Creating a proxy MEM based on reference wells

Abstract

A MSE-UCS correlation model of one or more reference wells is based on drilling parameters and well log data. The MSE-UCS correlation model is then used to generate a proxy mechanical earth model of an additional well being drilled using the drilling parameters of the additional well.

Description

CREATING A PROXY MEM BASED ON REFERENCE WELLSInventors: Cheolkyun Jeong, Sheng-hau Lin, Allan Reyes, Anke Simone Wendt, Vipin SharmaCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 560,277, entitled “Creating A Proxy MEM Based on Reference Wells” and filed March 1, 2024, which is incorporated by reference herein in its entirety.BACKGROUND

[0002] Mechanical Earth Models (“MEM”) relate to mechanical and petrophysical properties of geological formations. Engineers and geoscientists use MEMs to understand how rocks deform, and sometimes fail, in response to drilling, completion, and production operations. Conventional MEMs are generated by experts based on data obtained from wells drilled into the formations. MEM generation may include analyzing and interpreting well log data, which can be cost and time prohibitive to complete in every well drilled into a subsurface region. Creating conventional MEMs is therefore a time-consuming and expensive process. Thus, preparing a conventional MEM increases the time necessary to complete oilfield operations, since a MEM may be required to plan one or more drilling or completion operations. Additionally, the complexity and time-consuming nature of creating conventional MEMs limits their ability to be used to make real time decisions during the drilling of a well.

[0003] An exemplary output of a typical MEM is unconfined compressive strength (“UCS”) of the rock that is penetrated by the well. The UCS can be estimated from log data (such as acoustic log data) derived from sensors that are ran into the well. However, log data is not acquired in some wells.

[0004] Thus, there is a need to be able to estimate the UCS of the rock penetrated by a well based on drilling data.SUMMARY

[0005] Aspects of the present disclosure provide systems, apparatus, and methods for creating and using a correlation model that relates the mechanical specific energy (“MSE”) of a drilling operation of a well to the unconfined compressive strength (“UCS”) of the rock that is penetrated by the well. The correlation model relates the MSE to the UCS along the measureddepth (“MD”) of the well. In some aspects, the correlation model, along with the MSE calculated during the drilling of an additional well, are used to estimate the UCS of the rock that is penetrated by the additional well. In some aspects, the correlation model is used to estimate the UCS of the rock instead of acquiring log data from the additional well to estimate the UCS. In some aspects, the correlation model is used to facilitate the identification and implementation of remedial actions to change drilling parameters during the drilling of the additional well.

[0006] In an aspect, a method of drilling a well includes calculating a value of mechanical specific energy from drilling parameter data, the drilling parameter data obtained from one or more sensors while the well is being drilled. The method further includes estimating a value of unconfined compressive strength of rock penetrated by the well from the value of mechanical specific energy, and using the value of unconfined compressive strength to estimate a value of one or more geological formation properties of the rock penetrated by the well. The method further includes using the value of the one or more geological formation properties to determine a new value of a drilling parameter. The method further includes drilling the well based on the new value of the drilling parameter.

[0007] In another aspect, a method of drilling a well includes calculating a first mechanical specific energy along a measured depth of a first well from first drilling parameter data, the first drilling parameter data obtained from one or more sensors while the first well is being drilled. The method further includes calculating a first unconfined compressive strength of rock penetrated by the first well along the measured depth of the first well from first well log data, the first well log data obtained from one or more logging tools operated in the first well. The method further includes generating a correlation model from the first mechanical specific energy and the first unconfined compressive strength, and using the correlation model to estimate a second unconfined compressive strength of rock penetrated by a second well along a measured depth of the second well. The method further includes using the second unconfined compressive strength to estimate a value of one or more geological formation properties of the rock penetrated by the second well, and using the value of the one or more geological formation properties to determine a target value of a drilling parameter of the second well. The method further includes drilling the second well based on the target value of the drilling parameter.

[0008] In another aspect, a method includes calculating mechanical specific energy along a measured depth of a well from drilling parameter data, the drilling parameter data obtained from one or more sensors while the well is being drilled. The method further includes usingthe mechanical specific energy to estimate unconfined compressive strength of rock along the measured depth of the well. The method further includes using the unconfined compressive strength to estimate one or more geological formation properties of the rock along the measured depth of the well, and using the one or more geological formation properties to determine a strategy for stimulating the well. The method further includes stimulating the well based on the strategy.BRIEF DESCRIPTION OF DRAWINGS

[0009] The appended figures illustrate only exemplary embodiments and are therefore not to be considered limiting of the scope of the disclosure, as the disclosure may admit to other equally effective embodiments.

[0010] Figure 1 illustrates an example drilling site and wellbore system, according to one or more embodiments of the disclosure.

[0011] Figures 2A and 2B illustrate an exemplary flow chart of a method according to one or more embodiments of the disclosure.

[0012] Figure 3A illustrates a graph showing an identified Mechanical Specific Energy to Unconfined Compressive Strength (“MSE-UCS”) correlation of at least one reference well, according to one or more embodiments of the disclosure.

[0013] Figure 3B illustrates a graph showing the deviation track data of a reference well, according to one or more embodiments of the disclosure.

[0014] Figure 3C illustrates a graph showing the clustering of MSE data with respect to the drilling parameters of rotational speed and Weight On Bit (“WOB”), according to one or more embodiments of the disclosure.

[0015] Figure 4A illustrates a graph showing a first proposed MSE-UCS correlation model, according to one or more embodiments of the disclosure.

[0016] Figure 4B illustrates a graph showing a second proposed MSE-UCS correlation model, according to one or more embodiments of the disclosure.

[0017] Figure 5 illustrates generating a MSE-UCS correlation model based on an identified MSE-UCS correlation, the surface torque, rotational speed, inclination, and clustering of MSE data, according to one or more embodiments of the disclosure.

[0018] Figure 6 illustrates using the MSE-UCS correlation model to generate a proxy Mechanical Earth Model (“MEM”), according to one or more embodiments of the disclosure.

[0019] To facilitate understanding, identical reference numerals have been used, where possible, to designate identical elements that are common to the figures. It is contemplated that elements and features of one embodiment may be beneficially incorporated in other embodiments without further recitation.DETAILED DESCRIPTION

[0020] Aspects of the present disclosure provide systems, apparatus, and methods for creating a correlation model of the mechanical specific energy (“MSE”) obtained by analyzing drilling parameters of one or more reference wells and the unconfined compressive strength (“UCS”) of the drilled rock that is obtained from well log data from the one or more reference wells along the measured depth (“MD”) of the one or more reference wells. This correlation model (e.g., MSE-UCS correlation model) is used to generate a proxy Mechanical Earth Model (“MEM”) of one or more additional wells based on the only data collected from drilling parameters during the drilling of the one or more additional wells. In some embodiments, the correlation model, along with the MSE calculated during the drilling of an additional well, are used to monitor the drilling of the additional well. In some embodiments, the correlation model, along with the MSE calculated during the drilling of an additional well, are used to facilitate the identification and implementation of remedial actions to change drilling parameters during the drilling of the additional well.

[0021] Figure 1 illustrates an example drilling site 100 which has a drilling rig 105 (e.g., derrick) disposed over a field 101. As shown, a wellbore system 110 is being drilled into the subsurface region of the field 101 and into one or more target formations 102. As shown, the wellbore system 110 includes a mother well 120 and a plurality of lateral wells 130. Figure 1 illustrates that the plurality of lateral wells 130 includes a first lateral well 131 and a second lateral well 132. Additional lateral wells 130 may be drilled, such as a third lateral well 133 and a fourth lateral well 134 shown in dashed lines. The third lateral well 133 and fourth lateral well 134 represent additional wells that will be drilled into the one or more target formations 102. In some embodiments, any one or more of the first lateral well 131, the second lateral well 132, the third lateral well 133, or the fourth lateral well 134 may be drilled from a second mother well that is different from mother well 120. As will be explained below, the MSE-UCS correlation model can be created based on data obtained from prior drilled reference wells, suchas the first and second lateral wells 131, 132, to create a proxy MEM that is used during the drilling of the additional lateral wells 130, such as the third lateral well 133 and fourth lateral well 134.

[0022] One or more drill string including one or more tools are used to drill the mother well 120 and the first and second lateral wells 131, 132. An exemplary drill string 150 is shown extending into the mother well 120 that includes a drill bit 152 at the end of the drill string 150. The drilling rig 105 includes various gauges and sensors to record various drilling parameters as the drill string 150 drills a well. A control system 103 records the drilling parameters obtained during the drilling of the wellbore system 110, including data on the drilling of the mother well 120 and first and second lateral wells 131, 132. The drilling parameters include any one or more of borehole area (Areag), bit diameter (D), weight on bit (“WOB”), weight on bit with the pump-off (“W0Be”), rotational speed of the drill string or drill bit (such as revolutions per minute — “RPM”), rate of penetration (“ROP”), torque, flow rate (“Q”), pressure drop through the bit nozzle (“APS”), or hydraulic efficiency (“Jj”), etc. Torque may be the measured surface torque (“STOR”) or the measured downhole torque. In some embodiments, the drilling parameters include any one or more of a mud density, a mud viscosity, a pumping pressure, an applied back pressure, or a mud flowrate. The sensors used to acquire the data associated with the drilling parameters may be located at the drilling rig 105 or in one or more tools of the drill string 150. For example, the WOB may be measured by a weight sensor 141, the RPM may be measured by an RPM sensor 142, and the torque may be measured by a torque sensor 143 coupled to the top drive or rotary table of the drilling rig 105. The drilling parameters may be indexed to the measured depth (“MD”) of the well at the time the data was recorded. At least some of these drilling parameters are used to calculate the MSE. The MSE is the amount of energy required to remove a unit volume of rock during the drilling process. Below are a few example equations to calculate the MSE based on drilling parameters:Exemplary MSE Equation 1:WOB 120 * 7T * RPM * T orque MSE = AreagA -reag* ROPExemplary MSE Equation 2:4 * WOB 480 * RPM * Torque MSE =nD2 +D2* ROPExemplary MSE Equation 3:W0B„ 120 * 7T * RPM * Torque 1154 * q * APg* Q MSE = - - + - - — + - - - — -AreagAreag* ROP Areag* ROPExemplary MSE Equation 4:WOB 120 * it * RPM * T MSE = 0.35 * (- - + - — - — )AreaBAreaB* ROP

[0023] Well log data may be obtained by deploying a logging tool into from the mother well 120 and the first and second lateral wells 131, 132. This well log data may be obtained by a logging tool, such as a logging while drilling (“LWD”) module in the drill string used to drill the well, such as the LWD module 154 of drill string 150 shown in Figure 1. In some embodiments, a logging tool is deployed into the well, such as a wireline logging tool, after drilling. For example, a logging tool may be deployed into the wellbore system 110 on a wireline to obtain well log data after the drill string 150 is removed from the wellbore system 110.

