SPE-200006-MS

An Automated Physics-Based Workflow for Identification and Classificationof Drilling Dysfunctions Drives Drilling Efficiency and Transparency forCompletion Design

Gurtej Singh Saini, The University of Texas at Austin; Donald Hender, IPCOS; Chris James, Sathish Sankaran, andVikram Sen, Occidental Petroleum; Eric van Oort, The University of Texas at Austin

Copyright 2020, Society of Petroleum Engineers

This paper was prepared for presentation at the SPE Canada Unconventional Resources Conference originally scheduled to be held in Calgary, Alberta, Canada, 18 –19 March 2020. Due to COVID-19 the physical event was postponed until 29 September – 2 October 2020 and was changed to a virtual event. The official proceedingswere published online on 24 September 2020.

This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contentsof the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflectany position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the writtenconsent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations maynot be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.

AbstractIn this paper, a new workflow for data wrangling, cleansing, processing, automated identification andclassification of drilling dysfunctions (e.g. bit balling, drillstring vibrations, etc.) for any number of wellsis presented. Statistically-derived baseline rock strengths were used to improve drilling efficiency andtransparency in completions design. The methodology leverages commonly available rig surface sensordata and mud motor parameters. Data handling and processing across multiple databases was automated.Time-series data was converted to depth-based data, and drilling dysfunction indicator metrics (mechanicalspecific energy - MSE, depth-of-cut, etc.) were computed. Subsequently, the combined data set wassegmented based on formation tops, bottom hole assembly (BHA) used, and hole sections. A multivariatephysics-based decision tree methodology was then applied for identification and classification of drillingdysfunction. A baseline MSE was statistically derived and a subsurface rock strength database was createdfor every formation in every well within a given prospect.

Over 100 historical unconventional wells across two shale basins were analyzed using the newworkflow, and 10 different dysfunction categories were classified. The most commonly encountered drillingdysfunctions were whirl, stick-slip, and bit balling. An algorithm generates a set of reports per well basedon independent segments for individual drilling operations, including strip charts of drilling parameters anddysfunction indicators, with identification of potential (low/high) problem areas. Auto-generated scatterplots, identifying optimal control parameters for every geological formation at a given location, are used forplanning future wells and improving drilling performance. The calculated MSE baselines were averaged tobuild a repository of baseline MSEs per formation, which in turn can be utilized as a diagnostic tool for real-time MSE surveillance. The MSE baseline information can also be used as a representation of unconfinedcompressive strength (UCS), and thus used to formulate a reliable rock hardness map to enhance completiondesign.

Manual dysfunction analysis is time- and labor-intensive, and therefore often done either incompletely orbypassed altogether. Automating this process reduces time and effort significantly. The novelty of this work

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lies in the translation of the qualitative understanding of MSE to a reliable quantification of rock strengththrough the analysis of magnitudes and trends in drilling parameters and dysfunction indicators. Screeningout drilling dysfunctions enables a much better rock strength approximation to aid completion optimization.

Background and IntroductionOver the past five decades, Mechanical Specific Energy (MSE) quantification has been used in the oiland gas industry as a drilling analytics tool. MSE characterization can be used for pre-job planning, real-time monitoring/surveillance, and post-mortem analysis; e.g. predicting rate of penetration (ROP), drillingefficiency evaluation, diagnosing drilling dysfunctions for drilling parameter optimization, maximizingROP, pore pressure estimation, and rock strength correlation (Rickard W.M. et. al. 2019). In today'scost-sensitive and high-intensity unconventional market, MSE values can be used to predict unconfinedcompressive strength (UCS) within each frac stage. This enables the setting of perforation clusters in holesections with similar UCS values to minimize any negative effects of rock heterogeneity (Carpenter, C.,2016).

