Method for automating the applicability of job-related lessons learned

A computer-implemented method of placing a wellbore treatment into a wellbore penetrating a subterranean formation, comprising:

• designing a job design, wherein the job design comprises an inventory of materials, a pumping procedure, an inventory of pumping equipment, or combinations thereof;
• applying a set of metadata tags from a training set of job observations to the job design by comparing the job design to a training set of job designs;
• comparing the set of metadata tags of the job design to a database of job observations;
• retrieving a set of relevant job observations from the database in response to a comparison value exceeding a threshold value;
• alerting a user device to the relevant job observations from the database;
• generating a level two job design by modifying a level one job design with one or more relevant job observations in response to a probability value for the level two job design achieving a job objective with the one or more relevant job observations being greater than the probability value for the level one job design achieving a job objective without the one or more relevant job observations;
• and
• placing wellbore treatment in the wellbore, using cementing equipment, in accordance with the level two job design,
• wherein the metadata tags are validated by comparing a first classification grade using a first combination of the metadata tags used to form a group of metadata tags to a second classification grade using a second combination of the metadata tags used to form the group of metadata tags, to determine an error value, and
• wherein a machine learning model is trained such that the error value is reduced, by modifying the first combination or the second combination.

A computer-implemented method of placing a wellbore treatment within a wellbore utilizing a job observation of a well servicing operation, comprising:

• retrieving a job design comprising a pumping procedure, a bill of materials, an inventory of assigned pumping units, an inventory of downhole tools, an inventory of chemicals, or combinations thereof, wherein the pumping procedure comprises a series of sequential stages to achieve a job objective;
• retrieving one or more datasets of periodic pumping data indicative of the pumping procedure;
• receiving at least one dataset indicative of a change to the pumping procedure;
• generating a job observation;
• comparing a combination of metadata tags, referring to the at least one dataset of the job observation to the combinations of metadata tags of a plurality of historical job observations in a database;
• retrieving a set of historical job observations from the database with the combination of metadata tags that exceed a comparison threshold value;
• determining a portion of historical pumping procedure that corresponds to the job observation by comparing a set of historical job designs and historical job reports that correspond to the historical job observations from the database;
• determining a probability of achieving the job objective with the portion of the historical pumping procedure by accessing the job reports within the database;
• modifying the portion of the pumping procedure that corresponds to the job observation with the portion of the historical pumping procedure that corresponds with the historical job observation;
• recommending the portion of the pumping procedure that corresponds with the job observation be replaced with the portion of the historical pumping procedure to increase a probability score above a threshold value;
• and
• continuing the pumping procedure in response to the probability score being above the threshold value for achieving the job objective,
• wherein the metadata tags are validated by comparing a first classification grade using a first combination of the metadata tags used to form a group of metadata tags to a second classification grade using a second combination of the metadata tags used to form the group of metadata tags, to determine an error value, and
• wherein a machine learning model is trained such that the error value is reduced, by modifying the first combination or the second combination.