Private and Confidential Carrie Glaser, Chief Petrophysicist, Fracture ID Tim Foltz, Senior Geologist, Lario Oil & Gas Company Kit Clemons, VP – Geoscience, Lario Oil & Gas Company Petrophysical analysis of drillbit geomechanics data identifies production drivers in the Wolfcamp D
Tim Foltz, Senior Geologist, Lario Oil & Gas Company
Kit Clemons, VP – Geoscience, Lario Oil & Gas Company
Petrophysical analysis of drillbit geomechanics data identifies production drivers in the Wolfcamp D
Presenter
Presentation Notes
In this case study we used geomechanical properties acquired in several wolfcamp D lateral wells. The data was originally acquired to optimize completions. Lario’s completions organizations has built an effective workflow for on-the-fly adjustments to their stage-by-stage completions based on the mechanical logs and real-time treatment response.
Private and Confidential
Background: Wolfcamp D
2
Type Log
WFMP D Mean TOC Wolfcamp D Ro (Calc)
WFMP D Isochore WFMP D vClay
Presenter
Presentation Notes
Midland and Martin county, oriented in a north-south trend. Thickness of the WFMPD varies across our acreage from ~350’ to ~250’ thick, we are solidly in the oil window based on T-max, Ro and produced oil data, generally with TOC at ~3% - 4.2% and low V-clay values typically less than 20%. we acknowledge that the Cline and the Wolfcamp D are the same formation and age (Pennsylvanian) but use the RRC and PXD naming convention which generally refers to it as the Wolfcamp D primarily due to field rules and lease allocations.
Private and Confidential
Background: Wolfcamp D Production
3
Landing Points / target definitions(selected based on calculated HC Saturation and brittle mineral fraction)
Lario Wolfcamp D Production
P10
P50
P90
First 3mos Cum Oil
0.0%10.0%20.0%30.0%40.0%50.0%60.0%70.0%80.0%90.0%
100.0%
First 3 mon oil cum/lat ft
WD 3 Month Oil Cum Distribution(All Data)
Lario Wells
Presenter
Presentation Notes
For the Background Wolfcamp D Production slide: We pick our landing target based on resistivity, Delta Log R, Oil saturations and V-clay, using TOC and porosity to a lesser extent. We have seismic over most of our acreage and will also use it to target more brittle zones or to stay away from significant fractured/faulted zones. We of course also us the FracID data for targeting offset wells and then for real-time completion techniques. the takeaway is our wells represent better than the P30 of the whole field. I would point out that these are early time production values (3month cum oil only) but we have seen some P10 results and we think there were some operational challenges with those that fall below the P50 line (with respect to the Lario only probit).
Private and Confidential
Background: Drillbit Geomechanics
4
1960s – 1970s Drilling vibrations measured at surface are used for reservoir characterization with tri-cone bits (SPE 3604 by
Esso Production Research) PDC bits are introduced (1970s)
1980s Instrumented subs containing accelerometers are used by Shell Research and partners to characterize drill bit
behavior in a laboratory and develop surface tools to mitigate stick-slip
1990s Bit damage caused by torsional resonance (HFTO) recognized with PDC bits using downhole vibration sensors
(SPE 49204 Amoco E & P Technology Group) Stick-slip behavior characterized using down-hole accelerometers; development of high-frequency burst
recorders (SPE 52821 Baker Hughes Inteq)
2000s Commercialization of drilling diagnostics using at-bit accelerometers Development of high-bandwidth downhole vibration recorders used to characterize HFTO
Recent Advances 2014 – NOV “BlackBox” downhole dynamics logger improved to tri-axial recording system with 1kHz sample
rate; this allows development of processing algorithms for elastic properties 2017 – Improved NOV BlackBox tool (Eclipse) 2018 – Sanvean PuK tool becomes available for elastic property quantification with 100 Hz accelerometers and
1500 Hz sample rate 2018 – Halliburton in-bit sensor package (Cerebro) becomes available for elastic property quantification
Use of vibration data limited to lab and surface applications
Faster computing and higher fidelity downhole tools expand application of vibration data for mechanical formation properties primarily for completions applications.
MWD Vibration Data Stream Time-line
First generation of downhole vibration tools developed. Data is used for drilling optimization and bit design.
