SPE-201638-MS

Evaluation of Single Fracture Stage Performance with Integrated RockMechanics and Pressure Transient Analysis

Ahmed Farid Ibrahim, Mazher Ibrahim, Matt Sinkey, Thomas Johnston, and Wes Johnson, Shear Frac Group LLC

Copyright 2020, Society of Petroleum Engineers

This paper was prepared for presentation at the SPE Annual Technical Conference & Exhibition originally scheduled to be held in Denver, Colorado, USA, 5 – 7October 2020. Due to COVID-19 the physical event was postponed until 26 – 29 October 2020 and was changed to a virtual event. The official proceedings werepublished online on 21 October 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.

AbstractThe most common stimulation technique for shale production is multistage hydraulic fracturing. Estimatingfracture geometry is a focal parameter to judge the fracture operation and predict the well performance.Different direct and indirect techniques can be used for fracture diagnostics to estimates fracture geometries.The current study combines fracture measurements and pressure transient analysis to estimate fracturesurface area on each stage and to estimate production as a pseudo production log.

The numbers and kinds of fractures were calculated as a function of treating pressures, injection rates,proppant concentrations, and formation properties to compute fracture surface area (FSA). Pressure transientanalyses were then conducted with the leak-off data upon completion of each frac stage to estimate theproducing surface (PSA). The fall-off data was processed first to remove the noise and water hammeringeffects. The PTA diagnostic plots were used to define the flow regime and the data were matched with ananalytical model to calculate producing surface area.

Tensile and shear fractures are both created during the injection of frac fluids. Shear fractures are causedby movement in already existing natural (fluid expulsion) fractures found in all shale source rocks. Shearfractures form a pressure below the minimum horizontal stress. These shear fractures take advantage ofthe rock fabric and develop higher surface area than tensile fractures for the same given volumes of waterand sand.

FSA is a measure of permeability enhanced area due to hydraulic fracturing. Producing surface area isthe resulting effective flow areaconnected to the wellbore. Diagnostic plots showed a linear and radial flowregime depending on the formation and the completion design. Good correlations were found between PSAand FSA results. In general, higher FSA produces higher PSA. In cases where producing surface area washigher than expected from fracture surface area, communication was found with offset wells. When FSAhigher than PSA were found, it was usually caused by increased stress from too close offset wells.

Combining FSA and PSA measurements provides forecasts of production for each stage and helps tooptimize well spacing at the end of each frac stage.

2 SPE-201638-MS

IntroductionHistorically multistage hydraulic frac jobs pumped large volumes of water with different proppants andviscosity modifiers at high rates to fracture rock at high pressure (Ibrahim et al. 2018). There is movementtoward creating more fracture surface area by shear fracturing (Zhang et Li, 2016). Shear fracturing occurswhen existing natural fractures caused by fluid expansion are re-fractured by injection of thin fluid at lowerinjection rates. Shear fractures form at pressures below the minimum horizontal stress (Zhang et Li, 2016).They produce more fracture surface area per volume of water injected and typically place designed sandwith 30% less water and time.

The number of shear and tensile fractures were measured second-by-second from treating pressure datathat is cleaned and processed as fracs are pumped. It is now possible to maximize the number of shearfractures by adjusting fluid rates, sand concentrations and friction reducers live for maximum shearing.Fracture surface area (FSA) has units of square feet (or square meters), which are the units of permeability.When FSA increases, permeability and production increase. At the end of each frac stage after the pumpswere shut down, the producing surface area (PSA) was computed for each stage. Knowing the permeabilityincrease and the volume where the increase occurred, production forecasts were made from correlations ofFSA and PSA. (Ibrahim et al. 2020) showed that well performance depends on the fracture surface area andits' distribution around the wellbore.

Since the first known commercial hydraulic fracturing operation in 1949, the industry has tried tobetter understand and optimize hydraulic fracturing treatments. There are numerous fracture diagnostictechnologies shows in Table 1. The ability and main limitations of each technique are also listed in thetable. Indirect techniques include well testing, net pressure calculation, micro seismic and production dataanalysis. Direct diagnoses near-wellbore provide local measurements of fractures at the wellbore, usingradioactive tracers, temperatures, caliper and production logs, tiltmeters and borehole images.

Table 1—Capabilities and limitations for different direct and indirect fracture diagnostic methods after (Barree et al. 2002).

