Predicting Oil Production
Business Need​
Forecast mid term oil and gas production from wells and fit well-known oil-industry curves to extrapolate predictions 30+ years into the future.
Solution Overview​
This solution leverages multiple components of the Abacus platform to deliver automated production forecasting:
Step 1: Connect & Preprocess Data
- Data Source: Production data is ingested from a Snowflake table using our native connector
- Data Structure: The raw data contains daily production metrics at the well level
- Preprocessing: Daily production values are aggregated to monthly intervals, which provides the optimal balance between reducing noise and maintaining actionable granularity for operational decisions
Step 2: Build Forecasting Model
A time series forecasting model is developed using the Forecasting and Planning solution, which automatically handles seasonality, trends, and well-specific production patterns to generate monthly production forecasts. It predicts 12 months into the future.
Step 3: Curve Fitting
The curve fitting part was written purely in python and the code was hosted within a pipeline. The functions are then used to extrapolate predictions into the future.
How It's Used in Practice​
This solution operates as a fully automated workflow with minimal user intervention:
Automated Production Pipeline (runs monthly):
- Refreshes production data from Snowflake, executes forecasting models
- Executes forecasting models for all active wells
- Retrieves predictions from the feature group storage
- Applies curve fitting to forecasted values
- Writes results back to Snowflake for consumption by analytics tools
Model Retraining Pipeline (runs quarterly):
- Automatically retrains forecasting models every 3 months to incorporate recent production trends and maintain prediction accuracy
End users simply consume the forecasts and curve-fitting parameters from their existing BI tools and dashboards without needing to interact with the Abacus platform directly.