[0024] The logging tool may, for example, include acoustic instruments to obtain acoustic data. This acoustic data may be sonic measurements (e.g. compressional slowness (“DTCO”), compressional wave seismic velocity (“Vp”)). The data from the logging tool is used to determine the UCS of the rock along the wellbore, and the UCS may be indexed against the MD of the well at the time the data was recorded. Table 1, below, shows 10 different equations to calculate the UCS. However, these 10 equations form an exemplary, but non-exhaustive list. It is contemplated that other equations may be used to calculate the UCS from logging tool data.Table 1: Example UCS Equations

[0025] The correlation of MSE to UCS can be used to assist the understanding of the characteristics of the one or more layers, such as the target formation 102, within the field 101. However, establishing a MEM of the field 101 based on a certain relationship model between MSE and UCS is a significant challenge due to various factors complicating the correlation. The workflow to create conventional MEMs is notably time-intensive, requiring substantial expertise in data processing, geomechanical knowledge, and correlation identification.

[0026] Additionally, obtaining well log data in complex well trajectories is not always practical due to time, financial.. or techmcai constraints. For example, obtaining the well log data for each lateral well 130 may be cost and time prohibitive. Therefore, as disclosed herein, well log data from the one or more reference wells is analyzed and correlated with the MSE along the MD obtained by analyzing the drilling parameters of the one or more reference wells to generate a MSE-UCS correlation model. This MSE-UCS correlation model is then used tocreate a proxy MEM, where the proxy MEM estimates geomechanical properties in an additional well drilled into the field 101 using drilling parameter data obtained during the drilling of the additional well without obtaining the well log data for the additional well. Thus, the well log data obtained from a small number (e.g., one, two, or three) reference wells may be used to build a proxy MEM of a plurality of new additional wells.

[0027] Figures 2A and 2B illustrate an exemplary flow chart of an exemplary method 200. Method 200 includes a first group of operations 210 and a second group of operations 220. The first group of operations 210 includes operations 211, 212, 213, 214, and 215, which are directed to correlating the MSE to the UCS as determined along the MD of one or more reference wells (e.g., lateral wells 131, 132). The second group of operations 220 includes operations 221, 222, 223, 224, and 225, which are directed to using drilling parameter data obtained from an additional well (e.g., third or fourth lateral wells 133, 134) and the correlation of the MSE to the UCS of the one or more reference wells to generate a proxy MEM of the additional well.

[0028] Operation 211 includes obtaining drilling parameters from one or more reference wells, such as the first or second lateral wells 131, 132. Operation 211 includes indexing the drilling parameters to the MD of each corresponding reference well. The drilling parameters may be indexed to the MD based on an indexing rate, such as a depth-based indexing rate (e.g., every 0.5 units of MD, such as increments of every 0.5 ft (15.2 cm)).

[0029] Operation 212 includes calculating the MSE at each depth increment along the MD for each reference well based on the drilling parameters obtained at operation 211. The MSE may be calculated while the at least one reference well is being drilled. The calculated MSE is indexed to the MD of each respective reference well. For each reference well, operation 212 includes generating one or more sets of MSE data correlated to the MD of the respective reference well. In some embodiments, operation 212 includes generating a single set of MSE data indexed to the MD of each respective reference well. In some embodiments, operation 212 includes generating multiple sets of MSE data indexed to the MD of each reference well, each set representing an output of a different MSE equation. In some examples, operation 212 includes generating the multiple sets of MSE data with each set based on a different one of all four exemplary MSE equations.

[0030] Operation 213 includes obtaining well log data from the one or more reference wells. The well log data may be indexed to the MD of each corresponding reference well basedon an indexing rate, such as a depth-based indexing rate (e.g., every 0.5 units of MD, such as increments of every 0.5 ft (15.2 cm)). In some embodiments, the well log data and the drilling parameters are indexed to the MD of each corresponding reference well at the same depthbased indexing rate (e.g., every 0.5 units of MD, such as increments of every 0.5 ft (15.2 cm)). In some embodiments, the well log data and the drilling parameters are indexed to the MD of each corresponding reference well at different depth-based indexing rates. In some examples, the well log data is indexed at a higher depth-based indexing rate than the drilling parameters. In some examples, the well log data is indexed at a lower depth-based indexing rate than the drilling parameters.

[0031] Operation 214 includes calculating the UCS at each depth increment along the MD for each reference well based on the well log data obtained at operation 213. The UCS is indexed to the MD of each respective reference well. For each reference well, operation 214 includes generating one or more sets of UCS data correlated to the MD of the respective reference well. In some embodiments, operation 212 includes generating a single set of UCS data indexed to the MD of each respective reference well. In some embodiments, operation 212 includes generating multiple sets of UCS data indexed to the MD of each reference well, each set being output by a different UCS equation. For example, the UCS may be generated based on all 10 equations identified in Table 1.

[0032] Averaging and de-spiking techniques may be used during operation 212 and operation 214 to mitigate the effects of environmental factors that are unrelated to rock properties to ensure the accuracy of MSE and UCS data. Additionally, missing or null data points are addressed by a patching algorithm. For example, in some embodiments, for stable data channels, missing data is estimated by generating constant values, such as in segments where the data does not vary significantly.

[0033] Operation 212 and operation 214 may also include data processing procedures to correct for environment and instrument biases that would otherwise adversely impact the reliability of the MSE-UCS correlation model. For example, operation 212 and operation 214 may include calculating the MSE and UCS values using a quality control (“QC”) procedure to assess the data integrity of one or more input variables. For example, if the same MD is recorded at two different points in time, then the most recent recorded value may be assumed to be the more accurate value, thus establishing a minimum MD limit up to that point. The algorithm ensures that the MD consistently increases over time, such as by using a quality assurance (“QA”) algorithm during operation 212 and operation 214. The quality assurancealgorithm is used to detect and remove the anomalies in MSE and UCS data. Additionally, or alternatively, one or more mandatory input parameters may be defined for the drilling parameters used to calculate MSE. Table 2 illustrates an exemplary set of mandatory input parameters for the drilling parameters. In some examples, if the value of a particular drilling parameter is outside of the specified range, then the drilling parameter at the corresponding MD or point in time is excluded.Table 2

[0034] Operation 215 includes analyzing the MSE and UCS data using one or more algorithms, statistical techniques, modeling parameters, and / or factors to generate a MSE-UCS correlation model. Operation 215 includes pairing the UCS and MSE data is paired at each incremental value of MD. Operation 215 includes pairing also the UCS and MSE data for every combination of obtained UCS and MSE data sets, such as by pairing the MSE values output from Exemplary MSE Equation 1 with the UCS values output from each equation shown in Table 1. In some embodiments, operation 215 includes pairing UCS and MSE data from each reference well. Operation 215 includes analyzing each set of MSE and UCS data, to identify the closest relationship between MSE and UCS (e.g., identified MSE-UCS correlation). In some embodiments, operation 215 includes analyzing multiple MSE and UCS values produced by different associated equations to determine the most-closely related correlation. In some embodiments, the identified most-closely related MSE-UCS correlation is used as the MSE- UCS correlation model.

[0035] Once the identified MSE-UCS correlation is determined, the data is divided into distinct segments for scaling and fine-tuning, considering various factors such as drilling parameters, information derived from drilling parameters, and geological formations. A change point detection algorithm may be utilized to identify transition patterns in the MSE data and one or more drilling parameters, facilitating the application of different scaling factors to distinct data segments. In some embodiments, the change point detection algorithm may beused to identify transitions in the drilling efficiency. The change point algorithm can be used to identify the distinct segments that are subsequently scaled and fine-tuned.

[0036] The identified MSE-UCS correlation and various other factors are used to create the MSE-UCS correlation model. For example, consideration may be given to the well trajectory and other measurements (e.g., azimuthal data, surface torque, inclination of the wellbore, other drilling parameters, etc.) obtained from the reference wells to generate the MSE-UCS correlation model. For example, the trajectory of the well may change during drilling, and the trajectory of the well is also indexed to the MD. Thus, the trajectory may be used to account for a change in the values of the MSE and UCS in the identified MSE-UCS correlation at one or more values of the MD. Additionally, known properties of one or more geological formations that the reference well traverses may be taken into account, such as by considering the identified MSE-UCS correlation in light of the geological formation.

[0037] In some embodiments, operation 215 includes grouping and indexing the data to the MD based on an indexing rate, such as a depth-based indexing rate (e.g., 0.5 units of MD, such as increments of every 0.5 ft (15.2 cm)). In some embodiments, the indexing rate at operation 215 is the same as the indexing rate at operation 211 or at operation 213. In some embodiments, operation 215 includes generating the true vertical depth from the given measured depth, the azimuth data obtained from the drilling string, and the inclination data obtained from the drilling string. In some embodiments, operation 215 includes using the minimum curvature algorithm to compute the actual trajectory of the wellbore. In some embodiments, operation 215 includes using the computed trajectory as a factor when analyzing the identified MSE-UCS correlation to generate the MSE-UCS correlation model.

[0038] In some embodiments, operation 215 includes using a change point detection algorithm to automatically identify transition patterns in the correlation of the MSE to UCS within the identified MSE-UCS correlation, facilitating the application of different scaling factors to distinct data segments. Without being bound by theory, these distinct segments in the identified MSE-UCS correlation may be caused by a change in trajectory, geological formation, or a relationship with one or more drilling parameters. For example, operation 215 may include identifying changes in the drilling efficiency and use the drilling efficiency to facilitate application of different scaling factors to distinct data segments. Additionally, operation 215 may include using a clustering algorithm as an unsupervised machine learning method to perform the task of characterizing MSE patterns in the identified MSE-UCS correlation, taking into account the influences of various input data combinations. For example,the MSE data may be clustered based on one or more drilling parameters. Identifying clusters in the data helps to identify which the segments within the data and to explain UCS trends that corresponds with the MSE within the identified MSE-UCS correlation.

[0039] In some embodiments, operation 215 includes proposing a plurality, such as tens, hundreds, or thousands, of potential MSE-UCS correlation models based on different combinations of modeling parameters and / or factors. In some embodiments, operation 215 includes proposing potential MSE-UCS correlation models based on a differing UCS and MSE equations. Thus, operation 215 may include considering a broader range of models than could be realistically achieved manually to identify the most effective MEM based on the available data. Each proposed MSE-UCS correlation model may be scored based on a metric, such as a statistical analysis. In some examples, each proposed MSE-UCS correlation model may be scored based on a Pearson correlation, such as an Original Pearson Correlation or a Linear Regression Pearson correlation. In some examples, each of the proposed MSE-UCS correlation models may be evaluated by one or more of Root Mean Squared Error (“RMSE”), Mean Absolute Error (“MAE”), Kullback-Leibler divergence (“KL-divergence”), or Jensen-Shannon divergence (“JS-divergence”). In some examples, the MSE-UCS correlation model output by operation 215 may be the proposed MSE-UCS correlation model with the best score based on the metric used to evaluate the proposed MSE-UCS correlation models. In some embodiments, each proposed MSE-UCS correlation model may be evaluated based on multiple metrics to output the MSE-UCS correlation model.

[0040] The MSE-UCS correlation model derived in operation 215 is then used in the second group of operations 220. The second group of operations 220 includes operations 221 to 225. In some embodiments, method 200 includes the second group of operations 220 without performing the first group of operations 210. In some examples, the MSE-UCS correlation model created at operation 215 is pre-existing, and method 200 commences at operation 221.