Group Fracture Diagnostic Method Main Limitations

Surface tiltmeter mapping • Cannot resolve individual and complex fracture dimensions

• Mapping resolution decreases with depth (fracture azimuth ±3° at3,000-ft depth and ±10° at 10,000-ft depth)

Downhole tiltmeter mapping • Resolution in fracture length and height decreases as monitoring-welldistance increases

• Limited by the availability of potential monitoring wells

• No information about proppant distribution and effective fracturegeometry

Far fiield, duringfracturing

Microseismic mapping • Limited by the availability of potential monitoring wells

• Dependent on velocity-model correctness

• No information about proppant distribution and effective fracturegeometry

Radioactive tracers • Measurement in near-wellbore volume

• Provides only a lower limit for fracture height if fracture and wellpath are not aligned

Temperature logging • Thermal conductivity of different formations can vary, skewingtemperature log results

• Post-treatment log requires multiple passes within 24 h after thetreatment

• Provides only a lower limit for fracture height if fracture and wellpath are not aligned

Production logging • Provides only information about zones or perforations contributing toproduction in cased-hole applications

Near wellbore,after fracturing

Borehole image logging • Run only in open hole

SPE-201638-MS 3

Group Fracture Diagnostic Method Main Limitations

• Provides fracture orientation only near the wellbore

Downhole video • Run mostly in cased holes and provides information only about zonesand perforations contributing to production

• May have openhole applications

Net-pressure fracture analysis • Results depend on model assumptions and reservoir description

• Requires "calibration" with direct observations

Well testing • Results dependent on model assumptions

• Requires accurate permeability and reservoir pressure estimates

Model based

Production analysis • Results dependent on model assumptions

• Requires accurate permeability and reservoir pressure estimates

Different techniques have been used to evaluate the fracture operations including chemical tracers.Many projects used chemical tracers to evaluate the production contribution of each fractured stage and tounderstand the communication between the fractured wells (Tian et al. 2016). Success has been limited andthese tests are expensive. Real-time micro seismic monitoring (Gutierrez, Murillo et al. 2010), and fiberoptics (Pakhotina et al. 2020) methods are used for fracture geometry. Typically, micro seismic, fiber andtiltmeter studies are reserved for field pilot studies due to the high cost and are not economically feasiblyon large numbers of wells.

Rate transient analysis (RTA) and pressure transient analysis (PTA) are indirect methods that can be usedfor fracture diagnostics and estimate the fracture and formation parameters. Linear flow regime solution(El-Banbi and Wattenbarger 1998, Ibrahim and Wattenbarger 2006) can be used to estimate the fracturegeometry. RTA and PTA analysis estimate the effective fracture geometry and considerthe interference effectbetween the stages.

In this study, a combination of measured fractures and pressure transient analysis were used to estimatethe fracture and producing surface area for each stage. The computed fracture surface area is used as aproxy production log, where the relative numbers of fractures are used to allocate total well production andcorrelate fracture and producing surface area. This method is an improvement over simple allocation basedon the number of frac stages in a well.

MethodologyFor each stage, hydraulic fracturing operations started by injection of pad volume (without proppant)followed by injection of slurry with proppant. Fig. 1 shows a typical pressure and injection rate during thefracturing operation. Typical practice was to shut-in for a period after the pad and the main stage to recordpressure data and estimate instantaneous shut-in pressure (ISIP).

4 SPE-201638-MS

Figure 1—Typical pressure and injection rate during the fracturing operation including the injection and the fall-off periods.

During the injection period, the treating pressure, injection rate, proppant concentrations will be used tocalculate the number of shear and tensile fractures. The fractures kind and number were then correlated tothe fracture surface area (FSA).

During the leak-off period, pressure transient analysis of fall-off data was used to interpret producingsurface area (PSA) available for the fracturing fluid to leak.

Fracture MeasurementsShear Frac Group LLC (SFG) patented technique uses hydraulic fracturing data with a primarily focus onsurface or bottom hole treating pressure changes to compute the numbers and kinds of fractures. Fracturesurface area (FSA) was calculated using algorithms or neural networks. This approach converts pressure(energy) into rock stress for fracturing by cycling stress waves through the rock volume. Water dilatesfractures and draws sand into them with Bernoulli forces. Sand thus packs into the fractures, plugging themenough to reduce water leak-off. This converts energy from pressure into stress-causing shear failure or shearexpansion if the rock breaks. The process observed is dilation of fractures > sand filling > leak-off reduction> stress build-up > rock failure. This process is repeated. The goal of shear fracturing is to propagate stresswaves for thousands of seconds to shatter unconventional rocks and efficiently produce hydrocarbons.

