Latest Contributions by TomDeWitte

Scanning and Decoding Barcodes Part 2 of 5 By Tom DeWitte and Tom Coolidge Our first blog of this series provided an overview of the steps a utility can take to improve the productivity of the utility field worker. If you missed it, you can access it here. In this blog article, we will dig into the first step in automating field data collection. The first step in making life easier for the utility field worker is to deploy a configuration of ArcGIS Field Maps that minimizes the amount of manual data entry they must perform. As noted in the previous blog article, this is the first of four steps to automating field data collection. Minimize manual data entry Auto-populate what is already known Leverage sensors on your mobile device Use geography The use of barcodes to automate data entry is one method to minimize manual data entry. Barcodes Barcodes are currently used for many purposes across utility industries. Most utility field workers will have a barcode on their employee badge. This barcode encodes the unique identification of the employee. Another common use is to barcode machinery. This barcode encodes the manufacturer information about the device. Then there is the use of barcoding assets. In the natural gas industry, barcodes are applied to the plastic pipe, plastic device, or plastic fitting by the manufacturer. In industries that do not have a barcode industry standard, there are utility companies which are placing their own barcodes onto assets as they enter the warehouse. Barcodes are on many assets today, and soon seemingly will be everywhere. Simply having a barcode does not directly equate to productivity gains for the utility field worker. There needs to be a companion capability. This companion capability includes electronically capturing the barcode, and software to decode the barcode, then auto-populate the information directly to the asset record. Without this companion capability the utility field worker will have to manually read the 16-character case sensitive text string and without error manually enter it into the asset record. Manually entering barcodes is slow, prone to error, and a frustrating experience for field workers. Scanning the Barcode Two predominant methods for electronically capturing a barcode are optical and infrared scanning. Optical scanners are the camera built into your mobile device. Infrared scanners are external devices which Bluetooth connect to your mobile device. ArcGIS Field Maps supports both methods. If interested in using a handheld infrared barcode scanner, the Bluetooth device needs to support the keyboard wedge method for integration with ArcGIS Field Maps. Regardless of barcode scanning method, when ArcGIS Field Maps electronically captures a barcode the designated text field in your form will be automatically populated. There is no manual data entry. With the barcode now electronically captured and stored in the BARCODE field, software needs to be employed to decode the barcode value and auto-populate the appropriate data fields. Decoding the Barcode In many examples this decoding of the barcode can be accomplished with a small set of Arcade scripts. Each attribute which will be auto populated from the information encoded in the barcode will have an Arcade script. Deploying the Automation The method used to embed the software capability to decode the barcode in ArcGIS Field Maps is done through configuration. The Field Maps Web Application provides the administration environment to perform this configuration. The web application is used to define the Arcade script and the ability to apply this script to the table field. Each field to receive information from the decoded barcode values will have its own Arcade script. Field User Experience With these scripts now configured into the editing behavior of the layer, the automation is in place for the utility field worker to utilize. For the utility field worker, the field data collection is very simple. Open ArcGIS Field Maps and select the web map designated for the specific data collection task. Now you are ready to collect. The collection process with barcodes is to select the item to be collected and capture the barcode. The ArcGIS Field Maps application will automatically run the Arcade scripts immediately after the BARCODE field is populated. This allows the utility field worker to immediately be able to review the decoded content and verify it matches what was installed. As the above screenshot shows, a total of 8 data fields were automatically populated from information embedded in the BARCODE field. Including the BARCODE field, a single barcode scan results in nine data fields being populated with no user typing. Automating Data Entry The use of barcodes is only one example of how ArcGIS Field Maps can be used to free your utility field workers from manual data entry. Other opportunities to improve the productivity of utility field workers include:                 -auto-population of the date/time when the collection was performed                 -auto-population of who performed the data collection                 -auto-calculation and population of a pipe’s volume and surface area                 -auto-population of the projectID based on the asset’s location intersecting a project polygon                 -auto-population of the nearest address, based on the asset’s location These are just a few examples of how the automation capabilities in ArcGIS Field Maps can be used to improve the productivity of the utility field worker. About This Blog Series This blog article is the second in a series of five blog articles. Upcoming blogs will continue explaining in greater detail how to configure the Esri ArcGIS Field Maps mobile application to deploy these examples. PLEASE NOTE: The postings on this site are our own and don’t necessarily represent Esri’s position, strategies, or opinions. ... View more

