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AI for Gas Supply Chain Optimisation
manufacturing

AI for Gas Supply Chain Optimisation

Storengy, a French natural gas storage company, wants to anticipate its levels of gas storage requirements. Therefore, gas supply chain optimization is critical for procurement engineering. TADA predicts gas storage with a global score of 91%. And TADA can correlate gas storage with various factors such as the external temperature.

Industry

Oil & Gas

Project Duration and Effort

One week

Customer Benefits

  • A global score of 91% in predicting gas storage.
  • Evaluation in figures of the impact of the external temperature on the amount of gas stored.
  • Speed, once the tool is in place, TADA’s analysis takes a few minutes.

Problem to solve

Is it possible to anticipate the amount of gas to have readily available in storage at any one point in time?

Storengy is a French gas storage company. It works in partnership with GRTGaz, which provides wholesale gas. This gas is in turn distributed to the end consumer by Engie, a services company.

A gas storage has three main operating characteristics; 

(1) working gas volume

(2) withdrawal rate and 

(3) injection rate

The working gas volume is the gas buffering required over several days. The withdrawal defines the volume that can be withdrawn, i.e., consumed. 

Market players tend to own or contract natural gas storage flexibility primarily to manage the fluctuations in their portfolios. But, again, it is a supply chain issue.

TADA has predicted the volume of gas stored using a dataset made publicly available by Storengy. This dataset includes 741 records and the following parameters:

  • Gas Quantity Stocked (GWh): the amount of gas stored. It is a positive value representing natural gas stocked if there is some gas in storage. But it can also be negative if there is more natural gas delivered than stored,  
  • Distributor Forecast Base (GWh): the amount of natural gas estimated to be consumed during the day by Engie,
  • Distributor Daily Variation (GWh): the ability to ‘reserve’ GWh (gigawatt/hour) depending on the customers’ expected needs,
  • Transporter Daily Variation (GWh): the natural gas volume required to reach a balance. The wholesaler GRTGaz estimates it. This gas supply required is estimated every morning. It might be positive, i.e., there is enough gas in the network. It might also be negative, i.e., in case of a deficit of gas in the network for the day, 
  • Actual Previous Week (GWh): the values measured the previous week,
  • Previous Week Temperature (°C), 
  • Actual Previous Day (GWh): the natural gas values measured the last day,
  • Last Day Temperature (°C),
  • WeekDay,
  • Month,
  • Date.

 

It is challenging to reach a compromise between securing gas supply, storing enough gas, and dealing with the fluctuations of natural gas prices. Under normal market conditions, the available storage capacity is efficiently used and available at a relatively low cost. However, over the long term, unusual market conditions happen. That’s when there might be a shortage of natural gas storage flexibility in the system. Hence, providing good predictions using TADA is key to finding the right balance.

Objectives

  • Estimating the right amount of gas to store to achieve balance and stability.
  • Understand what impacts the gas storage amount required.

 

It poses the following question related to the natural gas industry: Can gas storage requirements be estimated daily with accuracy?

Solution

The TADA predictive models’ results reach a 91% global score, a 99% R2 based on actual data for gas storage. 

Storage plays a vital role in competitive natural-gas markets. Indeed, the consumption of natural gas varies more than gas production does. Furthermore, the injection season happens at entirely different times from the actual gas demand. Whether during the summer months or cold weather, it has no impact on gas injection timing. Demand is fluctuating. And to top it off, demand is often at a considerable distance from the gas production sources. Hence, there are significant variations in average prices. Furthermore, in North America and Europe, natural gas storage capacity measured by working volume is around 18% of the total consumption. As a consequence, there are fluctuations in natural gas prices. TADA has selected the following six main criteria out of the eleven available in the dataset:
  • Distributor Forecast Base (GWh),
  • Distributor Daily Variation (GWh), 
  • Previous Day Temperature (°C),
  • Month,
  • Transporter Daily Variation (GWh),
  • Date.

Analyzing TADA’s results, it does make sense that the actual gas stored depends on the month. Typically, more gas is consumed during cold weather than during the summer months. 

Since the financial crisis, natural gas production has been steadily increasing at a yearly average of 2.7%.

Customer Benefits

In one week, procurement engineers at Storengy gained the ability to:

  • Make accurate predictions of the gas storage amount required.
  • With an estimated global score of 91%.
  • Gather quantitative data about the impact of the previous day’s temperature on the gas stocks.