Two different fracture systems can be created during the injection of the fracture fluids; tensile (Fig. 2a)and shear fracturing (Fig. 2b). Shear fractures develop at lower pressures than tensile fractures and are thepreferred fractures for transferring energy. Shear fractures move more water at lower pressure because theyhave more surface area. Shear fractures only result when fluids enter the rock at a rate approximately equalto the rate at which the rock volume can accept fluid without tensile ruptureof the rock. When injectionrates and resulting pressures are too high, planar fractures are dominant. When injection rates are matchedto rock weaknesses, the treatment pressure is about 30% lower and shear fractures are in the majority.

SPE-201638-MS 5

Figure 2—Two images representing tinsel frac with clear bi-wingfracture (A) compared to shear fracs with secondary branches (B).

Fall off analysisFig. 3 shows the procedures used to conduct pressure transient analysis from falloff data after eachinjection stage. Injection rate, tubing head pressure (THP), and completion data were collected. THP wasthen converted to bottom hole pressure (BHP) using fluid density, tubing dimensions, and the formationdepth. The data were then processed to remove any noise or water hammer effects. Pressure gauges aresynchronized with rate gauges to make sure the falloff data started when the well was shut-in.

Figure 3—Procedures for analyzing fall off data after each stage.

Fracture surface area is created throughout the pumping (Belyadi et al. 2019). Fall off analyses measurethe connected surface area (PSA) available for fracturing fluid to leak off. That includes shear and tensilefractures connected to the wellbore.

Falloff analysis for the pressure data after the injection of slurry can be used to estimate the fracturecomplexity. Fig. 4 shows a diagnostic plot for the falloff data. A log-log plot was constructed for theanalyzable falloff period. The x-axis of the log-log plot is the elapsed time (Δt) with respect to shut-intime (tp) and the y-axis displays the pressure difference (Δp = p − pt=0) and the derivative of the pressuredifference with respect to the logarithm of the superposition time function. The derivative in Fig. 4 shows ½slope straight line which indicates linear flow regime of complex fractures followed by nonflow boundaryflow regime as the derivative declines. Fig. 5 shows another diagnostic plot for another stage from a differentwell. The derivative is a horizontal line that indicates a radial flow regime followed by a half slope linefor the linear flow regime.

6 SPE-201638-MS

Figure 4—Log-Log diagnostic plot for the falloff data for 5 clusters per stage case.

Figure 5—Log-Log diagnostic plot for the falloff data for 12 clusters per stage case.

FSA Versus PSA scenariosFSA measures the created surface area which measures the system permeability increase from the hydraulicfracture treatment. PSA from the falloff analysis measures the producing area that is available for fracturefluid to leak from the fractures into the formation. FSA usually correlates to the PSA. Fig. 6 shows threemain areas for the different scenarios as follow:

a. FSA = PSA is the general case when the stage has a good connection to wellbore.b. FSA > PSA is an indication of stage damage. Many possibilities could contribute to this but most like

due to fracture closure caused by increased stress from nearby offset wells.c. PSA > FSA is when the flow area is higher than the created stimulated areaindicating a communication

was found with an offset well.

SPE-201638-MS 7

Figure 6—scenarios for PSA compared to FSA fracture measurements during fracturing injection.

Results and Discussion

Field CaseIn this case, an injection and fall-off data were analyzed for a three-well pad as shown in Fig. 7. The threewells were drilled in the Eagle Ford shale formation with a spacing of 600 ft., with the nearest offset well at800 ft with two years on production before the new wells were completed. Cumulative gas production of theoffset was 0.55 Bscf. All wells in the study had a lateral lengthof two miles and completed with 43 stages.

Figure 7—gun barrel chart for the studied pad.

Fall off analyses were conducted on leak-off data after each stage. The initial fall-off data was maskedby the water hammering effect for around two minutes after the shut-in as shown in Fig. 8a. During theanalysis, these data were excluded from the data and the diagnostics were mainly based on the data afterit. Fig. 8b show The semi log MDH plot for the falloff data. The early pressure data were fluctuated dueto the water hammer effect at the shut-in. Late data was dominated by the fluid leak-off behavior whichis used in the diagnostic plot

8 SPE-201638-MS

Figure 8—pressure data and MDH plots to show the water Hammer effect.