MSE is the amount of work or energy being used per volume of rock drilled (IADC, 2015). Teale (1965)initially derived a mathematical representation consisting of two input components (thrust and torsionalforces) to model a rotating drilling system. He also introduced the conceptualization of minimum MSEbeing approximately equal to rock compressive strength assuming a perfect peak efficiency of the drillbit under atmospheric conditions. All input parameters (revolutions-per-minute (RPM), Torque (TOR),weight-on-bit (WOB), ROP, bit diameter) used in the MSE equation were measured at the surface duringdrilling operations. In field practice, peak bit efficiencies are typically much lower (30 to 40 percent range).Consequently, the magnitude of the MSE values are significantly higher than the rock strength of theformation being drilled (Dupriest, 2005). Scaling MSE values using a bit efficiency factor reduces this offsetto yield a more tangible reference frame for interpretation. The drill bit operates in a peak efficiency rangewhen MSE exhibits a constant trend after changing the drilling parameters (e.g. WOB, RPM). A significantincrease in MSE, either while drilling or while changing operating parameters, indicates inefficiencieswithin the drilling process and should be further diagnosed if no evident changes in drilled lithology haveoccurred. MSE is typically used as a qualitative "trend" indicator only, with its exact numerical value notbeing critical.

Using surface WOB and TOR measurements as MSE inputs will overestimate the drilling system's energyfor rock excavation (surface MSE >> downhole MSE). In practice, energy does not translate effectively toROP due to the following factors:

• axial & rotational frictional losses,

• drillpipe & annular pressure losses,

• bit-hydraulics,

• drilling dysfunctions.

Extensions of Teale's work has generated MSE variants to address such factors and improve the accuracy(see e.g. Chen et. al. (2018) and citations therein). Friction and drilling dysfunctions account for largestenergy losses along the drillstring. It is worth mentioning that energy loss due to drilling dysfunctions isconsidered a cause for reaching the drilling system's limit (max. ROP) known as the founder point, but is, interms of MSE, identified first as a cause after the problem appears. Sensors that measure downhole WOBand TOR would be ideal inputs for accurate computation of downhole MSE. When sensors near the bit oralong the bottom-hole-assembly (BHA) are not available, which is common in unconventional operations,mud motor parameters (i.e. maximum limits of differential pressure and flow rates) can provide valuable

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information about the torque delivered to the bit. This then, can easily be added to the MSE equation. In theabsence of a mud motor, calibrated torque and drag (T&D) models can be used as alternative solutions forestimating downhole WOB and TOR to capture frictional losses along the drillstring.

The use of MSE as a drilling dysfunction indicator are well-documented in SPE papers (see Willis, 2018and citations therein). Most recent techniques for identification and classification of drilling dysfunctionswere proposed by Menand & Mills (2017) and Ambrus et. al. (2017). Menand & Mills (2017) combinedthe standard MSE analysis (neglecting the WOB component) with the ratio of MSE over drilling strength.Ambrus et. al., (2017) defined criteria and method for extraction of probabilistic features (location andmovement/trend of a physical attribute) and aggregated the features into a Bayesian network to infer thebeliefs of drilling dysfunctions. Finally, screening out drilling dysfunctions yields much better rock strengthapproximations (min. MSE values) to map zones of like values along the well lateral. In fact, in orderto obtain reliable values for rock strength (e.g. UCS) it is essential that drilling dysfunctions and theirimpact on MSE values are properly identified and accounted for. Otherwise, highly erroneous "hard rock"characteristics may be automatically assigned to rock formations for which high MSE values are recorded,not because of their elevated rock strength but because of the effect of drilling dysfunctions.

MethodologyThe objective of this project, as shown in Figure 1, was to develop an automated diagnostic tool to analyzereal-time (RT) surface data trends, attempting (1) to identify when rotary drilling dysfunction is occurring,and (2) when occurring, to classify the type of dysfunction. The knowledge gained from this analysis wouldthen be used to optimize future drilling and completion operations.

Figure 1—Graphical representation of the objective of the project.

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To accomplish this, the following methodology was used. RT surface data first needs to be processed andcombined with contextual information about the rock being drilled as well as the details associated with thedrilling assembly used. All these data streams will need to be aggregated and cleansed, with the majorityof analysis time actually spent on these activities. Dysfunction indicator metrics then need to be calculatedusing the cleansed data. Deviations in trends from normal efficient drilling patterns can be indicators ofthe system experiencing drilling dysfunctions, and should be honed in on. This requires an understating ofthe physics of these dysfunctions as well as using the available RT data to define normal efficient drillingpatterns. Finally, the outcome of the analysis needs to be visualized in convenient graphs and heat maps.Thus, the steps involved in developing an automated diagnostic tool for drilling dysfunction identificationand classification can be summarized as shown in Figure 2.

