enabling cross discipline collaboration and forward

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1 GUSS14 - # # # Enabling Cross Discipline Collaboration and Forward Modeling Through Advanced Subsurface Geocellular Earth Modeling Ron Dusterhoft Halliburton This paper has been selected for presentation for the 2014 Gussow Geosciences Conference. The authors of this material have been cleared by all interested companies/employers/clients to authorize the Canadian Society of Petroleum Geologists (CSPG), to make this material available to the attendees of Gussow 2014 and online. ABSTRACT In many situations our industry today has become very focused on managing huge quantities of data looking for simple correlations enabling them to capture useful information applicable to asset planning and field development. One of the key issues with this approach is that it is always looking backward to determine the path forward. By nature this process is reactionary making it difficult to identify opportunities for real innovation. Having assessed the industry position, a new approach was examined where data and information could be continuously fed into an evolving sub-surface geocellular earth modeling tool. This would represent a significant change from the traditional modeling tools where changes are limited due to the time and effort required to collect and process new information, then work through the entire modeling process. This new approach requires a geocellular earth model that is capable of receiving new information continuously and updating quickly. Forward modeling in this way provides a single environment where geoscientists and engineers can work together to improve their understanding of the reservoir, leverage the latest generation of tools to model cause and effect behaviors, and establishes optimized field development solutions faster. Statistical tools are still very useful for monitoring performance, but this data is also used to calibrate design tools to enable continuous refinement to the subsurface geocellular model and forward modeling tools. By accomplishing this, a common collaboration environment is created where both geoscientists and engineers can collaborate and work with the most current and most relevant subsurface information and knowledge. This concept has been tested in a number of proof-of- concept projects that have shown very promising results which are discussed in during this presentation. INTRODUCTION In North America the move from more conventional reservoirs into tight, basin centered gas and now shale has resulted in a change in the way reservoir performance has

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Page 1: Enabling Cross Discipline Collaboration and Forward

1

GUSS14 - # # #

Enabling Cross Discipline Collaboration and Forward Modeling

Through Advanced Subsurface Geocellular Earth Modeling

Ron Dusterhoft

Halliburton

This paper has been selected for presentation for the 2014 Gussow Geosciences Conference. The authors of this material have been cleared by all interested

companies/employers/clients to authorize the Canadian Society of Petroleum Geologists (CSPG), to make this material available to the attendees of Gussow 2014

and online.

ABSTRACT

In many situations our industry today has become very

focused on managing huge quantities of data looking for

simple correlations enabling them to capture useful

information applicable to asset planning and field

development. One of the key issues with this approach is that

it is always looking backward to determine the path forward.

By nature this process is reactionary making it difficult to

identify opportunities for real innovation.

Having assessed the industry position, a new approach

was examined where data and information could be

continuously fed into an evolving sub-surface geocellular

earth modeling tool. This would represent a significant

change from the traditional modeling tools where changes

are limited due to the time and effort required to collect and

process new information, then work through the entire

modeling process. This new approach requires a geocellular

earth model that is capable of receiving new information

continuously and updating quickly. Forward modeling in this

way provides a single environment where geoscientists and

engineers can work together to improve their understanding

of the reservoir, leverage the latest generation of tools to

model cause and effect behaviors, and establishes optimized

field development solutions faster.

Statistical tools are still very useful for monitoring

performance, but this data is also used to calibrate design

tools to enable continuous refinement to the subsurface

geocellular model and forward modeling tools. By

accomplishing this, a common collaboration environment is

created where both geoscientists and engineers can

collaborate and work with the most current and most

relevant subsurface information and knowledge.

This concept has been tested in a number of proof-of-

concept projects that have shown very promising results

which are discussed in during this presentation.

INTRODUCTION

In North America the move from more conventional

reservoirs into tight, basin centered gas and now shale has

resulted in a change in the way reservoir performance has

Page 2: Enabling Cross Discipline Collaboration and Forward

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been monitored and documented. Where in conventional

reservoirs reservoir quality was carefully examined to

determine reserves in place and optimum completion

designs, the marginal economics and the large areal extent of

these reservoirs created something that has been referred to

as a statistical play or a resource play. The basic assumption

has been made is that reservoir quality is relatively consistent

and poor meaning that a large number of wells would be

required in a development and that statistical variation of

well performance was an acceptable result. The primary

focus here takes the form of a well factory approach where

the focus is placed on reducing drilling and completion costs

as much as possible to help improve the marginal economics

in this environment. Variation in well productivity became

tolerable provided economics were sustainable.

