Data Analytics

Focus on increased uptake to address sector challenges

The oil and gas industry faces several challenges. To overcome these and remain economically viable in the current low price environment, it needs to undertake operational changes. A plausible solution could be the large-scale adoption of data analytics. At present, big data analytics has a very low penetration, especially in the city gas distribution (CGD) sector.

The government aims to increase the share of piped natural gas (PNG) and compressed natural gas (CNG)  in the country’s energy mix to 20 per cent by 2025. It also declared CGD companies as public utilities and laid out a plan to increase the serviced cities from 81 to 326 by 2022. In order to achieve this stiff target, organisations will need to adopt a holistic approach by embedding analytics in all possible business processes. CGD companies must embrace digital technology in conjunction with analytics. This will help them achieve greater operational efficiency and better manage assets.

These companies can manage assets and improve pipeline capacity by deploying acoustic sensors and fibre optic sensors, detect leakages through smart pipeline inspection gauges (pigs), as well as use predictive analytics for improving responsiveness by decoding and modelling data patterns. In addition, this can help companies address the need to have a large workforce to manually maintain and monitor the pipeline network. Predictive analytics enables operators to mine data for equipment behaviour patterns, indicating problems that operators would not have noticed otherwise. For example, predictive asset analytics can identify when rotational assets like pumps and compressors are likely to face a failure.

Smart connected sensors, controllers and inspection technologies are being integrated with pipes, valves, regulators and meters to provide comprehensive, real-time asset and system information. Data from smart system components, consolidated and analysed in the cloud, feeds the supervisory control and data acquisition (SCADA) system to enable real-time optimisation of system operations. Meanwhile, the integration of this data with operations, maintenance and inspection information in enterprise systems enables enhanced risk and integrity management as well as better work planning and execution.

PNG is experiencing several challenges such as inaccurate billing, inappropriate usage and revenue leakage. Energy theft via supplementary pipeline connections and meter tampering negatively impacts profit margins. Smart gas metering can help remotely track and control gas delivery devices, detect anomalies and receive real-time alerts. For instance, a leading natural gas-focused energy company based in the US leverages Itron’s technology to remotely collect meter readings, improve billing accuracy and reliability, and reduce labour costs, thus translating into reduced tariffs for customers.

CNG as a fuel faces a threat from electric vehicles (EVs). Adapting to this new normal will require CGD companies to address fundamental challenges, such as reducing high queue times, ensuring adequate supply in all areas beyond urban centres, and predicting dry-outs and breakdowns. The efficiency and effectiveness of CNG services will determine its viability as compared to EVs. A few organisations are responding to these challenges by deploying analytics in station/areas, applying higher power for compression in stations and leveraging video analytics to optimise queues, among others.

Structured approach

Utilities need to first understand the “what for” aspect of their big data initiatives. Given that utilities have struggled to define the value of initiatives, they can begin with pilots and proof of concepts. These are a very pragmatic and result-driven way of convincing the management of the viability and value of analytics initiatives.

The next step for organisations is to begin with a pragmatic assessment of where they are in their analytics evolution. The aim is to move from basic reporting and business intelligence to the higher-value opportunities offered by predictive and real-time analytics.

Utilities wanting to move up the maturity curve need to focus on three areas:

  • Invest in the right data management tools: Despite the benefits of advanced analytics, adoption levels are low. Many utilities have recognised the importance of smart data tools. Therefore, it has become essential for gas utilities to use the right data management tools for increasing asset life and performance.
  • Invest in skill development: Analytics skills are in high demand across industries and the utility industry is not immune to the challenge of getting the right skills. Utilities should use a mix of in-house training, talent acquisition and partnership to plug the skills gap.
  • Create an effective governance model: Effective data governance will be critical for realising the value from analytics. This includes having a clear data management policy and the right governance approach. Governance can include data management steering committees or the appointment of a chief data officer to oversee data quality and ensure data is unlocked from different silos and successfully integrated.

Role of data analytics in the oil and gas industry

Big data and analytics may be new to some industries, but the oil and gas industry has long been dealing with large quantities of data to make technical decisions. In their quest to learn what lies below the surface and how to bring it out, energy companies have, for many years, invested in seismic software, visualisation tools and other digital technologies.

The rise of pervasive computing devices, affordable sensors that collect and transmit data, new analytics tools and advanced storage capabilities is opening up more possibilities every year. Oil producers can capture more detailed data in real time at lower costs and from previously inaccessible areas to improve oilfield and plant performance. Oil and gas companies will need to improve their analytics capabilities in order to compete in an industry where decisions are being made faster and the stakes are growing higher.

Data-driven analytics is becoming an important point of competitive differentiation in the oil and gas industry. It is a set of tools and techniques to infer patterns and trends in data, and construct predictive models, which can assist in decision-making and optimisation.

Oil and gas companies can leverage big data technologies, for instance, like the MapR Converged Data Platform which can be used for collecting, managing and rapidly analysing seismic drilling and production data. Moreover, companies can leverage data to acquire new insights that can help boost drilling and production performance while addressing safety and environmental concerns. Additionally, MapR can help businesses capitalise on big data to optimise their operations, achieve lower costs and boost their competitive edge.

Conclusion

Utilities should test both technical feasibility and how well technology and processes can be integrated into a given instance. They should prioritise initiatives which are most likely to bring demonstrable and measurable benefits and use these results to build a case for further build-out of smart gas distribution. Employees committed to the success of smart gas distribution will likely be more motivated to uncover ways to use the system to achieve a breakthrough performance. Smart gas distribution is more than a matter of technology and processes. It introduces a whole new way of thinking about organisational purposes and goals. Going forward, utilities would need to think and move beyond compliance to create cultures and businesses committed to safety.

 

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