Patent Publication Number: US-2023136646-A1

Title: Real time maximum horizontal stress calibration based on predicted caliper log while drilling

Description:
TECHNICAL FIELD 
     The present disclosure applies to monitoring parameters and optimizing conditions while drilling, such as drilling an oil well. 
     BACKGROUND 
     Maximum horizontal stress is one of the most critical parameters affecting the stability of a wellbore during drilling operations. Conventional systems typically do not provide direct measurements to validate the magnitude of maximum horizontal stress. When no direct measurement can be verified, the industry relies on theoretical solutions, which adds uncertainty to field operations. 
     SUMMARY 
     The present disclosure describes techniques that can be used for correcting the maximum horizontal stress value in real time while drilling and subsequently accounting for its effect when calculating the optimum mud weight. In some implementations, a computer-implemented method includes the following. A predicted breakout geometry is determined for a drilling operation of a petrochemical well. Determining the predicted breakout geometry uses an analytical elastic breakout model and includes determining a predicted breakout width, a predicted breakout depth, and a predicted breakout angle. The predicted breakout geometry is compared with an observed breakout geometry at an observed breakout angle determined in real time using real-time caliper log data obtained from a multi-finger caliper during the drilling operation. A maximum horizontal stress value in the analytical elastic breakout model is adjusted until the predicted breakout geometry matches the observed breakout geometry within a percentage threshold. Mud weight calculations for the drilling operation are updated in response to the comparing and adjusting. Drilling parameters for the drilling operation are changed in real time in response to the updating. 
     The previously described implementation is implementable using a computer-implemented method; a non-transitory, computer-readable medium storing computer-readable instructions to perform the computer-implemented method; and a computer-implemented system including a computer memory interoperably coupled with a hardware processor configured to perform the computer-implemented method, the instructions stored on the non-transitory, computer-readable medium. 
     The subject matter described in this specification can be implemented in particular implementations, so as to realize one or more of the following advantages. The magnitude of the maximum horizontal stress can be updated in real time while drilling. The term real-time can correspond to events that occur, for example, within a specified period of time, such as within a few seconds or a few minutes. This provides an advantage over conventional techniques that rely on equations to calculate the maximum horizontal stress when no direct measurement is available. Techniques can correct for the magnitude of maximum horizontal stress and subsequently provide updates to collapse and fracture mud weight pressure measurements. The techniques provide continuous measurements of breakout geometry and updates on the maximum horizontal stress. Corrected stress values can potentially be used in hydraulic fracturing applications. 
     The details of one or more implementations of the subject matter of this specification are set forth in the Detailed Description, the accompanying drawings, and the claims. Other features, aspects, and advantages of the subject matter will become apparent from the Detailed Description, the claims, and the accompanying drawings. 
    
    
     
       DESCRIPTION OF DRAWINGS 
         FIG.  1 A  is a diagram showing examples of a borehole breakout width/angle in conventional systems, according to some implementations of the present disclosure. 
         FIG.  1 B  includes a graph showing consistence in a theoretical plot and an experimental plot of breakout width, according to some implementations of the present disclosure. 
         FIG.  2    is a diagram of an example of a graph showing examples of breakout and in-situ stresses of a borehole in conventional systems, according to some implementations of the present disclosure. 
         FIG.  3    is a diagram showing an example of an elastic breakout model prediction, according to some implementations of the present disclosure. 
         FIG.  4    is a diagram showing an example of a process for modifying and correcting the magnitude of the maximum horizontal stress, according to some implementations of the present disclosure. 
         FIG.  5    is a graph showing an example of a caliper log without the calibration of maximum horizontal stress, according to some implementations of the present disclosure. 
         FIG.  6    is a graph showing an example of a caliper log with the calibration of maximum horizontal stress, according to some implementations of the present disclosure. 
         FIG.  7    is a screen print showing an example of a mud weight window using original maximum horizontal stress, according to some implementations of the present disclosure. 
         FIG.  8    is a screen print showing an example of the mud weight window using a 5% increase in maximum horizontal stress, according to some implementations of the present disclosure. 
         FIG.  9    is a flowchart of an example of a method for performing real-time maximum horizontal stress calibration based on a predicted caliper log while drilling, according to some implementations of the present disclosure. 
         FIG.  10    is a block diagram illustrating an example computer system used to provide computational functionalities associated with described algorithms, methods, functions, processes, flows, and procedures as described in the present disclosure, according to some implementations of the present disclosure. 
     
    
    
