In August of 2011, the Pipeline and Hazardous Materials Safety Administration (PHMSA) published the Distribution Integrity Management Program (DIMP) rule for the natural gas operators. The primary focus of this rule was for gas operators to perform a risk analysis of their system.
A secondary section (and primary focus of this case study) of the DIMP rule focused on documentation/reporting of specific manufacturing information of replacement material when a hazardous leak is repaired. More specifically, when a failure occurs, operators are now mandated to report the following information on gas couplings:
Historically, the industry has not collected/maintained this information when installing gas material. As a result, this information is typically unknown at the time of a leak repair.
Due to this rule, the gas industry subsequently adopted a barcoding standard for plastic pipe (ASTM-F2897). This standard would allow gas operators to easily collect the required information as plastic gas-specific material is installed underground.
The Memphis Light, Gas and Water (MLGW) Gas Department has since embarked on a pilot testing project. The project’s purpose was not only to address the PHMSA mandate, but also place the required barcode information into a spatial GIS reference via GPS technology.
MLGW acquired three high accuracy GPS (Trimble Geo 7xh) units each having a wireless Bluetooth connection to an external barcode scanner (Zebra D S3578). Each equipment pair was assigned to a specific crew to be utilized in the slightly modified daily routine.
One of the first tasks of the project was the discussion of what MLGW wanted to field collect. It was concluded that the following four gas features would be field collected:
Each of the above GPS features has an associated Enterprise GIS feature class. Even though the Enterprise database stores mains and services as linear feature classes, it was decided to collect these features as points. The collected “pipe points” would only be utilized as information and image storage of the linear feature.
A separate GIS staging feature dataset was created for these feature classes. The feature classes were created with only those fields designated for GPS attribute population. When possible, domains were assigned to enable crew drop down attribute selection, which increases efficiency and reduces error.
MLGW’s existing GIS fitting and valve feature classes leverage Esri subtypes. Several ArcFM™ autoupdaters have been designed to leverage specific subtypes for annotation and default field attribution. Since the GPS software lacked the ability to utilize the Esri subtype functionality directly, integer fields were created with domains mimicking the subtype valves. When the data is migrated, the subtype is populated and autoupdaters behave appropriately.
The typical daily process begins with the administrative creation of a GPS job. The job is given the name of its applicable MLGW work request. The job is then assigned to the crew conducting the field work. Each GPS is configured to automatically connect and utilize MLGW’s wireless network. As a result, when the crew’s assigned GPS is within signal range, transmission of the job is undertaken.
The crew carries out their normal day. The variation in the crew’s typical procedure is the GPS collection of gas features. Once the fitting, valve, and linear mains/services are installed, the crew collects the spatial location of each feature. While holding the GPS unit directly over the applicable feature, a submeter point is collected.
Upon completion of the location portion, applicable information attributes are entered. What provides the “Scooby Snacks” to the field crew is the automatic field population offered by the barcode scanner. A text field is designed to receive the value of the barcode. The user clicks on the field, scans the barcode, and the field is populated. Other fields are also populated automatically (via attribute query of the barcode field) as follows:
If necessary and/or desired, photographs may be taken of the feature/site. These photographs are stored with the collected feature.
Once the data has been completely collected for the job/work request, it is then closed. At this point, when the GPS is back within wifi range, the data will automatically transfer to the GPS database.
A crew leader can then verify the collected information and process it (differential correction and conversion to GIS staging database). Metadata attributes automatically populate applicable fields including job/work request name, crew, and average horizontal accuracy. The more attributes that are automatically entered (including those by the barcode scanner), the more efficient the crew becomes.
The crew leader informs the GIS staff to complete the process. The job’s work request data is now contained within the GIS gas staging area and needs to be migrated to the Enterprise GIS database. MLGW and SSP have designed and constructioned six tools to assist with the migration of features, attributes, and linked documents (i.e., photographs).
This being a pilot project, the MLGW workflow will be thoroughly tested over the next several months. It is entirely possible that alterations to the configured solution (i.e. workflow, software, etc) will be deemed necessary. While this case study centers on the field effort, a critical portion of the project also involves the conversion of data collected from the field and migrating into MLGW’s production ArcMap system. A subsequent "Part 2" will focus on tools designed to assist MLGW in the transferring the field-collected assets to the enterprise GIS.