/dailyproduction

This endpoint returns the daily gas production model for the lower 48, informed from pipeline scrapes, state data, and our short-term production forecast.

Endpoint

POST	https://hyperion.api.synmax.com/v3/dailyproduction

Usage

Python

daily_production(aggregate_by=[None], end_date=None, region=[None], start_date=None, sub_region=[None])

Returns the daily gas production model for the lower 48.

Object returned is a pandas DataFrame.

⮞ Setup the client.

from synmax.hyperion import HyperionApiClient, ApiPayload
access_token = 'your access token goes here'
client = HyperionApiClient(access_token=access_token)

For a more detailed guide on setting up the Hyperion client, please see the Quickstart page.

⮞ Use parameters to narrow down your request.

payload = ApiPayload(start_date='2021-05-01', end_date='2022-06-25', sub_region=['S LA', 'West - TX'])

Tip

Narrow down your request to receive a faster response. Using no parameters will fetch the entire dataset which may take a very long time.

⮞ Call the function.

result_df = client.daily_production(payload)

⮞ Print the results or save them for later.

# Print the entire response as a string.
print(result_df.to_string())

# Create a CSV file from the output.
result_df.to_csv('output.csv')

# Print the number of items per column.
print(result_df.count())

For more information on outputting to different formats, please see the pandas DataFrame documentation.

---

Parameters

Query

Parameter	Type	Description	Example
region	str arr	Limit search by a list of regions (clusters of multiple major producing basins).	["GULF", "WEST"]
sub_region	str arr	Limit search by a list of subregions (clusters of several counties forming a major producing basin).	["S LA", "West - TX"]
start_date	str [“YYYY-MM-DD”]	Limit search to a start date.	"2022-06-01"
end_date	str [“YYYY-MM-DD”]	Limit search to an end date.	"2022-07-15"
aggregate_by	str arr	Aggregate data by a list of parameters.	["date", "region"]

Aggregation

Aggregation provides an overview of overall productions levels for a particular operator or across a given region during a given timeframe.

Data is always implicitly aggregated by date. This means all other column data are grouped by date.

Supported parameters: date, region

Response

Parameter	Type	Description	Example
date	str [“YYYY-MM-DD”]	Forecasted month (unsynced).	“2022-11-01”
dry_gas	float	Gas volume in BCF (billions of standard cubic feet of gas).	23.842
region	str	Aggregation of multiple major producing basins.	“GULF”
sub_region	str	Aggregation of several counties, typically representing a major producing basin.	“S LA”

Note on Dry Gas Production

The dry gas production information for each day is not fully available on that same day, as some pipeline nomination data is still missing. The complete daily production data will be ready by the next day. So, for example, if you want the full data for April 12, 2023, you’ll need to wait until April 13, 2023, to get it.

---

Responses

Normal Output

date	dry_gas	region	sub_region
2020-06-08	0.3046666666666667	southeast	AL
2020-06-09	0.3046666666666667	southeast	AL
2020-06-10	0.3046666666666667	southeast	AL
2020-06-11	0.3046666666666667	southeast	AL
2020-06-12	0.3046666666666667	southeast	AL
2020-06-13	0.3046666666666667	southeast	AL
2020-06-14	0.3046666666666667	southeast	AL
2020-06-15	0.3046666666666667	southeast	AL
2020-06-16	0.3046666666666667	southeast	AL
2020-06-17	0.3046666666666667	southeast	AL

Note

Output data will return as “null” when input data is invalid or does not exist.

Aggregated Output

Aggregated by…

dateregion

payload = {
   'aggregate_by': 'date'
}

date	dry_gas
2022-11-01	97.74523344287729
2022-11-02	97.839740496358
2022-11-03	98.18611891481966
2022-11-04	98.37531152889612
2022-11-05	97.96266564040262
2022-11-06	98.49851707562956
2022-11-07	98.82688826428509
2022-11-08	98.72588165917888
2022-11-09	98.61454742178606
2022-11-10	98.79910324791804

Tip

To better explore using the aggregation functionality interactively, consider using our Excel Add-in. By familiarizing yourself with the available options, you can more efficiently query and process the data for your specific needs. To learn how to use the SynMax Excel Add-in, please refer to our Excel Add-in Documentation.