Presenter
Presentation Notes
The mechanical property data we are using comes from downhole accelerometers recording drilling-induced vibrations. Acquisition of vibration data generated by the bit-rock interaction while drilling began in the 1960’s with a tool that measured vibrations at the kelly. It was recognized at that time that different vibration patterns correlated to bit damage (Lutz et al, 1972). In the 1990’s, downhole vibrations sensors were deployed. These sensors were used to characterize and mitigate drilling inefficiencies such as stick-slip and bit whirl, reduce bit damage and ultimately optimize bit and BHA design (Warren and Oster, 1998, Robnett et al, 1999). Improvements in the downhole sensors, driven by the commercial value of drilling diagnostics and improved bit design, continued through the 2000s. In 2014, advances in tool design and a recognition of the potential value of mechanical properties not just to drilling engineers but also to unconventional well stimulation design led to a new technique for processing the downhole vibration data. The new models, using much broader bandwidth data than was possible with earlier versions of the tool, convert the vibration data to elastic mechanical properties of the formation. These properties were used in completions engineering applications including stress-balanced stage designs and like-rock groupings. Very recently, advances in the downhole tools and the workflows to process the vibrations data have been rapid. Simultaneously, workflows have been developed to expand the application of the data to include petrophysical reservoir characterization, as demonstrated here. Over the next several years, it is likely that this data stream will become ubiquitous, and the tools will become more sophisticated and better integrated with other downhole systems.
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Mechanical Property Sensitivities
5
1. Anisotropy – Clay Volume
In unconventional reservoirs, anisotropy is largely a function of clay mineral alignment, and is increased by the presence of low-aspect ratio organic content and laminar textures.
In contrast to the atomic measurement of a gamma ray tool, the vibration measurement is driven by bulk deformation of the rock while drilling. We hypothesize that this imparts sensitivity to laminar texture rather than the specific mineralogic content.
Sone and Zoback (2013)
Presenter
Presentation Notes
Mechanical properties can be used for petrophysical modeling because different mechanical logs have different sensitivities with respect to lithology. In fact, a critical assumption that we have to make is that the mechanical properties interpreted from the vibration data are ONLY a function of lithology and that other forces that would influence them either occur over very finite intervals (e.g. fractures) or are consistent for the length of the wellbore (e.g. net pressure). There are cases where these assumptions are not true, most notably in the presence of depletion. Sudden changes in the interpreted lithology should be evaluated in the context of the potential for these types of aberrations from the baseline assumption. The first thing we want to solve for in the petrophysical model is clay volume. The mechanical properties generated by the vibration data includes a measure of anisotropy comparable to an uncalibrated shear modulus. In unconventional reservoirs, this term is shown to strongly correlate to clay volume. It is important to understand that the vibration data is influenced by the deformation and destruction of the bulk rock, and therefore it inherently sensitive to the rock fabric. The degree of laminarity we assume to be most strongly driven by the laminar distribution of clay minerals, but that relationship may be variable and non-linear. In contrast, the gamma ray responds specifically to the radionuclide content of the mineral structure and clay mineral surfaces, which makes it sensitive only to mineralogy and not to texture. In this comparison, mechanical anisotropy may more accurately be thought of as an indicator of SHALE volume, but it is scaled to represent clay.
Private and Confidential
Mechanical Property Sensitivities
6
1. Anisotropy – Clay Volume
In unconventional reservoirs, anisotropy is largely a function of clay mineral alignment, and is increased by the presence of low-aspect ratio organic content and laminar textures.
In contrast to the atomic measurement of a gamma ray tool, the vibration measurement is driven by bulk deformation of the rock while drilling. We hypothesize that this imparts sensitivity to laminar texture rather than the specific mineralogic content.
This study will show that a lithology characterized by high organic content but low anisotropy is optimal for production. We can hypothesize that this rock will have low clay volume and high-aspect ratio organic content.