     Like reference numbers and designations in the various drawings indicate like elements. 
     DETAILED DESCRIPTION 
     The following detailed description describes techniques for correcting the maximum horizontal stress value in real time while drilling and subsequently accounting for its effect when calculating the optimum mud weight. This includes calibrating the magnitude of maximum horizontal stress in real time while drilling, which is critical for optimizing mud weight during drilling. The term real-time can correspond to events that occur, for example, within a specified period of time, such as within a few seconds or a few minutes. Various modifications, alterations, and permutations of the disclosed implementations can be made and will be readily apparent to those of ordinary skill in the art, and the general principles defined may be applied to other implementations and applications, without departing from scope of the disclosure. In some instances, details unnecessary to obtain an understanding of the described subject matter may be omitted so as to not obscure one or more described implementations with unnecessary detail and inasmuch as such details are within the skill of one of ordinary skill in the art. The present disclosure is not intended to be limited to the described or illustrated implementations, but to be accorded the widest scope consistent with the described principles and features. 
     Conventional systems do not provide a direct measurement of the maximum horizontal stress to validate its magnitude while drilling. The techniques of the present disclosure provide for a calibration of the maximum horizontal stress in real-time, including determining the maximum horizontal stress from equations using to minimum horizontal stress (σ h ) and vertical stress (σ V ). The term real-time can correspond to events that occur, for example, within a specified period of time, such as within a few seconds or a few minutes. The maximum horizontal stress can be calibrated based on the predicted caliper log while drilling. The maximum horizontal stress magnitude can be corrected based on the breakout’s width recorded in the real time caliper log. The maximum horizontal stress magnitude can be adjusted until convergence occurs the breakout’s width on the predicted caliper and real-time caliper log. The modified value of the maximum horizontal stress can then be used to re-calculated the mud weights. 
       FIG.  1 A  is a diagram showing an example  100  of a borehole breakout width/angle in conventional systems, according to some implementations of the present disclosure. It is well observed and accepted that the borehole breakout width/angle is expected to remain stable after the initiation even though the breakout depth  102  may continue to grow, as shown in  FIG.  1 A . For this reason, breakout width/angle of a borehole  104  is often used to reversely calculate the maximum horizontal stress (for example, in vertical wells).  FIG.  1 B  includes a graph  150  showing consistence in a theoretical plot  152  and an experimental plot  154  of breakout width, according to some implementations of the present disclosure. In  FIG.  1 B , the maximum horizontal stress σ Hmax  is shown in a y- direction and the minimum horizontal stress is shown in a x-direction; θ b  is the breakout width/angle with center at x-axis]. Axis  156  shows the magnitude of the maximum horizontal stress σ Hmax . Axis  158  shows the corresponding breakout width/angles (θ b ) at different maximum horizontal stress (σ Hmax ), relative to equation  160 . 
       FIG.  2    is a diagram of an example of a graph  200  showing examples of breakout and in-situ stresses of a borehole in conventional systems, according to some implementations of the present disclosure. For example, the following equation can be used to back-calculate the maximum horizontal stress from the breakout width/angle: 
     
       
         
           
             
               σ 
               
                 H 
                 m 
                 a 
                 x 
               
             
             = 
             
               
                 u 
                 c 
                 s 
                 + 
                 Δ 
                 
                   P 
                   W 
                 
                 + 
                 2 
                 
                   P 
                   p 
                 
               
               
                 1 
                 − 
                 2 
                 cos 
                 2 
                 
                   θ 
                   b 
                 
               
             
             − 
             
               σ 
               
                 h 
                 m 
                 i 
                 n 
               
             
             
               
                 1 
                 + 
                 2 
                 cos 
                 2 
                 
                   θ 
                   b 
                 
               
               
                 1 
                 − 
                 2 
                 cos 
                 2 
                 
                   θ 
                   b 
                 
               
             
           
         
       
     
