How much of the oil & gas industry’s recent underperformance comes from misapplication of large completions designs with tight spacing configurations? Though just about everybody you ask will agree that completions and spacing interact, 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 spacing” test in the Williston Basin, below. It’s likely that Middle Bakken and Three Forks wells interact to some degree, but 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?
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 — not 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, trained on data from the North Dakota Industrial Commission (NDIC). On the x-axis we have average in-zone lateral spacing, and 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.
The impact of going from wide to tight spacing is quite severe for the largest jobs — 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 provides engineers the tools to right-size designs for optimized development plans.