Sone and Zoback (2013)
Presenter
Presentation Notes
Mechanical properties can be used for petrophysical modeling because different mechanical logs have different sensitivities with respect to lithology. In fact, a critical assumption that we have to make is that the mechanical properties interpreted from the vibration data are ONLY a function of lithology and that other forces that would influence them either occur over very finite intervals (e.g. fractures) or are consistent for the length of the wellbore (e.g. net pressure). There are cases where these assumptions are not true, most notably in the presence of depletion. Sudden changes in the interpreted lithology should be evaluated in the context of the potential for these types of aberrations from the baseline assumption. The first thing we want to solve for in the petrophysical model is clay volume. The mechanical properties generated by the vibration data includes a measure of anisotropy comparable to an uncalibrated shear modulus. In unconventional reservoirs, this term is shown to strongly correlate to clay volume. It is important to understand that the vibration data is influenced by the deformation and destruction of the bulk rock, and therefore it inherently sensitive to the rock fabric. The degree of laminarity we assume to be most strongly driven by the laminar distribution of clay minerals, but that relationship may be variable and non-linear. In contrast, the gamma ray responds specifically to the radionuclide content of the mineral structure and clay mineral surfaces, which makes it sensitive only to mineralogy and not to texture. In this comparison, mechanical anisotropy may more accurately be thought of as an indicator of SHALE volume, but it is scaled to represent clay.
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2. Young’s Modulus - Porosity
Young’s Modulus has a strongly negative relationship to porosity.
In the workflow presented here, mineralogic drivers of Young’s Modulus variability are accounted for in order to estimate total porosity.
Mechanical Property Sensitivities
7
Shukla et al. (2013)
Presenter
Presentation Notes
In this workflow, we will leverage the relationship between Young’s Modulus and porosity. It should be noted that there are certainly alternative methods for this type of analysis, particularly if only a single parameter is needed. For example, a conceptually similar workflow was presented by in a 2010 paper from Halliburton and BP using a neural network workflow and cased hole logs to accomplish something similar to what we are doing here. Buller, D., Hughes, S. N., Market, J., Petre, J. E., Spain, D. R., & Odumosu, T. (2010, January 1). Petrophysical Evaluation for Enhancing Hydraulic Stimulation in Horizontal Shale Gas Wells. Society of Petroleum Engineers. doi:10.2118/132990-MS
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3. Poisson’s Ratio – Mineralogy
Poisson’s Ratio is used in this workflow to determine bulk mineralogy.
Once clay and kerogen are solved for using anisotropy and gamma ray, Poisson’s Ratio’s mineralogic sensitivity can distinguish between quartz and carbonate minerals.
Significant dilution of the clastic group with plagioclase can reduce the accuracy of this methodology.
Mechanical Property Sensitivities
8
Rock Physics Handbook and Others
Calcite
Dolomite
Quartz
Clay
Kerogen
Pyrite
Plagioclase
Anhydrite
0
0.1
0.2
0.3
0.4
0 10 20 30 40 50
Pois
son'
s R
atio
Young's Modulus (Mpsi)
Presenter
Presentation Notes
Mineralogy is calculated after solving for clay and kerogen volumes by leveraging the mineralogic-end member difference between quartz and the carbonate minerals.
Private and Confidential
3. Poisson’s Ratio – Mineralogy
Poisson’s Ratio is used in this workflow to determine bulk mineralogy.
Once clay and kerogen are solved for using anisotropy and gamma ray, Poisson’s Ratio’s mineralogic sensitivity can distinguish between quartz and carbonate minerals.
Significant dilution of the clastic group with plagioclase can reduce the accuracy of this methodology.
Four mineral groups are solved for in the lateral using MWD gamma ray, mechanical anisotropy and Poisson’s Ratio** estimated by drillbit vibration data.
* Accessory minerals are accounted for in correlative mineral group** Model assumes unity to be fully defined
0
0.1
0.2
0.3
0.4
0 10 20 30 40 50
Pois
son'
s R
atio
Young's Modulus (Mpsi)
Mechanical Property Sensitivities
9
Carbonate
Quartz/Feldspar
Clay
Kerogen
Pyrite*
Presenter
Presentation Notes
The model is simplified by having four mineralogic groups. The components of each group of determined by calibration to XRD and wireline logs. For example, the clay group will be a constant, specified ratio of individual clay mineral species, carbonate will be a defined ratio of calcite to dolomite, etc. Accessory minerals can be accommodated by relating them to one of these groups. This calibration is the first step in our workflow.