Part 1 of 5 By Tom DeWitte and Tom Coolidge Utilities collect a lot of data. This means utility field staff are spending a lot of time filling out forms.  Many companies today are undertaking efforts to convert paper forms to digital forms.  The basic justification for this effort is to eliminate the duplicate data entry of having an office person read, interpret, and recreate in the corporate enterprise systems what is captured in the field. But what about the field staff themselves, where is their benefit of digital transformation?  This blog series is about how to improve the productivity of field staff - how to automate data entry, leverage mobile device sensors, utilize intelligent software, and truly reduce the amount of time field staff spend filling out forms. Making life easier for field staff Have you ever met a utility field worker who walks into the office in the morning and says “Boy, I can’t wait to fill out forms today?” No, well me either. What is common to hear field workers complain about is spending too much time completing paperwork. The data utility field staff collect is vital to the successful operation and engineering of the utility system. Asking the field staff to stop collecting data is not an option.  But what about creating forms that auto-populate themselves to the maximum extent possible?  Stop typing The first step in making life easier for the utility field worker is to deploy solutions which minimize the amount of manual data entry they must perform. Today’s mobile devices and applications provide a wide range of capabilities to capture a large amount of information with minimal effort by the utility field worker. An example of this is barcodes.  The natural gas industry uses the ASTM F2897 barcode standard for its plastic pipe, fittings, and devices. A field worker equipped with a mobile application on a smart phone or tablet can use the device’s   camera to capture the barcode. This automatically inserts the barcode into the form. Then the mobile application can decode the information embedded in the barcode and auto-populate the appropriate form fields. If you are keeping score that is 8 form fields auto-populated and zero manual data entered for the utility field worker.  Barcodes can also be used on a worker's badge. Use the mobile device camera to read the badge and auto-populate the worker’s information into the form.  Take this idea a step further and have the mobile application compare the scanned worker ID against a table of operator qualifications to instantly verify that the worker has the current valid qualifications for performing the work, such as a weld or a plastic fusion. All of these intense data documentation and validation steps can be performed with no manual data entry.   Auto-populate what is already known The second step in making life easier for the utility field worker is to stop asking them to enter information the organization already knows. An example of this is project data.  Before a utility field worker drives up to a construction site, the project information is already well known within the utility planning, engineering, and permitting departments. A common utility practice is to create a project polygon to define the extent of the construction area. When using a geography-based mobile application this project polygon can be referenced to automatically insert project information into the form by having the field utility worker simply be within the extent of the project area. Another example is asset condition inspections, such as a valve inspection.  It is very common for a valve inspection form to ask for information about the valve. What size is it? Who was the manufacturer? What type of valve? The organization should already know the answer to these questions. The utility field worker simply needs to verify that the organization information is correct.  In this example the geography-based mobile application enables the utility field worker to simply click on the valve on the map to initiate the valve inspection. By selecting the valve from the map, the form automatically retrieves the information from the valve record and auto-populates the valve information portion of the form. All the field worker must do is review the information to verify it is correct. Asset Catalogs Another method for auto-populating what the organization already knows are lookup tables. When documenting new construction, these lookup tables are called Asset Catalogs.  