# US 2023/0055082 A1 · Method to Recommend Design Practices that Increase the Probability of Meeting Cementing Job Objectives > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2023/0055082 A1 - Document ID: US20230055082A1 - Status: Published application - Kind: A1 - Application number: 17/409,303 - Filed: 2021-08-23 - Published: 2023-02-23 - Earliest claimed: 2021-08-23 - Assignees: Halliburton Energy Services, Inc. - Applicants: Halliburton Energy Services, Inc. - Inventors: Siva Rama Krishna JANDHYALA; John Paul Bir SINGH; Krishna Babu YERUBANDI; Richard Frank VARGO, JR. - Web page: https://patentgrep.com/patent/US20230055082A1 ## CPC - E21B 33/14: for cementing casings into boreholes - G06F 30/27: using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model - E21B 2200/20: Computer models or simulations, e.g. for reservoirs under production, drill bits - E21B 2200/22: Fuzzy logic, artificial intelligence, neural networks or the like - E21B: EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A method of designing a cement pumping procedure of a wellbore isolation barrier with a design application receiving customer inputs comprising a plurality of wellbore data and a job objective. The design application can retrieve a cement pumping procedure from a data source comprising a series of sequential steps to achieve the job objective. The design application can load the customer inputs into the cement pumping procedure, access a database of best practices, calculate a probability score for achieving the job objective based on a model, and recommend modifying one or more steps of the cement pumping procedure with one or more best practices in response to the probability score being below a threshold. ## Claims ### Claim 1 A method of cementing in a wellbore penetrating a subterranean formation, comprising: receiving one or more customer inputs, by a design application executing on a computer system; initializing a design cement placement procedure, by the design application, by applying the customer inputs into a generic cement placement procedure; generating, by a machine learning process, a final cement placement procedure by modifying the design cement placement procedure with one or more prescribed methodologies in response to a probability value for the design cement placement procedure achieving a job objective with the one or more prescribed methodologies being greater than the probability value for the design cement placement procedure achieving a job objective without the one or more prescribed methodologies; and placing cement in the wellbore in accordance with the final cement placement procedure. ### Claim 2 (depends on 1) The method of claim 1, further comprising: retrieving the generic cement placement procedure, by the design application, from a database, wherein the generic cement placement procedure is a cement placement procedure comprising a series of sequential stages to achieve the job objective, wherein the stages comprise one or more steps with at least one generic methodology, and wherein the stages and the at least one generic methodology are machine learning inputs. ### Claim 3 (depends on 1) The method of claim 1, further comprising: calculating, by the design application, a bill of materials and a cement equipment list from the design cement placement procedure, and wherein the design application modifies the at least one generic methodology to a at least one design methodology with the bill of materials. ### Claim 4 (depends on 3) The method of claim 3, further comprising: modifying, by the design application, the design cement placement procedure by inputting the bill of materials, design cement placement procedure, and one or more customer inputs into one or more wellbore construction models, wherein the wellbore construction models comprise casing design model, casing stretch model, cement strength model, well control model, formation strength model, or dynamic loading model, wherein at least one wellbore construction model recommends modifying the bill of materials in response to a calculated design value being lower than a design threshold. ### Claim 5 (depends on 4) The method of claim 4, wherein: a machine learning process classifier identifies the at least one design methodology of the at least one stage of the design cement placement procedure; the machine learning process classifier determines a methodology grade for the design methodology within the database; the machine learning process classifier identifies a corresponding prescribed methodology within the database; wherein the machine learning process compares the methodology grade of the design methodology to the methodology grade of the corresponding prescribed methodology; and wherein the machine learning process retrieves the prescribed methodology in response to the prescribed methodology having a higher methodology grade. ### Claim 6 (depends on 5) The method of claim 5, further comprising: comparing, by the machine learning process, a first probability value for achieving a stage objective of at least one stage of the design cement placement procedure with at least one design methodology within at least one step of the stage to a second probability value for achieving the stage objective with a prescribed methodology, wherein the prescribed methodology is retrieved from the database, and wherein the prescribed methodology has a higher methodology grade than the design methodology; and replacing, by the machine learning process, the at least one design methodology within the at least one step of the stage with the prescribed methodology in response to the second probability value being greater than the first probability value for achieving a stage objective. ### Claim 7 (depends on 1) The method of claim 1, wherein: the one or more customer inputs comprise a plurality of wellbore data, a plurality of customer design inputs, and a job objective, and wherein the customer design inputs comprise a bill of materials and one or more construction plans. ### Claim 8 (depends on 7) The method of claim 7, wherein: the wellbore data comprises a wellbore location, a wellbore history, and a plurality of sensor data, wherein the wellbore location comprises well names, a lease location, global positioning satellite (GPS) coordinates, an internal designation, or a combination thereof; wherein the wellbore history comprises a wellbore survey, a wellbore drilling path, a wellbore production fluid analysis, a wellbore drilling fluid, and a wellbore construction history; and wherein the plurality of sensor data comprises well logging, wellbore production