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Petromechanical Workflow Diagram
10
Pilot well: petrophysics to mechanical properties
Lateral well: mechanical properties to petrophysics
Pilot well triple – combo data is used for petrophysical interpretation
(mineralogy, porosity)
Petrophysical data are used to model Young’s Modulus and Poisson’s
Ratio
Mechanical properties (Young’s Modulus, Poisson’s Ratio &
anisotropy) are calculated from drillbit vibrations and combined
with gamma ray in the lateral
The pilot well calibration is used to calculate petrophysical
properties from mechanical properties in the lateral wellbore
Pilot Well Model Calibration Lateral Well Model Application
Presenter
Presentation Notes
The workflow is designed to build calibration parameters in a pilot well using wireline and any available lithologic information. First a normal petrophysical model of porosity and mineralogy is generated, and from these, Young’s Modulus and Poisson’s Ratio are estimated. These parameters are then brought into the lateral where Young’s Modulus and Poisson’s Ratio are available from the vibration data, and the model can be reversed to calculate the petrophysical curves from the geomechanical curves.
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Petromechanics Workflow: Calibration
11
Wireline logs in a pilot well are used to estimate four-group mineralogy and porosity.
Vertical Pilot Well
Presenter
Presentation Notes
Stepping through the workflow in more detail, we start with a pilot well with triple combo data, plus or minus sonic. We interpret our four mineralogy groups and porosity.
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Petromechanics Workflow: Calibration
12
Wireline logs in a pilot well are used to estimate four-group mineralogy and porosity.
The mineralogy and porosity are used to model Poisson’s Ratio and Young’s Modulus. The modeled logs can be compared to the sonic data for additional calibration.
Variations between the mechanical properties can be attributed to errors in both models as well as physical differences manifested in the two methodologies.
Vertical Pilot Well
Presenter
Presentation Notes
The mineralogy and porosity are used to generate modeled Young’s Modulus and Poisson’s ratio, leveraging the mineralogic end member values for Poisson’s Ratio and the porosity dependence of Young’s Modulus. These modeled curves can be validated against sonic-derived mechanical properties, keeping in mind that the model represents a more static version of the properties (as does the vibration data).
Private and Confidential
Petromechanics Workflow: Calibration
13
Wireline logs in a pilot well are used to estimate four-group mineralogy and porosity.
The mineralogy and porosity are used to model Poisson’s Ratio and Young’s Modulus. The modeled logs can be compared to the sonic data for additional calibration.
Variations between the mechanical properties can be attributed to errors in both models as well as physical differences manifested in the two methodologies.
This modeling technique generates calibration parameters that allow us to reverse the model, calculating mineralogy and porosity from the Young’s Modulus and Poisson’s Ratio derived from drillbit vibration measurements.
Vertical Pilot Well
The model can now be applied in the lateral where only drillbit vibration data and gamma ray are acquired.
Presenter
Presentation Notes
In this case, the vibration data was also acquired in the pilot and can be normalized to the modeled Young’s and Poisson’s. This should guide any normalization that is required in the lateral wells, especially if there is a material difference between the modeled, static properties and the dynamic sonic properties, which mostly manifests in the Young’s Modulus. At this point you could reproduce your porosity and mineralogy using the vibration-derived data to QC the mechanical data. Otherwise the model is ready to be applied in the lateral.
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Petromechanics Workflow: Lateral Wells
14
Calibration parameters generated in the pilot are applied in the lateral wellbores to estimate mineralogy and porosity.
This interpretation leverages the mechanical property sensitivies previously discussed.
MWD gamma ray and mechanical data are likely to require normalization prior to interpretation; current generation of vibration recording tools are uncalibrated.
Mineralogy and porosity model applied in lateral producing wells using calibrated drillbit vibration data.
GR
0 –
200
PhiT
0 –
0.2
PR0.
1 –
0.4
YM 0 -1
0
Presenter
Presentation Notes
The model was applied in the seven available Wolfcamp D horizontal wells. Mechanical properties and gamma ray were initially normalized to the pilot. Clay volume is a direct normalization from the anisotropy curve (shear modulus).
Private and Confidential
Petromechanics Workflow: Lateral Wells
15
Calibration parameters generated in the pilot are applied in the lateral wellbores to estimate mineralogy and porosity.