[0041] At operation 221, drilling parameters are obtained during the drilling of an additional well (e.g., non-reference well), such as the third or fourth lateral wells 133, 134. The drilling parameters include any one or more of borehole area AreaB), bit diameter (D), weight on bit (“WOB”), weight on bit with the pump-off (“W0Be”), rotational speed of the drill string or drill bit (such as revolutions per minute — “RPM”), rate of penetration (“ROP”), torque, flow rate (“Q”), pressure drop through the bit nozzle (“APS”), or hydraulic efficiency (“? ”), etc. Torque may be the measured surface torque (“STOR”) or the measured downhole torque. In some embodiments, the drilling parameters of the additional well are input into the controlsystem 103. The drilling parameters of the additional well are indexed to the MD of the additional well. The drilling parameters may be indexed to the MD based on a an indexing rate, such as a depth-based indexing rate (e.g., every 0.5 units of MD, such as increments of every 0.5 ft (15.2 cm)).

[0042] Operation 222 includes calculating the MSE at each depth increment along the MD for the additional well based on the drilling parameters obtained from the additional well at operation 221. The MSE may be calculated while the additional well is being drilled. The calculated MSE is indexed to the MD of the additional well. Operation 222 includes generating one or more sets of MSE data correlated to the MD of the additional well. In some embodiments, operation 222 includes generating a single set of MSE data indexed to the MD of the additional well. In some embodiments, operation 222 includes generating multiple sets of MSE data indexed to the MD of the additional well, each set representing an output of a different MSE equation. For example, operation 222 may include generating the multiple sets of MSE data with each set based on a different one of all four exemplary MSE equations.

[0043] Operation 223 includes using the MSE-UCS correlation model from operation 215 (or a different pre-existing MSE-UCS correlation model) to analyze the MSE data obtained during the drilling of the additional well (e.g., operation 222) to create proxy (e.g., synthetic, pseudo) UCS data. In other words, the UCS data obtained from the reference well(s) is being used to estimate the UCS of the geological formation(s) penetrated during the drilling of the additional well based on the MSE obtained from the additional wells’ drilling parameters. Generating proxy UCS data saves cost and / or time in situations in which well log data is not being obtained during the drilling of the additional well.

[0044] The proxy UCS generated during operation 223 is an estimation of the UCS of the geological formation at each incremental value of the MD of the additional well based on the calculated MSE obtained during the drilling of the additional well at that MD as analyzed using the MSE-UCS correlation model. The proxy UCS, thus, may show the estimated UCS at each incremental value of the MD in real time during the drilling of the additional well. In other words, operation 223 is an iterative process, taking in MSE data as it is calculated from drilling parameters during operation 222 and applying the MSE data to the MSE-UCS correlation model to update the proxy UCS as the MD of the additional well increases.

[0045] In some embodiments, the calculation of the proxy UCS is depth-independent. In other words, the UCS value is not estimated based on the depth at which the MSE had beencalculated and compared to an identified MSE-UCS correlation model at that same or similar MD. Rather, a combination of factors, such as MSE values, torque, and RPM, may be considered. In some embodiments, operation 223 includes analyzing the MSE values of the additional well with a clustering algorithm to ascertain clusters in the data. The clustering patterns in the data may be a factor used to calculate the UCS. For example, the calculated MSE obtained in the additional well at a first MD range may be, upon analysis of the clustering algorithm, similar to a cluster pattern in the MSE data obtained from one or more reference wells at a second MD range. In some embodiments, operation 223 includes using the identified MSE-UCS correlation of the MSE-UCS correlation model for the cluster pattern at the second MD range to predict the UCS values (e.g., proxy UCS) of the additional well at the first MD range. In some embodiments, operation 223 includes identifying trends and / or patterns in MSE data that allows for the UCS of the geological formation penetrated by the additional well to be estimated based on a similar trend present in the MSE-UCS correlation model. Nevertheless, in other embodiments, the depth, such as MD, in the additional well pertaining to the drilling parameter data obtained at operation 211 can be a factor in determining the proxy UCS of the geological formation at that depth.

[0046] Operation 224 includes using the proxy UCS at each value of MD of the additional well to generate pseudo data, such as pseudo logging data, representing the geological formation at each increment of MD of the additional well. Exemplary pseudo data includes one or more of a pseudo DTCO (e.g., proxy DTCO, estimated DTCO), pseudo density, and / or pseudo shear slowness (e.g., DTS). Pseudo logging data includes an estimation of values of one or more parameters at each increment of MD of the additional well that could otherwise be obtained or derived from operating a logging tool in the additional well. In some embodiments, the pseudo data is estimated by using one or more of Equations 1 to 10 of Table 1. In some embodiments, the pseudo data is estimated by using one or more other equations or correlations between the particular data parameter and UCS.

[0047] The pseudo DTCO, for example, has been observed to be consistent with measured DTCO during an experimental blind drilling test. The experimental blind drilling test involved estimating a value of DTCO at each increment of MD of a test well, and comparing the estimated values with actual values measured or derived from operating a logging tool in the test well at the same increments of MD. The observed consistency demonstrated that the pseudo DTCO could be used for further estimations (such as the generation of a proxy MEM)with confidence that the further estimations would be acceptable approximations of actual values within a prescribed tolerance.

[0048] In some embodiments, operation 224 is omitted from the second group of operations 220.

[0049] In some embodiments, the second group of operations 220 includes operation 225. Operation 225 includes using the pseudo data generated at operation 224 to generate a proxy MEM of the additional well. In some embodiments, the proxy MEM is generated by a well- defined function using the pseudo data, such as the pseudo DTCO. In some embodiments, the proxy MEM is generated using other data, such as the proxy UCS, MSE, and / or drilling parameters in addition to the pseudo data.

[0050] In some embodiments, operation 225 is omitted from the second group of operations 220.

[0051] In some embodiments, the second group of operations 220 includes operation 230, at which one or more properties of the geological formations at each increment of MD of the additional well are estimated. In some embodiments, operation 230 includes using the proxy MEM to estimate the one or more properties of the geological formations penetrated by the additional well. In some embodiments, operation 230 includes using other data (such as the drilling parameter data of operation 221) to estimate the one or more properties of the geological formations penetrated by the additional well.

[0052] In some embodiments, operation 230 includes operation 232, at which the proxy MEM is used to derive one or more acoustic properties, such as the compressional wave seismic velocity (“Vp”) or the sonic shear wave velocity (“Vs”) of the geological formations at each increment of MD of the additional well.

[0053] In some embodiments, operation 230 includes operation 234, at which the proxy MEM is used to derive one or more properties of the rock of the geological formations at each increment of MD of the additional well. Exemplary rock properties include density and porosity. In some embodiments, operation 234 is performed using one or more of the acoustic properties derived at operation 232.

[0054] In some embodiments, operation 230 includes operation 236, at which the proxy MEM is used to derive one or more mechanical properties of the geological formations at each increment of MD of the additional well. Exemplary mechanical properties include one or more indices (such as Young’s modulus, Poisson’s ratio, or a brittleness index, etc.), and one or morestresses, such as total vertical stress (e.g., confining stress), effective vertical stress, or hoop stress. In some embodiments, operation 236 is performed using one or more of the acoustic properties derived at operation 232. In some embodiments, operation 236 is performed using one or more of the rock properties derived at operation 234.

[0055] In some embodiments, operation 230 includes operation 238, at which the proxy MEM is used to derive a pore pressure of the geological formations at each increment of MD of the additional well. In some embodiments, operation 238 is performed using one or more of the acoustic properties derived at operation 232. In some embodiments, operation 238 is performed using one or more of the rock properties derived at operation 234. In some embodiments, operation 238 is performed using one or more of the mechanical properties derived at operation 236.

[0056] In some embodiments, operation 230 includes one or more other operations in which one or more properties of the geological formations at each increment of MD of the additional well are estimated. In some embodiments, operation 230 is omitted from the second group of operations 220.

[0057] The method 200 thus allows for a proxy MEM relating to the additional well to be created based on a MSE-UCS correlation model obtained from one or more reference wells without the cost and time associated with creating a MEM manually by trained experts or the cost and time associated with obtaining well log data from the additional well. Additionally, any one or more of operation 221, operation 222, operation 223, operation 224, operation 225, and operation 230 may be conducted automatically in “real time.” In other words, the drilling engineers can view the proxy MEM and properties of the geological formations penetrated by the additional well while the geological formations are being drilled, rather than waiting for the painstaking process of generating a MEM manually. Thus, method 200 not only aids in well planning before drilling but also serves as a tool for real-time interpretation during the drilling process.

[0058] In some embodiments, “in real time” is instantaneously or nearly instantaneously, such as within 30 seconds, within 20 seconds, within 10 seconds, or within 5 seconds of data being obtained from the additional well, such as at operation 221. In other embodiments, “in real time” may be within minutes, depending on the quantity of data (e.g., each incremental value of the measured depth) of the well and the number of wells being evaluated. For example, “in real time” may be within 5 minutes, such as within 4 minutes, such as within 3 minutes,such as within 2 minutes, such as within 1 minute. There may be a delay of seconds or minutes between the drilling parameter data being obtained at a first MD value in the additional well and outputting the predicted UCS value (e.g., proxy UCS value) corresponding to that first MD. However, such a delay is a significant improvement over the timescale (e.g., several days) needed to generate a conventional MEM, and to make UCS predictions from the conventional MEM.

[0059] In some embodiments, the ultimate goal of the proxy MEM is not to create a perfect correlation but to capture salient geological features as accurately as possible in order to generate reliable predictions pertaining to the geological formations penetrated by the additional well. In some embodiments, use of the proxy MEM at operation 230 aims to produce trustworthy results in the evaluation processes of drilling the additional well using only drilling data of the additional well in combination with the MSE-UCS correlation model obtained from analysis of one or more reference wells. In some examples, operations 224 and 225 are performed without using logging data (e.g., sonic logging data) from the additional well.

[0060] Nevertheless, in other embodiments, logging data from the additional well may be used when performing at least one of operation 224 or operation 225. In some examples, data from a sonic log (such as obtained by a logging-while-drilling tool) of the additional well may be used to verify one or more items of the pseudo data generated at operation 224. In some examples, data from a sonic log (such as obtained by a logging-while-drilling tool) of the additional well may be used to generate one or more aspects of the proxy MEM at operation 225.

[0061] Additionally, the MSE-UCS correlation model is particularly useful to generate the proxy MEM for additional wells that are drilled within the general vicinity of the one or more reference wells. In some embodiments, the original proxy MEM may not provide accurate results for additional wells drilled in a different area of the field 101, such as being drilled approximately 50 miles (80.5 km) from the reference wells, due to natural variations in the geological formations within the field 101. In some examples, a new MSE-UCS correlation model is generated based on drilling one or more new reference wells prior to drilling new additional wells at a different location within the field 101. Nevertheless, in some embodiments, the original proxy MEM is used to provide an estimate of one or more property derived at operation 230 of a geological formation penetrated by an additional well being drilled in a different area of the field 101.

[0062] In some embodiments, method 200 includes operation 240, at which the estimations of properties of the geological formations penetrated by the additional well (derived at operation 230) are used to determine one or more drilling, completion, or operational strategies for the additional well. In some embodiments, operation 240 facilitates the identification and implementation of remedial actions to change drilling parameters during the drilling of the additional well. In some embodiments, the one or more drilling, completion, or operational strategies are initiated by the control system 103. In some embodiments, the one or more drilling, completion, or operational strategies are implemented by the control system 103. In some embodiments, the one or more drilling, completion, or operational strategies are implemented by personnel at the location of the additional well.

