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PHYSICS, MACHINES AND DATA




PAICE BUILDER USE CASE: PREDICTIVE ROD PUMP ANALYTICS IMPROVE EFFICIENCY, OIL
PRODUCTION, AND REVENUE

SEPTEMBER 20, 2021  Author: Abhishek Bihani Category: Thought Leadership

Like any manufacturer, upstream oil and gas producers continually seek the next
great efficiency improvement. They understand that even fractional gains in core
equipment efficiency can yield substantial benefits to production, revenue, and
equipment life. For oil producers, sucker rod pumps (SRPs), also known as beam
pumps, are under constant scrutiny for this reason.

SRP systems are the most widely used method of artificial lift in onshore oil
production. Designed for use on wells that cannot produce the well fluid on
their own, which is by far the majority, the systems consist of many surface and
subsurface components that must be periodically maintained to optimize
operational efficiency.

Sub-optimal production in a single well can cost a producer thousands of dollars
per day in lost revenue. Additionally, damaged rod pumping systems require
extremely expensive repair processes called workovers, making predictive
maintenance essential. A new approach was needed to accelerate efficiency
improvements.

Tignis developed a machine learning (ML) model using PAICe Builder to
demonstrate how easy it is to apply advanced analytics to automatically detect
sudden efficiency losses and emerging equipment issues. The SRP analytics built
into Tignis’ PAICe Builder monitor the rod pump cycle, identify anomalies,
trigger alerts, and quantify the production losses and impact on revenue on a
pump-by-pump basis. The potential impact on the bottom line is considerable.

Performance and diagnostic challenges

Any number of pump or rod issues can lead to failure or a drop in SRP process
efficiency, such as pump valve leaks, bent rods, wear, corrosion, insufficient
liquid supply, fluid buildup, gas interference, and sand production. These
failure modes can present themselves in different ways for each pump or failure,
making it difficult to write legacy condition-based monitoring alerts.

A dynamometer is one commonly used device to monitor SRP operation. These
devices plot the SRP’s rod load versus position through every cycle, which can
be compared to an ideal dynamograph to monitor if a pump is behaving normally.
Although this method is considered to be very adept at catching SRP issues, the
solution can be difficult to implement and maintain. Each SRP’s ideal
dynamograph must be tuned individually. Additionally, dynamographs must be
re-tuned as the reservoir and well properties, such as crude viscosity or fluid
ratios, change over time, resulting in significant maintenance costs.

One proposed methodology of monitoring SRPs is utilizing pure physics-based
models of the pump and well to produce the ideal process state and comparing the
ideal to real-time sensor data from the well. However, in practice, this
solution is not practical. Physics-based models of sucker rod pumps are
computationally taxing and cannot be run in real time on currently available
hardware. Calculations of a single cycle of the pump using readily available
physics-based models can take more than a minute to compute, meaning that the
calculation cannot keep up with the incoming real-time data.

Real-time analytics alternative

Tignis enables an automated approach to SRP monitoring by providing a tool that
allows process/operations engineers to quickly build real-time ML analytics on
physical systems and bring data-driven decision support to challenges across the
operation. To provide an example, a new rod pump analytics model was built into
PAICe Builder, powered by our proprietary Digital Twin Query Language (DTQL),
which helps automate detection and notification of SRP inefficiencies by
converting the SRP surface load and position into time-series data and running
ML-based predictive models against it in real time.

A key advantage of this method is that ideal operation of the SRP is based on
the pump’s historic behavior, meaning the analytic does not require manual setup
and tuning for each pump it is applied to. This analysis can be broadly applied
to many SRP manufacturers and applications. In addition, the model continues to
tune itself over time using new incoming data. This method allows the quick and
efficient detection of changes in well efficiency or operation.

Another advantage of using PAICe Builder for SRP analytics is that the embedded
machine learning drastically increases calculation times of ideal variables.
When trained on historic data, machine learning can predict ideal process
states, similar to a physics model, but much more efficiently. This enables
monitoring to happen in real time as data is being produced.

Not only does PAICe Builder uncover efficiency anomalies and immediately alert
the operations crew, but it also allows engineers to translate the efficiency
loss to the loss in barrels per day (bbl/day) produced, and then translates that
oil loss to the loss in potential revenue per day. All this supporting data is
included with the alert to help prioritize actions on SRP abnormalities.

Quantifying substantial business value 



The SRP analytics model targets conditions that cause insufficient pump rates,
such as friction between the pump and other subsurface components, equipment
wear, and corrosion. The figure above illustrates how dynamograph data reflects
the abrupt change in position and load behavior when a pump’s rate, or strokes
per minute (SPM), drops after six minutes of normal operation. Using a simple
rule set created in the PAICe Builder app, deviations like this are handled in
three steps:

1. Available sensor data, in this case the annular fluid height in the well,
flow rate out of the well, and surface pump position, is monitored to detect in
real time when a significant change in surface load behavior occurs and its
relationship to fluid production behavior, at which point an alert is issued
with this information and the following supplemental data points.

 



 

2. The analytics determine the ideal flow rate (846 bbl/day) had the anomaly not
occurred, as compared to the actual flow rate (506 bbl/day) as a result of the
anomaly, which translates to a production loss of 340 barrels of oil per day.

 



 

3. The resultant daily revenue loss is computed by multiplying the production
loss amount by oil price data that is either manually input or streamed into
PAICe Builder. In this case, at $60 a barrel, the theoretical 40% loss in pump
efficiency equates to $20,400 of lost revenue per day.

Armed with this knowledge, corrective actions can be prioritized based on their
value to the operation. Simple connectivity to data visualization tools such as
OSIsoft’s PI Vision allows operators to rank order the rod pumps by those
causing the most immediate or substantial revenue loss, allowing them to
concentrate their efforts to maximize production and profitability.

From this example, it is easy to see how early detection of efficiency anomalies
can save thousands of dollars per day. Additionally, the SRP analytics reveal
issues that don’t immediately impact oil production but can shorten pump life or
cause expensive repairs, allowing predictive maintenance to occur before
failure. One such example is a pump-off condition where the pump fills with
insufficient fluid during upstroke. This condition can lead to fluid pound,
causing accelerated stress and fatigue of subsurface SRP equipment, ultimately
leading to a premature need to perform an expensive workover of the pump and
other subsurface equipment.

The simplicity of the solution is also evident in how the analytics are
applicable to a broad range of rod pumps. The custom ML model can be deployed to
any rod pump that has sensor data, regardless of its manufacturer, rating, or
geographic location.

The SRP model is just one example of how, using PAICe Builder, customers can
quickly build a wide range of analytics for themselves to address whatever
process challenges they are facing. ML analytics use cases like this one make it
clear why artificial intelligence (AI) is the future of process control


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