Figure 2—Proposed implementation workflow.

Identification of dysfunction types and the indicator metricsRotary drilling is often negatively affected by drilling dysfunction. Often, there is a vibratory regime of thebit or drill string, that becomes excited by a certain combination of drilling parameters, leading to energybeing dissipated outside of the bit/rock interaction. Dysfunction frequently results in reduced penetrationrates, poor overall hole quality and downhole equipment damage. Drilling dysfunctions can primarily beclassified into three categories: (IADC, 2015) (Schlumberger, 2010) (Logan, 2015)

• Vibrations

○ Axial vibrations/ Bit bounce○ Torsional vibrations/ Stick slip○ Lateral vibrations/ Whirl

• Bit-related dysfunctions

○ Bit balling○ Bit wear

• Rock-related dysfunctions

○ Interfacial severity○ Bottom-hole balling

Various trends in surface data, observed in both raw feeds and computed values can give insight intothe presence and nature of rotary drilling dysfunction. For identification and classification of the differentdysfunctions, combinations of the following parameters were used (see IADC, 2015; Koederitz and Weis,2015; Pessier and Fear, 1992):

• Drilling Parameters- absolute values and the trends in the values (with respect to time and depth)

○ Surface WOB○ Rotary RPM○ Flowrate (Q)○ Differential pressure○ Rotary torque

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○ Rate of penetration (ROP)○ Standpipe Pressure (SPPA)

• Dysfunction Quantitation Metrics- absolute values and the trends in the values (with respect totime and depth)

○ Mechanical Specific Energy (MSE)○ Depth of Cut (DOC)○ Bit Aggressiveness (BA)○ Downhole Stick Slip Index (SSI)○ RPM at the bit

• Founder point chart (see below)

• Trends with respect to time and depth for the ratios of different dysfunction quantification metrics

○ DOC/WOB○ ROP/WOB○ SPPA/Q2

The dysfunction quantification metrics (MSE, DOC et al.) were computed using raw feed of RT data,as discussed in more detail below.

Founder point chart. A Founder point chart is a cross plot between drilling ROP and the applied WOB(IADC, 2015). Ideally, as different data points are plotted for similar values of RPM, the position of thesepoints gives an indication of the drilling efficiency. An example of a founder point chart is shown in Figure3. The chart is segmented into different regions based on potential dysfunction types. In case of efficientdrilling, all other conditions being same, the measured data points will center around the red line (markedin Figure 3 as efficient bit with expected DOC). This implies that as the applied WOB (for a given RPM)is increased, there is a linear increase in the ROP. However, in case of inefficient drilling (or presence ofdrilling dysfunctions), the points will deviate away to the right of the efficient drilling line.

Figure 3—Founder point chart (adapted from IADC, 2015).

For a given set of drilling conditions (i.e. the same BHA, formation, RPM and section) an approximateposition of the baseline (the red line) is established by using a combination of historical and RT data. Then,

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as the data starts deviating from this established baseline, i.e. starts falling into the different segments onthe founder point chart, a potential dysfunction is indicated making a probable classification possible.

Data wrangling: identification, extraction, cleansing and processingAn essential piece for creating an on-demand workflow for dysfunction identification and classificationis the development of an automated pipeline for data handling including data extraction, cleansing andprocessing. This data handling and processing methodology is shown schematically in Figure 4.

Figure 4—Data wrangling and processing methodology.