An Asset Catalog record contains the manufacturer specifications for an asset. For example, when installing a steel pipe, the organization already knows the nominal diameter, wall thickness, outside diameter, material, manufacturer, manufacture type, specified minimum yield strength (SMYS), and pipe coating type to name a few of the known characteristics.   When a mobile application can leverage a lookup table, the utility field worker experience is very straightforward.  Select the installed type of steel pipe from a picklist. The mobile application then uses the selected item to query the asset catalog table. The selected asset catalog table is read, and the information is used to auto-populate the new steel pipe record. Review the now populated asset information to verify it is correct. The mobile user has populated multiple data fields by only manually selecting a single value from a picklist. Once again, no typing is required. The mobile sensor array The third step for making life easier for the mobile field worker is to leverage the many sensors of your mobile device. Most phones and tablets available today come with a built-in GPS chip. When you have a geography-based mobile application which can leverage that GPS, it can do much more than simply place a dot on the map to show you your location.  The geography-based mobile application can auto-populate your form location descriptors such as address, city, state, zip code, and utility division. Area based descriptors of location such as utility division, city, state, and zip code can be determined by having the software automatically perform a point on polygon overlay using the GPS location as the point location. Some geography-based applications can also use the GPS location to perform a reverse geocode operation to determine the address. In both examples, the software automatically populates this information when the field worker taps on the screen to capture the GPS location. No other user input is required. Use geography to automate The fourth and final step for making life easier for the mobile field worker is to leverage geography. There are many examples in utility field data collection where multiple assets or locationally unique items need to be grouped together. A pressurized pipe system example is pressure test documentation. After construction of the pipe system, that new or modified section of pipe must be pressure tested to verify it will not leak. This documentation step requires that all assets which were part of the pressure test be grouped together and assigned a common Pressure Test ID. This can be a very time-consuming documentation step as a single pressure test can include dozens of individual assets. With a geography based mobile application the utility mobile worker can draw a polygon to encompass the assets which were pressure tested. The geography-aware mobile application automatically uses the utility field worker defined polygon to select all pipe segments, fittings, and devices within the polygon. Each selected asset is assigned the pressure test ID. Improve data quality and completeness These examples of how to make life easier for utility mobile workers also have a benefit for the office staff. This auto-population provides a more complete record of the asset. Pipe asset values such as wall thickness and SMYS are often not considered values which are required to be entered by the field worker. Human nature is that anything not required likely will not be populated. Yet, these values are critical to engineers determining the range of safe operation of the pipe network. Conclusion Digitally transforming utility field data collection needs to be more than creating a digital form to improve the productivity of the field staff.  It needs to automate data entry. These forms need to leverage mobile device sensors, utilize geography-aware mobile applications, and truly reduce the amount of time field staff spend filling out forms. About This Blog Series This blog article is the first in a series of five blog articles. Upcoming blogs will explain in greater detail how to configure the Esri ArcGIS Field Maps mobile application to deploy these examples. PLEASE NOTE: The postings on this site are our own and don’t necessarily represent Esri’s position, strategies, or opinions.   ... View more

ArcGIS for District Energy: Tracing Thermal Energy By Tom DeWitte and Tom Coolidge The purpose of a district energy system is to transport thermal energy to customers (district heating) or to transport thermal energy away from customers (district cooling). It sounds simple, yet the looped nature of district energy pipe networks makes them some of the most complicated utility pipe networks. Modeling flow through this complicated network of energy plants, pipes, pumps, valves, and eventually buildings is not easy. Yet, it is vital to planners, engineers, and operators of the pipe network to understand how their product moves to and from their customers. This gets even more complicated if the district energy organization utilizes circulation zones with heat exchangers. In that system configuration, the water flow is not the same as the thermal energy flow. This requires an application and data model smart enough to differentiate between water flow and thermal energy flow. This is where ArcGIS with its ArcGIS Utility Network capability and District Energy Data Model can help.  