To a greater extent, the fiscal results of corporations in different sectors of the oil and gas industry rely on the performance of intricate, capital-intensive assets. Initially, oil and gas companies lacked neither the tools nor the expertise required to run these process facilities at their optimal capacity. But things have changed for the better. The rapid technological advancements in data analytics tools and methods have freed up the production capability of multifaceted process facilities. It has also increased returns on asset investment in the oil and gas industry. A report[1] by McKinsey shows that advanced analytics has the potential to yield returns of up to 50 times the investment within a few months after implementation.

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The reason behind the popularity of big data in the oil and gas industry is the need to improve oil and gas exploration and efficiency in production. In this article, we look at what data analytics is and its areas of application in the oil and gas industry.

oil and gas industry

What do you need to know about data analytics in the oil and gas industry?

CHARACTERISTICS OF BIG DATA:

Volume

Volume represents the size of data information. The source of these datasets could be from a data recording tool or a sensor. These huge datasets present a processing challenge because of issues related to storage, sustainability, and analysis. Data analytics comes in handy to provide both managing and analysis apparatus for the growing data quantities.

data analytics

Velocity

Velocity denotes the speed of data broadcasting and processing. It also signifies the speed of data production. The problem with the velocity aspect is the few number of accessible processing units vis-à-vis the volume of data.

Today, the speed of data generation is high in the oil and gas industry, given that data of 5 exabytes can be produced in only 2 days. This is equal to the total volume of data generated by the human species until 2003[2].

Read more about Big Data and its business impacts

Variety

Variety signifies the multiple forms of data that are produced, stored, and evaluated. The data recording tools and sensors come in different varieties. Therefore, the produced data can be in various sizes and formats like image, text, video, or audio.

The data can be classified in three forms:
• Structured
• Semi-structured
• Unstructured

Veracity

Veracity stands for the quality and efficacy of the generated data for the function of analysis and decision-making. The data ought to be effectively processed and filtered for the purpose of data analysis, or else the outcomes wouldn’t be reliable.

In the oil and gas industry, the veracity of generated data is challenging because the data generated originates from subsurface facilities and may contain ambiguity.

Value

The value of Big Data investment assets is of utmost importance to any organization. By analyzing massive datasets, Big Data can expose the underlying trends and assist data engineers in predicting the likely issues. The ability to know the future working capabilities of the equipment and pinpoint the malfunctions early can increase a company’s competitive edge.

data

Complexity

Apart from the above 5 Vs, complexity is another crucial characteristic of Big Data. It refers to the difficulty of the underlying problem for which data collection is being performed. For these difficulties, Big Data tools may be quite useful.

Application of big data in the oil and gas industry

Data analytics has become popular due to the ever-increasing amount of data produced in the oil and gas industry. This is mainly because of developments in seismic acquisition devices, fluid front monitoring geophones, channel counting, and carbon capture and sequestration sites that produce huge datasets which require out-of-the-ordinary processing and analysis infrastructure.

oil and gas industry

Below are some of the ways in which big data analytics is used in the oil and gas industry:

IMPROVE OCCUPATIONAL SAFETY

The safety of personnel and the environment during the drilling process is a major concern for the oil and gas sector. According to a research study by Tarrahi and Shadravan[3], data analytics managed potential risk and enhanced safety in this sector. The research was conducted based on a statistical report by the Bureau of Labor Statistics (BLS), which comprised 846 injury sources from 1278 sectors between 2011 and 2014.

According to Pettinger[4], the data collected from safety inspections can be applied to create safety predictive analytics. This involves regularly collecting the safety indicator data and incorporating it into predictive analytics.

OPTIMIZATION OF UPSTREAM, MIDSTREAM, AND DOWNSTREAM OPERATIONS

Big Data analytics helps to improve operations in 3 areas in the oil and gas industry:
• Upstream
• Midstream
• Downstream

Let’s take a closer look at them:

UPSTREAM

Analyzing seismic data in exploration

The interpretation of seismic data calls for high-tech processing computers with strong visualization capabilities. This is where big data comes in to analyze the seismic data. Upstream analytics starts with gathering seismic data with sensors placed in the potential location of interest scanning for petroleum sources.

Next, the data is combined, cleaned, processed, and evaluated to select the ideal drilling location. Seismic data can also be pooled along with other datasets, including the organization’s past data on earlier drilling operations.

datasets

According to a research study by Joshi et al.[5], data analytics was used to evaluate the micro-seismic datasets to conjure up fracture propagation maps in hydraulic fracturing. In this study, the Hadoop platform was preferred over conventional tools to handle and process huge volumes of data produced by micro-seismic tools.