[0063] In some embodiments, operation 240 includes operation 241, at which a proxy mud weight window is generated. The proxy mud weight window is determined using the output of any one or more of operation 230, operation 232, operation 234, operation 236, or operation 238. In some examples, the proxy mud weight window generation includes a prediction of fracture gradients of the rocks being drilled. The proxy mud weight window includes an estimation of upper and lower boundaries of the value of the density of the mud used in the drilling of the additional well. In some examples, the proxy mud weight window includes a minimum mud density required to balance a pore pressure or avoid collapse of the rock being drilled. In some examples, the proxy mud weight window includes a maximum mud density above which the rock being drilled is at risk of fracturing. In some examples, the minimum or maximum mud densities are calculated to include one or more safety factors. In some embodiments, the proxy mud weight window includes an estimation of the corresponding upper and lower boundaries for the pressure of the mud at each increment of MD of the additional well.

[0064] In some embodiments, the proxy mud weight window includes an identification of locations along the MD of the additional well where the rock will be unstable, where a kick may occur, and / or where mud losses may occur. In some embodiments, the proxy mud weight window includes a predicted value (or predicted range of values) of one or more of a kick pressure, a rock breakdown pressure, a shear failure window, and / or a volume or rate of mud loss along the MD of the additional well.

[0065] In some embodiments, operation 241 includes an analysis of the equivalent circulating density (“ECD”) of the mud in the additional well. In some examples, operation 241 includes a determination of the pressure of the mud at each increment of MD of theadditional well taking account of the pumping pressure, the flowrate of the mud, mud rheology (density, viscosity, etc.), and other pertinent data (such as hole size). In some embodiments, the ECD analysis includes an evaluation of one or more hypothetical scenarios in which one or more operating parameters are varied. In some examples, the one or more operating parameters include mud density, mud viscosity, pumping pressure, applied back pressure, or mud flowrate. In some embodiments, the ECD analysis includes generating a set of recommended values for the one or more operating parameters.

[0066] In some embodiments, operation 241 includes generating a set of recommended values for the one or more operating parameters for a Managed Pressure Drilling (“MPD”) operation. In some embodiments, operation 241 includes a comparison of one or more hypothetical scenarios simulating a conventional drilling operation with one or more hypothetical scenarios simulating an MPD operation. In some embodiments, operation 241 includes generating a set of recommended values for the one or more operating parameters based on the comparison.

[0067] In some embodiments, method 200 includes generating an alert based on a proximity of the determined pressure of the mud to the estimation of the upper and lower boundaries for the pressure of the mud at each increment of MD of the additional well. In some embodiments, method 200 includes generating a recommended course of action based on the alert and / or the hypothetical scenarios. In some examples, the recommended course of action includes a prescribed change to a value of one or more of mud density, mud viscosity, pumping pressure, applied back pressure, or mud flowrate. In some examples, the recommended course of action includes maintaining a current value of one or more of mud density, mud viscosity, pumping pressure, applied back pressure, or mud flowrate.

[0068] For example, in some embodiments, operation 241 includes using an output of any one or more of operation 230, operation 232, operation 234, operation 236, or operation 238 to determine a reference pressure of the rock penetrated by the additional well. In some examples, the reference pressure includes one of a fracture pressure, a pore pressure, or a pressure at which wellbore stability is compromised (such as estimated at operation 242, below). In some embodiments, operation 241 includes determining a first calculated mud pressure in the additional well from the drilling parameter data obtained at operation 221. In some embodiments, operation 241 includes determining a second calculated mud pressure in the well based on a hypothetical value of the drilling parameter that is different from an actual value of the drilling parameter obtained at operation 221. In some embodiments, operation 241 includescomparing the reference pressure to the first and second calculated mud pressures. In some embodiments, operation 241 includes selecting the hypothetical value of the drilling parameter as the new value of the drilling parameter according to the comparing. In some embodiments, method 200 includes generating the recommended course of action to alter the value of the drilling parameter to the new value of the drilling parameter. In some embodiments, the new value of the drilling parameter includes one or more of a new value of mud density, a new value of mud viscosity, a new value of pumping pressure, a new value of an applied back pressure, a new value of mud flowrate, a new value of WOB, or a new value of RPM.

[0069] In some embodiments, method 200 includes performing the recommended course of action. In some examples, performing the recommended course of action is initiated by the control system 103. In some examples, the recommended course of action is performed by the control system 103. In some examples, the recommended course of action is performed by personnel at the location of the additional well. In some embodiments, performing the recommended course of action at least partially avoids, alleviates, or cures the causing of exposed rock in the additional well to fracture. In some embodiments, performing the recommended course of action at least partially avoids, alleviates, or cures the causing of an influx of fluid from exposed rock into the additional well. In some embodiments, performing the recommended course of action at least partially avoids, alleviates, or cures the causing of exposed rock in the additional well to collapse.

[0070] In some embodiments, operation 240 includes operation 242, at which a wellbore stability analysis is performed. The wellbore stability analysis is performed using the output of any one or more of operation 230, operation 232, operation 234, operation 236, operation 238, or operation 241. In some embodiments, operation 242 is performed as drilling progresses through increments of MD of the additional well.

[0071] In some embodiments, operation 242 includes predicting a likelihood of rock failure occurring. In some embodiments, operation 242 includes predicting a type of rock failure (e.g., fracture, borehole collapse, sloughing, shear failure, sand failure, or the like) occurring. In some embodiments, operation 242 includes predicting a severity of rock failure (if rock failure occurs).

[0072] In some embodiments, operation 242 is performed using the current drilling parameter data obtained at operation 221 to analyze wellbore stability during the drilling of the additional well. In some embodiments, operation 242 is performed to analyze wellbore stabilityduring the completion phase of the additional well. In some embodiments, operation 242 is performed using the one or more operating parameters of the one or more hypothetical scenarios generated at operation 241. In some embodiments, one or more outputs of operation 242 are used as inputs to operation 241. In some embodiments, operation 241 and operation 242 are performed iteratively.

[0073] In some embodiments, operation 242 is performed to analyze wellbore stability during the operation of the additional well to produce fluids from (or to inject fluids into) the geological formation(s) penetrated by the additional well.

[0074] In some embodiments, method 200 includes generating an alert based on the determined likelihood of rock failure occurring. In some embodiments, method 200 includes generating an alert based on the predicted type of rock failure. In some embodiments, method 200 includes generating an alert based on the predicted severity of rock failure.

[0075] In some embodiments, method 200 includes generating a recommended course of action based on the alert. In some examples, the recommended course of action includes a prescribed change to a value of one or more of mud density, mud viscosity, pumping pressure, applied back pressure, or mud flowrate. In some examples, the recommended course of action includes maintaining a current value of one or more of mud density, mud viscosity, pumping pressure, applied back pressure, or mud flowrate.

[0076] In some embodiments, method 200 includes performing the recommended course of action. In some examples, performing the recommended course of action is initiated by the control system 103. In some examples, the recommended course of action is performed by the control system 103. In some examples, the recommended course of action is performed by personnel at the location of the additional well. In some embodiments, performing the recommended course of action at least partially avoids, alleviates, or cures the causing of rock failure occurring during the drilling of the additional well. In some embodiments, performing the recommended course of action at least partially avoids, alleviates, or cures the causing of rock failure occurring during the completion phase of the additional well. In some embodiments, performing the recommended course of action at least partially avoids, alleviates, or cures the causing of rock failure occurring during the operation of the additional well subsequent to drilling and completion.

[0077] In some embodiments, operation 240 includes operation 243, at which a drilling optimization analysis is performed. The drilling optimization analysis is performed using theoutput of any one or more of operation 230, operation 232, operation 234, operation 236, operation 238, operation 241, or operation 242. In some embodiments, operation 243 is performed as drilling progresses through increments of MD of the additional well.

[0078] In some embodiments, operation 243 includes analyzing mud parameters and ECD, such as described above for operation 241. In some embodiments, operation 243 includes using one or more of the hypothetical scenarios generated at operation 241, described above. In some embodiments, operation 243 includes generating the one or more hypothetical scenarios that are analyzed at operation 241. In some embodiments, operation 243 includes generating one or more hypothetical scenarios additional to the one or more hypothetical scenarios generated at operation 241.

[0079] In some embodiments, method 200 includes generating an alert such as described above with respect to operation 241. In some embodiments, method 200 includes generating a recommended course of action based on the alert and the one or more hypothetical scenarios of operation 241 or operation 243. In some examples, the recommended course of action includes a prescribed change to a value of one or more of mud density, mud viscosity, pumping pressure, applied back pressure, or mud flowrate. In some examples, the recommended course of action includes maintaining a current value of one or more of mud density, mud viscosity, pumping pressure, applied back pressure, or mud flowrate.

[0080] In some embodiments, method 200 includes performing the recommended course of action. In some examples, performing the recommended course of action is initiated by the control system 103. In some examples, the recommended course of action is performed by personnel at the location of the additional well. In some embodiments, performing the recommended course of action at least partially avoids, alleviates, or cures the causing of operational inefficiencies in the drilling process that otherwise would make the drilling process more time-consuming and expensive.

[0081] In some embodiments, operation 243 includes analyzing one or more drilling parameters, such as WOB and RPM. In some embodiments, operation 243 includes an evaluation of one or more hypothetical drilling scenarios in which the one or more drilling parameters are varied. In some embodiments, the evaluation includes estimating an MSE for each hypothetical drilling scenario, and comparing the estimated MSE with the MSE obtained at operation 222. In some embodiments, operation 243 includes generating a set of recommended values for the one or more drilling parameters.

[0082] In some embodiments, method 200 includes generating an alert based on the comparison of the MSE obtained at operation 222 with an estimated MSE for one or more of the hypothetical drilling scenarios. In some embodiments, method 200 includes generating a recommended course of action based on the alert and the one or more hypothetical drilling scenarios. In some examples, the recommended course of action includes a prescribed change to a value of one or more of WOB or RPM. In some examples, the recommended course of action includes maintaining a current value of one or more of WOB or RPM.

[0083] In some embodiments, operation 243 includes using an output of any one or more of operation 230, operation 232, operation 234, operation 236, or operation 238 to create a drilling performance model of the additional well. In some examples, the drilling performance model simulates any one or more of a rate of penetration while drilling, a risk of compromising wellbore stability (such as determined at operation 242), or an estimation of MSE. In some embodiments, operation 243 includes using the drilling performance model to determine a hypothetical drilling performance using a hypothetical value of a drilling parameter that is different from an actual value of the drilling parameter obtained at operation 221. In some examples, the hypothetical drilling performance includes one of a hypothetical rate of penetration or a hypothetical mechanical specific energy. In some embodiments, operation 243 includes comparing the hypothetical drilling performance to one of an actual rate of penetration or an actual value of mechanical specific energy determined at operation 222. In some embodiments, operation 243 includes selecting the hypothetical value of the drilling parameter as the new value of the drilling parameter according to the comparing. In some embodiments, method 200 includes generating the recommended course of action to alter the value of the drilling parameter to the new value of the drilling parameter. In some embodiments, the new value of the drilling parameter includes one or more of a new value of mud density, a new value of mud viscosity, a new value of pumping pressure, a new value of an applied back pressure, a new value of mud flowrate, a new value of WOB, or a new value of RPM.

[0084] In some embodiments, method 200 includes performing the recommended course of action. In some examples, performing the recommended course of action is initiated by the control system 103. In some examples, the recommended course of action is performed by the control system 103. In some examples, the recommended course of action is performed by personnel at the location of the additional well. In some embodiments, performing the recommended course of action at least partially avoids, alleviates, or cures the causing ofoperational inefficiencies in the drilling process that otherwise would make the drilling process more time-consuming and expensive.

