In this article we define land ownership mapping and identify some purposes of land ownership mapping.
We present this article to help in the necessary understanding to perform automated land ownership mapping.
We also identify the basic technological areas required to perform automated land ownership mapping and discuss these areas in some detail.
The target audiences for this paper are those who are considering automated land ownership mapping or those who are attempting to perform automated land ownership mapping.
What is automated land ownership mapping? Automated land ownership mapping is the activity of producing informational maps from queried land ownership records stored in databases or spreadsheets. Ownership here shall mean any decision-oriented control over acreage that is created by legal instruments.
What are the purposes of automated land ownership mapping? The purpose of automated land ownership mapping includes the association of ownership (and other basic economic and legal information) with geologic and production data for the ultimate goal of determining whether to acquire additional ownership, drill, produce, determine status of operations, or to sell ownership.
Why is automated land ownership mapping important? Land ownership mapping shortens the path and time required for land departments, exploration, and production departments to exchange information necessary to make decisions pertaining to the application of the company’s resources and efforts. As such, automated land ownership mapping is a method of exchanging critical information between departments that may have not existed in the past. Many organizations have not yet experienced the increase in efficiency and heightened economies that are created by such exchanges. For more on the initial obstacles (and costs) that can occur when initiating automated land ownership mapping, see our white paper on data standards at: http://www.infopipe.com/datastds.htm
What technologies are required for automated land ownership mapping? Automated land ownership mapping requires four areas of technology exist and work in unison:
The Process of Automated Land Ownership Mapping.
The general process for each technology can be briefly described as follows:
The main technologies required parallel these processes.
Data (and Data Standards)
In order to perform land ownership mapping we need two general types of information: ownership and map information. These information must be presented in a fashion that is amenable to extracting interests, dates, locations, etc., in order to efficiently compare and ultimately query the data.
Map information is the information required to create the land images (called polygons). Map information must be sufficiently standardized in order to identify or create the land images. For example, in congressional states meridian, township, range, section, and aliquot descriptions would be sufficient to create representations of the land images that would be associated with the land ownership information.
The map and ownership information must also be standardized as to format for the particular information to be used. For example, one must choose whether ownership interests are fractions of one or percentages. Dates must be standardized. Finally, only certain formats may be input into particular information fields, ie, you should not enter a date into an interest amount field.
Map information must be specified in a manner that the land image creation and naming software can interpret the information and produce the necessary land images. For example, in congressional states aliquot descriptions, such as NE/4SW/4 or NESW must be interpretable. In addition, Lots and tracts should be interpretable. Examples might be Lots 1-3, 4-7, 13 or Tract 14, etc. Finally, we must be able to handle irregular tracts. The most general tools will allow for your naming of such tracts. For example, !MyTract would specify that a tract named "MyTract" would be the tract that would be associated with ownership information. We typically call such tracts as BangName tracts because they are specified with an exclamation point prefix, commonly called a bang symbol.
The map and ownership information must be formatted and standardized in such a way that the automated mapping software (such as Infopipe’s OwnerImage Builder) can read the information, format that information for mapping, and produce data for subsequent maps.
There are several sources of data. Each requires its own data standards to be useful. In addition, each data source may have different information as required for the purposes of the source organization.
We have identified the following general sources of land information:
Field Brokers – This is data generally compiled and completed in spreadsheet form by field landmen.
Internal Data – This is data that is generally entered into the company’s internal land database or accounting system for the purpose of rental payments and working interest allocations.
Vendor Data – This is land data supplied by vendors that process data from county court houses or from Federal Land Records Databases. For example, Infopipe supplies Federal Land Records data that is ready to map
Land Image Creation and Naming.
In order to create land images and to assign systematic names to images it is important for the land mapping tool (of which Infopipe’s OwnerImage Builder is one) to be able to create or associate unique land images to land descriptions.
One method used in congressional surveys is to create a land image name that represents the location. For example, in Infopipe’s OwnerImage Builder tool the map information is transformed into a list of unique land image names using the meridian, township, range, section, and description to create the land image name. Table 1 shows examples of the input map information and the land image name that results.
Table 1.
| Description | Land Image Name - Creation Method |
| Mer 6, T74N, R27E, Sec. 3, NENE | 6_74N27E3aA - Auto-Created |
| Mer 6, T74N, R27E, Sec. 3, !6_74N27E3_1 | 6_74N27E3_1 - Digitizing required |
| Mer 6, T74N, R27E, Sec. 3, Lot 1 | 6_74N27E3L1 - Digitizing required |
| Mer 6, T74N, R27E, Sec. 3, S2 | 6_74N27E3S - Auto-Created |
where A=NE, B=NW, C=SW, D=SE and lower case is prefix.
Why is it necessary to create land image names? Our purposes include being able to take a legal description, find the land image that represents this legal description, and create maps (presentation) that represent and differentiate the land ownership data associated with the legal description. There are two types of land images: auto-created and digitized.
Auto-created land images (dynamic polygon) are usually created in congressional surveys by "slicing" up a section polygon (from a landgrid database) using a recursive software tool commonly referred to as a "polygonizer". The polygonizer interprets the aliquot (commonly called the quarter-quarter call, but is not limited to strict quarter-quarter), retrieves the section polygon, and determines the polygon within the section that represents the aliquot call. Polygonizers are typically complex pieces of software that must be able to "slice" a section to any level of definition and do so very quickly.
Digitized land images (static polygon) are images that must be created from other images or descriptions. Scanning and digitizing a map would create such land images. Any special tracts would be created in this manner. These tracts are created in advance of mapping and are named using naming conventions (as is the case for Lots and Tracts) or have special names assigned (such as BangName tracts).
Once the tracts are created, then any query may identify the land image that is required to map ownership information.
The essence of automated land ownership mapping is the association of standardized ownership information with auto-created and digitized land images.
Querying.
Querying is the activity of requesting land records that meet certain criteria to produce map components to be displayed. For example, we might request a display of land images that represent lands where we hold acreage based upon production (HBP). Or we might wish to display land images that represent where two particular companies are in partnership.
The process of querying should allow for the creation of many types of map components: color-filled land images, symbols (hung off of specified corners of a parcel), and outline-oriented text information. In addition, other queries may be used to alter the patterns or borders of the color-filled images in order to present more information without increasing the amount of text on the maps.
Examples of map components might include:
Expiration Period Maps – color-filled land images or symbols where land records show expiring leases within a particular time period.
Interest Maps – color-filled land images or symbols where land records show a particular level of interest acquired by either individual companies or by a collection of companies.
Lessee-Interest-Expiration Date Text Images – Text outlines displayed on a land image that specify the Lessee, Lessee Interest, Expiration Date for a lease.
Lease Name-Number Maps – Text displayed on a color-filled-land image that specify the lease name or number of the lease associated with the parcel.
Land Type Maps – color-filled land images or symbols where land records show a particular type of land, ie, Federal, Fee, State.
HBP Maps – color-filled land images or symbols where land records show leases held by production.
Unit Maps – color-filled land images or symbols where land records show leases within a particular production unit.
Price Map – color-filled land images or symbols where land records differentiate the prices paid for leases.
Summary.
In this article we have defined automated land ownership mapping, identified the purposes of land mapping, and identified the importance of land mapping. We also presented the main areas of technology required to perform automated land ownership mapping and discussed these briefly.If you have any suggestions or comments regarding this article, please call or email us.
Email: info@infopipe.com