Optimizing drilling operations

According to a study by Yin et al.[6], data analytics was employed to identify the invisible non-production time (INPT) using the gathered real-time logging information. The researchers enhanced the drilling processes by using mathematical statistics, AI, and cloud computing, and thus optimizing the INPT.

Optimizing reservoir engineering

Data analytics can be used to gather and process data required by oil and gas corporations to boost their reservoir production. Several distributed downhole sensors are used to collect the data. Bello et al.[7] made use of the collected data to create a reservoir management application through Big Data analytics. By doing so, companies can access timely and relevant information about variations in temperature, reservoir pressure, acoustics, and flow. Ultimately, they have better control over their operations which leads to increased reservoir profitability.

MIDSTREAM

Oil and gas transportation

A logistical challenge affecting the oil and gas sector is the safe transportation of petroleum. To minimize potential risks, companies employ sensors and prognostic maintenance. This helps to identify different malformations in tankers and pipelines, including:

• Stress corrosion
• Fatigue cracks
• Seismic ground displacement

It might be interesting for you 10 Use Cases of Big Data in Logistics

Production

A number of internal and external variables affect the production costs incurred by oil and gas companies. Data analytics can be used to boost production efficiency and realize cost savings through multiple scenarios.

Moreover, you can use Big Data to create forecasts and flow methods that will help you perform automated decline assessments. This can help in pinpointing the underlying pattern in terms of production data.

DOWNSTREAM

Predictive maintenance

Oil and Gas companies can use predictive analysis to develop simulations that predict maintenance incidences. Ideally, predictive maintenance brings down the cost associated with untimely downtime maintenance.

analytics

Take, for instance, equipment like gas compressors. The analysis begins by first forecasting the performance of the gas compressor. This involves evaluating its present and historical data. Next, depending on the equipment’s service life criteria and malfunction conditions, engineers can further refine the performance prediction of the device. With data analytics, you can put all this information in a transparent report or sheet and use it to make more informed maintenance decisions. These predictive reports built from the utilization of data analysis can considerably minimize downtime as well as maintenance expenses.

Final thoughts

Advanced analytics is developing at a rapid rate. It is expected that soon there will be fully autonomous control systems for multifaceted processing facilities in oil and gas companies. This is based on the fact that more industry executives believe that Big Data is the solution to boost their business operations. Oil and gas industries that have adopted advanced data analytics are more likely to become the future industry leaders.

If you want to know more about data analytics and how this field can boost your company’s operations, take a look at our data analytics consulting services. We are an AI consulting company. Data analytics is where we thrive. If you want to want more, feel free to drop us a line! Our Team will help you make the most of big data in your organization.

References

[1]Mickinsey.com. Why oil and gas companies must act on analytics. URL: https://www.mckinsey.com/industries/oil-and-gas/our-insights/why-oil-and-gas-companies-must-act-on-analytics. Accessed 10 December, 2021
[2] J. Ishwarappa, J. Anuradha. A Brief Introduction on Big Data 5Vs Characteristics and Hadoop Technology, vol. 48 (2015), pp. 319-324, 10.1016/j.procs.2015.04.188, Accessed 10, December, 2021
[3] S. Park, M. Roh, M. Oh, S. Kim, W. Lee, I. Kim, et al. Estimation model of energy efficiency operational indicator using public data based on Big data technology 28th Int. Ocean Polar Eng. Conf., Sapporo, International Society of Offshore and Polar Engineers (2018), pp. 894-897. Accessed 10, December, 2021
[4] C.B. Pettinger. Leading indicators, culture and Big Data: using your data to eliminate death ASSE Prof. Dev. Conf. Expo, American Society of Safety Engineers, Orlando (2014). Accessed 10, December, 2021
[5] P. Joshi, R. Thapliyal, A.A. Chittambakkam, R. Ghosh, S. Bhowmick, S.N. Khan.  OTC-28381-MS Big Data Analytics for Micro-seismic Monitoring (2018), pp. 20-23. Accessed 10 December, 2021
[6] Q. Yin, J. Yang, B. Zhou, M. Jiang, X. Chen, C. Fu, et al. Improve the Drilling Operations Efficiency by the Big Data Mining of Real-time Logging SPE/IADC-189330-MS (2018). Accessed 10 December, 2021
[7] O. Bello, D. Yang, S. Lazarus, X.S. Wang, T. Denney, B.H. Incorporated Next Generation Downhole Big Data Platform for Dynamic Data-driven Well and Reservoir Management. Accessed 10 December, 2021

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