Thermal Energy and District Heating When modeling a district heating pipe network, it is vital to understand where the energy starts and where it goes. For district heating, the start is typically the energy plant. From the energy plant the heated water or steam transports the thermal energy through pipes, pumps, heat exchangers, valves, more pipes and eventually it to a building. The customer in that building consumes the thermal energy to heat their home. In a full loop system, the now cooled water returns to the energy plant to be reheated. Thermal Energy and District Cooling When modeling a district cooling pipe network, the thermal energy starts with the customer and is delivered to the cooling plant where it is typically released into the air. In a full loop system, after the thermal energy is released to the air, it is sent to chiller units to further remove thermal energy. This results in an additional decrease to the water temperature. Once cooled it is transported through pipes, pumps, heat exchangers, valves, and more pipes before it reaches the customer's building. At the building the chilled water absorbs the customer’s waste heat and transports it back to the cooling plant. Leveraging Utility Network Modeling this real-world transportation of thermal energy through a pipe network, requires more than an understanding of how the pipes, pumps, heat exchangers, valves and other components are connected. It requires the ability to understand which pumps are operating, and which valves are closed. It also needs to differentiate between cathodic protection wires and leak detection wires. These wires are connected to the pipes but do not transmit thermal energy. To accurately represent these additional real-world complexities, we need a more advanced connectivity model. We need to leverage the Utility Network. The Utility Network is an extension to ArcGIS. ArcGIS is the geographic information system (GIS) provided by Esri. What Stops Thermal Energy Flow With a Utility Network comes the Trace geoprocessing tool. A configuration of this tool is what will be used to perform the thermal energy flow trace. To correctly configure the thermal energy flow trace requires a real-world understanding of what can stop the thermal energy flow. In the real-world devices which are closed, such as a valve, or are not operating, such as a pump, will impede the flow of the thermal energy. Add to this list any pipe segment or device which is not in service, such as retired pipe segments or pipe segments which are proposed but have not yet been built. Lastly, there is the need to exclude the cathodic protection wires and leak detection wires which are part of the pipe network but do not conduct thermal energy flow. When using the District Energy Utility Network Foundation data model, those constraints look like this: Pipe segment or asset is not in service: Lifecycle Status Does not equal In Service Valve is closed: Device Status is equal to closed Pump is not operating: Device Status is equal to closed Cathodic Protection wires: Category is equal to CP Only Leak detection wires: Category is equal to Leak Detection Only Configuring the Trace Tool The Trace geoprocessing tool in ArcGIS Pro has many parameters. Here is how to transpose the constraints into the specific settings in the Trace tool. The type of trace will be a downstream trace, leveraging the DHC domain network, and the DHC Energy Tier. Setting the Tier to “DHC Energy Tier” is important as it sets the flow source as the energy plant/chiller plant, versus the “DHC Pressure” tier which would set the source as in-system pumps. The constraints will be added as Traversability Barriers. If any one constraint is true, the trace will not traverse beyond the asset. When this trace is run, it will return all pipe segments, devices, fittings, and customer service points which are receiving thermal energy from the designated start location. This includes both the supply and return portions of the pipe network. If you are a district heating organization and only interested in the thermal energy flow supplying energy to the customer, you can add a filter barrier: Pipe segments are only supply lines: Line Asset Type does not equal supply This will return a selection set of the pipe network which shows how the thermal energy traverses the pipe network from the designated location to the customer. The same configuration will work on the district cooling pipe network. Sharing the Trace Now that you know how to configure the trace tool to perform a thermal energy flow trace, how do you share this with others in your organization. And, how do you share it in a way that does not require everyone to manually perform this configuration of the Trace Tool. This is where the new functionality introduced at ArcGIS 10.9, called Trace Configurations is useful. Trace Configurations is the ability to store a configuration of the Trace Tool within the Geodatabase. When the Thermal Energy Flow trace is stored in the Geodatabase, other desktop, web, and mobile users do not need to know how to configure the tool. A simple check of the “Use Trace Configuration” option removes all the configuration options and replaces it with a simple pulldown menu for the end user to select from. Having the trace configuration centrally stored and accessible to end users ensures that everyone is running a properly configured thermal energy flow trace. Summary Planners, engineers, and operators require this type of advanced flow modeling to help them perform their daily activities. Thermal energy flow modeling is just one of the many types of water, thermal, cathodic protection, leak detection flow analysis which can be configured with the ArcGIS Utility Network.  These trace capabilities help to remove some of the complexity of maintaining and operating a district energy pipe system. PLEASE NOTE: The postings on this site are our own and don’t necessarily represent Esri’s position, strategies, or opinions. ... View more

