Understanding the Spacing, Completions, and Geological Influences on Decline Rates and B Values
- Wednesday, July 28th at 11:15 AM | Room 371
- Theme 9: EUR and Performance Prediction: Decline Curve Analysis and Beyond II
D. Niederhut, A. Cui, C. Macalla, J. Reed (Novi Labs)
The decline curve is one of the most important elements of the oil and gas industry. It is critical for defining the rate of revenue and cash flow for upstream participants. Therefore, understanding and predicting its shape using the Arps framework can help producers constrain critical top and bottom line business metrics. This study uses machine learning to explore which engineering and subsurface features/variables most influence each of the Arps parameter values.
Separate datasets for the Midland and Williston basins were constructed by combining publicly reported production and completions data with several subsurface geology and calculated interwell spacing features. An advanced set of algorithms was used to fit unbiased modified hyperbolic decline curves to every well’s historical production. With these best fit equation parameters as calibration points, an ensemble of decision trees was built to model the wells’ arps parameters using a multitude of geological, completions, and spacing features. Shapley values were then generated to quantify the importance of each feature and their impacts on the arps parameters, initial production (qi), initial decline (di), and b-factor (b). These were inspected for between-stream and between-basin trends.
In general, most would hypothesize that operational features can strongly influence qi and di; however, their ability to change b is minimal. Larger completions and longer lateral lengths indicate higher qi across all production streams. These same features are associated with higher di for oil but lower for gas. This suggests that after initial flush production from creating a bigger stimulated rock volume, the reservoir cannot deliver oil to the wellbore at a high enough rate. Tight interwell spacing can drive higher qi and di. Completing closely spaced wells could be creating more fracture complexity but ultimately forces wells to compete for more of the same resources. Of the geological features, clay volume was uniquely influential for oil qi and di, while water saturation for water qi but not di.
Most engineers have an intuition about how different basins respond to pad design and completions via their impact on Arps equation parameters – here, we can validate or refute these in a data-driven fashion. Our method has enough specificity to pinpoint the responses to completions and interwell spacing, which empowers engineers to apply different strategies for managing the tradeoffs between initial production and decline in a location-specific manner.