The first step of the methodology is establishing a seamless channel for pulling the different categoriesof data from the different databases. The drilling data required for this analysis can be classified into thefollowing categories:

• Operational performance information, obtained from one second RT drilling data

• Well profile information, obtained from the well survey and the well plan data

• BHA, bit and motor information, obtained from the pre-drill plans

• Formation top information, obtained from the offset wells

• Mud check information and details, obtained from the well's daily operational data

• Specification details for the drilling motors used for the various bit runs

This data was spread across multiple database types (SQL as well as noSQL) and was collected at differentfrequencies with varying degrees of resolution. The data was collected from the following databases:

• A noSQL database

• A SQL database

• Internal geologic top SQL database

• Mud motor library from manufacturers' spec sheets (stored in an internal database)

Figure 5 summarizes the presence of the different categories of data in the different databases.

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Figure 5—Data availability across the various databases.

Challenges associated with the data wrangling step included:

• Data Availability

○ Data was spread across multiple databases○ The nomenclature was different for different wells, for instance the motor description for the

same motor was different for multiple wells

• Data Quality

○ There were missing intervals of data○ There were calibration issues with some of the required data channels (e.g. WOB and differential

pressure not zeroed properly in the field)○ Data values showed occasional errors and unrealistic values (e.g. block height over 200 feet,

WOB over 1000 klbs etc.)○ Block height references changed throughout the well run (possibly due to multiple calibrations

in the equipment)

These challenges were all addressed using multiple automated scripts, following the spider bot scriptmethodology proposed by Saini et al. (2018). Some of the scripts created to handle these issues includeddata parsing scripts, auto-zero WOB scripts, scripts to handle changing references, etc. Finally, all datawas collected and combined into a single time-based data framework, i.e. for every time-based data point,values were defined for the corresponding formation top, BHA number, motor details, mud information,section, etc.

The time-based data was subsequently converted to depth-based data for 0.5-foot intervals. To performthis conversion, all the time data was accumulated for the given 0.5-foot interval and then averagedappropriately to assign a value for the corresponding depth. For instance, if while drilling from 12,000 to12,000.5 feet there were 30 on-bottom drilling data points available, then average values of the drillingparameters (e.g. RPM, WOB, torque etc.) were calculated, and ROP was estimated based on the ratio ofthe depth drilled (0.5 feet) to the on-bottom drilling time taken to drill that depth. The different drillingdysfunction indicator metrics (discussed in the previous section), the various ratios etc. were then calculatedon this depth converted data.

The final and the most important step of the data handling process is the segmentation of data basedon formation tops, BHA number and sections. For instance, BHA number 1 drilling in the vertical sectionin formation number 1 would be analyzed independent of BHA number 1 drilling in vertical section information 2, and so on. The purpose of this was to make sure that there was a direct and valid comparisonof the trends in the data for dysfunction identifications. Figure 6 illustrates this segmentation step.

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Figure 6—Illustration of the segmentation step.

Calculation of the dysfunction indicator metricsDysfunction indicator metrics as identified in the first step of the methodology were calculated on the time-based and the depth-based data for every individual segment. For the purpose of this project, the followingmetrics definitions were used (see Ambrus et al., 2017; Chen et al., 2018):

Mechanical Specific Energy (MSE):

(1)

(2)

In the above equations WOB is the weight on bit measured at the surface (pounds - lbs.), RPM is thesurface rotary speed (revolutions per minute – rev/min), and flowrate is rate of flow of the drilling mudmeasured at the surface (gallons per minute - gpm). Abit is the surface area of the drillbit (inches squared- in2), and ROP is the drilling rate of penetration (feet drilled per hour - ft/hr). Torquebit is the estimatedvalue of torque at the drillbit (foot-pounds - lbs-ft), while Diff Press. is the differential pressure measured atsurface (pounds per square inch - psi). The quantities Diff Pressmax(motor) and Torquemax(motor) respectively, arethe differential pressure (psi) and the torque ratings (lbs-ft) of the motor, read from the motor specificationsheet. The constant RPG is revolutions per gallon rating of the motor also derived from its specificationsheet. Equation 1 outputs the MSE value in psi.

Depth of Cut (DOC):

(3)

The DOC is measured in inches drilled per revolution.Bit Aggressiveness (BA):

(4)

where Dbit is the diameter of the drillbit in inches, and the calculated BA is a dimensionless number.Stick Slip Index (SSI):

(5)

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where Torquebit,max, Torquebit,min and Torquebit,average are respectively the maximum, minimum and averagedownhole torque values in a specified time interval, measured in lbs-ft. The calculated SSI is a dimensionlessnumber.