Thru 2021 the Esri Gas team worked with multiple natural gas organizations to help them deploy ArcGIS Field Maps to construction crews. This effort builds on the Tracking and Traceability capabilities such as GPS, Laser Range Finder, and barcode scanner integration accomplished prior to 2021. The 2021 effort resulted in many enhancements to automating the construction documentation process. A few of these enhancements include: Realtime decoding of the barcode Compatible unit table lookup for non-barcoded plastic and steel assets Automatic assignment of Construction Project ID to collected assets Automatic assignment of PressureTestID to all tested assets This video demonstrates how a configuration of ArcGIS Field Maps can be used to address the mobile as-builting needs of gas organizations. ... View more

By Tom DeWitte and Tom Coolidge Last month, Esri released an updated version of the District Energy Data Model. This release continues Esri’s practice of maintaining a template data model ready “out-of-the-box” to manage district cooling, district heating, and steam data within an Esri geodatabase. Why District Energy Data Model The goal of the Esri District Energy Data Model is to make it easier, quicker, and more cost-effective for district heating, district cooling, and steam utilities to implement the ArcGIS platform. Esri accomplishes this by freely providing a data model that takes full advantage of the capabilities of the geodatabase. The data model is created and tested with ArcGIS products to ensure that it works. This significantly reduces the complexity, time, and cost to implement a spatially enabled district energy data repository. District Energy Enterprise Data Management For many district energy utility enterprises, deploying the ArcGIS platform that leverages the concepts of a service-oriented web gis is more than loading the district energy data model into an enterprise geodatabase. It requires additional steps such as creating an ArcGIS Pro map configured for publishing the data model and publishing of the Pro map to create the required map and feature services. To help simplify these additional steps performed with the industry data model, Esri has embedded the data model into a new ArcGIS for district energy solution.  The new solution is called District Energy Utility Network Foundation. This solution provides the data model, sample data, and an ArcGIS Pro project configured with tasks and performance optimized maps. You can access this solution from the Esri ArcGIS for District Energy solution site. A data dictionary for this data model is available online. Best Practice Use of ArcGIS This updated version of the District Energy data model is configured to take advantage of the latest capabilities provided by the ArcGIS technology. This includes recent enhancements such as contingent values, attribute rules, and the utility network. In this data model you will find contingent value configurations to restrict the valid types of pipe insulation and pipe material based on whether the pipe transits steam, condensate, heated water, or chilled water. Included are many attribute rules to automate attribute population as well as provide data quality checks. This data model includes utility network subnetwork configurations for defining pressure zones, circulation areas, leak detection zones, cathodic protection zones, and energy zones. And let’s not forget the thousands of connectivity, containment, and association rules of the utility network. Embeds Industry Business Rules Business rules are a great way to share industry knowledge. Sometimes it is simple, such as ensuring that the maximum operating temperature is greater than the standard operating temperature, or that the in-service date occurred after the date of installation. Others are more complicated, such as knowing when to create a containment association between two assets.  Thanks to the combined knowledge of many persons across the industry, these business rule examples and many more are included with this data model. Modeling Flow thru your Pipe Network District energy systems often have a dual set of pipes in a single trench. One flows from the energy facility to the customer, the other flows from the customer back to the energy facility. This makes modeling flow through a district energy pipe system unique among the pipe utilities. This data model supports the dynamic tracing of water flow, heat flow, cathodic electric flow, and leak detection circuits across the dual pipe network. A Foundation to Build Upon The official name of this first district energy solution is District Energy Utility Network Foundation. The name was intentional as this spatially-enabled data management solution is the foundation from which many more district energy solutions can be added. With this foundation, district energy organizations can provide analytics, visualization, and data collection solution to their users. PLEASE NOTE: The postings on this site are our own and don’t necessarily represent Esri’s position, strategies, or opinions. ... View more

