SPE-172793-MS

An Improved Drilling Simulator for Operations, Research and Training

Vassilios C. Kelessidis, Shehab Ahmed, Texas A&M University at Qatar/WellCruiser; Alexandros Koulidis,Technical University of Crete, Greece

Copyright 2015, Society of Petroleum Engineers

This paper was prepared for presentation at the SPE Middle East Oil & Gas Show and Conference held in Manama, Bahrain, 8–11 March 2015.

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.

Abstract

This work describes the functionalities of the drilling simulator to optimize well drilling utilizing offsetdata offering also training modules for novice and experienced drillers. It fully simulates the drillingprocess using WOB, RPM, flow, survey and lithology data and predicts ROP. It is based on Payzonesimulator, introduced some years ago. The improved simulator has been tested to compare performancewith drilling data from sandstone and carbonate reservoirs. The simulator has also built in functionalitieslike prediction of rock strength, finding optimal rheological model from viscometer data, and optimizingbit nozzle selection.

The simulator determines optimum conditions for drilling new wells utilizing offset data. It allows forfine tuning of the process. Validation is by matching predicted versus actual drilling curve. Onceaccomplished, a new well can be drilled, in similar environmental conditions, but with optimized drillingparameters. Training is accomplished with simple and challenging exercises, teaching students the drillingprocess, and allowing different drilling scenarios. It can be used for research to identify the importanceof drilling parameters for drilling efficiency.

We have tested performance and predictions using literature field data and compared performance inprimarily sandstone and carbonate reservoirs. Overall, the simulator is capable of simulating anyformation type. Our results also show that the improved functionalities, stand-alone calculators for rockstrength, rheological parameter determination and optimization of bit nozzle performance have signifi-cantly enhanced the simulator capabilities.

The results show that the simulator is very good tool for designing new wells using offset data. Itprovides good interactive training tool for students and engineers allowing experimentation of parametersaffecting drilling process. We compared the simulator performance with respect to formation, sandstoneversus carbonate reservoir and report similarities and differences.

IntroductionThere have been several attempts from prior researchers to model drilling process [1]. This has provenvery difficult as formation properties, which essentially govern drilling conditions and primarily drillingadvancement, i.e. rate of penetration (ROP) are ever changing as drilling advances in deeper horizons. Onthe other hand, the ability to predict drilling advancement is essential for a successful drilling campaign.

This can be especially useful in the drilling design phase so that various scenarios can be tested in orderto design with optimal drilling rig operating parameters.

Prior evidence has demonstrated that rate of penetration depends on two group of parameters, drillingrig and formation parameters. In the first group one identifies Weight-On-Bit (WOB), bit rotational speed(RPM) and Hydraulic Parameters (mud flow rate, mud rheology and mud density), Bit and Bit Condition.From the formation group the most essential parameters affecting penetration rate are local stresses, rockcompaction, fluid pore pressure and mineralogical content which is related strongly to lithology ofsubsurface formations.

Rock drillability, an essential parameter for penetration rate prediction in a drilling process is an everelusive parameter. Never-the-less, researchers have executed both lab and field scale experiments in orderto define rock drillability and relate it to operator dependent changable parameters such as the onesidentified above, i.e., WOB, RPM and hydraulics. Such experiments can be lengthy and very expensive.If a model which describes drilling process is available it can be entered in a simulator which can be usedto predict penetration rate under different conditions. Of course one needs real data from previous similarwells in order to verify such predictions. Thus, monitoring of several operating parameters is more thanessential for such successful application of the simulator. Drilling data are gathered from sensors installedon the rig, which include, surface weight-on-bit, torque, rotary speed, pump pressure, flow rate, nozzleconfiguration, detailed bit grading.

Availability fo such data coming from real time monitoring of the drilling process allows esitmationof optimum weight-on-bit, pump pressures and rotary speeds [2]. Similar simulators were developed in thepast [3,4] but almost all had the disadvantage of requiring a large number of parameters to be knowna-priori which make the use of the simulators very complex and non-flexible in terms of simulatingvarying and changing drilling conditions. Another approach that was followed [5, 6] was the use of adrilling process model and then using drilling data one could infer information regarding rock drillabilityallowing for drilling optimization.

The development of the simulator described in this work was based on a totally different concept.Rather than applying a complicated model of rock-bit interaction leading to the prediction of penetrationrate under a given set of conditions, a simpler approach is taken using a developed model based on Teale’sconcept of mechanical specific energy [7]. Successful application requires the use of offset well data. Thesimulator has been developed not only for operation but for training as well [8, 9]. Verification of goodpredictions was done through use of lab generated data [10], while Kelessidis et al., in a series of articles[11, 12, 13] have demonstrated that the simulator could be used with sufficient accuracy to simulate thedrilling process, provided that proper monitoring is applied in offset wells and with good knowledge ofrock strength data. Recently, data from casing while drilling were also simulated with good results [14].