[0085] In some embodiments, operation 243 includes analyzing the performance of the drilling bottom hole assembly (“BHA”) used to drill the additional well. In some embodiments, operation 243 includes an evaluation of one or more hypothetical BHA scenarios in which the effects of varying aspects such as stabilizer selection and location within the BHA are simulated. In some embodiments, the evaluation includes estimating an MSE for each hypothetical BHA scenario, and comparing the estimated MSE with the MSE obtained at operation 222. In some embodiments, operation 243 includes generating a set of recommendations for the BHA, such as a recommended stabilizer type and stabilizer location within the BHA.

[0086] In some embodiments, method 200 includes generating an alert based on the comparison of the MSE obtained at operation 222 with an estimated MSE for one or more of the hypothetical BHA scenarios. In some embodiments, method 200 includes generating a recommended course of action based on the alert and the one or more hypothetical BHA scenarios. In some examples, the recommended course of action includes a prescribed change to a type of stabilizer or a location of a stabilizer in the BHA. In some examples, the recommended course of action includes maintaining a current type of stabilizer or a location of a stabilizer in the BHA.

[0087] In some embodiments, method 200 includes performing the recommended course of action. In some examples, performing the recommended course of action is initiated by the control system 103. In some examples, the recommended course of action is performed by personnel at the location of the additional well. In some embodiments, performing the recommended course of action at least partially avoids, alleviates, or cures the causing of operational inefficiencies in the drilling process that otherwise would make the drilling process more time-consuming and expensive.

[0088] In some embodiments, operation 240 includes operation 244, at which a drilling trajectory analysis is performed. The drilling trajectory analysis is performed using the output of any one or more of operation 230, operation 232, operation 234, operation 236, operation 238, operation 241, operation 242, or operation 243. In some embodiments, operation 244 is performed as drilling progresses through increments of MD of the additional well.

[0089] In some embodiments, operation 244 includes an evaluation of one or more hypothetical trajectory scenarios in which the trajectory of the additional well is altered from the planned trajectory. In some examples, the trajectory is varied in each hypothetical trajectory scenario while terminating at the same planned end point in 3 -dimensional space in each hypothetical trajectory scenario. In some embodiments, the evaluation includes performing a wellbore stability analysis (such as in operation 242) for each hypothetical trajectory scenario. In some embodiments, the evaluation includes estimating an MSE for each hypothetical trajectory scenario, and comparing the estimated MSE with the MSE obtained at operation 222. In some embodiments, operation 244 includes generating a recommended drilling trajectory for the remainder of the additional well.

[0090] In some embodiments, method 200 includes generating an alert based on a comparison of the wellbore stability analysis resulting from operation 242 with the wellbore stability analysis performed for one or more of the hypothetical trajectory scenarios. In some embodiments, method 200 includes generating an alert based on a comparison of the MSE obtained at operation 222 with an estimated MSE for one or more of the hypothetical trajectory scenarios. In some embodiments, method 200 includes generating a recommended course of action based on the alert and the one or more hypothetical trajectory scenarios. In some examples, the recommended course of action includes a prescribed change of the drilling trajectory. In some examples, the recommended course of action includes maintaining the current drilling trajectory.

[0091] In some embodiments, method 200 includes performing the recommended course of action. In some examples, performing the recommended course of action is initiated by the control system 103. In some examples, the recommended course of action is performed by the control system 103. In some examples, the recommended course of action is performed by personnel at the location of the additional well. In some embodiments, performing the recommended course of action at least partially avoids, alleviates, or cures the causing of rock failure occurring during the drilling of the additional well. In some embodiments, performing the recommended course of action at least partially avoids, alleviates, or cures the causing of rock failure occurring during the completion phase of the additional well. In some embodiments, performing the recommended course of action at least partially avoids, alleviates, or cures the causing of rock failure occurring during the operation of the additional well subsequent to drilling and completion. In some embodiments, performing the recommended course of action at least partially avoids, alleviates, or cures the causing ofoperational inefficiencies in the drilling process (due to the drilling trajectory) that otherwise would make the drilling process more time-consuming and expensive.

[0092] In some embodiments, operation 240 includes operation 245, at which a casing strategy analysis is performed. The casing strategy analysis is performed using the output of any one or more of operation 230, operation 232, operation 234, operation 236, operation 238, operation 241, operation 242, operation 243, or operation 244. In some embodiments, operation 245 is performed as drilling progresses through increments of MD of the additional well.

[0093] In some embodiments, operation 245 includes an analysis of the viability of proceeding with drilling the additional well to the current planned casing setting depth. In some embodiments, operation 245 includes an analysis of the viability of proceeding with drilling the additional well to the current planned end depth (also known as “total depth” or “TD”) beyond the current planned casing setting depth. In some of such embodiments, operation 245 includes a simulation of drilling the remainder of the additional well to TD with a hole size different from (e.g., smaller than) the current hole size. In some embodiments, the analysis includes performing a wellbore stability analysis (such as in operation 242) for each remaining section of the additional well that is yet to be drilled. In some embodiments, the analysis includes estimating an MSE for each remaining section of the additional well that is yet to be drilled, and comparing the estimated MSE with the MSE obtained at operation 222.

[0094] In some embodiments, operation 245 includes an evaluation of one or more hypothetical casing scenarios in which the depth of setting a casing in the additional well is altered from the planned setting depth. In some examples, one or more of the hypothetical casing scenarios includes a simulation of drilling the remainder of the additional well beyond the hypothetical casing setting depth to the planned TD. In some of such examples, one or more of the hypothetical casing scenarios includes a simulation of drilling the remainder of the additional well to TD with a hole size different from (e.g., smaller than) the current hole size. In some embodiments, the evaluation includes performing a wellbore stability analysis (such as in operation 242) for each hypothetical casing scenario. In some embodiments, the evaluation includes estimating an MSE for each hypothetical casing scenario, and comparing the estimated MSE with the MSE obtained at operation 222. In some embodiments, operation 245 includes generating a recommended casing setting depth for the additional well.

[0095] In some embodiments, method 200 includes generating an alert based on a comparison of the wellbore stability analysis resulting from operation 242 with the wellbore stability analysis performed to assess the viability of proceeding with drilling the additional well to the current planned casing setting depth. In some embodiments, method 200 includes generating an alert based on a comparison of the wellbore stability analysis resulting from operation 242 with the wellbore stability analysis performed for one or more of the hypothetical casing scenarios. In some embodiments, method 200 includes generating an alert based on a comparison of the wellbore stability analysis performed to assess the viability of proceeding with drilling the additional well to the current planned casing setting depth with the wellbore stability analysis performed for one or more of the hypothetical casing scenarios.

[0096] In some embodiments, method 200 includes generating an alert based on a comparison of the MSE obtained at operation 222 with an estimated MSE for each remaining section of the additional well that is yet to be drilled. In some embodiments, method 200 includes generating an alert based on a comparison of the MSE obtained at operation 222 with an estimated MSE for one or more of the hypothetical casing scenarios. In some embodiments, method 200 includes generating an alert based on a comparison of an estimated MSE for each remaining section of the additional well that is yet to be drilled with an estimated MSE for one or more of the hypothetical casing scenarios.

[0097] In some embodiments, method 200 includes generating a recommended course of action based on the alert and the one or more hypothetical casing scenarios. In some examples, the recommended course of action includes a prescribed change of the current planned casing setting depth. In some examples, the recommended course of action includes maintaining the current planned casing setting depth.

[0098] In some embodiments, method 200 includes performing the recommended course of action. In some examples, performing the recommended course of action is initiated by the control system 103. In some examples, the recommended course of action is performed by the control system 103. In some examples, the recommended course of action is performed by personnel at the location of the additional well. In some embodiments, performing the recommended course of action at least partially avoids, alleviates, or cures the causing of rock failure occurring during the drilling of the additional well. In some embodiments, performing the recommended course of action at least partially avoids, alleviates, or cures the causing of rock failure occurring during the completion phase of the additional well. In some embodiments, performing the recommended course of action at least partially avoids,alleviates, or cures the causing of rock failure occurring during the operation of the additional well subsequent to drilling and completion. In some embodiments, performing the recommended course of action at least partially avoids, alleviates, or cures the causing of operational inefficiencies in the drilling process (due to the casing strategy) that otherwise would make the drilling process more time-consuming and expensive.

[0099] In some embodiments, operation 240 includes operation 246, at which a completion strategy is generated. The completion strategy is generated using the output of any one or more of operation 230, operation 232, operation 234, operation 236, operation 238, operation 241, operation 242, operation 243, operation 244, or operation 245. In some embodiments, operation 246 is performed as drilling progresses through increments of MD of the additional well.

[0100] In some embodiments, operation 246 includes identifying one or more target zones of the geological formations penetrated by the additional well upon which to complete the additional well. In some examples, the one or more target zones include one or more zones from which to produce an in situ fluid, such as oil or gas. In some examples, the one or more target zones include one or more zones into which to inject a fluid, such as water. In some embodiments, operation 246 includes identifying a type of completion for each of the one or more target zones. Exemplary types of completion include an open hole completion, a cased hole completion, a perforating strategy, a sand control completion, or the like. An exemplary perforating strategy includes a size of perforating gun, a type of perforating charge, an orientation pattern of perforations, a number of perforations per unit length of borehole, or the like.

[0101] In some embodiments, operation 246 includes an analysis of a preselected completion strategy. In some embodiments, the analysis includes estimating a productivity or injectivity of the one or more target zones. In some embodiments, operation 246 includes an evaluation of one or more hypothetical completion scenarios in which the identification of the one or more target zones or the type of completion is altered from the preselected completion strategy. In some examples, one or more of the hypothetical completion scenarios includes a simulation of a productivity or injectivity of the one or more target zones with different types of completion. In some embodiments, operation 246 includes generating a recommended completion strategy for the additional well.

[0102] In some embodiments, method 200 includes generating an alert based on a comparison of the productivity or injectivity of the preselected completion strategy with an estimated productivity or injectivity of one or more of the hypothetical completion scenarios. In some embodiments, method 200 includes generating a recommended course of action based on the alert and the one or more hypothetical completion scenarios. In some examples, the recommended course of action includes a prescribed change of the completion strategy. In some examples, the recommended course of action includes maintaining the current completion strategy.

[0103] In some embodiments, method 200 includes performing the recommended course of action. In some examples, performing the recommended course of action is initiated by the control system 103. In some examples, the recommended course of action is performed by personnel at the location of the additional well. In some embodiments, performing the recommended course of action at least partially avoids, alleviates, or cures the causing of operational inefficiencies in the operation of the additional well subsequent to drilling and completion. In some examples, performing the recommended course of action at least partially avoids, alleviates, or cures a propensity for sand failure and sand production in the additional well. In some examples, performing the recommended course of action facilitates the additional well having a greater productivity or injectivity than as originally planned. In some examples, performing the recommended course of action at least partially avoids or alleviates a requirement for future remedial intervention during the operational life of the additional well.

[0104] In some embodiments, operation 240 includes operation 247, at which a stimulation strategy is generated. The stimulation strategy is generated using the output of any one or more of operation 230, operation 232, operation 234, operation 236, operation 238, operation 241, operation 242, operation 243, operation 244, operation 245, or operation 246. In some embodiments, operation 247 is performed as drilling progresses through increments of MD of the additional well.