In this environment, normal means of data acquisition such

as open hole logs and coring are often eliminated to reduce

cost and speed up operations. In vertical or deviated wells

targeting sand lenses, cased hole gamma ray is often used to

identify completion intervals while in horizontal shale

applications a geometric perforating and completion strategy

is often deployed resulting in equally spaced fractures along

the entire horizontal lateral, not accounting for the possibility

of any reservoir heterogeneity.

In statistical plays there have been several relationships

captured to help optimize well and completion performance.

In many cases simple relationships like production vs

proppant placed per foot of pay or lateral length or in other

cases the volume of fluid injected used per foot of pay or

lateral length. While these relationships may be useful, it is

actually very difficult to relate anything back to reservoir

quality, and these relationships become a standard

performance measure with little reservoir significance. One

example is shown in Figure 1 where the results of a 50,000

well study was performed and documented by Roth et al

where some very useful information was obtained through

statistical analysis of a very large sample of wells.

Figure 1: Results of a large statistical field study in the

Bakken performed by Roth

Well performance and asset value shifted from detailed

reservoir characterization to the use of simple decline curve

type analysis to establish recoverable reserves and asset

value. Essentially initial well performance has become a

primary tool in establishing well performance. The problem

here is that the basic assumption about statistical

equivalence from one well to the next is often wrong. In

many cases there is significant reservoir heterogeneity from

one well to the next and often from one productive interval

to the next meaning that not taking time to understand

reservoir quality can result in large stimulation treatments

being pumped into reservoir sections that have no potential

of ever paying out. The result is increased well cost without

positive production response, essentially exactly the opposite

result of what was intended.

Another unintended result of statistical approaches to field

development is that a certain number of failures are required

to establish boundaries. These wells can add a significant

cost to a project without generating adequate production or

sustainable economics.

In tight gas applications, Schubarth et al demonstrated that

taking the time and effort to run open hole logs to high grade

quality gas sands and eliminate stages in low quality intervals

could have a huge impact on production economics by

eliminating unnecessary expenses and focusing on improving

completion designs in the higher quality reservoir sections.

Figure 2 shows a comparison of results using statistical

completion techniques perforating all sands based upon

Page 3: Enabling Cross Discipline Collaboration and Forward

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cased hole gamma ray logs with a more reservoir centric

approach using open hole logs to high grade perforation

locations and targeting higher quality sands

Figure 2: Results of a tight gas study comparing results of

statistical methods versus a reservoir centric approach

In tight gas reservoirs most of the wells will be either vertical

or deviated making it relatively easy and cost effective to

perform open hole logging to capture reservoir information

in the near wellbore. In shale, however, the vast majority of

the wellbores drilled during a development will be horizontal

making it much more difficult and costly to obtain open hole

log information. The reservoir quality indicators for shale

reservoirs are also significantly different than conventional

reservoirs. In fact a very strong case can be made suggesting

that the reservoir quality indicators for shale will vary

significantly from one field to the next and even from one

portion of a reservoir to another due do variations in thermal

maturity, reservoir fluid properties , TOC content, effective

porosity, pore pressure and stresses. Because of this, shale

assets tend to be much more complex in nature and

achieving the greatest impact in economic performance

requires a much more in depth understanding of the

reservoir. In these assets the creation of an integrated sub

surface model creates an environment for collaboration

between geoscientists and engineers and enables the use of

forward looking engineering tools to model and anticipate

well and field behavior using predictive techniques.

The Use of Earth Modeling in Shale Assets

In unconventional shale reservoirs the initial industry belief

was that these would be large homogeneous reservoirs that

could be treated as a resource or statistical play with little if

any variation from one well to the next. From an

engineering perspective this meant that wells could be easily

drilled and completed following a standard well template

making drilling cost and efficiency the key operational

drivers. After several years of deploying this approach it

became apparent that there was much more variation in well

performance than expected including a large number of

underperforming wells.

More recent studies of these reservoirs at pore scale levels

have revealed that the flow and production mechanisms are

extremely complex and in many cases the reservoirs were

much more heterogeneous that first thought. It has taken

several years, but reservoir understanding of shale has

started to catch up to our engineering abilities to drill and

complete horizontal wells with multi-stage hydraulic

fracturing and the importance of collecting key data to help

construct detailed subsurface models is becoming more

common.

