/longtermforecast#

This endpoint returns long-term production forecasts use public producer guidance with an adjustment for private producer activity of the lower 48 and its subregions for the next 12 months.

Warning

A throttling mechanism with a rate limit of 300 requests per minute has been implemented for this endpoint. This is temporary and may be adjusted in the future.

Endpoint#

POST

https://hyperion.api.synmax.com/v3/longtermforecast

Usage#

Python#

long_term_forecast(end_date=None, region=[None], start_date=None, sub_region=[None])#

Returns a long term production forecast for the next 12 months for the lower 48. The forecast is provided at a monthly granularity.

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.long_term_forecast(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

end_date

string

Limit search to an end date. Date formatting - [“YYYY-MM-DD”]

"2022-07-15"

region

string array

Limit search by a list of regions (clusters of multiple major producing basins).

["GULF", "WEST"]

start_date

string

Limit search to a start date. Date formatting - [“YYYY-MM-DD”]

"2022-06-01"

sub_region

string array

Limit search by a list of subregions (clusters of several counties forming a major producing basin).

["S LA", "West - TX"]

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

string

Forecasted month (unsynced). Date formatting - [“YYYY-MM-DD”]

“2022-11-01”

gas_daily

float

Average rate of gas production expressed per day (in BCF/day).

23.842

oil_daily

float

The amount of oil produced in a single day (in BBLS, or barrels).

112.9570761

region

string

Aggregation of multiple major producing basins.

“GULF”

state_ab

string

Abbreviation of the state.

“LA”

Responses#

Normal Output#

date

gas_daily

oil_daily

region

sub_region

2022-12-01

0.269483871

southeast

AL

2023-01-01

0.269483871

southeast

AL

2023-02-01

0.269483871

southeast

AL

2023-03-01

0.2

southeast

AL

2023-04-01

0.2932

10.0

southeast

AL

2023-05-01

0.14287821

10.0

southeast

AL

2023-06-01

0.288166667

10.0

southeast

AL

2023-07-01

0.289258065

10.0

southeast

AL

2023-08-01

0.279419355

10.0

southeast

AL

2023-09-01

0.4

10.0

southeast

AL

Note

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

Aggregated Output#

Aggregated by…

payload = {
   'aggregate_by': 'date'
}

date

gas_daily

oil_daily

2027-01-01

110.229465131

13074.375628571

2027-02-01

110.541306062

13573.777517447

2027-03-01

110.82943534

13628.26997467

2027-04-01

111.844971557

13684.59321951

2027-05-01

111.133382874

13715.688904682

2027-06-01

110.611841747

13775.90870968

2027-07-01

111.09850583

13829.514445008

To get the aggregated version of the LTF for the Lower48, use the following query:

payload = ApiPayload(aggregate_by=["date"], region=["gulf", "midwest", "northeast", "southeast", "west"])

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.