[0105] In some embodiments, operation 247 includes identifying one or more objective zones of the geological formations penetrated by the additional well in which to perform a fracture stimulation. In some embodiments, the one or more objective zones are a subset of the one or more target zones of operation 246. In some embodiments, operation 247 includes simulating a productivity or injectivity of the one or more objective zones. In some embodiments, operation 247 includes performing one or more simulations of a post-fracturing productivity or injectivity of the one or more objective zones. In some embodiments, operation247 includes performing the one or more simulations in which each simulation includes a different type or size of fracture stimulation.

[0106] In some embodiments, operation 247 includes an analysis of a preselected stimulation strategy. In some embodiments, the analysis includes estimating a productivity or injectivity of the one or more zones identified in the preselected stimulation strategy. In some embodiments, operation 247 includes an evaluation of one or more hypothetical stimulation scenarios in which the identification of the one or more objective zones or the type or size of fracture stimulation is altered from the preselected stimulation strategy. In some examples, one or more of the hypothetical stimulation scenarios includes a simulation of a productivity or injectivity of the one or more objective zones with different types or sizes of fracture stimulation.

[0107] In some embodiments, operation 247 includes generating a recommended stimulation strategy for the additional well. In some embodiments, method 200 includes generating a recommended course of action based on the recommended stimulation strategy for the additional well. In some examples, the recommended course of action includes details of a fracturing operation, such as type of fracturing operation (e.g., propped fracture, acid fracture, etc.) or a size of fracture to be created (e.g., vertical and / or horizontal extent).

[0108] In some embodiments, one or more outputs of operation 247 are used as inputs to operation 246. In some embodiments, operation 246 and operation 247 are performed iteratively.

[0109] In some embodiments, method 200 includes generating an alert based on a comparison of the productivity or injectivity of the preselected stimulation strategy with an estimated productivity or injectivity of one or more of the hypothetical stimulation scenarios. In some embodiments, method 200 includes generating a recommended course of action based on the alert and the one or more hypothetical stimulation scenarios. In some examples, the recommended course of action includes a prescribed change of the stimulation strategy. In some examples, the recommended course of action includes maintaining the current stimulation strategy.

[0110] In some embodiments, method 200 includes performing the recommended course of action. In some examples, performing the recommended course of action is initiated by the control system 103. In some examples, the recommended course of action is performed by personnel at the location of the additional well. In some embodiments, performing therecommended course of action at least partially avoids, alleviates, or cures the causing of operational inefficiencies in the operation of the additional well subsequent to drilling and completion. In some examples, performing the recommended course of action at least partially avoids, alleviates, or cures a propensity for sand failure and sand production in the additional well. In some examples, performing the recommended course of action facilitates the additional well having a greater productivity or injectivity than as originally planned. In some examples, performing the recommended course of action at least partially avoids or alleviates a requirement for future remedial intervention during the operational life of the additional well.[OHl] In some embodiments, any one or more of operations 241, 242, 243, 244, 245, 246, or 247 may be performed in real time, such as according to a timing as described above with respect to operations 221, 222, 223, 224, 225, and 230. In some embodiments, the output of any one or more of operations 241, 242, 243, 244, 245, 246, or 247 may be displayed on a monitor, dashboard, or graphical interface. In some embodiments, the alerts of any one or more of operations 241, 242, 243, 244, 245, 246, or 247 may be displayed on a monitor, dashboard, or graphical interface. In some embodiments, the recommended courses of action of any one or more of operations 241, 242, 243, 244, 245, 246, or 247 may be displayed on a monitor, dashboard, or graphical interface.

[0112] In some embodiments, any one or more of operations 240, 241, 242, 243, 244, 245, 246, or 247 may be omitted from method 200.

[0113] In some embodiments, artificial intelligence or machine learning is used to implement one or more aspects of one or more operations of method 200. For example, the first group of operations 210 may be used to train a programmable computer about the one or more formations that the lateral wells 130 are being drilled into. In some embodiments, method 200 includes updating the MSE-UCS correlation model based on trends in the data obtained during the drilling of one or more additional wells. In some embodiments, method 200 includes learning one or more characteristics about one or more formations based on each subsequent well; thus, each proxy MEM for each additional well may build upon the analysis performed to generate a prior proxy MEM.

[0114] In some embodiments, the programmable computer includes, or is electronically coupled to, the control system 103. In some embodiments, the control system 103 may be located at the drilling site 100, such as being located on or near the drilling rig 105. In some embodiments, the control system 103 may be remote to the drilling rig 105. In someembodiments, the control system 103, even if remote, is in communication with the drilling rig 105.

[0115] The control system 103 can be used to control one or more operations of the drilling rig 105. For example, the control system 103 may be used to control the drill string during the drilling of the reference wells and can be used to control the drill string during the drilling of additional wells.

[0116] The programmable computer and / or the control system 103 may include a programmable central processing unit (“CPU”) which is operable with a memory (e.g., non- transitory computer readable medium and / or non-volatile memory) and support circuits. In some embodiments, the support circuits of the programmable computer are coupled to the CPU of the programmable computer, and include cache, clock circuits, input / output subsystems, power supplies, and the like to facilitate performing one or more operations of method 200. In some embodiments, the support circuits of the control system 103 are coupled to the CPU of the control system 103, and include cache, clock circuits, input / output subsystems, power supplies, and the like, and combinations thereof coupled to the various components of the drilling rig 105, to facilitate data-gathering at, and / or operation of, the drilling rig 105. For example, in one or more embodiments the CPU is one of any form of general purpose computer processor used in an industrial setting, such as a programmable logic controller (“PLC”), for controlling various polishing system components and sub-processors. The memory, coupled to the CPU, is non-transitory and is one or more of readily available memory such as random access memory (“RAM”), read only memory (“ROM”), floppy disk drive, hard disk, or any other form of digital storage, local or remote.

[0117] In some embodiments, the memory is in the form of a computer-readable storage media containing instructions (e.g., non-volatile memory), that when executed by the CPU, facilitates the generation of the MSE-UCS correlation model, the proxy MEM, and the proxy mud weight window. In some embodiments, the memory is in the form of a computer-readable storage media containing instructions (e.g., non-volatile memory), that when executed by the CPU, facilitates the data-gathering at, and / or operation of, the drilling rig 105. The instructions in the memory are in the form of a program product such as a program that implements the methods of the present disclosure (e.g., middleware application, equipment software application, etc.). The program code may conform to any one of a number of different programming languages. In one or more embodiments, the disclosure may be implemented as a program product stored on computer-readable storage media for use with a computer system.The program(s) of the program product define functions of the embodiments (including the methods and operations described herein).

[0118] Illustrative computer-readable storage media include, but are not limited to: (i) non- writable storage media (e.g., read-only memory devices within a computer such as CD-ROM disks readable by a CD-ROM drive, flash memory, ROM chips or any type of solid-state nonvolatile semiconductor memory) on which information is permanently stored; and (ii) writable storage media (e.g., floppy disks within a diskette drive or hard-disk drive or any type of solid- state random-access semiconductor memory) on which alterable information is stored. Such computer-readable storage media, when carrying computer-readable instructions that direct the functions of the methods described herein, are embodiments of the present disclosure.

[0119] The various methods and operations disclosed herein (such as method 200, operations 211-215, 221-225, 230-238, and 240-247) may generally be implemented under the control of the CPU of the programmable computer and / or of the control system 103 by the CPU executing computer instruction code stored in the memory as, e.g., a software routine. When the computer instruction code is executed by the CPU, the CPU conducts operations in accordance with the various methods and operations described herein. In one or more embodiments, the memory (a non-transitory computer readable medium) includes instructions stored therein that, when executed, cause the method and operations described herein (such as method 200, operations 211-215, 221-225, 230-238, and 240-247) to be conducted. The operations described herein can be stored in the memory in the form of computer readable logic.

[0120] Figures 3 A-3C illustrate an exemplary analysis of MSE and UCS data obtained from reference wells. Figure 3A illustrates graph 300, which shows the identified MSE-UCS correlation obtained during operation 215, which is the most favorable pair of MSE and UCS as plotted over the MD based on the drilling parameters and well log data obtained from the reference wells. The MSE values are shown by line 301 (e.g., darker line) and the UCS values are shown as line 302 (e.g., lighter line). The lower X-axis shows the MSE on a standard scale and the upper X-axis shows the UCS on a standard scale. The MSE and the UCS increase from left to right, as shown by the arrows. The Y-axis shows the MD on a standard scale. The MD increases from the top of the graph 300 toward the bottom of the graph 300, as shown by the arrow.

[0121] The programmable computer and / or the control system 103 calculates MSE from the drilling parameters based on one or more MSE equations as part of operation 212. Theprogrammable computer and / or the control system 103 also calculates the UCS based on one or more UCS equations as part of operation 214. The programmable computer and / or the control system 103 then determines during operation 215 the best correlation between the MSE and UCS (e.g., identified MSE-UCS correlation shown in graph 300) based on the combinations of MSE values and UCS values obtained during operations 212 and 214, respectively.

[0122] The programmable computer and / or the control system 103 may also analyze the identified MSE-UCS correlation illustrated in graph 300 against one or more factors as part of operation 215 to generate the MSE-UCS correlation model. For example, the programmable computer and / or the control system 103 may also generate and analyze the deviation track data of one or more of the reference wells. Figure 3B illustrates graph 310 showing the deviation track data of a reference well on a standard scale, which shows the change in degree of the trajectory of the drill bit from vertical. The programmable computer and / or the control system 103 system may also analyze the MSE data using the clustering algorithm to ascertain clustering of MSE values with respect to one or more drilling parameters. In some embodiments, the programmable computer and / or the control system 103 may also analyze the MSE and the proxy UCS to calculate the drilling efficiency (e.g., a factor that describes the efficiency of transmitting the penetration power of the rig to the rock) and may cluster the MSE data based on drilling efficiency. Figure 3C illustrates a cluster graph 320 showing the result of running the clustering algorithm to examine the MSE data along the MD with respect to the drilling parameters of RPM and WOB. The clustering algorithm revealed distinct clustering within the data. The programmable computer and / or the control system 103 may then use the changing point algorithm to analyze the data, such as the clustering results, to segment data into a first segment 331, a second segment 332, and a third segment 333 along the MD. This segmentation can be applied to graph 300 and graph 310. Different scaling and auto tuning may be applied to each segment 331-333 to create the MSE-UCS correlation model.

[0123] The clustering results may also be analyzed to evaluate if certain data should be excluded. For example, a first portion 321a of the of the results shown in the third segment 333 exceeds a threshold 322, while a second portion 321b is shown being less than the threshold 322. Indeed, most of the MSE data plotted in the third segment 333 exceeds the threshold 322. The programmable computer and / or the control system 103 may determine the data in the second portion 321b to be unreliable, and thus may exclude the MSE values shown in the second portion 321b. The programmable computer and / or the control system 103 may checkother factors before excluding the data, such as comparing the identified MSE-UCS correlation as well as factors such as the deviation track of the wellbore. For example, the programmable computer and / or the control system 103 may analyze the third segment 333 of the data represented by graph 310. The programmable computer and / or the control system 103 may determine that the MSE values along the third segment 333 have generally a greater magnitude and frequency of fluctuation (e.g., bumpier) as opposed to the UCS data. The programmable computer and / or the control system 103 may then look to another factor, such as the deviation track data shown in graph 310 over the third segment 333. The programmable computer and / or the control system 103 may determine that the fluctuations in the MSE may have been caused by the non-stable trajectory of the drill string over the third segment 333. The programmable computer and / or the control system 103 may then, based in part on the MSE fluctuations and deviation track of the drill string, decide to exclude the MSE values represented by the second portion 321b.