Earth modeling provides a means to integrate several key

geoscience disciplines into a single environment to provide a

detailed subsurface model that can be used to help make

several key decisions as follows:

Identification of higher quality reservoir sections or

sweet spots

Identification of larger scale geo hazards such as

faults and surfaces

Identification of smaller, sub seismic hazards such as

clay rich ductile layers that may introduce drilling

and completion difficulties

Presence, density and preferential directions of

natural fracture systems

Mapping of key reservoir attributes including TOC,

effective porosity, mechanical properties, and

preferential stresses

Most shale reservoirs are source rocks or self-sourced

meaning that the hydrocarbon that is still present within

these reservoirs was generated from organic material within

the rock when exposed to time, temperature and pressure.

Understanding the burial history of these rocks is critical to

determine how much hydrocarbon has been generated, how

Page 4: Enabling Cross Discipline Collaboration and Forward

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much has migrated out of the interval, how much

hydrocarbon is left within the reservoir and the state,

pressure and mobility of that hydrocarbon that is left in the

reservoir. Detailed petroleum systems analysis and source

rock modeling can be used to create larger basin models to

help gain regional understanding of the production potential

of a source rock as well as the hydrocarbon and state of the

hydrocarbon in place.

Local tuning of this understanding can then be accomplished

through the use of core analysis, geochemistry and

petrophysical interpretation. This will require wells to be

drilled where pre-existing wells with the required information

and core may not already exist. Some key parameters that

are obtained during this phase of the analysis include:

Mineralogy

Total Organic Content (TOC)

Maturity

Mechanical rock properties including Poisson’s

Ratio, Young’s Modulus and Brittleness

Brinnell hardness

Effective porosity

Water saturation

Estimation of the hydrocarbon in place

Estimation of the state of the hydrocarbon in place,

whether it is gas, wet gas, condensate or oil

Once this has been accomplished, 3D seismic can be used to

map surfaces and along with geostatistics integrate

properties between the wells to create a detailed geo cellular

subsurface earth model. At this stage, this model may quite

course due to scarce data, but it still becomes the most

comprehensive concentration of information available and

becomes a collaboration hub to help direct the key decisions

mentioned earlier. Dusterhoft et al provides an outline of

one basic procedure that has been summarized in Figure 3a,

3b and 3c.

Figure3a: Petroleum systems analysis core and

geochemistry

Figure 3b: Petrophysics and seismic integration

Figure 3c: Subsurface earth model

Once the initial subsurface earth model has been created, it

enables several engineering workflows to improve well

placement, completion design and completion optimization

as shown in a workflow schematic described by Dhal et al and

shown in Figure 4.

Page 5: Enabling Cross Discipline Collaboration and Forward

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One unique thing about this workflow is that as additional

well, drilling, logging, completion and production information

becomes available, the subsurface earth model is updated on

a regular basis creating generation n+1 providing the most up

to date and current information available for the asset team

to collaborate over and utilize engineering tools to perform

forward looking predictive modeling and uncertainty analysis.

This is a key difference between the conventional field data

and production analytics, which looks back to create trends

and relationships going forward. This type of sensitivity

modeling creates a much better opportunity to identify new

and often unexpected relationships that can lead to

significant improvement drilling and completion

opportunities. Following are examples of how collaboration

over this common subsurface earth model can be used to

improve well and completion designs to improve production

and economic success.

Figure 4: Field development process and workflows

developed over a common subsurface earth model.

Reservoir Simulation and Uncertainty Analysis for

Completion Optimization

Extracting a volume of the reservoir from the subsurface

earth model and placing this information into a detailed

reservoir simulator can enable several completion concepts

and ideas to be tested and evaluated against different

reservoir properties. This type of analysis makes it possible

to determine which reservoir and completion attributes have

the greatest impact on well performance and allow engineers

to utilize this information for improved completion design.

One such study has been described by Kumar et al which

looks at sensitivity of reservoir properties, reservoir fluid

properties and completion characteristics for a region in the

Eagle Ford formation in Southwest Texas. In this case 572

simulations were performed to fully evaluate effect ranges of

specific parameters to determine the impact and sensitivity

on well performance. While this seems like a lot of cases,

automation of this process enables this work to be done

relatively fast so results can be observed in one or two days.