Hydraulic Horsepower (HHP):

(6)

where

(7)

where TFA is the total flow area of the drillbit (in2) and can be easily calculated based on the size of thenozzles used, and Cd is the discharge coefficient associated with the bit. Typically, the value of Cd is assumedto be 0.95 (Wells and Pessier, 2003). The density of drilling mud, ϱmud, is specified in pounds per gallon(ppg). ΔPbit is the estimated pressure drop across the drillbit (psi), while the HHP is measured in horsepower(hp).

A key reason behind using these particular metrics definitions was to be able to calculate the metrics usingonly the commonly available surface measurements, bit and BHA information, and mud motor parameters,making the methodology viable for low cost land operations that are not utilizing downhole high frequencymeasurements.

Dysfunction identification and classificationThe next step was to develop and apply a technique for identifying and classifying the potential regions ofdysfunctions in the various segments identified in the earlier steps. This required the following:

• Estimation of a baseline MSE value for every segment

• Determination of trends in the values of the different surface parameters

• Determination of trends in the values of dysfunction indicator metrics

• Development of a hybrid data-based and physics-based model for dysfunction classification

Baseline MSE for a rock, as defined for this project, corresponds to the amount of energy required tobreak the rock during efficient drilling, i.e. when there are no drilling dysfunctions. Since drilling a wellinvolves drilling through different formation types, i.e. rocks of different strengths, the baseline MSE valuefor every formation is quite different. Thus, an algorithm was developed to calculate the baseline MSEvalues for every given drilling segment based on the following steps:

• All the collected MSE data was separated into bins

• All the bins in the lowest quantile (P25) were identified and selected

• Out of these bins, the bin with the greatest number of points was selected

• The mid-point of the selected bin was assigned as the baseline MSE for the segment

Figure 7 shows an example for calculation of the baseline MSE for two different formations. The midpointof the bin selected as the baseline (i.e. baseline MSE) is marked in red.

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Figure 7—Examples of baseline MSE estimation.

The baseline MSE values estimated for the different segments with different drilling operations (slideand rotary drilling) were then weighted and combined to estimate a single baseline MSE value for everyformation. This algorithm was then implemented on all the wells in the database to calculate the baselineMSE values for all the different formation tops encountered. All the data was then combined using anarithmetic weighting scheme, based on the number of data points collected per formation top per well, toget a single baseline MSE value for every formation.

For the measured drilling parameters and the calculated dysfunction indicator metrics, trends with respectto time and depth were determined. For any parameter or metric, the trend could either be increasing,constant or decreasing. The following procedure was followed for trend determination:

• For the value of the given variable, the absolute change in its value over one time or depth step(i.e. value [k] – value[k-1]) was computed

• The backward derivative for the rate of change of the variable's value over one step was calculated

• Using the estimated baseline MSE for the given formation, the data was split into two categoriesor dataframes: baseline and non-baseline. A data point was classified as baseline if the MSE valuefor that data point was less than or equal to the baseline MSE, or categorized as non-baseline dataotherwise

• If the gamma ray log information was available, further segmentations were done on the data forsimilar gamma ray values

• For the baseline data (for the given formation and for the given sub-interval based on similar gammaray log values), a normalized centroid (with respect to the median values of WOB and ROP in theinterval) value was calculated, as shown in Figure 8

• For every point in the non-baseline dataframe, their normalized (with respect to the median valuesfor baseline WOB and ROP) distance from the baseline centroid was calculated. This distance wasused later in the hybrid model as one of the checks to classify the dysfunction type

• Lastly, based on heuristics determined by observing multiple datasets, thresholds were defined forrate of change of the various parameters or metrics with depth and time. Comparisons against thesethresholds were used to classify the trend in the variable at the given depth or time segment

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Figure 8—Estimation of the centroid on the normalized founder point chart.

Finally, a multivariate physics-based decision tree approach was developed for automatically classifyingthe different dysfunction types. A schematic of this approach is shown in Figure 9.