      where ucs is the unconfined compressive strength; (σ H  and (σ h  are the maximum and minimum horizontal stresses  202  and  204 , respectively; θ b   206  is the wellbore angle measured from the maximum horizontal stress direction where the breakout starts; P p  is the formation pore pressure; and ΔP W  is the wellbore pressure above the formation pore pressure, as per equation  210 . θ b  is an angle  208  measured from the minimum horizontal stress to the edge of breakout; and P w  is a wellbore pressure  210 . 
       FIG.  3    is a diagram showing an example of an elastic breakout model prediction  300 , according to some implementations of the present disclosure. Crescent-shaped areas  302  represent a predicted breakout region. In this example, φ bo  is a predicted breakout angle  304  in degrees, and d bo  is a predicted caliper  306  (for example, 0.24 meters (m)). A drill bit diameter used for the elastic breakout model prediction  300  can be 0.2 m, for example. The elastic breakout model prediction  300  is plotted relative to a maximum horizontal stress (SH max )  308  and a minimum horizontal stress (SH min )  310 . 
       FIG.  4    is a diagram showing an example of a process  400  for modifying and correcting the magnitude of the maximum horizontal stress, according to some implementations of the present disclosure. The process  400  can be used in real-time while drilling vertical, horizontal, or inclined wells, for example. 
     At step  402 , breakout geometry is predicted using an analytical or numerical model, including predicting a breakout angle (or breakout width) and a breakout depth (with constitutive behavior of the rock known). For example, caliper data can be predicted using poroelastoplastic models. In this first step, input data  410  can be used that includes parameters such as a pore pressure, a minimum horizontal stress azimuth, and a tensile strength. The step can also use the maximum horizontal stress (σ h ), maximum vertical stress (σ V ), a cohesion friction angle UCS, and a Young Modulus Poisson’s ratio for horizontal wells which are typically drilled in the direction of minimum horizontal stress direction. The model uses the geomechanical properties from the 1D Mechanical Earth Model (MEM) , the constitutive behavior of the formation measured in the rock mechanics lab, as well as real-time data including equivalent circulating density (ECD) which can be converted into well pressure. The model can use any of the existing analytical solutions or solutions given by numerical models for computing effective stresses in combination with any of the shear failure criteria. Examples of analytical solutions for effective stresses include various elastic solutions and poroelastic solutions well-known to the industry. Examples of shear failure criteria include Mohr-Coulomb, Drucker-Prager, modified Lade, and Mogi-Coulomb techniques.  FIG.  3    illustrates a breakout prediction using an elastic solution in combination with the Mohr-Coulomb criterion, for example. A result of the first step is an initial estimation of σ H . 
     At step  404  includes comparing the real-time caliper log data (observed breakout’s depth and angle, for example) with a multi-finger caliper while drilling. This includes comparing a breakout’s depth observed in real time with a predicted breakout depth. Real-time caliper data  414  obtained from a data link  412  can be used in the calculations. 
     Assuming all of the rock properties that were used in the calculation of the predicted caliper were verified except for the maximum horizontal stress, At step  406  includes adjusting the maximum horizontal stress value in the breakout model until the predicted breakout geometry matches the one observed from the real-time caliper log data. For example, adjusting the maximum horizontal stress value can include making adjustments until matching is within a threshold, for example, 5% or less. Matching can be done by comparing only the breakout depth, or only the breakout angle, or both. By modifying the maximum horizontal stress magnitude in real-time to match the observed breakout geometry, a more accurate representation of the magnitude of the maximum horizontal stress can be obtained. The maximum horizontal stress of 1D mechanical earth modeling (1D MEM) (the numerical representation of rock mechanical properties and the state of in situ stresses along a borehole) can be updated in real-time, and the corrected values can be used to re-calculate the optimum mud weight while drilling. 
     If at step  406 , convergence has not occurred, then at step  416  the predicted breakout depth is adjusted by an adjustment percentage (for example, 1%). If the predicted break-out depth is greater than the real-time break-out depth, then the predicted break-out depth can be decreased by 1%. Otherwise, if the predicted break-out depth is less than the real-time break-out depth, then the predicted break-out depth can be increased by 1%. Then processing can proceed to step  408 . 
     At  408 , mud weight calculations are updated using a corrected σ H . The updated mud weight calculations can be used during drilling operations, for example, to make real-time changes in drilling parameters. 
     The maximum horizontal stress is one of the critical parameters in the 1D MEM that has a huge impact when calculating the critical collapse and fracture gradients. The maximum horizontal stress affects the mud weight window and the stability of the wellbore (as described with reference to  FIGS.  7  and  8   ). 
       FIG.  5    is a graph showing an example of a caliper log  500  without the calibration of maximum horizontal stress, according to some implementations of the present disclosure. The graph  500  includes a real-time caliper plot  502  and an uncorrected maximum horizontal stress plot  504 . The plots  502  and  504  are plotted relative to a caliper-measured borehole size  506  (for example, in inches) and a well depth  508  (for example, in feet). 
       FIG.  6    is a graph showing an example of a caliper log  600  with the calibration of maximum horizontal stress, according to some implementations of the present disclosure. The graph  600  includes a real-time caliper plot  602  and an corrected maximum horizontal stress plot  604 . The plots  602  and  604  are plotted relative to a caliper-measured borehole size  606  (for example, in inches) and a well depth  608  (for example, in feet). 
       FIG.  7    is a screen print showing an example of a mud weight window  700  using original maximum horizontal stress, according to some implementations of the present disclosure. The mud weight window  700  includes plots of a collapse gradient  702 , a fracture gradient  704 , a stable gradient  706 , a pore pressure  708 , a minimum horizontal stress  710 , a fracture mud weight (MW)  712 , and a collapse mud weight  714 . Elements of the mud weight window  700  are plotted relative to a wellbore inclination angle  716  (for example, in degrees) and a drilling mud weight  718  (for example, in pounds per cubic foot (pcf)). 
     In this example, the input summary  720  includes the following, for example. Analysis settings identify an elastic model (for example, no time involved), a borehole condition (for example, “impermeable”), and a radial ratio without breakout (for example, 1). Wellbore geometry information includes a true vertical depth (TVD) of 13,284 feet, a borehole radius of 0.24 feet, an average inclination of 87.49 degrees, and an average azimuth of 173.43 degrees. In situ stresses and pore pressure include an overburden stress gradient of 21.78 pounds per gallon (ppg), a maximum horizontal stress gradient of 27.54 ppg, a minimum horizontal stress gradient of 18.13 ppg, a pore pressure gradient of 10.15, and a maximum horizontal stress azimuth of 75 degrees. Elastic properties include a Young’s Modulus of 7.94 megapounds per square inch (Mpsi) and a Poisson’s ratio of 0.23. Failure criteria and strength properties include a failure condition (for example, Mohr-Coulomb), a cohesion of 2,342.15 pounds per square inch (psi), a friction angle of 42.83 degrees, and a tensile strength of 536.17 psi. 