A tornado chart showing the sensitivity for both oil and gas

cases is shown in Figure 5. Here it is interesting to note that

for a liquids rich reservoir that is more oil prone, PVT

properties of the reservoir fluid has the highest impact on

well performance. Of course this is a parameter that is not

controlled, but it suggests that in liquids rich shale reservoirs

it is critically important to understand the PVT properties of

the reservoir and incorporate this information into the

completion design to achieve the best results. It is also

interesting to note that matrix permeability is the third most

important parameter after fracture length. Unfortunately we

rarely know what the matrix permeability is for a given

reservoir, especially in these ultralow permeability reservoirs.

This also demonstrates the importance of tuning the earth

model to ensure that the best possible information is

captured for permeability across an entire asset. Fracture

length, fracture spacing (number of fractures) and fracture

conductivity also have significantly positive impact on well

performance and must be optimized in the completion

design.

Page 6: Enabling Cross Discipline Collaboration and Forward

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Figure 5: Sensitivity results utilizing 572 reservoir

simulations for an Eagle Ford well.

A similar study was used by Dahl et al to help understand

how to maximize liquids production from a retrograde

condensate region of the Barnett Shale in Wise County,

Texas. This is an area where interaction of the hydraulic

fracture and natural fractures often results in significantly

complex fracture networks. This type of analysis was utilized

here to determine the significance of more effectively

stimulating and propping natural fractures. While this was

seen to have only a modest impact on gas productivity, it was

a completely different story for liquid hydrocarbon

production. Figure 6 shows the production sensitivity to

natural fracture simulation for gas production, while Figure 7

shows the sensitivity for oil production. Here it can be seen

that more effective stimulation of natural fractures in a

retrograde condensate leads to significant increases in

connected fracture area which leads to significantly more oil

production. Gas production, on the other hand is impacted,

but not to the same extent as the liquids. Again a strong

understanding of the PVT properties of the reservoir fluids is

essential, but capturing representative samples of fluid from

these reservoirs can be very difficult and in some cases nearly

impossible. For this reason compositional modeling to create

an equation of state using produced hydrocarbon

composition has been utilized throughout these studies. The

results of this type of modeling has proven to be far more

reliable than the use of black oil or modified black oil

solutions.

Figure 6: Barnett condensate sensitivity analysis example,

impact of natural fracture stimulation on gas production.

Figure 7: Barnett condensate sensitivity analysis example,

impact of natural fracture stimulation on oil production.

This observation lead to a number of significant completion

design changes including utilization of a new complex

fracture model to help understand how to more effectively

stimulate secondary, narrow fractures and connect them

more effectively to the wellbore. Figure 8 and Figure 9 show

the results of the best well in the region as compared to the

latest well drilled and completed using this new completion

solution. In this case the new technique supported the

reservoir simulation work achieving the same cumulative oil

in 4 months that had taken the best well utilizing the

conventional completion approach 2 years to achieve.

Page 7: Enabling Cross Discipline Collaboration and Forward

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Figure 8: Barnett Condensate production performance of

best well prior to completion design changes

Figure 9: Barnett Condensate production performance of

latest well utilizing new completion designs to stimulate

natural fractures

In the Barnett case it is important to note that the

completion design changes implemented to more effectively

stimulate natural fractures were not intuitive and were not

apparent based upon the existing well performance in the

area. In this case the use of earth modeling and reservoir

sensitivity analysis provided unique insight into a very

complex problem and enabled the engineers to create a step

change improvement in well performance.

Hydraulic Fracture Design Considerations

In unconventional assets hydraulic fracture design has

become more of a statistical process as well where

incremental improvement and innovation are used to create

a treatment schedule that is often replicated across an entire

field without consideration of the stratigraphic well location

or the reservoir heterogeneity. Based upon vertical well

experience, the fracture initiation point could be carefully

selected by identifying the locations within the well to

perforate. In a horizontal well, however, the location of the

wellbore defines the fracture initiation point anywhere along

the well, so the stratigraphic location of the well becomes

critical.

In shale reservoirs, it is also highly desirable to take

advantage of natural fractures whenever possible to

maximize the production potential. In order to accomplish

this, a much more detailed understanding of the subsurface is

required including natural fracture joint behavior and local

stresses around the wellbore.

If the engineer and geoscientists are able to identify all of the

important parameters needed for the completion design, the

earth model can be used as a tool to capture and model

these properties across the asset.

For areas where complex fracture growth is expected, Dahl et

al provides a fracture design workflow that incorporates the

use of the earth model, statistical tools to create a

representative fracture fabric for the reservoir, and complex

fracture models coupled with reservoir simulation to create

an optimized fracture treatment based on production

potential.