Figure 9—Schematic of the decision tree for dysfunction classification.

The goal of the developed method was to gauge the absolute values as well as trends in the differentparameters and metrics, and systematically proceed down a branch of the tree until a dysfunction type wasreached. Using this methodology, nine types of dysfunctions were identified, and the rest (which could notbe clearly identified), were classified as "undefined". The following is the list of the various dysfunctionsthat were identified based on the devised algorithm:

1. Whirl2. Bit Balling3. Bottom hole Balling

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4. Bit Wear5. Interfacial Severity6. Stick Slip7. Formation Change8. Inefficient with respect to the founder point9. Potential Drilling System Limit10. Unidentified Dysfunction

Visualization and analysisOnce the process of data handling, dysfunction metrics calculation, dysfunction identification andclassification was automated to be implemented on any well (or any selected number of wells), the nextstep was to present the insights gathered from analysis to the end-users (meaning operations personnel, fieldengineers etc.) in a succinct manner. The goal with this was to organize and display data as plots, such thatjust the right amount of information was presented to the end users, without overwhelming them with toomuch information. To accomplish this, the following main outputs were used:

• Reports for every segment indicating the potential regions of dysfunctions and classifying thedysfunction in those regions

• Heat maps or scatter plots for drilling parameter optimization for any number of wells

• Repository of baseline MSEs for all formations drilled over all the drilled wells

Implementation and ResultsThe described workflow was implemented on over 100 wells drilled across more than 20 drilling pads in twodifferent unconventional shale fields. This section discusses some results of this implementation organizedas following:

• Auto-generated dysfunction reports for every section

• Repository of baseline MSE values for all formations encountered while drilling all the wells

• Heat maps generated to optimize drilling parameters for future drilled wells

• Example case of dysfunction diagnosis and classification for bit balling

Dysfunction reportsPrimary output of this workflow is automated generation of reports for every individual segment, as shownin Figure 10. The report starts by stating the formation name, the baseline MSE calculated for the drillingsegment, the bit dull grade and the BHA number for the given well. Inside the report, there are sevenpages of charts that guide the reader (drilling engineer or operations teams) systematically to lead them todysfunction identification and classification.

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Figure 10—Example of report generated for a single segment.

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Some of the plots included in the report are:

• MSE, WOB/DOC and the ratio between MSE/(WOB/DOC)

• The founder plots (ROP versus WOB and ROP versus Differential pressure)

• The DOC, MSE, ROP traces with respect to depth

• The drilling parameter traces (WOB, torque, flowrate etc.) with respect to depth

• A chart marking the zones of MSE above the baseline value and the identified potentialdysfunctions in those regions

Repository of baseline MSE valuesAnother output of the project was generation of a repository of the baseline MSE values for the twofields under investigation. The baseline MSEs calculated for individual wells were collected, weighted andcombined to build a repository of the baseline MSEs for every formation encountered while drilling theanalyzed 100 wells. To verify the consistency of these values, they were compared against the UCS valuesobtained from both sonic well logs as well as data obtained from laboratory core tests. These baseline MSEscan be potentially used as a diagnostic tool for real-time MSE surveillance, and for aiding in completionsdesign. The left hand side of Figure 11 depicts the position of more than 70 wells in one of the fields, andthe table on the right demonstrates an example of the generated baseline repository using the well data. Ineffect, for every formation top a single baseline MSE value is defined. As more wells are drilled throughthese formations, resulting in collection of more data, the baseline MSE values are further refined.

Figure 11—Baseline MSE values calculation for wells in a shale field.

Scatter maps for drilling optimizationAdditional output of the analysis is the automated generation of drilling parameter scatter plots or heat mapsfor any selected set of wells. These plots can be generated to identify optimal drilling parameters (WOB,RPM, differential pressure, etc.) with regards to minimizing MSE and/or maximizing ROP for any set ofwells selected (on a pad by pad basis, or any custom selection). The primary application of such scatterplots is finding optimal parameters for drilling future wells. Figure 12 depicts an example of the generatedscatter plots using 4 wells drilled on a given pad, while drilling in the formation designated as formation 1.