       FIG.  8    is a screen print showing an example of the mud weight window using a 5% increase in maximum horizontal stress, according to some implementations of the present disclosure. The elements plotted in  FIG.  8    are plotted differently than in  FIG.  7   . 
     An input summary  720 , which can be displayed on the mud weight window  700 , identifies specific inputs to the mud weight window  700 , including the following, for example. Analysis settings identify an elastic model (for example, no time involved), a borehole condition (for example, “impermeable”), and a radial ratio without breakout (for example, 1). Wellbore geometry information includes a TVD of 13,284 feet, a borehole radius of 0.24 feet, an average inclination of 87.49 degrees, and an average azimuth of 173.43 degrees. In situ stresses and pore pressure include an overburden stress gradient of 21.78 ppg, a maximum horizontal stress gradient of 26.11 ppg, a minimum horizontal stress gradient of 18.13 ppg, a pore pressure gradient of 10.15, and a maximum horizontal stress azimuth of 75 degrees. Elastic properties include a Young’s Modulus of 7.94 Mpsi and a Poisson’s ratio of 0.23. Failure criteria and strength properties include a failure condition (for example, Mohr-Coulomb), a cohesion of 2,342.15 psi, a friction angle of 42.83 degrees, and a tensile strength of 536.17 psi. 
       FIG.  9    is a flowchart of an example of a method  900  for performing real-time maximum horizontal stress calibration based on a predicted caliper log while drilling, according to some implementations of the present disclosure. For clarity of presentation, the description that follows generally describes method  900  in the context of the other figures in this description. However, it will be understood that method  900  can be performed, for example, by any suitable system, environment, software, and hardware, or a combination of systems, environments, software, and hardware, as appropriate. In some implementations, various steps of method  900  can be run in parallel, in combination, in loops, or in any order. 
     At  902 , a predicted breakout geometry is determined for a drilling operation of a petrochemical well. Determining the predicted breakout geometry uses an analytical elastic breakout model and includes determining a predicted breakout width, a predicted breakout depth, and a predicted breakout angle. For example, determining the predicted breakout geometry can be based on a pore pressure, a maximum horizontal stress azimuth, a tensile strength, a maximum horizontal stress (σ h ), a maximum vertical stress (σ V ), a cohesion friction angle UCS, and a Young Modulus Poisson’s ratio. In some implementations, the analytical elastic breakout model can use geomechanical properties from a one-dimensional (1D) Mechanical Earth Model (MEM) and real-time data including an equivalent circulating density (ECD). The analytical elastic breakout model can support computing effective stresses in combination with shear failure criteria. For example, computing the effective stresses can include computing various elastic solutions and poroelastic solutions. The shear failure criteria can include Mohr-Coulomb, Drucker-Prager, modified Lade, and Mogi-Coulomb techniques, for example. From  902 , method  900  proceeds to  904 . 
     At  904 , the predicted breakout geometry is compared with an observed breakout geometry at an observed breakout angle determined in real time using real-time caliper log data obtained from a multi-finger caliper during the drilling operation. As an example, a breakout angle can be predicted in the breakout region of the crescent-shaped areas  302 , as described with reference to  FIG.  3   . From  904 , method  900  proceeds to  906 . 
     At  906 , a maximum horizontal stress value in the analytical elastic breakout model is adjusted until the predicted breakout geometry matches the observed breakout geometry within a percentage threshold. For example, adjusting the maximum horizontal stress value in the analytical elastic breakout model can include incrementally adjusting the predicted breakout depth by 1%. From  906 , method  900  proceeds to  908 . 
     At  908 , mud weight calculations for the drilling operation are updated in response to the comparing and adjusting. For example, mud weight calculations can be corrected as part of the process  400  which is described with reference to  FIG.  4   . From  908 , method  900  proceeds to  910 . 
     At  910 , drilling parameters for the drilling operation are changed in real time in response to the updating. For example, systems used to control drilling equipment can be provided with updated parameters based on suggested changes. After  910 , method  900  can stop. 
     In some implementations, in addition to (or in combination with) any previously-described features, techniques of the present disclosure can include the following. Customized user interfaces can present intermediate or final results of the above described processes to a user. The presented information can be presented in one or more of textual, tabular, or graphical format, such as through a dashboard. The information can be presented at one or more of on-site locations (such as at an oil well or other facility), on the Internet (such as on a webpage), on a mobile application (or “app”), or at a central processing facility. The presented information can include suggestions, such as suggested changes in parameters or processing inputs, that the user can select to implement improvements in a production environment, such as in the exploration, production, and/or testing of petrochemical processes or facilities. For example, the suggestions can include parameters that, when selected by the user, can cause a change or an improvement in drilling parameters (including speed and direction) or overall production of a gas or oil well. The suggestions, when implemented by the user, can improve the speed and accuracy of calculations, streamline processes, improve models, and solve problems related to efficiency, performance, safety, reliability, costs, downtime, and the need for human interaction. In some implementations, the suggestions can be implemented in real-time, such as to provide an immediate or near-immediate change in operations or in a model. The term real-time can correspond, for example, to events that occur within a specified period of time, such as within one minute or within one second. In some implementations, values of parameters or other variables that are determined can be used automatically (such as through using rules) to implement changes in oil or gas well exploration, production/drilling, or testing. For example, outputs of the present disclosure can be used as inputs to other equipment and/or systems at a facility. This can be especially useful for systems or various pieces of equipment that are located several meters or several miles apart, or are located in different countries or other jurisdictions. 