Figure 10 provides a visualization of this completion design

and optimization workflow.

Earth ModelingMicroseismic AnalysisImage Log Analysis

Representative Fracture Fabric within the Reservoir

Complex Fracture Modeling

Reservoir Simulation and Uncertainty Analysis

Figure 10: Hydraulic fracture design workflow for modeling

and optimizing complex hydraulic fracture systems

Page 8: Enabling Cross Discipline Collaboration and Forward

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The engineering tools used to complete this workflow are

very new incorporating advanced statistical microseismic

analysis, stochastic natural fracture modeling, induced

hydraulic fracture modeling and very advanced, highly

refined unstructured gridding within a compositional

reservoir simulator. These tools combined create a very

powerful engineering design tool that requires collaboration

and cooperation between engineers and geoscientists to

execute.

Horizontal Well Correlation to Improve Well Placement and

Perforating in Horizontal Wells

Another application that is significantly enhanced through

the use of a collaborative subsurface earth model is the use

of horizontal well correlation to identify the optimum well

locations within the reservoir and then, based upon well

location, local stress conditions and local reservoir

characteristics create an optimum scheme for perforating

and fracture stage break down.

While well log interpretation for horizontal wells can be very

difficult, creating an environment where the stratigraphic

placement of the wellbore can be observed alongside the log

curves makes it possible to determine where the wellbore is

in a good reservoir location for stimulation and also where

the wellbore is located in a poor location. This tool makes it

possible to identify and map geo hazards relative to the

wellbore and provide insight to the completions engineer

from a fracture treatment design perspective.

This environment creates a powerful tool to plan and design

the wellbore to get it located in the correct place, create a

geosteering plan to ensure the well is placed as designed as

well as the capability to establish where to perforate and how

to stimulate the well based upon both local and far field

stress and reservoir quality. One such case is shown in Figure

11 for an Eagle Ford well where the wellbore cuts across

several stratigraphic layers with different stress and reservoir

characteristics. In this case different completion scenarios

can be assessed with the potential of significantly reducing

the total well cost by the elimination of fracture stages in low

quality reservoir sections along the wellbore.

Figure 11: Horizontal Well Correlation displayed to enable

better well planning and easier interpretation for

completion optimization

CONCLUSIONS

For unconventional shale assets, subsurface earth

modeling creates a point where geoscientists and engineers

can collaborate and enable more advanced engineering

applications.

Engineering tools populated using an earth model can be

used to create many representative realizations to identify

opportunities for improved well performance through both

well placement and completion design.

Displaying information from the earth model combined

with LWD and geosteering information creates a very

powerful environment for well placement and completion

optimization.

ACKNOWLEDGMENT

The author would like to thank Halliburton for the

permission to present this material.

REFERENCES

Roth, Murray; Roth, Michael, “Lifecycle Optimization of

Unconventional Plays: A Bakken Case Study,” URTeC

1922509 presented at the Unconventional Resource

Technology Conference in Denver, Aug 25-27, 2014

Page 9: Enabling Cross Discipline Collaboration and Forward

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Schubarth, SK, Mullen, MJ, Seal, CA, Woodall, RS, “Reservoir

Description Techniques Improves Completions Economics

Piceance Basin Masaverde Play,” SPE 39918 Presented at the

Rocky Mountain Regional/Low-Permeability Reservoirs

Symposium, Denver Co, April 5-8, 1998

Dusterhoft, Ron. Williams, Ken. Kumar, Amit. Croy, Matt.

“Understanding Complex Source Rock Petroleum Systems to

Achieve Success in Shale Developments,” SPE 164271

Presented at the SPE Middle East Oil and Gas Show and

Exhibition, Manama, Bahrain, March 10-13, 2013.

Dahl, Jeff. Spaid, John. McDaniel, Buddy. Grieser, Bill.

Dusterhoft, Ron. Johnson, Bill. Siddiqui, Shameem.

“Accelerating Shale Asset Success through Applied Reservoir

Understanding” URTeC: 1920572, presented at the

Unconventional Resources Technology Conference, Denver,

Co, August 25-27, 2014.

Kumar, Amit. Dusterhoft, Ron. Siddiqui, Shameem.

“Completion and Production Strategies for Liquies-Rich Wells

in Ultra-low-permeability Reservoirs,” SPE 166177 presented

at the SPE Annual Technical Conference and Exhibition, New

Orleans, LA, Sept 30 – Oct 2, 2013.