This interpretation leverages the mechanical property sensitivies previously discussed.
MWD gamma ray and mechanical data are likely to require normalization prior to interpretation; current generation of vibration recording tools are uncalibrated.
Mineralogy and porosity model applied in lateral producing wells using calibrated drillbit vibration data.
GR
0 –
200
PhiT
0 –
0.1
Direct comparison to production, without relying on geosteering or interpolation between pilots can be made with this calibrated model.
PR0.
1 –
0.4
YM 0 -1
0
Presenter
Presentation Notes
With a petrophysical interpretation available now in a significant number of Wolfcamp D wells, petrophysical parameters can be compared directly to production. In this study, the production data that is available is single well 30 day cumulative production (a smaller number of the wells had 60- and 90-day production data). The relationships shown here are based on the average petrophysical properties of the stimulated wellbore.
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Results: Raw Data Correlations
16
Cross-plots are mean properties versus 30-day cumulative oil corrected for stimulated lateral foot.
One well was removed from the data set due to operational issues resulting in a loss of ~50% of stages.
With no additional interpretation, near-wellbore anisotropy exerts the strongest influence on early production.
Young’s Modulus exhibits a reverse trend relative to what might be anticipated; this is likely due to the impact of clay consistent with the anisotropy interpretation.
Poisson’s Ratio and gamma ray have no meaningful correlation to early production.
Gamma Ray Anisotropy
Poisson’s Ratio Young’s Modulus
30 – day Cumulative Oil per Stimulated Lateral Foot
30 – day Cumulative Oil per Stimulated Lateral Foot
30 – day Cumulative Oil per Stimulated Lateral Foot
30 – day Cumulative Oil per Stimulated Lateral Foot
Presenter
Presentation Notes
We first examined correlations between production and the raw data. The properties calculated from the vibration data include anisotropy, Young’s Modulus and Poisson’s Ratio; MWD gamma ray was also acquired. Gamma ray and Poisson’s Ratio have no clear trend with respect to production. This may be a result of different components of the system pulling these data in opposite directions, i.e. clay is bad but kerogen is good so the result on the gamma ray is confused. If you had no other data or ability to interpret the mechanical anisotropy, that solution by itself is a reasonably good predictor of production. Young’s Modulus, and therefore porosity, is a strong predictor but in the opposite direction that you would predict. This is very likely driven by the role of clay in the system, and the fact that that the average values for the wells do not allow us to represent a truly tight lithotype.
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Results: Kerogen Volume Drives ProductionAverage kerogen volume, which leverages the difference between the mechanical anisotropy and gamma ray, has a stronger correlation to production than either input variable.
30 – day Cumulative Oil per Stimulated Lateral Foot
Presenter
Presentation Notes
The strongest relationship
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Outlier: Normalization Caveat?
18
Operational issues include cement in casing that was drilled out before stimulation
Relationship between rock properties and operational challenges is unclear at this time
Significant gamma ray normalization implies a great deal of uncertainty in kerogen interpretation
Outlier well reduces r2 of kerogen relationship to 0.53
VTI Anisotropy relationship is not significantly impacted
Anisotropy without Outlier Anisotropy with Outlier
Kerogen vs Production with outlier
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Discussion
19
1. Normalization of inputs
2. Near wellbore measurement
3. Short production time
4. Mapping mechanically-derived petrophysics back to wireline, core
Caveats and Challenges
1. Map mechanical facies tied to production back to pilot to refine landing zone
2. Incorporate high volume of reservoir characterization data into geologic models
3. Manage stage treatment parameters to reduce costs
Cost-Effective Acreage Management
1. Continue to evaluate study conclusions over longer production windows
2. Using Wolfcamp D evaluation as a blueprint, evaluate other benches
3. Refine Wolfcamp D target in future wells
Forward Plans
Presenter
Presentation Notes
Near wellbore measurement Quality of reservoir or quality of stimulation? Short production time Longer production, relationship between early and late production will be key Normalization of inputs Unavoidable at this time, needs to be done with caution and as blind as possible. Mapping mechanically-derived petrophysics back to wireline, core Is mechanical sensitivity the critical piece and is it captured in wireline? Can we tie directly to core, seismic?
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