       FIG.  10    is a block diagram of an example computer system  1000  used to provide computational functionalities associated with described algorithms, methods, functions, processes, flows, and procedures described in the present disclosure, according to some implementations of the present disclosure. The illustrated computer  1002  is intended to encompass any computing device such as a server, a desktop computer, a laptop/notebook computer, a wireless data port, a smart phone, a personal data assistant (PDA), a tablet computing device, or one or more processors within these devices, including physical instances, virtual instances, or both. The computer  1002  can include input devices such as keypads, keyboards, and touch screens that can accept user information. Also, the computer  1002  can include output devices that can convey information associated with the operation of the computer  1002 . The information can include digital data, visual data, audio information, or a combination of information. The information can be presented in a graphical user interface (UI) (or GUI). 
     The computer  1002  can serve in a role as a client, a network component, a server, a database, a persistency, or components of a computer system for performing the subject matter described in the present disclosure. The illustrated computer  1002  is communicably coupled with a network  1030 . In some implementations, one or more components of the computer  1002  can be configured to operate within different environments, including cloud-computing-based environments, local environments, global environments, and combinations of environments. 
     At a top level, the computer  1002  is an electronic computing device operable to receive, transmit, process, store, and manage data and information associated with the described subject matter. According to some implementations, the computer  1002  can also include, or be communicably coupled with, an application server, an email server, a web server, a caching server, a streaming data server, or a combination of servers. 
     The computer  1002  can receive requests over network  1030  from a client application (for example, executing on another computer  1002 ). The computer  1002  can respond to the received requests by processing the received requests using software applications. Requests can also be sent to the computer  1002  from internal users (for example, from a command console), external (or third) parties, automated applications, entities, individuals, systems, and computers. 
     Each of the components of the computer  1002  can communicate using a system bus  1003 . In some implementations, any or all of the components of the computer  1002 , including hardware or software components, can interface with each other or the interface  1004  (or a combination of both) over the system bus  1003 . Interfaces can use an application programming interface (API)  1012 , a service layer  1013 , or a combination of the API  1012  and service layer  1013 . The API  1012  can include specifications for routines, data structures, and object classes. The API  1012  can be either computer-language independent or dependent. The API  1012  can refer to a complete interface, a single function, or a set of APIs. 
     The service layer  1013  can provide software services to the computer  1002  and other components (whether illustrated or not) that are communicably coupled to the computer  1002 . The functionality of the computer  1002  can be accessible for all service consumers using this service layer. Software services, such as those provided by the service layer  1013 , can provide reusable, defined functionalities through a defined interface. For example, the interface can be software written in JAVA, C++, or a language providing data in extensible markup language (XML) format. While illustrated as an integrated component of the computer  1002 , in alternative implementations, the API  1012  or the service layer  1013  can be stand-alone components in relation to other components of the computer  1002  and other components communicably coupled to the computer  1002 . Moreover, any or all parts of the API  1012  or the service layer  1013  can be implemented as child or sub-modules of another software module, enterprise application, or hardware module without departing from the scope of the present disclosure. 
     The computer  1002  includes an interface  1004 . Although illustrated as a single interface  1004  in  FIG.  10   , two or more interfaces  1004  can be used according to particular needs, desires, or particular implementations of the computer  1002  and the described functionality. The interface  1004  can be used by the computer  1002  for communicating with other systems that are connected to the network  1030  (whether illustrated or not) in a distributed environment. Generally, the interface  1004  can include, or be implemented using, logic encoded in software or hardware (or a combination of software and hardware) operable to communicate with the network  1030 . More specifically, the interface  1004  can include software supporting one or more communication protocols associated with communications. As such, the network  1030  or the interface’s hardware can be operable to communicate physical signals within and outside of the illustrated computer  1002 . 
     The computer  1002  includes a processor  1005 . Although illustrated as a single processor  1005  in  FIG.  10   , two or more processors  1005  can be used according to particular needs, desires, or particular implementations of the computer  1002  and the described functionality. Generally, the processor  1005  can execute instructions and can manipulate data to perform the operations of the computer  1002 , including operations using algorithms, methods, functions, processes, flows, and procedures as described in the present disclosure. 
     The computer  1002  also includes a database  1006  that can hold data for the computer  1002  and other components connected to the network  1030  (whether illustrated or not). For example, database  1006  can be an in-memory, conventional, or a database storing data consistent with the present disclosure. In some implementations, database  1006  can be a combination of two or more different database types (for example, hybrid in-memory and conventional databases) according to particular needs, desires, or particular implementations of the computer  1002  and the described functionality. Although illustrated as a single database  1006  in  FIG.  10   , two or more databases (of the same, different, or combination of types) can be used according to particular needs, desires, or particular implementations of the computer  1002  and the described functionality. While database  1006  is illustrated as an internal component of the computer  1002 , in alternative implementations, database  1006  can be external to the computer  1002 . 
     The computer  1002  also includes a memory  1007  that can hold data for the computer  1002  or a combination of components connected to the network  1030  (whether illustrated or not). Memory  1007  can store any data consistent with the present disclosure. In some implementations, memory  1007  can be a combination of two or more different types of memory (for example, a combination of semiconductor and magnetic storage) according to particular needs, desires, or particular implementations of the computer  1002  and the described functionality. Although illustrated as a single memory  1007  in  FIG.  10   , two or more memories  1007  (of the same, different, or combination of types) can be used according to particular needs, desires, or particular implementations of the computer  1002  and the described functionality. While memory  1007  is illustrated as an internal component of the computer  1002 , in alternative implementations, memory  1007  can be external to the computer  1002 . 
