Get Accurate Well Spacing and Landing Metrics
Gun Barrel

Get Accurate Well Spacing and Landing Metrics

Charles Connell  •  

Creating a data set that contains trusted well spacing, well landing, and parent/child relationships is a required first step of many reservoir engineering workflows.

Comparing analog wells, understanding PDP well performance, creating type curves, or building machine learning models all depends on this initial step. These comparisons can be done manually for a small data set, but it is prohibitively time consuming to manually look at all wells in an AOI, let alone across an entire basin. While the metrics can be sourced from public data providers, the default classifications often do not align with the internal subsurface characterizations done by an asset team.

The above screenshot from PetroAI shows a gun barrel for the selected well at the top. The summarized well spacing and landing calculations are shown in the middle table. The bottom table lists all the identified well pairs with their classification and offset distance.

PetroAI addresses these challenges by letting asset teams blend public and private data together and then execute a configurable pipeline that calculates well spacing, lands the wells against their internal structure grids, classifies all well-to-well relationships, and creates gun barrel images.

Wells are landed using a team’s internal structure grids and public drilling surveys. The lateral can even be divided into any number of segments with each segment landed independently. Different segments could land in different intervals and the system will report the most common landing interval in addition to the landing location of each segment. Other calculations, like the relative landing position inside a zone is calculated.

Well spacing is most often calculated perpendicular to the lateral. However, this method does not account for the orientation of Shmax, the direction in which fracs actually travel.

PetroAI performs well spacing calculations in stress space, interpolating the orientation of Shmax at each well (or well segment) location. This orientation creates the plane in which the offset wells are identified and the horizontal distances calculated. Vertical distance is also calculated for all offset wells.

Two configurable inputs are used by the PetroAI calculation pipeline to identify and classify offset wells as parent, child, or sibling: maximum offset distance (can be vertical, horizontal, or direct) and the time between completion dates. All well to well pairs are identified and then classified appropriately. The parent/child/sibling relationship is focused on well pairs. A well might be a parent of one well and a child of another. However, the count of parents, siblings, and childs are rolled up to the well level.

Gun barrel images are automatically created for every well using internal structure grids.

When a specific well is selected, it becomes the “active” well and is placed at the zero-horizontal location in the gun barrel image. All identified offset wells are positioned in stress space around the active well and identified as either parent, child, or sibling. The actual structure is interpolated to the well locations and shown in the gun barrel image to provide an accurate representation of the well positions with respect to the subsurface.

Through its automated prediction pipeline, PetroAI generates key outputs for asset teams.

The result is more accurate well landing and spacing information available instantly through the PetroAI Cloud interface. Teams eliminate hours of monotonous work while also being able to deliver highly technical work.

114 Main Street Ste. 400
Houston, TX 77002
4-5609 Avenue du Parc
Montreal QC, H2V 4S8