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Figure 12—Scatter plots generated for a single formation drilled across multiple wells in a given pad.

These plots can be used as post-drilling analysis tools to find the optimum drilling parameters for a givenformation. For instance, in this case the optimal parameters, as circled in the Figure 12, are:

• WOB: 40 to 45 Klbs

• RPM: 195 RPM or 205 RPM

• Differential pressure: 900 to 1,000 psi

This combination of parameters would result in an MSE value of around 30 kpsi, at an ROP ofapproximately 350 ft/hr. These identified optimal drilling parameters can then be used for planning thenext well in the given pad. As more data from drilling multiple wells is accumulated and analyzed, thesescatterplots can be refined more to better guide in the selection of optimum drilling parameters for everyencountered formation.

Example of dysfunction diagnosis and classificationThe results of the dysfunction identification and classification algorithm were manually examined andvalidated by subject matter experts for some of the 100 wells. The steps below discuss in a step-by-stepmanner the working of the algorithm for an actual well example.

Figure 13 illustrates key information from the given well; the well name, section, formation top, BHAnumber, baseline MSE value and the bit grade. The following steps were applied:

1. As a first step, the algorithm finds the regions with MSE values greater than the baseline MSE, andflags them for further analysis. The areas highlighted in light purple color in Figure 13 are flaggedfor further analysis.

2. Flagged regions are further analyzed for trends of different dysfunction indicator metrics. Based onthe evaluated trends in MSE and ratios of metrics such as WOB/DOC etc., an assessment is madeof whether the trend is due to dysfunctions such as vibration and bit wear, bit balling etc. or due to

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an increase in formation strength. Since the section being analyzed corresponds to a single formationintersected with the same BHA, the likelihood of significant formation change is low. Pulled bitcondition is considered at this point. The dull grading of the bit used in this particular well sectionwas 1-1 (inner and outer cutter condition, respectively), i.e. the bit was in good condition with lowwear when pulled. Therefore, there is a good chance that the dysfunction being encountered is bitballing (Figure 14).

3. To determine with certainty the type of dysfunction, the next step is evaluating the position of the dataon the founder plot with respect to the ideal founder line, and comparing the data location on the plotwith the potential dysfunction signatures. As shown in Figure 15, the ROP is plotted against WOBfor baseline and non-baseline data to generate the founder plot. On comparing the position of the datawith the dysfunction signatures, it is confirmed that the observed dysfunction is indeed bit balling.

4. Finally, to further ascertain and verify the dysfunction as bit balling, trends in RPM, WOB and ROPare evaluated. It can be seen in Figure 16 that for a constant trend in RPM values, the ROP trenddecreases while the WOB shows an increasing trend. This behavior is fully consistent with the bitballing phenomenon.

Figure 13—MSE data with marked dysfunction regions (light purple color) and the corresponding well information.

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Figure 14—Utilizing trends to rule out improbable dysfunctions.

Figure 15—Using founder chart to further establish the identification of bitballing as the cause of the observed dysfunction at a higher probability.

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Figure 16—Using the trends in drilling parameters to identify bit balling.

This finally leads the algorithm to classify this specific dysfunction as bit balling, as indicated by thegenerated output information page as a part of the final reporting step with dysfunctions labeled, shownin Figure 17.

Figure 17—The generated output information page

Figure 18 summarize the various steps of the dysfunction classification methodology used to identifybit balling.

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Figure 18—Dysfunction identification and classification for bit balling.

If, for instance, this algorithm was implemented in RT and was able to detect bit balling earlier, it wouldhave resulted in potential savings of almost two hours just for the given 1500 feet segment, as shown inFigure 19. A reduction in the surface WOB to around 35Klbs for the same downhole RPM (of around 260)would have resulted in an increase in ROP of the system from 150 ft/hr to about 200 ft/hr. A simplisticimplementation of this, as displayed in plot on the right in Figure 19, shows that this could potentially haveresulted in the section being drilled faster by almost 2 hours.

Figure 19—Potential savings resulting from possible RT implementation.