     The application  1008  can be an algorithmic software engine providing functionality according to particular needs, desires, or particular implementations of the computer  1002  and the described functionality. For example, application  1008  can serve as one or more components, modules, or applications. Further, although illustrated as a single application  1008 , the application  1008  can be implemented as multiple applications  1008  on the computer  1002 . In addition, although illustrated as internal to the computer  1002 , in alternative implementations, the application  1008  can be external to the computer  1002 . 
     The computer  1002  can also include a power supply  1014 . The power supply  1014  can include a rechargeable or non-rechargeable battery that can be configured to be either user- or non-user-replaceable. In some implementations, the power supply  1014  can include power-conversion and management circuits, including recharging, standby, and power management functionalities. In some implementations, the power-supply  1014  can include a power plug to allow the computer  1002  to be plugged into a wall socket or a power source to, for example, power the computer  1002  or recharge a rechargeable battery. 
     There can be any number of computers  1002  associated with, or external to, a computer system containing computer  1002 , with each computer  1002  communicating over network  1030 . Further, the terms “client,” “user,” and other appropriate terminology can be used interchangeably, as appropriate, without departing from the scope of the present disclosure. Moreover, the present disclosure contemplates that many users can use one computer  1002  and one user can use multiple computers  1002 . 
     Described implementations of the subject matter can include one or more features, alone or in combination. 
     For example, in a first implementation, a computer-implemented method includes the following. A predicted breakout geometry is determined for a drilling operation of a petrochemical well. Determining the predicted breakout geometry uses an analytical elastic breakout model and includes determining a predicted breakout width, a predicted breakout depth, and a predicted breakout angle. The predicted breakout geometry is compared with an observed breakout geometry at an observed breakout angle determined in real time using real-time caliper log data obtained from a multi-finger caliper during the drilling operation. A maximum horizontal stress value in the analytical elastic breakout model is adjusted until the predicted breakout geometry matches the observed breakout geometry within a percentage threshold. Mud weight calculations for the drilling operation are updated in response to the comparing and adjusting. Drilling parameters for the drilling operation are changed in real time in response to the updating. 
     The foregoing and other described implementations can each, optionally, include one or more of the following features: 
     A first feature, combinable with any of the following features, where determining the predicted breakout geometry is based on a pore pressure, a maximum horizontal stress azimuth, a tensile strength, a maximum horizontal stress (σ h ), a maximum vertical stress (σ V ), a cohesion friction angle UCS, and a Young Modulus Poisson’s ratio. 
     A second feature, combinable with any of the previous or following features, where the analytical elastic breakout model uses geomechanical properties from a one-dimensional (1D) Mechanical Earth Model (MEM) and real-time data including an equivalent circulating density (ECD). 
     A third feature, combinable with any of the previous or following features, where the analytical elastic breakout model supports computing effective stresses in combination with shear failure criteria. 
     A fourth feature, combinable with any of the previous or following features, where computing the effective stresses includes computing various elastic solutions and poroelastic solutions. 
     A fifth feature, combinable with any of the previous or following features, where the shear failure criteria include Mohr-Coulomb, Drucker-Prager, modified Lade, and Mogi-Coulomb techniques. 
     A sixth feature, combinable with any of the previous or following features, where adjusting the maximum horizontal stress value in the analytical elastic breakout model includes incrementally adjusting the predicted breakout depth by 1%. 
     In a second implementation, a non-transitory, computer-readable medium stores one or more instructions executable by a computer system to perform operations including the following. A predicted breakout geometry is determined for a drilling operation of a petrochemical well. Determining the predicted breakout geometry uses an analytical elastic breakout model and includes determining a predicted breakout width, a predicted breakout depth, and a predicted breakout angle. The predicted breakout geometry is compared with an observed breakout geometry at an observed breakout angle determined in real time using real-time caliper log data obtained from a multi-finger caliper during the drilling operation. A maximum horizontal stress value in the analytical elastic breakout model is adjusted until the predicted breakout geometry matches the observed breakout geometry within a percentage threshold. Mud weight calculations for the drilling operation are updated in response to the comparing and adjusting. Drilling parameters for the drilling operation are changed in real time in response to the updating. 
     The foregoing and other described implementations can each, optionally, include one or more of the following features: 
     A first feature, combinable with any of the following features, where determining the predicted breakout geometry is based on a pore pressure, a maximum horizontal stress azimuth, a tensile strength, a maximum horizontal stress (σ h ), a maximum vertical stress (σ V ), a cohesion friction angle UCS, and a Young Modulus Poisson’s ratio. 
     A second feature, combinable with any of the previous or following features, where the analytical elastic breakout model uses geomechanical properties from a one-dimensional (1D) Mechanical Earth Model (MEM) and real-time data including an equivalent circulating density (ECD). 
     A third feature, combinable with any of the previous or following features, where the analytical elastic breakout model supports computing effective stresses in combination with shear failure criteria. 
     A fourth feature, combinable with any of the previous or following features, where computing the effective stresses includes computing various elastic solutions and poroelastic solutions. 
     A fifth feature, combinable with any of the previous or following features, where the shear failure criteria include Mohr-Coulomb, Drucker-Prager, modified Lade, and Mogi-Coulomb techniques. 
     A sixth feature, combinable with any of the previous or following features, where adjusting the maximum horizontal stress value in the analytical elastic breakout model includes incrementally adjusting the predicted breakout depth by 1%. 
