SYSTEMS AND METHODS FOR FLUID END EARLY FAILURE PREDICTION
A method of monitoring hydraulic fracturing equipment includes training a machine learning model on training data obtained from a plurality of hydraulic fracturing operations. The training data includes a corpus of operational data associated with the hydraulic fracturing operations and corresponding health conditions associated with one or more hydraulic pump fluid ends. The method further includes receiving a set of operational data associated with an active hydraulic fracturing operation, processing the set of operational data using the trained machine learning model, and determining, based on the trained machine learning model and the input set of operational data, one or more estimated health conditions of a hydraulic pump fluid end used in the active hydraulic fracturing operation.
1 . A method of monitoring hydraulic fracturing equipment, comprising:
training a machine learning model on training data obtained from a plurality of hydraulic fracturing operations, the training data including a corpus of operational data associated with the hydraulic fracturing operations and corresponding health conditions associated with one or more hydraulic pump fluid ends;
receiving a set of operational data associated with an active hydraulic fracturing operation;
process the set of operational data using the trained machine learning model; and
determine, based on the trained machine learning model and the input set of operational data, one or more estimated health conditions of a hydraulic pump fluid end used in the active hydraulic fracturing operation.
2 . The method of claim 1 , wherein the set of operational data includes one or more of environmental conditions, equipment specifications, operating specifications, equipment hours, damage accumulation data, vibration parameters, temperature parameters, flow rate parameters, pressure parameters, speed, and motion counts associated with the active hydraulic fracturing operation.
3 . The method of claim 1 , wherein the one or more estimated health conditions of the hydraulic pump fluid end include an estimated time to failure.
4 . The method of claim 1 , wherein the one or more estimated health conditions of the hydraulic pump fluid end include indications associated with a plurality of different failure modes.
5 . The method of claim 4 , further comprising:
determining, from the trained machine learning model, which parameters of the set of operational data are correlated with certain failure modes.
6 . The method of claim 1 , further comprising:
receiving and processing the set of operational data through the machine learning model in real time; and
generating an alert indicating a predicted failure.
7 . The method of claim 1 , further comprising:
obtaining actual health and failure conditions of the hydraulic pump fluid end; and
updating the trained machine learning model by correlating the set of operational data with the actual health and failure conditions.
8 . A method of monitoring hydraulic fracturing equipment, comprising:
training a machine learning model on training data obtained from a plurality of hydraulic fracturing operations, the training data including a corpus of operational data associated with the hydraulic fracturing operations and corresponding health conditions associated with one or more hydraulic fracturing equipment;
receiving a set of operational data associated with an active hydraulic fracturing operation;
processing the set of operational data using the trained machine learning model; and
determining, based on the trained machine learning model and the input set of operational data, one or more estimated health conditions of a hydraulic fracturing equipment used in the active hydraulic fracturing operation.
9 . The method of claim 8 , wherein the set of operational data includes one or more of environmental conditions, equipment specifications, operating specifications, equipment hours, damage accumulation data, vibration parameters, temperature parameters, flow rate parameters, pressure parameters, speed, and motion counts associated with the active hydraulic fracturing operation.
10 . The method of claim 8 , wherein the one or more estimated health conditions of the hydraulic fracturing equipment include an estimated time to failure.
11 . The method of claim 8 , wherein the one or more estimated health conditions of the hydraulic fracturing equipment include indications associated with a plurality of different failure modes.
12 . The method of claim 11 , further comprising:
determining, from the trained machine learning model, which parameters of the set of operational data are correlated with certain failure modes.
13 . The method of claim 8 , further comprising:
receiving and processing the set of operational data through the machine learning model in real time; and
generating an alert indicating a predicted failure.
14 . The method of claim 8 , further comprising:
obtaining actual health and failure conditions of the hydraulic fracturing equipment; and
updating the trained machine learning model by correlating the set of operational data with the actual health and failure conditions.
15 . The method of claim 8 , wherein the hydraulic fracturing equipment includes at least one of a hydraulic pump, a fluid end, a power end, power generation equipment, pump iron, and manifold system.
16 . A hydraulic fracturing system, comprising:
a pump comprising a fluid end;
one or more additional hydraulic fracturing equipment;
a plurality of sensors configured to measure a plurality of operational parameters of the hydraulic fracturing system during an active hydraulic fracturing operation; and
a control system, the control system configured to:
receive a set of operational data associated with the active hydraulic fracturing operation, the set of operational data including the plurality of operational parameters;
process the set of operational data using a trained machine learning model; and
determine, based on the trained machine learning model and the set of operational data, one or more estimated health conditions of the fluid end.
17 . The system of claim 16 , wherein the set of operational data includes one or more of environmental conditions, equipment specifications, operating specifications, equipment hours, damage accumulation data, vibration parameters, temperature parameters, flow rate parameters, pressure parameters, speed, and motion counts associated with the active hydraulic fracturing operation.
18 . The system of claim 16 , wherein the trained machine learning model utilizes training data, the training data including a corpus of historical operational data associated with historical hydraulic fracturing operations and corresponding health conditions associated with one or more hydraulic pump fluid ends used in the historical hydraulic fracturing operations, respectively.
19 . The method of claim 16 , wherein the one or more estimated health conditions of the fluid end include an estimated time to failure.
20 . The method of claim 16 , wherein the one or more estimated health conditions of the hydraulic fracturing equipment include indications associated with a plurality of different failure modes, and wherein the trained machine learning model indicates which parameters of the set of operational data are correlated with certain failure modes.