[0124] The programmable computer and / or the control system 103 may illustrate each of the graphs 300, 310, and 320 on a user interface.

[0125] The programmable computer and / or the control system 103 generates one or more proposed MSE-UCS correlation models based on analyzing the identified MSE-UCS correlation against multiple factors and modeling parameters, including grouping, scaling, and tuning segments of MSE and UCS differently based on the factors and modeling parameters.

[0126] Figure 4A illustrates a graph 400 that illustrates a first proposed MSE-UCS correlation model 410. In this case, the programmable computer and / or the control system 103 grouped, scaled, and tuned the MSE data represented by line 301 in graph 300 to line 411 and the UCS data represented by line 302 in graph 300 to line 412 shown in Figure 4A based on the deviation track data shown in graph 310. The MSE and MD are presented on standard scales. As shown by the arrows, the MSE increases from left to right, and the MD increases from the top toward the bottom of the graph 400.

[0127] Figure 4B illustrates a graph 430 that illustrates a second proposed MSE-UCS correlation model 440. In this case, the programmable computer and / or the control system 103 grouped, scaled, and tuned the MSE data represented by line 301 in graph 310 to line 441 and the UCS data represented by line 302 in graph 300 to line 442 shown in Figure 4B based on the clustering results shown in graph 320. The MSE and MD are presented on standard scales. Asshown by the arrows, the MSE increases from left to right, and the MD increases from the top of the graph 430 toward the bottom of the graph 430.

[0128] The programmable computer and / or the control system 103 then scored the first proposed MSE-UCS correlation model 410 and the second proposed MSE-UCS correlation model 440 based on a metric, such as a Pearson correlation. In this case, the programmable computer and / or the control system 103 chose the second proposed MSE-UCS correlation model 440 based on the metric. The programmable computer and / or the control system 103 will now use this second proposed MSE-UCS correlation model 440 to conduct the second group of operations 220.

[0129] The programmable computer and / or the control system 103 may generate a proposed MSE-UCS correlation model based on one factor or based on a combination of factors. For example, the programmable computer and / or the control system 103 could have used the known characteristics of geological formations that the reference well traversed to generate first proposed MSE-UCS correlation model 410 in addition to the deviation track data.

[0130] Figure 5 illustrates generating a MSE-UCS correlation model based on a combination of factors. Graph 510 shows which shows the identified MSE-UCS correlation as plotted over the MD based on the drilling parameters and well log data obtained from the reference wells during a field test. Line 511 (e.g., darker line) represents the MSE value and line 512 (e.g., lighter line) represents the UCS value in graph 510. Graph 520 shows the MSE- UCS correlation model output by the programmable computer and / or the control system 103 based on the identified MSE-UCS correlation illustrated by graph 510 and a combination of factors during operation 215. These factors included surface torque (graph 530), RPM (graph 540), inclination (graph 550), and the clustering (graph 560) of the calculated MSE values in graph 510 based on the STOR, RPM, and inclination. Line 521 (e.g., darker line) represents the MSE value and line 522 (e.g., lighter line) represents the UCS value in graph 520. The MSE, UCS, surface torque (“STOR”), RPM, inclination (“Inc”), and MD are presented on standard scales. As shown by the arrows, the MSE, UCS, STOR, RPM, Inc increase from left to right, and the MD increases from the top of toward the bottom in each graph 510, 520, 530, 540, 550, 560.

[0131] The programmable computer and / or the control system 103 then, as part of operation 215, created data sets, shown as graphs 530, 540, 550, and 560, based on variousdrilling parameters. Graph 530 shows the surface torque over the MD. Graph 540 shows the RPM over the MD. Graph 550 shows the inclination of the wellbore over the MD.

[0132] Graph 560 shows the clustering of the calculated MSE values in graph 510 based on the STOR, RPM, and inclination. Graph 560 is based on the clustering of the MSE values calculated from the MSE equation used to generate graph 510. A shown in graph 560, the programmable computer and / or the control system 103 identified six distinct cluster patterns in the data, namely first cluster pattern 561, second cluster pattern 562, third cluster pattern 563, fourth cluster pattern 564, fifth cluster pattern 565, and sixth cluster pattern 566. The programmable computer and / or the control system 103 used these factors to generate the MSE- UCS correlation model shown in graph 520 which was used to create a Proxy MEM of an additional well. In some embodiments, each cluster pattern 561-566 may include a correlation coefficient between MSE and UCS that is different to a correlation coefficient between MSE and UCS of any one or more of each other cluster pattern 561-566.

[0133] The programmable computer and / or the control system 103 may illustrate one or more of the graphs 510, 520, 530, 540, 550, and 560 on a user interface. In some embodiments, each graph may be shown on the user interface simultaneously.

[0134] Figure 6 illustrates using the MSE-UCS correlation model to generate a proxy UCS during operation 223. Graph 610 illustrates the MSE-UCS correlation model shown in graph 520 generated from the reference wells. Graph 620 illustrates a portion of MSE data obtained from the drilling parameters of an additional well during operation 221. As shown, the MSE data from the additional well in graph 620 has been analyzed by a clustering algorithm to identify clusters in the MSE values, namely clustering relationship based on the same clustering parameters used to generate graph 560. In other words, the programmable computer and / or the control system 103 identifies patterns in the MSE data from the additional well that are similar to patterns identified from the reference wells, even if the patterns are not occurring in a similar MD range. The programmable computer and / or the control system 103 can then use the correlation between the MSE and UCS in the identified MSE-UCS correlation that corresponds to the identified cluster to predict the UCS. The MSE, UCS, and MD are presented on standard scales. As shown by the arrows, the MSE and UCS increase from left to right, and the MD increases from the top of toward the bottom in each graph 610, 620, 630.

[0135] Graph 630 illustrates the proxy UCS (e.g., predicted UCS) based on the MSE data obtained from the additional well along the MD shown in graph 620 and the identified MSE-UCS correlation shown in graph 610. The proxy UCS can be used to generate pseudo data, such as pseudo DTCO, in operation 632 to facilitate creating a proxy MEM at operation 225, which can be used during operations 230-238 and during operations 240-247.

[0136] The programmable computer and / or the control system 103 may illustrate one or more of the graphs 610, 620, and 630 on a user interface. In some embodiments, each graph may be shown on the user interface simultaneously.Example Aspects

[0137] Implementation examples are described in the following numbered aspects:

[0138] Aspect 1 includes a method of drilling a well, the method including calculating a value of mechanical specific energy from drilling parameter data, the drilling parameter data obtained from one or more sensors while the well is being drilled. The method further includes estimating a value of unconfined compressive strength of rock penetrated by the well from the value of mechanical specific energy, and using the value of unconfined compressive strength to estimate a value of one or more geological formation properties of the rock penetrated by the well. The method further includes using the value of the one or more geological formation properties to determine a new value of a drilling parameter. The method further includes drilling the well based on the new value of the drilling parameter.

[0139] Aspect 2 includes the method of Aspect 1, such that using the value of unconfined compressive strength to estimate the value of one or more geological formation properties includes: generating a mechanical earth model from the value of unconfined compressive strength; and estimating the value of one or more geological formation properties using the mechanical earth model.

[0140] Aspect 3 includes the method of Aspect 1 or Aspect 2, such that the one or more geological formation properties includes an acoustic property, a rock density, a rock porosity, a mechanical property, or a pore pressure.

[0141] Aspect 4 includes the method of Aspect 3, such that the acoustic property includes a sonic compressional wave velocity or a sonic shear wave velocity.

[0142] Aspect 5 includes the method of Aspect 3, such that the mechanical property includes a value of a brittleness index, a total vertical stress, an effective vertical stress, or a hoop stress.

[0143] Aspect 6 includes the method of any of Aspects 1 to 5, such that estimating the value of unconfined compressive strength includes: inputting the value of mechanical specific energy into a correlation model, the correlation model comprising reference mechanical specific energy values derived from a drilling operation in a reference well correlated with reference unconfined compressive strength values derived from a logging operation in the reference well; and correlating the value of mechanical specific energy with a value of reference unconfined compressive strength in the correlation model.

[0144] Aspect 7 includes the method of Aspect 6, such that the reference mechanical specific energy values derived from the reference well are calculated from drilling parameter data from the reference well; and the reference unconfined compressive strength values derived from the reference well are calculated from log data from the reference well.

[0145] Aspect 8 includes the method of any of Aspects 1 to 7, such that using the value of the one or more geological formation properties to determine a new value of a drilling parameter includes: using the value of the one or more geological formation properties to determine a reference pressure of the rock penetrated by the well, the reference pressure including one of a fracture pressure, a pore pressure, or a pressure at which wellbore stability is compromised; determining a first calculated mud pressure in the well from the drilling parameter data, the drilling parameter data including an actual value of the drilling parameter; determining a second calculated mud pressure in the well based on a hypothetical value of the drilling parameter that is different from the actual value of the drilling parameter; comparing the reference pressure to the first and second calculated mud pressures; and selecting the hypothetical value of the drilling parameter as the new value of the drilling parameter according to the comparing.

[0146] Aspect 9 includes the method of any of Aspects 1 to 8, such that using the value of the one or more geological formation properties to determine a new value of a drilling parameter includes: using the value of the one or more geological formation properties to create a drilling performance model; using the drilling performance model to determine a hypothetical drilling performance using a hypothetical value of the drilling parameter that is different from an actual value of the drilling parameter, the hypothetical drilling performance including one of a hypothetical rate of penetration or a hypothetical mechanical specific energy; comparing the hypothetical drilling performance to one of an actual rate of penetration or the value of mechanical specific energy; and selecting the hypothetical value of the drilling parameter as the new value of the drilling parameter according to the comparing.

[0147] Aspect 10 includes the method of any of Aspects 1 to 9, such that the new value of the drilling parameter comprises one or more of a mud density, a mud viscosity, a pumping pressure, an applied back pressure, a mud flowrate, a weight on bit, or a rate of rotating the bit.

[0148] Aspect 11 includes a method of drilling a well, the method including calculating a first mechanical specific energy along a measured depth of a first well from first drilling parameter data, the first drilling parameter data obtained from one or more sensors while the first well is being drilled. The method further includes calculating a first unconfined compressive strength of rock penetrated by the first well along the measured depth of the first well from first well log data, the first well log data obtained from one or more logging tools operated in the first well. The method further includes generating a correlation model from the first mechanical specific energy and the first unconfined compressive strength, and using the correlation model to estimate a second unconfined compressive strength of rock penetrated by a second well along a measured depth of the second well. The method further includes using the second unconfined compressive strength to estimate a value of one or more geological formation properties of the rock penetrated by the second well, and using the value of the one or more geological formation properties to determine a target value of a drilling parameter of the second well. The method further includes drilling the second well based on the target value of the drilling parameter.

