Automated Electric Submersible Pump (ESP) failure analysis

A method for automating root cause identification of electric submersible pump (ESP) failures post-breakdown, the method comprising:

• receiving data associated with at least one ESP, the received data including numerical features, categorical features, and textual features;
• encoding the numerical features, the categorical features, and the textual features of the received data into a machine-readable format, the encoding including:
• encoding the numerical features using a data engineering pipeline to generate standardized numerical values;
• encoding the categorical features using one-hot vector encoding or cardinality encoding based on a threshold comparison against a number of categories;
• and
• encoding the textual features by:
• matching one or more non-repeated words for a first textual feature of the textual features to a set of pretrained oil and gas specific word embeddings to create a first matrix of vocabulary words and corresponding word embeddings;
• classifying failure components using the first matrix to generate a second matrix of trained word embeddings;
• matching the second matrix to one or more tokenized sentences;
• and
• reducing dimensionality of the matched second matrix to generate a third compressed embeddings matrix,
• wherein the classifying the failure components is performed with a bi-directional Long Short Term Memory (LSTM) recurrent neural network, and
• wherein the reducing the dimensionality of the matched second matrix is performed with an autoencoder neural network;
• and
• predicting at least one failure component class or category of the at least one ESP using the encoded numerical features, the encoded categorical features, and the encoded textual features with a trained multi-input multinomial classification model.

A method for automating root cause identification of electric submersible pump (ESP) failures post-breakdown, the method comprising:

• receiving data associated with at least one ESP, the data including numerical features, categorical features, and textual features;
• encoding the numerical features, the categorical features, and the textual features of the received data into a machine-readable format, the encoding including respective pathways for each of the numerical features, the categorical features, and the textual features, the pathways including:
• for the numerical features, encoding the numerical features using a data engineering pipeline to generate standardized numerical values;
• for the categorical features, encoding the categorical features using one-hot vector encoding or cardinality encoding based on a threshold comparison against a number of categories;
• and
• for the textual features, encoding the textual features by:
• matching one or more non-repeated words for a first textual feature of the textual features to a set of pretrained specific word embeddings to create a first matrix of vocabulary words and corresponding word embeddings;
• classifying failure components using the first matrix to generate a second matrix of trained word embeddings;
• matching the second matrix to one or more tokenized sentences;
• and
• reducing dimensionality of the matched second matrix to generate a third compressed embeddings matrix,
• wherein the classifying the failure components is performed with a bi-directional Long Short Term Memory (LSTM) recurrent neural network, and
• wherein the reducing the dimensionality of the matched second matrix is performed with an autoencoder neural network;
• and
• predicting at least one failure component of the at least one ESP using the encoded numerical features, the encoded categorical features, and the encoded textual features with a trained multi-input multinomial classification model.

An apparatus for automating root cause identification of electric submersible pump (ESP) failures post-breakdown, comprising:

• a computing system including one or more processors;
• and
• a non-transitory computer-readable medium storing program code that, upon execution by the one or more processors, causes the apparatus to:
• receive data associated with at least one ESP, the received data including numerical features, categorical features, and textual features;
• encode the numerical features, the categorical features, and the textual features of the received data into a machine-readable format, the encoding including:
• encoding the numerical features using a data engineering pipeline to generate standardized numerical values;
• encoding the categorical features using one-hot vector encoding or cardinality encoding based on a threshold comparison against a number of categories;
• and
• encoding the textual features by:
• matching one or more non-repeated words for a first textual feature of the textual features to a set of pretrained oil and gas specific word embeddings to create a first matrix of vocabulary words and corresponding word embeddings;
• classifying failure components using the first matrix to generate a second matrix of trained word embeddings;
• matching the second matrix to one or more tokenized sentences;
• and
• reducing dimensionality of the matched second matrix to generate a third compressed embeddings matrix,
• wherein the classifying the failure components is performed with a bi-directional Long Short Term Memory (LSTM) recurrent neural network, and
• wherein the reducing the dimensionality of the matched second matrix is performed with an autoencoder neural network;
• and
• predict at least one failure component of the at least one ESP using the encoded numerical features, the encoded categorical features, and the encoded textual features with a trained multi-input multinomial classification model,
• wherein the multi-input multinomial classification model has been trained using second numerical features, second categorical features, and second textual features from at least one other ESP.

An apparatus for automating root cause identification of electric submersible pump (ESP) failures post-breakdown, comprising:

• a computing system including one or more processors;
• and
• a memory storing program code that, when executed by the one or more processors, causes the one or more processors to:
• process, clean, and standardize received data associated with at least one ESP, the received data including numerical features, categorical features, and textual features;
• encode the numerical features, the categorical features, and the textual features of the received data into a machine-readable format, the encoding including:
• encoding the numerical features using a data engineering pipeline to generate standardized numerical values;
• encoding the categorical features using one-hot vector encoding or cardinality encoding based on a threshold comparison against a number of categories;
• and
• encoding the textual features by:
• matching one or more non-repeated words for a first textual feature of the textual features to a set of pretrained oil and gas specific word embeddings to create a first matrix of vocabulary words and corresponding word embeddings;
• classifying failure components using the first matrix to generate a second matrix of trained word embeddings;
• matching the second matrix to one or more tokenized sentences;
• and
• reducing dimensionality of the matched second matrix to generate a third compressed embeddings matrix,
• wherein the classifying the failure components is performed with a bi-directional Long Short Term Memory (LSTM) recurrent neural network, and
• wherein the reducing the dimensionality of the matched second matrix is performed with an autoencoder neural network;
• and
• predict at least one failure component of the at least one ESP using the encoded numerical features, the encoded categorical features, and the encoded textual features with a trained multi-input multinomial classification model,
• wherein the multi-input multinomial classification model has been trained using second numerical features, second categorical features, and second textual features from at least one other ESP.