Utility and Pipeline Data Model 2021 is Released By Tom DeWitte and Tom Coolidge Esri’s Utility and Pipeline Data Model (UPDM) 2021 is available now. This release continues Esri’s practice of maintaining a template data model ready “out-of-the-box” to manage natural gas and hazardous liquid pipe system data within an Esri geodatabase. This release includes enhancements to keep up with changes in industry practice, regulatory requirements, and previous implementation feedback. Gas and Pipeline Enterprise Data Management For many gas utility and pipeline enterprises, deploying the ArcGIS platform is more than simply loading the UPDM 2021 data model into an enterprise geodatabase. That’s because ArcGIS leverages the concepts of a service-oriented web GIS. It requires additional steps, such as creating an ArcGIS Pro map configured for publishing the data model, publishing of the Pro map to create the required map and feature services and, perhaps, configuring a location referencing system. To help simplify these additional steps performed with UPDM 2021, Esri has embedded UPDM 2021 into a newly renamed ArcGIS for Gas solution. The new solution is called Gas and Pipeline Referencing Utility Network Foundation. This solution provides UPDM 2021, sample data, and an ArcGIS Pro project configured with tasks and performance optimized maps. You can access this solution from the Esri ArcGIS for Gas solution site. A full data dictionary of UPDM 2021 is available online. A change log documenting the full list of changes incorporated into UPDM 2021 is also available online. Enhancements for Managing Cathodic Protection Interest in leveraging ArcGIS to manage Cathodic Protection (CP) data has grown significantly over the last few years. Recent implementations have shown an interest in modeling a more detailed representation of CP systems. With UPDM 2021, our industry data model now includes models for managing the following CP assets: Linear Anode AC Mitigation Wire Decoupler Grounding Point Cathodic Assembly Grounding Mat Feedback from Pipeline Implementations Pipeline implementations of UPDM have been many and varied. These previous implementations have resulted in feedback that has resulted in dozens of adjustments to improve how we model pipeline assets. Two specific enhancements based on previous implementations to highlight are the defining of the attributes “odorized” and “piggable” as Utility Network Network Attributes. Having “odorized” and “piggable” as Network Attributes provides additional capabilities for leveraging tracing. Tracing is the ability to understand how the components of a pipe system are connected, and how the gas and hazardous liquids flow through the pipe system. With this new template configuration, pipeline operators can trace across their pipe system and see where the “piggable” portion stops when it reaches an asset which is tagged as not piggable. Similarly, planners and engineers can trace to see what portion of their pipe system is “odorized.”                    Feedback from Gas Utility Implementations Previous implementations of UPDM in Gas Utilities have also resulted in feedback to help fine-tune the gas and pipeline industry data model. Some of these adjustments include improved modeling of relief valves, flanges, and taps to name a few. Other enhancements include adjustments to the Utility Network rulebase. What is UPDM UPDM is a geodatabase data model template for operators of pipe networks in the gas and hazardous liquids industries. UPDM is a moderately normalized data model that explicitly represents each physical component of a gas pipe network from the wellhead to the customer meter, or a hazardous liquids pipe network from the wellhead to the terminal or delivery point, in a single database table object.     UPDM is the only industry model which can manage a single representation of the entire pipe system.  For many companies around the world this single data repository aligns well with enterprise practices to vertically integrate business processes and operations.  Why UPDM The goal of the Esri UPDM is to make it easier, quicker, and more cost-effective for pipeline operators and gas utilities to implement the ArcGIS platform. The Esri UPDM accomplishes this by freely providing a data model that takes full advantage of the capabilities of the geodatabase. The data model is created and tested with ArcGIS products to ensure that it works. This significantly reduces the complexity, time, and cost to implement a spatially enabled hazardous liquid or gas pipe system data repository. Looking Forward to UPDM 2022 A wise man once said “change is the only constant.” This is a great quote when thinking about UPDM going forward. The Esri development team will continue to enhance the capabilities of ArcGIS. Industry will continue to evolve its practices. To continue adjusting to industry practices and incorporating new ArcGIS capabilities, UPDM will continue to evolve.  This evolution will help assure gas utilities and pipeline operators that their GIS industry-specific data model is current with their needs. PLEASE NOTE: The postings on this site are our own and don’t necessarily represent Esri’s position, strategies, or opinions.       ... View more