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The same underlying algorithm was used in a similar fashion to identify and classify the other 8dysfunctions on all the analyzed wells.

ConclusionsThe paper discusses the development of an automated workflow for data wrangling, cleansing, processing,and dysfunction identification and classification. Initially, the developed workflow was applied to historicaldata sets (combined real-time surface measurements & contextual data) as proof of concept, although theend goal is to deploy this drilling technology as a near real-time application. The method can be easilyperformed stand-by-stand while drilling, but the outcome may not yield a single dysfunction diagnosis dueto the absence of bit-grading details; at the end of a run or section the analysis must be repeated for aconclusive result. The workflow simplifies the diagnostic scope allowing the drilling team to conduct furtherMSE testing to isolate the root-cause. In conclusion, the following are the key contributions of the work:

• Automating the process of data handling and identification of dysfunction regions reduces timeand effort associated with manual analysis. Further, automating the process of data mining allowsfor the capture of those pieces of information that otherwise could be overlooked if a human wereto sift through the data, benefiting continuous improvement without missed opportunities.

• The developed methodology for dysfunction identification and classification uses a hybridapproach, by combining physics-based models with data measurements that require only surfacedata. The use of surface data allows for the possibility of this method to be used for RT dysfunctionidentification and classification, particularly on lower-cost land operations where no downholehigh-frequency data is available.

• The repositories created for baseline MSE values for different formations offer applications to bothdrilling and completions teams. They not only serve as a diagnostic tool to identify dysfunctionsduring RT drilling, but can also aid completions teams in designing and optimizing completionsplans.

• Segmenting and independently analyzing unique sections (with regards to BHA type, formation,section being drilled in etc.) for a well, and subsequently combining and deriving knowledge fromanalysis of equivalent sections of multiple wells allows for continuously improving future wells.For instance, information collected from offset wells can be presented as heat maps for makingrecommendations about drilling parameters for the next well to be drilled.

• Identified and labeled dysfunction types on the generated reports can be used as training sets forsubsequent supervised learning to build RT dysfunction detection algorithms.

The presented methodology was implemented in a modular and plug-and-play manner by developingcustomized functions in python. This allows for automated rotary drilling diagnostics at scale, therebyhelping with drilling optimization, in turn enabling continuous performance improvement and improvedhole quality.

AcknowledgementsThe authors are thankful to the following colleagues for their support and valuable feedback: Kate Ruddy,Chad Hough, Yuxing Ben, Will Tank, Justin Stone, and Sanjay Paranji.

NomenclatureAbit : Area of the drillbit, inches squared (in2)BA : Bit aggressiveness, dimensionlessCd : Discharge coefficient associated with the bit, dimensionless

Dbit : Diameter of the drillbit, inches (in)

SPE-200006-MS 21

Diff Pressmax(motor) : Differential pressure rating of the motor, pounds per square inches (psi.)Diff Press : Differential pressure at the surface, pounds per square inches (psi.)

DOC : Depth of cut, inches per revolution (in/rev)Flowrate : Rate of flow of the drilling mud, gallons per minute (gpm)

HHPbit : Hydraulic horsepower delivered at the bit, horsepower (hp)MSE : Mechanical specific energy, pounds per square inches (psi.)RPG : Revolutions per gallon of the motor (rev/gal)RPM : Surface rotary speed, revolutions per minute (rev/min)

SSI : Stick slip index, dimensionlessTFA : Total flow area of the drillbit, inches squared (in2)

Torquebit : Estimated torque at the bit, foot-pounds (lbs-ft)Torquebit,average : Average downhole torque in the given time interval, foot-pounds (lbs-ft)

Torquebit,max : Maximum downhole torque in the given time interval, foot-pounds (lbs-ft)Torquebit,min : Minimum downhole torque in the given time interval, foot-pounds (lbs-ft)

Torquemax(motor) : Torque rating of the motor, foot-pounds (lbs-ft)WOB : Surface weight on bit, pounds (lbs.)ΔPbit : Pressure drop across the bit, pounds per square inches (psi.)ϱmud : Density of the drilling mud, pounds per gallon (ppg)

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