     In a third implementation, a computer-implemented system includes one or more processors and a non-transitory computer-readable storage medium coupled to the one or more processors and storing programming instructions for execution by the one or more processors. The programming instructions instruct the one or more processors to perform operations including the following. A predicted breakout geometry is determined for a drilling operation of a petrochemical well. Determining the predicted breakout geometry uses an analytical elastic breakout model and includes determining a predicted breakout width, a predicted breakout depth, and a predicted breakout angle. The predicted breakout geometry is compared with an observed breakout geometry at an observed breakout angle determined in real time using real-time caliper log data obtained from a multi-finger caliper during the drilling operation. A maximum horizontal stress value in the analytical elastic breakout model is adjusted until the predicted breakout geometry matches the observed breakout geometry within a percentage threshold. Mud weight calculations for the drilling operation are updated in response to the comparing and adjusting. Drilling parameters for the drilling operation are changed in real time in response to the updating. 
     The foregoing and other described implementations can each, optionally, include one or more of the following features: 
     A first feature, combinable with any of the following features, where determining the predicted breakout geometry is based on a pore pressure, a maximum horizontal stress azimuth, a tensile strength, a maximum horizontal stress (σ h ), a maximum vertical stress (σ V ), a cohesion friction angle UCS, and a Young Modulus Poisson’s ratio. 
     A second feature, combinable with any of the previous or following features, where the analytical elastic breakout model uses geomechanical properties from a one-dimensional (1D) Mechanical Earth Model (MEM) and real-time data including an equivalent circulating density (ECD). 
     A third feature, combinable with any of the previous or following features, where the analytical elastic breakout model supports computing effective stresses in combination with shear failure criteria. 
     A fourth feature, combinable with any of the previous or following features, where computing the effective stresses includes computing various elastic solutions and poroelastic solutions. 
     A fifth feature, combinable with any of the previous or following features, where the shear failure criteria include Mohr-Coulomb, Drucker-Prager, modified Lade, and Mogi-Coulomb techniques. 
     Implementations of the subject matter and the functional operations described in this specification can be implemented in digital electronic circuitry, in tangibly embodied computer software or firmware, in computer hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Software implementations of the described subject matter can be implemented as one or more computer programs. Each computer program can include one or more modules of computer program instructions encoded on a tangible, non-transitory, computer-readable computer-storage medium for execution by, or to control the operation of, data processing apparatus. Alternatively, or additionally, the program instructions can be encoded in/on an artificially generated propagated signal. For example, the signal can be a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to a suitable receiver apparatus for execution by a data processing apparatus. The computer-storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of computer-storage mediums. 
     The terms “data processing apparatus,” “computer,” and “electronic computer device” (or equivalent as understood by one of ordinary skill in the art) refer to data processing hardware. For example, a data processing apparatus can encompass all kinds of apparatuses, devices, and machines for processing data, including by way of example, a programmable processor, a computer, or multiple processors or computers. The apparatus can also include special purpose logic circuitry including, for example, a central processing unit (CPU), a field-programmable gate array (FPGA), or an application-specific integrated circuit (ASIC). In some implementations, the data processing apparatus or special purpose logic circuitry (or a combination of the data processing apparatus or special purpose logic circuitry) can be hardware- or software-based (or a combination of both hardware- and software-based). The apparatus can optionally include code that creates an execution environment for computer programs, for example, code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of execution environments. The present disclosure contemplates the use of data processing apparatuses with or without conventional operating systems, such as LINUX, UNIX, WINDOWS, MAC OS, ANDROID, or IOS. 
     A computer program, which can also be referred to or described as a program, software, a software application, a module, a software module, a script, or code, can be written in any form of programming language. Programming languages can include, for example, compiled languages, interpreted languages, declarative languages, or procedural languages. Programs can be deployed in any form, including as stand-alone programs, modules, components, subroutines, or units for use in a computing environment. A computer program can, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data, for example, one or more scripts stored in a markup language document, in a single file dedicated to the program in question, or in multiple coordinated files storing one or more modules, sub-programs, or portions of code. A computer program can be deployed for execution on one computer or on multiple computers that are located, for example, at one site or distributed across multiple sites that are interconnected by a communication network. While portions of the programs illustrated in the various figures may be shown as individual modules that implement the various features and functionality through various objects, methods, or processes, the programs can instead include a number of sub-modules, third-party services, components, and libraries. Conversely, the features and functionality of various components can be combined into single components as appropriate. Thresholds used to make computational determinations can be statically, dynamically, or both statically and dynamically determined. 
     The methods, processes, or logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform functions by operating on input data and generating output. The methods, processes, or logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, for example, a CPU, an FPGA, or an ASIC. 
     Computers suitable for the execution of a computer program can be based on one or more of general and special purpose microprocessors and other kinds of CPUs. The elements of a computer are a CPU for performing or executing instructions and one or more memory devices for storing instructions and data. Generally, a CPU can receive instructions and data from (and write data to) a memory. 
     Graphics processing units (GPUs) can also be used in combination with CPUs. The GPUs can provide specialized processing that occurs in parallel to processing performed by CPUs. The specialized processing can include artificial intelligence (AI) applications and processing, for example. GPUs can be used in GPU clusters or in multi-GPU computing. 
     A computer can include, or be operatively coupled to, one or more mass storage devices for storing data. In some implementations, a computer can receive data from, and transfer data to, the mass storage devices including, for example, magnetic, magneto-optical disks, or optical disks. Moreover, a computer can be embedded in another device, for example, a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive. 