A diagnostic plot was then used to define the flow regime. Fig. 9 show a linear flow regime representedby a half-slope line. For the same pressure drop, the location of the derivative curve defines the value for

, where Ac is the stimulated surface area and k is the effective permeability. Fig. 9 shows the diagnosticplot for three stages. Linear flow was observed in the three stages. However, the derivative for stage Amoved up which an indication of lower stimulated area.

SPE-201638-MS 9

Figure 9—log-log diagnostic plot for three stages with a different stimulated area.

The next step as shown in Fig. 3 was to match the pressure with an analytical model (Lougheed et al.2013). A multistage fractured horizontal well model was used to match the pressure and its derivative data.The well length was assumed to be the stage length of 160 ft and the number of fractures was assumed to bethe number of the clusters. The analytical model has matched the fall-off pressure and the derivative databy changing the stimulated area (Fig. 10). The producing surface area (PSA) can be calculated as follows.

(1)

Where; hf is the height of the fracture, Nf is the number of fractures, and Xf is the fracture half-length.The same analysis was conducted on each stage to estimate the producing surface area. Fig. 11

summarizes the PSA from the fall-off analysis for well 2. The results show the distribution of the stimulatedarea around the well. A complex fracture system was observed where the stimulated area was different fromone stage to another. This technique provides an important result for the distribution of the fracture areaaround the wellbore, where the well performance is different from uniform fracture length in all stages witha complex system as shown by (Ibrahim et al. 2020).

10 SPE-201638-MS

Figure 10—analytical model matching for pressure data (A), diagnostic plot data (B).

SPE-201638-MS 11

Figure 11—PSA Results for Well-2 based on fall off analysis.

Measurements were conducted to calculate the number of shear and tensile fractures. FSA was thencalculated based on these counts. Fig. 12 shows the agreement was found between the PSA and FSA. Theestimation of PSA required 15-30 minutes fall off data after each stage. Correlating FSA to PSA can beused then to estimate the stimulated area for the stages with missing fall off data, or to estimate how manyfractures are required to achieve a desired flow area.

Figure 12—PSA from the fall-off analyses are in a good agreement with FSA from geomechanics analysis in Well-2.

A similar analysis was conducted for wells 1 and 3 in Figure 7. Fig. 13 shows good agreement betweenthe PSA and FSA in well 1. Some stages (highlighted with green) had high PSA comparing to FSA. Apossible reason for this behavior is the interference of these stages with offset well stages, as was observedby offset pressure evaluation. The offset well produced for 2 years at the completion of well-1. Agrawaland Sharma (2018) presented that in depleted reservoirs, stress changes arising from reservoir drainagesignificantly affect growth of fractures, attracting them towards depleted regions. According to Mayorgaand Coenen (2019) during hydraulic fracturing, tip energy is usually diverted towards the parent well, dueto the reduction of localized stresses caused by production. In our well 1 fluid leak off is interpreted to behigher than was predicted from measured fracture surface area. As a result, the available area for a fluidleak-off in these stages could be higher than the area created by the fracturing fluid injection.

12 SPE-201638-MS

Figure 13—Comparison between FSA and PSA for Well-1.

Method ValidationIn this section, the shear counts were validated as a production log method. As the shear counts are correlatedto the stimulated surface area. It can be used to allocate the well production per each stage. Fig. 14 shows acomparison between shear count per each stage and cumulative oil production based on the chemical tracer.Some stages were missing from the chemical tracer analysis; however, shear counts can be measured foreach stage during the fracturing operation using the available data of treating pressure and injection rate.

Figure 14—validating the shear count calculations with chemical tracers.

Future WorkThe current study results will be used to predict the future production on stage level and evaluate thecompletion efficiency for the well.

ConclusionsThis study combined pressure transient with fracture counting mechanism to diagnose hydraulic fracturingperformance per stage. The main conclusions drawn from this study are summarized as follows:

1. Analyzing the available fall off data after each stage can be used to estimate producing area.2. Good agreement was found between producing surface area (PSA) and fracture surface area (FSA)

in stages without interference from offset wells.3. Measured fracture surface area (FSA) can be used for production allocation from each stage.

SPE-201638-MS 13

4. Combing PSA and FSA measurements can provide forecasts of production at the completion of eachstage in addition to define the percentage of interference between wells.

NomenclaturesFSA fracture surface area from sum of fracture counts

hf the height of the fractureNf the number of fractures

PSA Producing fracture surface area from pressure fall-off analysisXf the fracture half-length

(Δt) time with respect to shut-in time (tp)Δp the pressure difference between the shut-in pressure and the flowing pressure.