[0149] Aspect 12 includes the method of Aspect 11, such that generating the correlation model includes: generating a plurality of proposed correlation models, each proposed correlation model based on one or more of the first drilling parameter data, a trajectory of the first well, or clustering of values of the first mechanical specific energy; and selecting one of the proposed correlation models based on a statistical analysis.

[0150] Aspect 13 includes the method of Aspect 12, such that a first proposed correlation model of the plurality of proposed correlation models includes a first proposed mechanical specific energy calculated from a first mechanical specific energy equation, and a first proposed unconfined compressive strength calculated from a first unconfined compressive strength equation; a second proposed correlation model of the plurality of proposed correlation models includes a second proposed mechanical specific energy calculated from a second mechanical specific energy equation, and a second proposed unconfined compressive strength calculated from a second unconfined compressive strength equation; and the first mechanical specific energy equation is different from the second mechanical specific energy equation.

[0151] Aspect 14 includes the method of any of Aspects 11 to 13, and further includes calculating a second mechanical specific energy along the measured depth of the second well from second drilling parameter data, the second drilling parameter data obtained from one or more sensors while the second well is being drilled; and inputting the second mechanical specific energy into the correlation model to generate values of the second unconfined compressive strength along the measured depth of the second well.

[0152] Aspect 15 includes the method of Aspect 14, and further includes identifying a first plurality of clusters of values of the first mechanical specific energy relative to one or more drilling parameters of the first well, wherein the first plurality of clusters includes a first cluster of values at a first measured depth range; and identifying a second plurality of clusters of values of the second mechanical specific energy relative to one or more drilling parameters of the second well, wherein the second plurality of clusters includes the first cluster of values at a second measured depth range.

[0153] Aspect 16 includes the method of Aspect 15, such that using the correlation model to estimate the second unconfined compressive strength along the measured depth of a second well includes: identifying that a first value of the second mechanical specific energy at a first measured depth of the second well is contained in the first cluster of values at the second measured depth range; inputting the first value of the second mechanical specific energy into the correlation model; and generating a value of the second unconfined compressive strength based on the first measured depth range of the correlation model.

[0154] Aspect 17 includes a method, the method including calculating mechanical specific energy along a measured depth of a well from drilling parameter data, the drilling parameter data obtained from one or more sensors while the well is being drilled. The method further includes using the mechanical specific energy to estimate unconfined compressive strength of rock along the measured depth of the well. The method further includes using the unconfined compressive strength to estimate one or more geological formation properties of the rock along the measured depth of the well, and using the one or more geological formation properties to determine a strategy for stimulating the well. The method further includes stimulating the well based on the strategy.

[0155] Aspect 18 includes the method of Aspect 17, such that using the one or more geological formation properties to determine the strategy includes: identifying one or moreobjective zones of the rock along the measured depth of the well; and simulating a productivity or injectivity of the one or more objective zones.

[0156] Aspect 19 includes the method of Aspect 18, such that simulating the productivity or injectivity of the one or more objective zones comprises simulating a post-fracturing productivity or injectivity of the one or more objective zones.

[0157] Aspect 20 includes the method of Aspect 19, such that simulating the post-fracturing productivity or injectivity of the one or more objective zones comprises performing multiple simulations in which each simulation includes a different type or size of fracture stimulation.

[0158] The apparatus, systems, and methods of the present disclosure facilitate the estimation of UCS for the rock penetrated by a well while alleviating a need to run dedicated logging tools into the well. The apparatus, systems, and methods of the present disclosure facilitate the analysis of drilling performance of the well while the well is being drilled. The apparatus, systems, and methods of the present disclosure facilitate the identification and implementation of remedial actions while the well is being drilled. The apparatus, systems, and methods of the present disclosure facilitate the identification of a completion strategy and a stimulation strategy for the well while the well is being drilled.

[0159] It is contemplated that any one or more elements or features of any one disclosed embodiment or example may be beneficially incorporated in any one or more other non- mutually exclusive embodiments or examples. While the foregoing is directed to embodiments of the present disclosure, other and further embodiments of the disclosure may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.

[0160] The following claims are not intended to be limited to the aspects shown herein, but are to be accorded the full scope consistent with the language of the claims. Within a claim, reference to an element in the singular is not intended to mean “one and only one” unless specifically so stated, but rather “one or more.” Unless specifically stated otherwise, the term “some” refers to one or more. No claim element is to be construed under the provisions of 35 U.S.C. §112(f) unless the element is expressly recited using the phrase “means for”. All structural and functional equivalents to the elements of the various aspects described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassedby the claims. Moreover, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims.

Claims

WHAT IS CLAIMED IS:

1. A method of drilling a well, comprising: calculating a value of mechanical specific energy from drilling parameter data, the drilling parameter data obtained from one or more sensors while the well is being drilled; estimating a value of unconfined compressive strength of rock penetrated by the well from the value of mechanical specific energy; using the value of unconfined compressive strength to estimate a value of one or more geological formation properties of the rock penetrated by the well; using the value of the one or more geological formation properties to determine a new value of a drilling parameter; and drilling the well based on the new value of the drilling parameter.

2. The method of claim 1, wherein using the value of unconfined compressive strength to estimate the value of one or more geological formation properties comprises: generating a mechanical earth model from the value of unconfined compressive strength; and estimating the value of one or more geological formation properties using the mechanical earth model.

3. The method of claim 1, wherein the one or more geological formation properties includes an acoustic property, a rock density, a rock porosity, a mechanical property, or a pore pressure.

4. The method of claim 3, wherein the acoustic property includes a sonic compressional wave velocity or a sonic shear wave velocity.

5. The method of claim 3, wherein the mechanical property includes a value of a brittleness index, a total vertical stress, an effective vertical stress, or a hoop stress.

6. The method of claim 1, wherein estimating the value of unconfined compressive strength comprises: inputting the value of mechanical specific energy into a correlation model, the correlation model comprising reference mechanical specific energy values derived from adrilling operation in a reference well correlated with reference unconfined compressive strength values derived from a logging operation in the reference well; and correlating the value of mechanical specific energy with a value of reference unconfined compressive strength in the correlation model.

7. The method of claim 6, wherein: the reference mechanical specific energy values derived from the reference well are calculated from drilling parameter data from the reference well; and the reference unconfined compressive strength values derived from the reference well are calculated from log data from the reference well.

8. The method of claim 1, wherein using the value of the one or more geological formation properties to determine a new value of a drilling parameter comprises: using the value of the one or more geological formation properties to determine a reference pressure of the rock penetrated by the well, the reference pressure including one of a fracture pressure, a pore pressure, or a pressure at which wellbore stability is compromised; determining a first calculated mud pressure in the well from the drilling parameter data, the drilling parameter data including an actual value of the drilling parameter; determining a second calculated mud pressure in the well based on a hypothetical value of the drilling parameter that is different from the actual value of the drilling parameter; comparing the reference pressure to the first and second calculated mud pressures; and selecting the hypothetical value of the drilling parameter as the new value of the drilling parameter according to the comparing.

9. The method of claim 1 , wherein using the value of the one or more geological formation properties to determine a new value of a drilling parameter comprises: using the value of the one or more geological formation properties to create a drilling performance model; using the drilling performance model to determine a hypothetical drilling performance using a hypothetical value of the drilling parameter that is different from an actual value of the drilling parameter, the hypothetical drilling performance including one of a hypothetical rate of penetration or a hypothetical mechanical specific energy; comparing the hypothetical drilling performance to one of an actual rate of penetration or the value of mechanical specific energy; andselecting the hypothetical value of the drilling parameter as the new value of the drilling parameter according to the comparing.

10. The method of claim 1, wherein the new value of the drilling parameter comprises one or more of a mud density, a mud viscosity, a pumping pressure, an applied back pressure, a mud flowrate, a weight on bit, or a rate of rotating the bit.

11. A method of drilling a well, comprising: calculating a first mechanical specific energy along a measured depth of a first well from first drilling parameter data, the first drilling parameter data obtained from one or more sensors while the first well is being drilled; calculating a first unconfined compressive strength of rock penetrated by the first well along the measured depth of the first well from first well log data, the first well log data obtained from one or more logging tools operated in the first well; generating a correlation model from the first mechanical specific energy and the first unconfined compressive strength; using the correlation model to estimate a second unconfined compressive strength of rock penetrated by a second well along a measured depth of the second well; using the second unconfined compressive strength to estimate a value of one or more geological formation properties of the rock penetrated by the second well; using the value of the one or more geological formation properties to determine a target value of a drilling parameter of the second well; and drilling the second well based on the target value of the drilling parameter.

12. The method of claim 11, wherein generating the correlation model comprises: generating a plurality of proposed correlation models, each proposed correlation model based on one or more of the first drilling parameter data, a trajectory of the first well, or clustering of values of the first mechanical specific energy; and selecting one of the proposed correlation models based on a statistical analysis.

13. The method of claim 12, wherein: a first proposed correlation model of the plurality of proposed correlation models includes a first proposed mechanical specific energy calculated from a first mechanical specificenergy equation, and a first proposed unconfined compressive strength calculated from a first unconfined compressive strength equation; a second proposed correlation model of the plurality of proposed correlation models includes a second proposed mechanical specific energy calculated from a second mechanical specific energy equation, and a second proposed unconfined compressive strength calculated from a second unconfined compressive strength equation; and the first mechanical specific energy equation is different from the second mechanical specific energy equation.

14. The method of claim 11, further comprising: calculating a second mechanical specific energy along the measured depth of the second well from second drilling parameter data, the second drilling parameter data obtained from one or more sensors while the second well is being drilled; and inputting the second mechanical specific energy into the correlation model to generate values of the second unconfined compressive strength along the measured depth of the second well.

15. The method of claim 14, further comprising: identifying a first plurality of clusters of values of the first mechanical specific energy relative to one or more drilling parameters of the first well, wherein the first plurality of clusters includes a first cluster of values at a first measured depth range; and identifying a second plurality of clusters of values of the second mechanical specific energy relative to one or more drilling parameters of the second well, wherein the second plurality of clusters includes the first cluster of values at a second measured depth range.

16. The method of claim 15, wherein using the correlation model to estimate the second unconfined compressive strength along the measured depth of a second well comprises: identifying that a first value of the second mechanical specific energy at a first measured depth of the second well is contained in the first cluster of values at the second measured depth range; inputting the first value of the second mechanical specific energy into the correlation model; and generating a value of the second unconfined compressive strength based on the first measured depth range of the correlation model.

17. A method, comprising: calculating mechanical specific energy along a measured depth of a well from drilling parameter data, the drilling parameter data obtained from one or more sensors while the well is being drilled; using the mechanical specific energy to estimate unconfined compressive strength of rock along the measured depth of the well; using the unconfined compressive strength to estimate one or more geological formation properties of the rock along the measured depth of the well; using the one or more geological formation properties to determine a strategy for stimulating the well; and stimulating the well based on the strategy.

18. The method of claim 17, wherein using the one or more geological formation properties to determine the strategy comprises: identifying one or more objective zones of the rock along the measured depth of the well; and simulating a productivity or injectivity of the one or more objective zones.

19. The method of claim 18, wherein simulating the productivity or injectivity of the one or more objective zones comprises simulating a post-fracturing productivity or injectivity of the one or more objective zones.

20. The method of claim 19, wherein simulating the post-fracturing productivity or injectivity of the one or more objective zones comprises performing multiple simulations in which each simulation includes a different type or size of fracture stimulation.

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