  By Tom DeWitte and Tom Coolidge The natural gas industry’s drive to a lower or net zero carbon footprint is focused on actions that generally can be organized in three groups: (1) reducing methane emissions from operations, (2) reducing methane emissions from customer consumption, and (3) embracing new sources and uses of alternative lower carbon fuels. This blog focuses on a key activity central to reducing methane emissions from operations – finding and fixing leaks. Managing methane emissions is without a doubt as critical a set of tasks as any every natural gas pipe organization performs.  For the owners and operators of the pipe networks which transport this critical energy source, managing methane emissions starts with surveillance programs to identify leaks. Class 1 leaks are immediately fixed. But low-level leaks are studied via engineering analysis to rank the risk and consequence of those leaks. Once analyzed, construction activities can be scheduled to repair the identified and qualified leaks. This very straightforward set of tasks to “find the leak” and to “fix the leak” have been going on for decades. So, what’s new in the world of methane emission reduction? What’s new for methane emission reduction is the availability of new sensor platforms to “find the leak”. Many Platforms for Detecting Methane Finding the leak has long been a human-intensive process of walking the pipe network. While walking, field technicians will carry methane emission detectors. Once a leak is found, a gas leak report is created to document the location, quantity, and likely source of the leak. Then came the ability to mount methane sensors on cars and trucks. These vehicles could cover more miles of pipe than the walkers. Advancements by manufacturers of these devices have further enhanced these vehicle- mounted sensors to better estimate the location of the source of the leak. These potential leaks are then investigated by field technicians to verify and complete the gas leak report.   More recently, methane emission sensors have been mounted on aerial platforms ranging from drones to helicopters to planes. In addition to aerial mounted sensors, there are now satellites circling our planet in low earth orbit with methane sensors. These satellite-based sensors sell their data and analytics, providing gas pipe operators with yet another capability to survey the pipe network and “find the leak”. Still Need Those Humans These newer tools to surveil the pipe network do not replace the regulatory requirements of Leak Survey. But they do provide a new affordable capability for higher frequency monitoring. Then when a methane emission is detected, a field technician can be sent out to the emission area to validate the sensor reading and fill out the gas leak report with the details needed for the organization to “fix the Leak”. As powerful as these additional methane detections are, humans are still required to verify and document every leak. Need a Common Repository Adding additional platforms of methane detection opens new opportunities to reduce methane emissions.  It also creates a new, but manageable, data management issue. With these new sensor platforms to identify potential methane emissions, a common data management system is needed to capture, track, and document the status of these potential leaks. With location being critical to each potential leak, A geographical information system (GIS) is the best data management system for aggregating and managing this information.   Critical Information for Gas Pipe System Management Combining the information from these many methane detection platforms into a single data management system is not the finish line.  It is at best mid span. This data still needs to flow into analytical programs such as DIMP and TIMP. This data still needs to be monitored with dashboards, summarized with reports, and communicated to gas executives, regulators, and the public. This data needs to flow to construction and mobile viewing devices enabling the gas organization to “fix the Leak”. That is the finish line. The natural gas industry is enjoying a technological revolution. New technologies are continuously being applied to better solve gas industry problems.  And the gas industry is embracing and implementing these new capabilities at a faster pace than ever before.  Placing your GIS at the center of your methane emission management ensures the data collected by all these new sensor platform technologies is fully leveraged throughout your gas organization. PLEASE NOTE: The postings on this site are our own and don’t necessarily represent Esri’s position, strategies, or opinions. ... View more