Method to Recommend Design Practices that Increase the Probability of Meeting Cementing Job Objectives

A method of training a machine learning process for constructing a wellbore, comprising:

• retrieving, by a machine learning process executing on a computer system, a completed cement placement procedure;
• identifying, by a machine learning classifier of the machine learning process, a format of the completed cement placement procedure, wherein the format comprises a job objective, a plurality of stages, a plurality of wellbore data, and a plurality of measured field data;
• retrieving, by the machine learning classifier, the plurality of stages from the completed cement placement procedure;
• producing, by the machine learning process, a combined methodology grade for each completed stage by comparing at least one completed stage of the plurality of stages to the plurality of measured field data corresponding to the completed stage, and wherein the combined methodology grade comprises a plurality of completed methodologies within the completed stage;
• validating, by the machine learning process, the combined methodology grade by comparing a predictive stage objective grade using the completed methodology grade to the completed stage objective grade to determine an error value, and wherein the completed stage objective grade comprises the plurality of measured field data, a completed job objective grade, and a plurality of completed stage objective grades;
• and
• training the machine learning process to reduce the error value.

A method of placing cement within a wellbore utilizing a historical database of wellbore construction, comprising:

• retrieving, by a managing application executing on a computer system, a final cement placement procedure comprising a series of sequential stages to achieve a job objective, a plurality of customer wellbore data, and a plurality of stage objectives, and wherein each stage of the final cement placement procedure includes one or more prescribed methodologies;
• transporting a cement blend and a plurality of cement pumping equipment to a well site, wherein the cement blend is included in the final cement placement procedure;
• beginning the final cement placement procedure by the managing application;
• retrieving, by the managing application, one or more data sets of periodic pumping data indicative of the final cement placement procedure;
• beginning, by the managing application, a current stage of the final cement placement procedure, wherein the current stage is one of the stages of the final cement placement procedure;
• alerting, by the managing application, if the one or more prescribed methodology included in the current stage of the final cementing placement procedure is not found;
• determining, by the managing application, a probability of achieving the stage objective without the prescribed methodology based on machine learning process by accessing a database of methodologies for cement placement procedures;
• recommending, by the machine learning process, one or more prescribed methodologies to apply to one or more steps within the current stage or subsequent to the current stage of the final cement placement procedure to increase a probability score above a threshold value;
• and
• continuing the final cement placement procedure, by the managing application, in response to the probability score being above the threshold value for achieving the job objective.