The Payzone simulator allows for defining different lithologies along the wellbore, in terms the rocktype, strength, and abrasivity. Rock-type data are usually available in a typical LAS file, strength data inform of Unconfined Compressive Strength (UCS) are sometimes given, while in many time they are notgiven, and in the latter cases, estimation is made, either from databases for similar rocks or they areestimated from sonic data, although these are fairly crude estimates [1]. This parameter will be furtherexplored later. When all the data is loaded, the simulator is tuned to reproduce the drilling performanceobserved in the offset or reference well by adjusting some key parameters of model specific factors. Thismatching is performed on the drilling-time curve which essentially gives rate of penetration. Then anywell can be re-drilled to see if a better set of operating conditions can be specified. In the same way, anew well can be “drilled” and its drilling performance optimized [8-9]. The matching is satisfied throughthe drilling time curve, when one compares actual drilling time curve versus the simulated one (Figure 1).

Once the actual data is fully and properly simulated, one then can apply different scenarios in terms

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of operating rig parameters to see what the effect might be on the penetration rate and thus determineoptimized values. Such an attempt is shown in Figure 2 from the example shown in Figure 1, where extra4.5 tons of WOB, extra 50 RPM and extra 53 gpm have been applied, showing that it is possible to reducedrilling time, which is the slope of the curve, and quantifying it, when increasing some of the rig operatingparameters.

Application of the same approach for the first time to casing drilling data showed also considerablepromise (Figure 3) indicating that the system is flexible enough to accommodate difficult and new drillingsituations.

Figure 1—Simulations of real drilling data. North Sea Well. WOB 9.5-21.5 tons; RPM 50-180; Q 635 gpm (adapted from [11]).

Figure 2—Optimization of rig operating parameters for the well shown in Figure 1.

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While the simulator has had good successes in the past, several improvements needed to be made,primarily in terms of extending operating conditions, updating the databases for drilling bit data andcasing data, as well as implementing certain calculators like determination of rock strengths (UCS),rheology calculators and drilling bit optimization. Prior experience did not allow for determination ofcapabilities of simulator to simulate drilling process in any type of formation. Hence, major objective ofthis work was to determine if there are any issues or difficulties and what these differences might be ifdrilling through sandstone or carbonate formations, the majority of producing oil & gas reservoirs.

Methodology and data

The main features of the model predicting drilling rate are given by Equations (1) and (2), namely,

(1)

where

(2)

Here, ROP is penetration rate, RPM is rotary speed, tooth_length is the average length of the bit teeth,UCS is unconfined compressive strength of the rock, D is bit diameter, WOB is applied weight on bit. Thefollowing modifiable by the user constants are used:

● C is a constant;● (aggressivity) is a formation and bit characteristic constant ranging between 20% and 100% and

normally is given the value of 35%;● (curv) is a formation-bit interaction constant and it is usually given the value of 1.5;● (flow_factor) is a constant, ranging between 50% and 100% and defines the capability of the

system to adequately clean the bit front by the cuttings.

A similar rock-bit interaction model, with adjustable parameters, is the one that has been presented byTeale [7] who introduced the concept of specific energy, the energy required by the rig to drill a unit

Figure 3—Simulation and actual casing-while-drilling data for 100-1800ft, 2.5-7.0 tons WOB, 65 RPM, shale section (adapted from [14]).

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volume of rock. The model has been used by many researchers and practitioners in the past and is givenas,

(3)

where, (Abit) is the bit diameter, (�) is the coefficient of friction between drill string and formation,converting applied WOB to torque, and (eff) is the efficiency of transmitting the rock destruction powerof the drilling rig to the rock. It has been shown that predictions from Equation (3) and Equations (1 &2) are essentially the same [11], while the values of UCS could be used to estimate rock drillability [1,12].

In this work data was utilized from publicly available end-of-well reports. Results from a total of foursections from two wells, Well_1 and Well_2 will be presented, covering sandstone and carbonateformations. Two cases are primarily carbonate formations (Well_1-Section_1 & Well_1_Section_2) andtwo cases are primarily sandstone formations (Well_1-Section_3 & Well_2-Section_1).

More specifically, Well_1-Section_1 data is the top part of the well covering 80 to 3214ft, whileWell_1-Section_2 data is deeper covering the section between 7380ft to 10270ft. The first set of sandstonedata is used from the same Well_1-Section_3, covering data between 3313 to 6348ft, while the second setcomes from Well_2-Section_1 covering the depth of 1746ft to 2573 ft.

