Flowstate LDS has multiple methods available for detection of possible leaks. In some scenarios, many methods perform well and can work together to provide corroboration of a leak alarm. In other scenarios, one method may work better than others in accommodating operational challenges on a line such as slack conditions or intermittent flow.
Our engineers will work with your team to assess your pipeline and make recommendations on the best methods to implement. The following gives a generalized overview of our approach to segment classification and solutions.
Class A segments are characterized by having consistent flow, minimal noise, and simple topology (inlets and outlets).
Because of these conditions, the Class A segment offers the least complexity and is therefore a candidate for most model types. For the same reason, Class A segments will also typically have faster detection times possible.
Class B segments are still fairly consistent and exhibit low levels of measurement noise. However, operational conditions such as intermittent flow may lead to complexities such as slack or packing/unpacking. In addition, shorter flow cycles or activity on the line may pose difficulties for methods like signature recognition.
For these reasons, some Class B segments may not be candidates for certain model types. In addition, detection times may be longer for Class B segments.
Class C segments posses many challenges for leak detection. Noisy data – often due to less accurate devices – can reduce the sensitivity possible for leak detection. Often flow in gathering systems is inconsistent and intermittent. Gathering systems may also have complex topology – with many injections complicating the hydraulics.
For these reasons, Class C segments are often very limited in the methods available to them and often over-short monitoring may be the only viable solution.
|Class||Data Failure||Over/Short||Rupture||SVB 2%||SVB 1%||Leak Signature|
Classifying a segment provides a starting point for the types of leak detection methods that will be developed and delivered. We will work with your team to determine both methods that will be used and the performance requirements that will be established (i.e. leak size, time to detection, and false alarm rate).
There may be other contributing factors on a segment that may influence which models are selected for a segment. These could include things such as:
Nominally, a minimum of three weeks of operational data is needed to use machine learning to train and test a model for a particular pipeline.
However, if the line is a Class B or Class C segment with irregular or inconsistent flow, a larger window of data will need to be collected to adequately learn the operations.