Oilfield Job – A Mudlogger’s Career Advancement to Data Engineer and Beyond

The oil and gas industry is desperately looking for workers at all levels. They would prefer experienced workers, but beggars can’t be choosers – many of their most experienced staff are reaching retirement age in the next few years and they need those skills transferred before it is too late. Besides roustabouts, a mudlogger is another entry level oilfield job which leads to better things. Many senior staff on oil rigs started off as mudloggers.

A mudlogger:

  • connect various sensors to the drilling apparatus and install specialized equipment
  • collects geological samples of rock cuttings from the oil well (as part of the oil drilling process)
  • monitor gases coming up out of the wellbore as an indicator of hydrocarbons
  • prepares and analyses them geologically
  • writes a report on them
  • enters the information into the database.

Mudloggers work 12-hour shifts, and there are always 2 of them on an oil rig to ensure 24-hour coverage. The job is strenuous and challenging, especially when you have to install equipment and collect samples while drilling is actively going on. You have to be diligent, because part of your duties includes monitoring the level of dangerous gas which can cause a well blowout.

There is high turnover in this oilfield job. Most mudloggers work for oil services companies – not directly for the major companies like Shell or BP. Larger service companies require you to have a geology degree, and expect you to move up the career ladder quickly. Most mudloggers are young, in their early twenties and single. It is rare to see a middle-aged mudlogger. After 6 months to two years of work, you would ideally gain promotion to data engineer, with more responsibilities. As a data engineer, you will also troubleshoot problems which arise, and maintain and repair sensors as needed. For many mudloggers, the eventual aim is to become the wellsite geologist.

Although a mudlogger is an entry level oilfield job, you will earn at least $50,000 annually. Recent information from the American Association of Petroleum Geologists’ April 2008 meeting showed that graduate students with Masters and PhD degrees were receiving salaries of $80,000 to $110,000. Compare this to $55,000 in 2003.

Another perk of your job is travel. Many oil services companies have operations all over the world. For example, Geoservices has service contracts throughout oil rigs on the North Sea. Their employees get the opportunity to travel throughout northern Europe – Norway, Denmark and Holland – when they are off-duty. Working 2 weeks on, 2 weeks off means that you have plenty of time to explore the countries where you are based.

Some new hires hope to use a mudlogger oilfield job to get hired for bigger things by a major oil company like Exxon. This strategy has mixed success. In the United States, many oil wells are owned by wildcatters, who sell their oil to the oil companies. In the North Sea, too, many subcontractors and service companies are used to operate offshore oil rigs. Typically, companies like Shell have only a token presence on board these offshore oil rigs – the company man. Everyone else works for the contractor.

Right now, geology graduates with advanced degrees are being headhunted even before they graduate. But not everyone can go to graduate school, and not every geology student can score straight A’s to attract a company like Halliburton. If your results are only average, your best chance to get an oilfield job is to use proven oil rig employment placement services.

Should Data Science Be Taken As Career?

Being regarded as the hottest job by Harvard Business Review does not assure an individual that it is a promising career. This question needs to be answered in a proper way. Analysis of this topic will be done in this article so that it enables the reader/interested person in deciding whether to take it up as a career or not.

The Learning:

A number of courses are available offline and online that allow an individual to learn the basics of data science and get a certification. These organizations can be established ones like Google, Harvard and Adobe or can be other private organizations that have earned a name by getting their students placed.

The courses can be specialized ones or general to give an idea of data science and getting the students ready to explore the world of data science. This course is considered to be an ever expanding one and needs a student to give ample amount of time to reflect on and be creative to bring out new things in the data science field. This is something that would never get out of fashion as the creativity skill can never be a forte of a machine.

The Earning:

For the youth, this question holds importance. The learning part becomes the foundation for the package that would be provided to a learner. It can be done by a fresher or existing job holders. The fresher can further get experience in the field. The job candidate can get promotions on the basis of the certification as this is in demand. This field is considered to be the most rewarding in the current scenario as well as in the days to come. People with good command over machine learning and statistical tools with mathematical touch can pursue their career. Yes, it demands a lot, but the hikes are also lucrative as the skill is not available in the market. As the competition increases in the global market and more and more companies fight for the demand absorption to maximize profits, the analysts are the professionals that are looked upon by these business houses. They promise high packages in lieu of the success of the projects. Many websites have rated it as the most rewarding job of the year.

Conclusion:

Giving a clear number in terms of the package is not made in the article. This is because the compensation provided by different organizations varies according to the type of work. But as the world ascends even more towards the information age, data, science becomes an indispensable job that would only gain importance. The accuracy of a professional and good command over the tools would ensure the growth in this sector.

To conclude, no doubt, this is going to be a career that one can follow. Being a part of the smart age, this is a job made for a person who is smart with number crunching and analysis skills. If you are someone who enjoys doing it, then go for it.

A Career in Data Science

Data science employs concepts and methods of data analysis, machine learning and statistics to derive an understanding and analysis of the data related phenomenon. Disciplines of Mathematics, Statistics, Computer science, and Information technology contributes to their theories and techniques in the establishment of the field of Data Science. The establishment of Data science as an independent term is a recent phenomenon. Earlier, it was used as an alternative for the term Computer Science. Interaction of data with certain processes and representation of data through various program forms the study area of computer science. The manipulation, storage, and communication of digital information require proficient use of algorithms. Computer Science facilitates the usage of these algorithms. A Computer Scientist learns to design software systems and gains in-depth knowledge of the theory of computation.

Knowledge of data helps you ask appropriate questions and derive insights from big data, it teaches you how to manipulate data sets and allows you to gain the skill of visualization of your own findings in a convincing manner. A well-designed course trains you about how to handle data science tools. The tools which build the foundation are Mathematical tools and computational tools. The in-depth understanding of these tools and proficiency in handling these tools helps one in suggesting data-driven solutions in the business.

Mathematical and applied are two aspects and to learn data science, one has to gain an understanding of both of these aspects. Probability, statistics, and machine learning come under the scope of Mathematical aspect while applied aspects help you gain knowledge of data science, languages which includes Python, MATLAB, JAVA, SQL. It also helps gives you an understanding of the usage of the specific toolkit. The applied aspects let you into the real data world. Training in a data science course gives you expertise in the collection of big data as well as its analysis and cleansing. This training assists you in executing analysis of big data on a large scale. It also trains you on how to communicate your findings in a compelling manner.

The term which shares a very close association with data science is Machine learning. Machine learning deals with algorithms to draw patterns out of data and to make predictions. For this purpose of making predictions and drawing out patterns, machine learning employed methods of data modeling. While making predictions, machine learning trains predictive models by making use of tagged data. The awareness of ground truth gives rise to observations which qualify themselves as tagged data. This task of making prediction includes training of models to enable them on how to prefigure the unknown data from tagged data. The training of models can be done by employing various methods. While some of these methods are simple, like regression, the others are complex, like neural nets. While discovering patterns out of data, machine learning tries to look for some patterns or search for some data associations in a situation where tagged data is absent. While there are more categories to machine learning, these two comprises of the core categories.