Groundwater monitoring and analytics are at the heart of optimizing operations, improved ROI,

and sustainable groundwater management.  Smart Water systems provide the foundation for

understanding past and current conditions, as well as predictive analysis.  Today’s smart water

technologies build on monitoring capabilities, and offer groundwater supply managers data-driven

intelligence to improve operational efficiency, resiliency, and sustainability efforts. 

 

The growth of the Internet of Things (IoT), cost-effective sensors, and powerful analytics and intelligence tools combine to provide the means for achieving these lofty goals.  However, realizing these outcomes demands an effective platform for data collection, transmission, storage, and analysis to deliver actionable outcomes.  This article briefly touches on important elements to consider with regard to smart water systems, then explores use cases in the business-critical areas of operational efficiency, resiliency, and sustainability.

   

The Right “Big Data”:  Let’s begin at the beginning – what data is needed.   Must-have metrics include groundwater depth, extraction volume (flowrate), and energy used to deliver groundwater to the surface.  This data set establishes a core set of direct-read metrics and allow the development of useful Key Performance Indicators (KPIs) that provide essential visibility

and analytics.  UAM employs sensors with a “light footprint” (e.g., sonic groundwater depth sensor)

that install easily and don’t impede an operator’s O&M.  Beyond this core data set, sensors that

provide motor/pump temperature and vibration data, water quality information such as turbidity,

critical lubricant levels, and other application-specific requirements round out tailored packages. 

It is critical that your data set is sufficient and of the proper quality to support end uses/decisions

(O&M, stakeholder and regulatory reporting, etc.).

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Telemetry:  Next, the data must be delivered to the cloud where it is organized and stored for

visualization and analysis.  Today’s gateways are capable of powerful edge computing, are compatible

with major industrial protocols, and are of rugged industrial design to operate in harsh environments. 

These traits translate to resilient, at-well hardware capable of comprehensive alerting/alarming and

delivering a time-based data set to the cloud via a reliable and uninterrupted cellular connection.

 

Visualization & Analytics:  While hardware is important, real value is delivered through effective

cloud-based collection, organization, visualization and analysis of the data.  UAM leverages

Microsoft’s Azure database infrastructure and its Power BI business intelligence platform to complete these crucial tasks.  Power BI, the sector-leading intelligence platform, affords UAM the ability to deliver a comprehensive set of interactive dashboards that extract and clearly present data and KPIs for informed decision-making.   The platform employs AI, machine learning, and advanced analytics not only to visualize information but also discover hidden, actionable insights that support better decision-making and maximize ROI.  Let’s look at a few related use cases, we’ll dive deeper in future articles.

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Operational Efficiency:   Improving operational efficiency encompasses many factors, including optimizing production – minimizing cost per unit volume while maximizing asset lifespan.   A fundamental KPI used in optimizing wellfield efficiency is specific capacity. Specific capacity is defined as the pumping rate (gpm) divided by drawdown in feet.  Specific capacity obtained just after a well is drilled and properly developed or rehabilitated establishes the baseline

for comparison against future values.  As the well’s performance

degrades, its specific capacity value drops, representing decreasing

flow and/or increasing drawdown and lost efficiency.  UAM’s Smart Well

supports evaluation of individual, group, or entire wellfield performance

for this KPI.

 

Use Case:  As many well-owners do, this use case operator

generally operated wells to significant reduced flow or failure. 

Accurate KPIs such as specific capacity were only available

with new or recently rehabbed wells.  With the addition of

a SmartWater system, real-time and historical KPIs, including

specific capacity are available.  Visualization and analytics for this important metric consists of a baseline value, real-time value,

and average values over user-selected timeframes.  Supplementing this data are KPI rate of change, variance against baseline, along with projected future values.  This set of KPIs and analytics

now give the wellfield manager a powerful decision-oriented data

set to optimize production, cost, and maintenance cycles.  Alerts

and alarms at key threshold values ensure failure risk and

sub-optimal performance are identified and addressed in a timely

manner.  When coupled with financial information, the operator

knows well-specific and wellfield values, impacts on costs, and

return on maintenance.

 

Resiliency:  Operational resiliency can broadly be defined as the

anticipation and avoidance of problems within the scope of the

operation, including workforce, operations, legal, financial,

compliance, environmental, safety, security, and other natural threats to the system. 

 

Use Case:  Many clients identify workforce/labor availability as a significant issue impacting their operations.  This is particularly true with today’s pandemic environment, workforce availability, and loss of institutional knowledge due to baby-boomer retirements.  In this use case, a water system with approximately 100 wells, has a full-time resource applied to managing well lubrication systems.  To ensure proper operation requires that the

maintenance resource continuously moves from well to well, servicing

each serially.  Due to this approach, there have been many instances of

significant equipment damage and downtime due to loss of lubricant

supply or line blockage. 

 

Smart Water systems can significantly reduce failure risk and level of effort

associated with this maintenance function via continuous ultrasonic level

monitoring using tank level and lubricant use rate metrics.  The later metric

identifies situations where lubricant levels are sufficient, but application rate

falls below required amounts due to line blockage/failure.  Machine learning

is also employed to identify variance in normal operational profiles.

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Sustainability:  Groundwater/aquifer depth and extraction volumes are vital metrics

used by water managers to support sustainability efforts.  A Smart Water system can

develop time intelligence-based visualization/analytics for these fundamental aquifer

sustainability metrics. 

Use Case:  For the use cases below, located in California’s Central Valley and involved

in SGMA, a single well is profiled – the first dashboard depicts minimum and maximum

groundwater levels along with median, first and third quartiles and depth variance for

a user-selected timeframe.  Developing this data is critical in evaluating sustainable

use of the aquifer.

 

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The following dashboard presents groundwater depth on a monthly basis for a single asset.  This type of evaluation can be done for individual wells, groups of wells, wellfields, and at the groundwater basin level.  Filtering allows evaluating data sets while wells are operating as well as when they are inactive.  As shown, this visualization provides significant insight into aquifer levels throughout the year.  This evaluation can also be used to support optimized Dispatch as well as providing insight into issues such as subsidence.

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The continuously growing capabilities of Power BI allow for extensive in-depth analysis of wellfield data.  Future articles will provide more detailed discussion of Smart Water’s suite of dashboards with use cases demonstrating their application.