Use of Data Analytics in Oil and Gas Production

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Production analytics enables the Oil and Gas companies to have dedicated resources for carrying out data aggregation and production optimization using previous data. This data arrives from different sectors of the oil and gas industry, such as drilling, production, gas exploration, boring, etc. The world is constantly changing, and the industries from almost all sectors are heavily dependent on processed data to carry out their day to day operations. In this scenario, the oil and gas industry seems to be catching up in utilizing big data to derive valuable insights to garner more profit. Raw and crude data is of little use to any industry unless it is aggregated and analyzed and deemed fit to forecast the potential information and keep costs under check.

Need for Oil and Gas Production Analytics

The biggest challenge for the Oil and Gas industries is the operational complexities backed by a large and crude data hub. Apart from these, there are other exogenous factors, such as humidity and temperature that needs to be taken into account. The available bandwidth of the simulation tools do not take into account the exogenous factors, and their algorithm does not update dynamically, thereby creating a glitch between the forecast and the actual performance. There is, therefore, a need for analyzing real performance data which can only be derived out of advanced analytics.

Benefits of incorporating Oil and Gas Production Analytics

The key idea over integration of production analytics with technology is to optimize the operational complexities with a thorough knowledge base of geophysics and geology to carry out future predictions with minimal flaws and to avoid failures. The idea is not to replace the conventional models but instead, assist them in bridging the performance gap efficiently.

Lowered RiskĀ 

Carefully carried out production analytics helps in minimizing the risk associated with a project, thereby enabling accurate forecasts. For example, big data analytics already done for one kind of geographical area gives a clear understanding of the layering of rocks in that area. This inference can be applied to regions having similar rock structures to avoid previous mistakes.

Cost Efficiency

Storing and handling large volumes of unprocessed data is a menace for the Oil and Gas producers which significantly impact their production costs. In such a scenario, use of production analytics enables them to capture it in real-time and analyze data to carry out real-time visualization and subsequently generate real-time alerts.

Improved Accuracy

Oil and gas industries have a heavy dependency on human resources, material supply, and logistics. The production analytics helps in understanding in the subsurface mapping and in zeroing down on the best drilling locations in order to avoid future disappointments and wastage of resources.

Improved Efficiency

The machines engaged in the O & G facilities are in constant operation for long schedules. The data analytics guides the engineers to carry out the timely replacement of the spare parts and avoid downtime due to breakdowns, thereby saving up on production costs. With the availability of historical data, the maintenance engineers can carry out predictive analysis and replace a component even before the downtime occurs. In order to achieve this, the machines are fitted with data sensors to track real-time efficiency and predict malfunctioning.

Automation

The laborers in the Oil and Gas Industries are subject to extremely precarious and life-threatening work conditions. Due to the advancements in production analytics, almost 60-90 percent of the manual tasks can be automated. This will help minimize human errors and increase the efficiency of routine jobs. The cloud-based services collect the offshore data and notify the automation support team of the potential threats and failures way in advance, thereby preventing any mishaps and accidents.

Production Analytics Key Requisites

Now that the benefits of having Production Analytics in place have been carved out, let’s understand the prerequisites that the Oil and Gas facilities need in order to make the most of it.

  1. Availability of production data.
    2. Skills and capabilities to use advanced analytics and convert them into actionable insights.
    3. Ability to rethink the conventional ways of working to identify and eliminate major bottlenecks and inefficiencies.
    4. Ability to adapt to agile ways of working by carrying out short pilot projects and use those insights in transforming the operating models.

Challenges:

The production analytics of Oil and gas data has its own set of challenges.

  1. The cost associated with the storage and analysis of such a large infrastructure of data turns out to be huge. That’s one of the primary reasons a lot of companies are still adhering to the traditional methods.
  2. The requirement of skilled oil personnel, scientists, and petroleum engineers for combining big data with the nitty-gritty of the Oil and gas industry. Engineers need to collaborate with the data scientists for deducing the accurate insights and solve issues.
  3. Transfer of huge chunks of data from the digital oilfields to the processing facilities.
  4. Data ownership issues.
  5. Lack of business support and awareness about production data analytics.

Use of production analytics is not a question anymore. It is rather the need of the hour to be receptive to the use of historical data and apply it for better prospects and productivity in the Oil and Gas industry.

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