sample, and well control data. ### Claim 9 (depends on 7) The method of claim 7, wherein: the job objective comprises wellbore isolation, a location of top of cement, a kick off plug, a shoe test, or a combination thereof. ### Claim 10 (depends on 1) The method of claim 1, further comprising: transporting a cement blend and a plurality of cement pumping equipment to a well site, wherein the cement blend is included in the cement placement procedure; beginning the cement placement procedure by the design application; retrieving, by the design application, one or more data sets of periodic pumping data indicative of the cement placement procedure; mixing a cement slurry, by the cement pumping equipment, per the cement placement procedure; pumping the cement slurry per the cement placement procedure; alerting, by the design application, if the prescribed methodology included in the cementing placement procedure is not found; calculating, the machine learning process, a probability score of for achieving the job objective based on a probability model; recommending, by the machine learning process, one or more prescribed methodology to apply to one of more subsequent steps of the cement placement procedure to increase the probability score above a threshold value; and continuing the cement placement procedure, by the design application, in response to the probability score being above the threshold value for achieving the job objective. ### Claim 11 (depends on 1) The method of claim 1, further comprising: transporting a downhole tool to a well site, wherein the downhole tool is included in the cement placement procedure; beginning the cement placement procedure by the design application; coupling the downhole tool with a casing via the cement placement procedure; retrieving, by the design application, one or more data sets of periodic pumping data indicative of the cement placement procedure; alerting, by the design application, if a prescribed methodology included in the cementing placement procedure is not found; calculating, by the machine learning process, a probability score of for achieving the job objective based on a probability model; recommending, by the machine learning process, one or more prescribed methodologies to apply to one of more subsequent steps of the cement placement procedure to increase the probability score above a threshold value; and continuing the cement placement procedure, by the design application, in response to the probability score being above the threshold value for achieving the job objective. ### Claim 12 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. ### Claim 13 (depends on 12) The method of claim 12, wherein: the completed cement placement procedure comprises a series of sequential stages to achieve the job objective, and wherein each completed stage of the cement placement procedure includes at least one completed methodologies. ### Claim 14 (depends on 13) The method of claim 13, further comprising: identifying, by the machine learning classifier, at least one completed methodology within a completed stage of the completed cement placement procedure by comparing a training set of training stages comprising training methodologies to the completed stage comprising the least one completed methodologies of the completed cement placement procedure, wherein the least one completed methodology corresponding to the completed stage are inputs to the machine learning process. ### Claim 15 (depends on 14) The method of claim 14, further comprising: grading, by the machine learning process, the at least one completed methodologies corresponding to the completed stage by comparing the completed stage objective to a plurality of measured field data, and wherein the completed methodology grade comprises a weighting factor and a methodology value. ### Claim 16 (depends on 15) The method of claim 15, further comprising: validating, by the machine learning process, the completed methodology grade and corresponding completed stage by comparing the completed methodology grade to one or more prescribed methodologies within a database, and modifying the completed methodology grade in response to the completed methodology grade grading higher than the prescribed methodology. ### Claim 17 (depends on 15) The method of claim 15, further comprising: saving the methodology grade and corresponding completed stage to a database. ### Claim 18 (depends on 15) The method of claim 15, further comprising: training the machine learning process with supervised learning in response to the machine learning classifier recognizing the format of the completed cement placement procedure. ### Claim 19 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. ### Claim 20 (depends on 19) The method of claim 19, wherein the wellbore data comprises wellbore diameter, casing size, casing thread type, pressure, temperature, formation geology, and wellbore trajectory. ### Claim 21 (depends on 19) The method of claim 19, wherein the database is on the computer system, a local network, a local data source, or a remote data source; and wherein the remote data source is a server, a computer, a virtual computer, a virtual network function, or data storage device. ### Claim 22 (depends on 19) The method of claim 19, wherein the job objective comprises a successful test of a barrier, wherein the barrier is a cement sheath disposed within an annulus formed between an outside of a casing disposed within the well and an inner wall of the wellbore. ### Claim 23 (depends on 19) The method of claim 19, further comprising: transporting a downhole tool to a well site, wherein the downhole tool is included in the cement placement procedure; beginning the cement placement procedure by the managing application; coupling the downhole tool with a casing via the cement placement procedure; retrieving, by the managing application, one or more data sets of periodic pumping data indicative of the cement placement procedure; alerting, by the managing application, if a prescribed methodology included in the cementing placement procedure is not found; calculating, by the managing application, the probability score of for achieving the job objective based on the machine learning process; recommending, by the machine learning process, one or more prescribed methodology to apply to one of more subsequent steps of the cement placement procedure to increase the probability score above a threshold value; and continuing the cement pumping procedure, by the managing application, in response to the probability score being above the threshold value for achieving the job objective.