Results and Discussion

Carbonate Data

Data analyzed for the first carbonate section are shown in Figure 4 which shows WOB, RPM, Q and bitsthat were used to drill throughS this section.

Figure 4—Rig operational data for the Well_1-Section_1 - carbonate section. (80 to 3214ft).

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In order to predict UCS, the following equation was used based on available sonic data for theformations drilled [15],

(4)

Lithology data are shown in Figure 5. Data, taken from the end-of-well report, were given every 15ftand were averaged every 50ft in order to have good but not very lengthy execution. Of course data canbe simulated every one foot. Based on the results from equation (1), UCS was estimated for every layer.For each bit run and every layer, the main adjustment factor of the bit, the user selectable parameter,ROP_factor, was manipulated in order to get good matching of ROP (Rate-Of-Penetration, ft/h) of welldata and simulation data. Final values of ROP_factor, after achieving this matching are shown versusdepth in Figure 6.

The results show that ROP_factor ranged between 2 to 10, in order to give good matching of ROP, asseen in Figure 7. Such values of ROP_factor are well within the normal range one would expect whichshows the validity of the approach. Similar results have been reported previously [12, 14, 15]. There islittle discrepancy between predicted and actual ROP but this could easily be alleviated with further

Figure 5—Lithology data for Well_1_Section_1. Depth is given in (ft), Rock Strength (UCS) is given in ksi�000’s psi.

Figure 6—ROP_factor for Well_1-Section_1, Carbonate Formations.

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adjustment of ROP_factor. In Figure 8 the final simulated drilling time curve is depicted together withlithology data.

A second section from same Well_1 was taken, deeper, between 7380 ft and 10270 ft, consisting ofonly limestone and dolomite. Following similar approach, ROP_factor was adjusted to match ROP dataand this variation is shown in Figure 9.

Figure 7—Matching of ROP_data (ROP Report) and ROP_simulation (ROP Payzone) for Well_1-Section_1, Carbonate Formations.

Figure 8—Simulated drilling time curve for Well1. In the left the lithology is shown, Carbonate Formations.

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Fairly good matching is observed for almost all ROP data, with the exception of the point at around9750ft, which shows an extremely high, for the records of this well, ROP value. This particular point hasan ROP_factor of 10. There may be additional factors playing a role at this particular depth. The data ofFigure 9 show that ROP_factor again is adjusted between the values of 2 to 10, similar to results from theprevious section.

Sandstone DataSandstone data were used from Well_1 from a section of 1010 m to 1935 m (3313 - 6348 ft). Rigoperational data are shown in Figure 11 and UCS data were computed from sonic log using equation (2)[16].

(5)

Figure 9—Variation of ROP_factor versus depth for Well_1-Section_2; Carbonate Formations.

Figure 10—Matching of ROP_data (ROP Report) and ROP_simulation (ROP Payzone) for Well_1-Section_2; Carbonate Formations.

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The ROP_factor range needed in order to match ROP data is shown in Figure 12 and it takes a bit ofsmaller values than carbonates, ranging between 1.0 and 7.5.

The comparison between ROP predictions and actual data is shown in Figure 13 while Figure 14 showsthe actual drilling time plot together with associated lithology.

Figure 11—Rig operational data for sandstone. Data from Well_1-Section_2.

Figure 12—ROP Factor for sandstone data. Well_1-Section_2; Sandstone Formations.

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The results show very good matching between predictions and measurements, even at the very highROP rates at the top sections, approaching 140 ft/h, about 14 times the average ROP rate for the remainderof the sandstone section, pointing out to the fact that matching sandstone data appears to be bit easier thancarbonate data.

The second section of sandstone data is from Well_3-Section_1, for a depth range of 1746 to 2573 m,using 12-1/4’’ bit. Similar approach was followed and the ROP_factor range versus depth is shown inFigure 15 while ROP matching is shown in Figure 16. The ROP_factor ranged between 2.0 and 8.0, whileROP matching can be considered good, for the average ROP values ranging between 5 and 40 ft/h.

Figure 13—Matching of sandstone ROP_data (ROP Report) and ROP_simulation (ROP Payzone) for Well_1-Section_3; SandstoneFormations.

Figure 14—Simulated drilling time curve for Well_1-Section_3. In the left the lithology is shown; Sandstone Formations.

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Comparison of simulation: Carbonate - Sandstone FormationsOne of the aims of this work was to compare the efficacy and adaptability to simulate carbonate andsandstone data. This comparison could be done quantitatively, by comparing the range of ROP_factorsand qualitatively by firstly, comparing the degree of matching of ROPs, but not going through rigorousstatistical evaluation because many of the operating parameters are different between the cases andsecondly, by the difficulty and time spent to achieve ROP matching.

