oil and gas well spacing and completions: untangling complex interactions with machine learning

The oil & gas industry is underperforming recently. How much is due to misapplication of large completions designs with tight oil well spacing configurations? Just about everybody you ask will agree that completions and spacing interact. The crazy part is that engineering teams have often failed to evaluate the two in tandem.

Completions results from parent wells or exterior wells do not extrapolate to interior wells. In stacked plays, even four- or six-well pads may be composed of mostly exterior wells. Consider the hypothetical “tight well spacing” test in the Williston Basin, below. It’s likely that Middle Bakken and Three Forks wells interact to some degree. Yet, they certainly do not show the interference that same-zone wells do.

Do you think large completions designs will perform the same on this four-well pad as they would on full row development?

Untangling Data with Machine Learning Methods

Fortunately, we now have enough data in most major unconventional plays to untangle this problem with empirical, machine learning-based methods. These tools are great at handling both complex variable interactions and nonlinear relationships. Additionally, they can handle multiple spacing inputs to properly describe & evaluate the above scenario. As opposed to just one “spacing” value, but distance to all the possible neighbors.

Below, we’re showing one of the outputs of our machine learning model for the Williston Basin. The model is trained on data from the North Dakota Industrial Commission (NDIC).

On the x-axis we have average in-zone lateral spacing. On the y-axis we have spacing impact on performance (720-day cumulative oil), with the dots colored by proppant intensity. The model has learned that different completion sizes will result in different spacing-related degradation. Larger jobs will cause more well interference, whereas smaller jobs will cause less.

Oil Well Spacing & Production Model

The impact of going from wide to tight spacing is quite severe for the largest jobs. This leads to a loss of over 30% production for the large jobs compared to a much more mild ~10% degradation with small jobs. Clearly, completions tests from parent wells and widely-spaced wells do not translate to full row development. (And infill drilling is another question entirely!)

Maximizing returns requires thoughtful design considerations — in many cases, the largest jobs or the tightest designs will crush returns. Machine learning software for oil and gas from Novi Labs provides engineers the tools to right-size designs for optimized development plans.

Ted Cross

Ted Cross is Director of Product Management at Novi, where he builds powerful machine learning data and analytics products to solve the toughest problems in unconventional oil and gas. Prior to Novi, Ted was a Senior Geologist with ConocoPhillips.