Loading…
This layer is editable and shared with the public. To prevent unwanted editing, unshare this item or approve it for public data collection.
Finish setting up your layer
Describe your item below. Add fields on the Data tab. Configure editing on the Settings tab. Configure drawing and pop-ups through Map Viewer or Visualization tab.
Open
Creating Service

Deep learning model to detect oil well pads from Sentinel-2 imagery. A brief summary of the item is not available. Add a brief summary about the item.

‎Deep learning package from

Managed by

New notebook runtime available. You can update the runtime from the settings tab of the item details page.

Item created: Oct 7, 2021 Item updated: Jan 2, 2025 Number of downloads: 3,797

Snapshot last refreshed:

1980 characters left.

Description

The Oil & Gas industry in the United States has witnessed significant growth in the last decade. Development in technology and the extraction process has given access to reserves that were earlier out of reach. This has led to increased production in many regions and most remarkably in the Permian Basin. With increasing production, the number of well pads is also going up. Well pads are relatively flat areas that are cleared for drilling and extraction of oil and natural gas. This deep learning model can automate the detection of well pads and can be used to monitor the progress of new drilling. It can provide competitive intelligence as well as potentially help identify illegal drilling. Additionally, knowing where the new well pads are coming up can enable better planning and resource allocation.

Using the model
Follow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.

Fine-tuning the model
This model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.

Input
Sentinel-2 imagery in the form of raster product, mosaic dataset, or image service. For best results, imagery has to be analyzed at 5 meter resolution.

Output
Feature class denoting well pads.

Applicable geographies
The model is expected to work well in the Permian Basin (west Texas and southeastern New Mexico) in the United States.

Model architecture
The model uses the FasterRCNN model architecture implemented using ArcGIS API for Python.

Accuracy metrics
The model has an average precision score of 0.924.

Sample results
Here are a few results from the model.


image1

Image2

Image3

An in-depth description of the item is not available.

Layers

Ground Layers

Tools

Tables

Basemap

Project Contents:

Solution Contents

Contents

Layers

Screenshots

Terms of Use

https://downloads.esri.com/blogs/arcgisonline/esrilogo_new.png This work is licensed under the Esri Master License Agreement.

No special restrictions or limitations on using the item's content have been provided.

Comments (0)

Sign in to add a comment.
Item Information

LowHigh

Item Information

LowHigh

Make your item easy to find, understand, and use by providing this information.

    Details

    Dashboard views: Desktop

    Source: (Image Collection)

    Creating data in:

    Published as:

      Other Views:

        Dependent items in the recycle bin

          Applicable: 2d

          Size: 147.829 MB

          Attachments size: 0 KB

          ID: fab4f3a85abd41ce886338ed85246146

          Image Count: 0

          Image Properties

          Layer Drawing

          Using tiles from a cache

          Dynamically from data

          Share
          Owner

          Esri Managed by:
          esri_analytics

          Folder

          Categories

          This item has not been categorized.

          Assign Category
          Tags

          oil well, well pads, permian basin, fasterrcnn, dlpk, deep learning package

          Edit Tags
          Credits (Attribution)
          No acknowledgements.

          Esri Inc.

          ArcGIS Navigator link
          URL View
          WMTS View
          Your tile layer is ready to use
          This tile layer will automatically create tiles as needed and cache them for future use. No further configuration is required. View the Settings tab to change the default options. Build tiles manually for specific scales and extents to improve display performance for the first person to view the tile layer at that scale and extent. Tiles must exist if the layer will be used offline.
          All items were exported successfully
          ${numberOfItems} item(s) were exported successfully. Some item(s) skipped or failed to export.
          See description for more information
          Cannot import
          Export packages from newer portal versions cannot be imported to older versions.
          Loading…