     Computer-readable media (transitory or non-transitory, as appropriate) suitable for storing computer program instructions and data can include all forms of permanent/non-permanent and volatile/non-volatile memory, media, and memory devices. Computer-readable media can include, for example, semiconductor memory devices such as random access memory (RAM), read-only memory (ROM), phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and flash memory devices. Computer-readable media can also include, for example, magnetic devices such as tape, cartridges, cassettes, and internal/removable disks. Computer-readable media can also include magneto-optical disks and optical memory devices and technologies including, for example, digital video disc (DVD), CD-ROM, DVD+/-R, DVD-RAM, DVD-ROM, HD-DVD, and BLU-RAY. The memory can store various objects or data, including caches, classes, frameworks, applications, modules, backup data, jobs, web pages, web page templates, data structures, database tables, repositories, and dynamic information. Types of objects and data stored in memory can include parameters, variables, algorithms, instructions, rules, constraints, and references. Additionally, the memory can include logs, policies, security or access data, and reporting files. The processor and the memory can be supplemented by, or incorporated into, special purpose logic circuitry. 
     Implementations of the subject matter described in the present disclosure can be implemented on a computer having a display device for providing interaction with a user, including displaying information to (and receiving input from) the user. Types of display devices can include, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), a light-emitting diode (LED), and a plasma monitor. Display devices can include a keyboard and pointing devices including, for example, a mouse, a trackball, or a trackpad. User input can also be provided to the computer through the use of a touchscreen, such as a tablet computer surface with pressure sensitivity or a multi-touch screen using capacitive or electric sensing. Other kinds of devices can be used to provide for interaction with a user, including to receive user feedback including, for example, sensory feedback including visual feedback, auditory feedback, or tactile feedback. Input from the user can be received in the form of acoustic, speech, or tactile input. In addition, a computer can interact with a user by sending documents to, and receiving documents from, a device that the user uses. For example, the computer can send web pages to a web browser on a user’s client device in response to requests received from the web browser. 
     The term “graphical user interface,” or “GUI,” can be used in the singular or the plural to describe one or more graphical user interfaces and each of the displays of a particular graphical user interface. Therefore, a GUI can represent any graphical user interface, including, but not limited to, a web browser, a touch-screen, or a command line interface (CLI) that processes information and efficiently presents the information results to the user. In general, a GUI can include a plurality of user interface (UI) elements, some or all associated with a web browser, such as interactive fields, pull-down lists, and buttons. These and other UI elements can be related to or represent the functions of the web browser. 
     Implementations of the subject matter described in this specification can be implemented in a computing system that includes a back-end component, for example, as a data server, or that includes a middleware component, for example, an application server. Moreover, the computing system can include a front-end component, for example, a client computer having one or both of a graphical user interface or a Web browser through which a user can interact with the computer. The components of the system can be interconnected by any form or medium of wireline or wireless digital data communication (or a combination of data communication) in a communication network. Examples of communication networks include a local area network (LAN), a radio access network (RAN), a metropolitan area network (MAN), a wide area network (WAN), Worldwide Interoperability for Microwave Access (WIMAX), a wireless local area network (WLAN) (for example, using 802.11 a/b/g/n or 802.20 or a combination of protocols), all or a portion of the Internet, or any other communication system or systems at one or more locations (or a combination of communication networks). The network can communicate with, for example, Internet Protocol (IP) packets, frame relay frames, asynchronous transfer mode (ATM) cells, voice, video, data, or a combination of communication types between network addresses. 
     The computing system can include clients and servers. A client and server can generally be remote from each other and can typically interact through a communication network. The relationship of client and server can arise by virtue of computer programs running on the respective computers and having a client-server relationship. 
     Cluster file systems can be any file system type accessible from multiple servers for read and update. Locking or consistency tracking may not be necessary since the locking of exchange file system can be done at application layer. Furthermore, Unicode data files can be different from non-Unicode data files. 
     While this specification contains many specific implementation details, these should not be construed as limitations on the scope of what may be claimed, but rather as descriptions of features that may be specific to particular implementations. Certain features that are described in this specification in the context of separate implementations can also be implemented, in combination, in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations, separately, or in any suitable sub-combination. Moreover, although previously described features may be described as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can, in some cases, be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination. 
     Particular implementations of the subject matter have been described. Other implementations, alterations, and permutations of the described implementations are within the scope of the following claims as will be apparent to those skilled in the art. While operations are depicted in the drawings or claims in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed (some operations may be considered optional), to achieve desirable results. In certain circumstances, multitasking or parallel processing (or a combination of multitasking and parallel processing) may be advantageous and performed as deemed appropriate. 
     Moreover, the separation or integration of various system modules and components in the previously described implementations should not be understood as requiring such separation or integration in all implementations. It should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products. 
     Accordingly, the previously described example implementations do not define or constrain the present disclosure. Other changes, substitutions, and alterations are also possible without departing from the spirit and scope of the present disclosure. 
     Furthermore, any claimed implementation is considered to be applicable to at least a computer-implemented method; a non-transitory, computer-readable medium storing computer-readable instructions to perform the computer-implemented method; and a computer system including a computer memory interoperably coupled with a hardware processor configured to perform the computer-implemented method or the instructions stored on the non-transitory, computer-readable medium.