the year 2023.?I hope that 2024 will be better, calmer, kinder, and more productive. Today, I want to share a case with you that demonstrates the power of variability and social skills. ? Recently, I moved to a new house and didn't know any of the neighbors when I suddenly needed internet access. Deciding that it would be faster and easier to pick a password for some Wi-Fi?, I went on GitHub to look for useful scripts. And here's what I found: https://github.com/Cyber-Dioxide/Wifi-Brute - this guy suggests trying several thousand of the most popular passwords. Funny, I thought. But the neighbors are likely using standard passwords from routers, and for gigabit devices, this is usually an 8-character password from the characters of the lowercase English alphabet and numbers. So, I supplemented this guy's code with the generation of passwords under this mask:

```
def test(face, x, ts):
wifi_name = x.ssid # WiFi network name

```

But is the game worth the candle? First of all, checking the strength of someone else's Wi-Fi without permission is illegal?, and secondly, I was stopped by the thought to first calculate how much time it would take me to find a variant.
?????????????????????????????

Ok, guys. Here's the right answer. The number of possible password combinations is determined by the formula:
N = m ^ k, where N is the number of possible combinations, m is the number of allowed characters for each sign of the password, k is the length of the password.
Consequently, the number of possible passwords consisting of 8 characters will be: N = 36 ^ 8 = 2,821,109,907,456
To prevent the router from blocking, you need to make pauses, and it turns out that it takes about 15 seconds to check one password. And the whole job of picking such an 8-character password will take 42,316,648,611,840 seconds or 705,277,476,864 minutes or 11,754,624,614.4 hours or 489,776,025.6 days or 16,325,867.52 months or 1,360,488.96 years? So I just went and met the neighbors, who kindly offered me to use their ? until my own internet was connected) May you only meet kind and responsive people in the new year!??

#life

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I also found a book Machine Learning in the Oil and Gas Industry with a repository on GitHub.

Give it a like ? if you're interested in learning more works with practical case studies.

⚒Need more practical usefulness? Then check out the "Processing Digital Geoscience Data with Python" series of articles.

In the repository: parsing any type of data, analyzing QC, working with a database, feeding data for geophysical inversion - machine learning / deep learning, and more (I haven't explored it fully yet).

According to the author this tutorial is as beginner-friendly as possible, so some of the words I use may be too simple

#knowledge

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SpringerLink

Machine Learning in the Oil and Gas Industry

This book shows how to apply machine and deep learning to solve some of the challenges in the oil and gas industry. It begins with a brief discussion of the oil and gas exploration and production life cycle in the context of data flow through the different…

?Hi there!
Today I found an interesting paper that came out recently and can be a good foundation for any engineer who wants to use Python in their work. The author is talking about his book here?

? I couldn't find where to get the electronic version, but it seems you can order the paper version here.

What drilling problems are you solving with Python?
Write in the comments, if you have something to say it will be 100% useful for other people?

#knowledge

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YouTube

Digitalization: Python Application for Drilling and Geoscience Engineers

The integration of digital technologies through wired drill pipes has transformed the traditional well-drilling process. In this book Behzad Elahifar and co-author Erfan Hosseini explore Python's applications in drilling, including real-time data analysis…

Hi there, today I want to share some valuable information with you...

? AI Summarization Tools to Make Your Life Easier! ?

With the plethora of content available, finding the essence can be a daunting task. But, thanks to AI, we have powerful tools that can help summarize even the most complex documents. Here's a rundown of some brilliant tools:

1️⃣ Yandex: Russia's tech giant offers a summarization tool that's worth checking out. (Note: The platform works only in Russian for the time being.)

2️⃣ WordTune: Boasts two distinct features:

*✍️ Write and Paraphrase: Optimizes your writing content.
? Read & Summarize:* An AI assistant to help you understand key points from complex articles.

3️⃣ AnySummary App: Summarize any file, including audio and video! Free tier offers:

? 3 files/day
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Check it out4️⃣ ChatPDF: Process up to ? 3 PDFs (120 pages each) daily for FREE!
Dive in here: ChatPDF5️⃣ humata.ai: Work on PDFs with size limits. Free version has a cap at ? 20 pages while premium offers up to ? 500 pages. ?This is my favourite, as it seems to be the most convenient service.
Explore here6️⃣ Explainpaper: Ever stumbled upon a confusing segment in a PDF? Upload and get clear explanations at Explainpaper.

7️⃣ Kagi Summarizer: A universal link-based search system with a summarizer. They even have a free tier! ? Give it a try.8️⃣ Summarize.Tech for Videos: Make sense of long YouTube videos ?, be it lectures, live events, or meetings, with AI.
Start summarizing here.The world of AI is ever-evolving, and these tools are a testament to its potential. Dive in and find which one works best for you! ?

#knowledge

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Ok, I haven't posted on the channel for a while, I've had a lot going on (moving to another country and the other niceties of life).
Today I want to share with you a resource that will help you keep up to date with the oil and gas industry. In the world of information, working with data, being able to analyse it and make predictions (not like a fortune teller, but scientifically based) is becoming a fairway. While others are sceptical about the black box, others are learning the mathematical laws it contains?‍?.

This is our advantage. ?The ability to doubt and to seek the truth.

One last question: if anyone has any information on open geological/geophysical data for North America, please write to me.

#knowledge

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❔Does anyone know what a Restricted Tripping Report is?

Hello everyone! ?I haven't posted for an entire month, and now I'll share what I've been up to. I've delved into the development of machine learning cases. One of them is a model for predicting drilling ROP. I fully understand that speed is a function of drilling parameters (+pipe friction, wellbore bends, etc.) and the strength of the rock, which depends on the integrity of the wellbore. But the idea is to take the first step in creating a tool to predict the necessary drilling parameters to achieve the desired speed. What's more, we're developing a recommendation system to monitor parameters for safe drilling. Cool, right?*?*I'm not interested in writing about well-known libraries like numpy and scikit-learn. What's fascinating is discussing approaches to acquiring new knowledge! How can one do this quickly and efficiently? In the past, novice programmers learned everything from documentation; then resources like stackoverflow emerged, making it easier and faster to find answers. Now, with the evolution of neural networks, we have the opportunity to combine the best practices from all three approaches. I love good documentation; it organizes information in the mind. However, finding truly great manuals is challenging, and creating one is even more so.

So, let's list the skill set of a drilling engineer with ML knowledge:
Python
SQL, noSQL
* libraries and packages for data analysis and machine learning (Scikit-learn, Statsmodels, Pandas, Numpy, Scipy, Xgboost, among others).

It's also recommended to understand the procedure for working with time series, as for evaluating drilling parameters in real-time, the data should arrive with a time reference down to hours and minutes.

And most importantly, a comprehension of drilling technology and knowledge of engineering calculations is essential. This isn't just a craft where early drillers randomly selected the density of the solution and the load on the drill bit. It's science - it's the future.#knowledge

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GitHub

GitHub - Devenir-Glorieux/rop_predict: Predicting the rate of penetration during well drilling. The first step in creating a recommendation…

Predicting the rate of penetration during well drilling. The first step in creating a recommendation system for optimising drilling parameters. - GitHub - Devenir-Glorieux/rop\_predict: Predicting t...

⚡️Hi, did you know that you can open any GitHub repository in the browser version of VScode?

Lifehack to learn code quickly and easily.

Just paste this "vscode.dev/" into the address bar of the page before "https://github.com/....".

#knowledge

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