ReferencesAgrawal, Shivam, and Sharma, Mukul M. 2018. Impact of Pore Pressure Depletion on Stress Reorientation and Its

Implications on the Growth of Child Well Fractures. Presented at the SPE/AAPG/SEG Unconventional ResourcesTechnology Conference, Houston, Texas, USA. 2018/8/9/. https://doi.org/10.15530/URTEC-2018-2875375.

Barree, R. D., Fisher, M. K., and Woodroof, R. A. 2002. A Practical Guide to Hydraulic Fracture DiagnosticTechnologies. Presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas. 2002/1/1/.https://doi.org/10.2118/77442-MS.

El-Banbi, Ahmed H. and Wattenbarger, Robert A. 1998. Analysis of Linear Flow in Gas Well Production. Presented atthe SPE Gas Technology Symposium, Calgary, Alberta, Canada. 1998/1/1/. https://doi.org/10.2118/39972-MS.

Emrani, A. S., Ibrahim, A. F., and Nasr-El-Din, H. A. 2017. Evaluation of Mobility Control with Nanoparticle-StabilizedCO2 Foam. Presented at the SPE Latin America and Caribbean Petroleum Engineering Conference, Buenos Aires,Argentina. 2017/5/17/. https://doi.org/10.2118/185551-MS.

Gutierrez Murillo, Guillermo, Sanchez, Adrian C., Rios, Austreberto et al 2010. Microseismic Hydraulic FractureMonitoring To Determine The Fracture Geometry In Coyotes Field, Chicontepec. Presented at the SPE Latin Americanand Caribbean Petroleum Engineering Conference, Lima, Peru. 2010/1/1/. https://doi.org/10.2118/139155-MS.

Ibrahim, Ahmed F., Nasr-El-Din, Hisham A., Rabie, Ahmed et al 2018. A New Friction-Reducing Agent for Slickwater-Fracturing Treatments. SPE Production & Operations 33 (03): 583–595. https://doi.org/10.2118/180245-PA.

Ibrahim, Ahmed Farid, Ibrahim, Mazher, Sinkey, Matt et al 2020. Integrated RTA and PTA Techniques to Study SingleFracture Stage Performance. Presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference,Virtual. 2020/7/20/. https://doi.org/10.15530/urtec-2020-2121.

Ibrahim, Mazher Hassan and Wattenbarger, Robert A. 2006. Analysis of Rate Dependence in Transient Linear Flow inTight Gas Wells. Presented at the Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, UAE.2006/1/1/. https://doi.org/10.2118/100836-MS.

Lougheed, Dylan, Ewens, Steve David, and Santo, Marty. 2013. Is That Radial Flow? What Can be Learned From BuildupAnalysis of Multiply-Fractured Horizontal Wells In Unconventional Reservoirs. Presented at the SPE UnconventionalResources Conference-USA, The Woodlands, Texas, USA. 2013/4/10/. https://doi.org/10.2118/164525-MS.

Mayorga, Justin and Coenen, Erica. 2019. Standardizing Offset Pressure Characterization for Infill Completion inUnconventional Reservoirs. Presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference,Denver, Colorado, USA. 2019/7/31/. https://doi.org/10.15530/urtec-2019-1109.

Pakhotina, Iuliia, Sakaida, Shohei, Zhu, Ding et al 2020. Diagnosing Multistage Fracture Treatments with DistributedFiber-Optic Sensors. Presented at the SPE Hydraulic Fracturing Technology Conference and Exhibition, TheWoodlands, Texas, USA. 2020/1/28/. https://doi.org/10.2118/199723-MS.

Scott, Michael Paul, Johnson, Raymond Leslie, Datey, Ashish et al 2010. Evaluating Hydraulic Fracture Geometry fromSonic Anisotropy and Radioactive Tracer Logs. Presented at the SPE Asia Pacific Oil and Gas Conference andExhibition, Brisbane, Queensland, Australia. 2010/1/1/. https://doi.org/10.2118/133059-MS.

Tian, Wei, Shen, Tong, Liu, Junrong et al 2016. Hydraulic Fracture Diagnosis Using Partitioning Tracer in Shale GasReservoir. Presented at the SPE Asia Pacific Hydraulic Fracturing Conference, Beijing, China. 2016/8/24/. https://doi.org/10.2118/181857-MS.

Zhang, Z., and Li, X. The Shear Mechanisms of Natural Fractures during the Hydraulic Stimulation of Shale GasReservoirs. Materials 2016, 9, 713. https://doi.org/10.3390/ma9090713