For the latter and for the cases examined in this work we can report that no essentiall differences wereencountered when simulating carbonate and sandstone data. For the data analyzed it appears thatsimulating very high ROP was more successful in sandstone than in carbonate formations. Furthermore,the ‘easiness’ of the application was very similar, albeit both situations did require quite extensive runningtime in order to match actual data.

In Figure 17 we compare ROP_factors of carbonate and sandstone data from the same well, while infigure 18 we put all analyzed data together. Close inspection reveals that ROP_factors are of the same

Figure 15—ROP Factor for sandstone data. Well_3-Section_1; Sandstone Formations.

Figure 16—Matching of sandstone ROP_data (ROP Report) and ROP_simulation (ROP Payzone) for Well_3-Section_1; SandstoneFormations.

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order of magnitude, showing similar variation and ranging between about 2 and 10, with sandstone datarequiring the smallest ROP_factor at around 8.0.

Drilling OptimizationHaving matched the data, without any variation in the difficulties between carbonate and sandstoneformations, one can proceed to determine whether the values used of the main rig parameters, WOB,RPM, Q were optimal. For the latter of course (Q), cuttings transport and hydraulics is of importance butit contributes also to the imrpovement of ROP.

For a given section, one can work on ROP_factor and tune the simulator to match ROP, as it was doneand shown for example, in Figure 16 above. Then by saving the plot, one can run different scenarios bychanging the values of the operating parameters, like WOB, RPM, Q, even the mud weight. This is

Figure 17—Comparison of ROP factor for sandstone and carbonate formation from same Well_1 (Sections 1 and 3).

Figure 18—Comparison of ROP_factor of carbonates and sandstones from this work for all data analyzed.

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implemented in the simulator by the function ‘Custom Drilling’: One can set the desired depth on thedrilling time plot, and then custom drilling can commence, producing a depth-time curve which can beoverlayed on the screen. Several runs can then be run and such runs are shown in Figure 18.

One can see that an increase by 50% on WOB resulted in an increase of ROP (the slope of the curve)by about 40%. Increase in ROP is linear with flow rate and an increase by 31% in Q resulted in an increaseby 31% in ROP, while an increase of 50% in RPM resulted in an increase of about 14% in ROP.

One would then wonder what the combined effect of individual parameter changes may have overallin the ROP. A simulation run, not shown in Figure 18 showed that if one increases the parameter valuesas described above, then the combined increase in ROP would be approximately 100%. This brings outthe education aspect of the tool which allows the trainee to experiment and see in front of him the impacton ROP of any changes one can think of, of the rig operating parameters, keeping them of course withinthe operational constraints, or even challenge these constraints.

It is worth noting, referring to Figure 18, which is a screen shot of the main simulator screen, thesimulator also predicts applied torque, stand-pipe pressure and bit hydraulic power. It allows for many ofthe rig functions to be incorporated in the drilling process (casing, bit change, mud change) and has a costfunction to monitoring drilling costs.

ConclusionsThe drilling simulator was described which allows modeling and simulating the drilling process,maniftested as rate of penetration, taking into account rig data and formation properties. The model istuned using offset well data. Matching of well data is achieved with few adjustable parameters, the majorone being the ROP_factor, a drilling bit parameter, which lumps all parameters affecting rock-bitinteraction.

Figure 18—Optimization of drilling process for sandstone data (Well_3-Section_1) for the section between 7500ft and 8000ft. Normalconditions were of WOB�40000lbf; RPM�80; Q�643gpm; mud weight�9.7lbm/gal.

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Literature field data, of approximately 3000 ft each, covering two sections in carbonates and twosections in sandstones from two wells were analyzed to determine whether there were any differences inthe simulation of different formation types.

Matching of field data were achieved in all simulations attempted, with good matching observed,giving thus further strong evidence that the approach is good and sound. No significant differences insimulation were observed among the two types of formation. The adjustable factor took similar range ofvalues, between 1 and 10, thus giving further evidence that the approach is sound.

Optimization of data can be done, once the system is fine-tuned, by having the process of customdrilling, which allows to re-drill the particular section, changing desired parameters. Such application tothe cases studied revealed that an increase in WOB could give a large boost in ROP, while a combinationof increases in rig operational parameters could almost double the rate of penetration. Of course in a realsituation, one would have to allow for constraints for any increases implemented in rig operatingparameters.

Custom drilling process, available now in the simulator, enhances significantly the training aspects ofthe simulator, because the students can redrill any particular section, after having tuned the simulator andthey can see immediately the benefits or not of the changes implemented in one or more of the operatingrig parameters by monitoring rate of penetration.

AcknowledgementsThe authors would like to acknowledge Professor George Cooper, the founder of the simulator, whoseguidance and support throughout the development of this work has been instrumental.

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