Top-Down, Intelligent Reservoir Modeling of Oil and Gas...

Int. J. Oil, Gas and Coal Technology, Vol. 5, No. 1, 2012 1

Copyright © 2012 Inderscience Enterprises Ltd.

Top-Down, Intelligent Reservoir

Modeling of Oil and Gas

Producing Shale Reservoirs; Case

Studies

Shahab D. Mohaghegh* Intelligent Solutions, Inc. and

Department of Petroleum & Natural Gas Engineering,

West Virginia University, Morgantown, West Virginia, 26506, USA

E-mail: [email protected]

*Corresponding author

Ognjen Gruic Saeed Zargari West Virginia University Colorado School of Mines

E-mail: [email protected] E-mail: [email protected]

Amirmasoud Kalantari Grant Bromhal West Virginia University U.S. Department of Energy, NETL

E-mail: [email protected] Email: [email protected]

Abstract: Producing hydrocarbon (both oil and gas) from Shale plays has attracted much

attention in recent years. Advances in horizontal drilling and multi-stage hydraulic

fracturing have made shale reservoirs a focal point for many operators. Our understanding

of the complexity associated with the flow mechanism in the natural fracture and its

coupling with the matrix and the induced fracture, impact of geomechanical parameters and

optimum design of hydraulic fractures has not necessarily kept up with our interest in these

prolific and hydrocarbon rich formations.

In this paper we discuss the application of a new reservoir modeling approach to history

matching, forecasting and analyzing oil and gas production from shale reservoirs. In this

new approach instead of imposing our understanding of the flow mechanism and the

production process on the reservoir model, we allow the production history, well log, and

hydraulic fracturing data to force their will on our model and determine its behavior. In

other words, by carefully listening to the data from individual wells and the reservoir as a

whole, we developed a data-driven model and history match the production process and

validate our model (using blind production history) from shale reservoirs. The validated,

history matched model is used to forecast future production from the field and to assist in

planning field development strategies. In the validation context, the “blind production

history” is referred to the last several months of production history that is not used during

the training and history matching process and has been used to validate the forecast of the

Top-Down Model (TDM).

This is a unique and innovative use of pattern recognition capabilities of Artificial

Intelligence and Data Mining (AI&DM) as a workflow to build a full field reservoir

simulation model for forecasting and analysis of oil and gas production from shale

formations. Examples of three case studies in Lower Huron and New Albany shale

formations (gas producing) and Bakken shale (oil producing) is presented in this article.

Keywords: Top-Down Modelling, Shale Reservoir, Reservoir Modelling, Reservoir Simulation.

Reference to this paper should be made as follows: Mohaghegh, S.D., Gruic, O., Zargari, S., Kalantari, M. (2011) „Top-Down, Intelligent Reservoir Modelling of Oil and Gas Producing Shale Reservoirs; Case Studies‟, Int. J. Oil, Gas, and Coal Technology, Vol. x, Nos. x/x/x, pp.xx–xx.

mailto:[email protected]

12 S.D. MOHAGHEGH, O. GRUIC, S. ZARGARI, A. KALANTARI & G. BROMHAL

Biographical notes:

S.D. Mohaghegh received his PhD in Petroleum & Natural Gas Engineering from Penn State University in 1991. He is currently Professor at the Department of Petroleum & Natural Gas Engineering, West Virginia University. He is the president and founder of Intelligent Solutions, Inc. His current research interests include application of Artificial Intelligence & Data Mining (AI&DM) in smart fields, carbon sequestration and fluid flow in unconventional reservoirs.

O. Gruic received his MS degree in Petroleum and Natural Gas Engineering from West Virginia University in 2011. Currently he is a Reservoir Engineer with Stratagen Engineering. His current research interest includes application of Artificial intelligence and Data Mining (AI&DM) in smart fields, unconventional reservoir engineering, and well stimulation in unconventional reservoirs.

S. Zargari received Master of Science degree in Petroleum and Natural Gas Engineering from West Virginia University in 2010. He is currently a PhD student in Petroleum Engineering at Colorado School of Mines. His current research interests are Reservoir Characterization and Modeling in unconventional reservoirs.

A. Kalantari-Dahaghi received BS in petroleum engineering from Petroleum University of Technology and MS in Petroleum and Natural Gas Engineering from West Virginia University in 2010. Currently, he is a Ph.D. student at WVU. His current research interests include modelling and simulation of unconventional gas resources (especially shale gas reservoirs), applicability and simulation of CO2 sequestration in shale gas reservoirs, artificial Intelligence application in shale gas reservoir modelling.

G. S. Bromhal received bachelors‟ degrees in Civil Engineering and Mathematics from West Virginia University in 1995. He received a Masters from Carnegie Mellon in Civil and Environmental Engineering in 1997 and a PhD from Carnegie Mellon in Environmental Engineering in 2000. His research interests include modeling two-phase flow in porous media, from the pore level to the reservoir scale, modeling, experiments, and field research related to carbon sequestration and hydrocarbon recovery. He is the recipient of the 2007 Hugh Guthrie Award for Innovation at NETL, and the 2010 USGS Director‟s Award for Exemplary Service to the Nation.

1 INTRODUCTION

This article reviews a new reservoir simulation and

modeling technology called Top-Down, Intelligent

Reservoir Modeling (Top-Down Modeling - TDM) as it is

applied to shale formations with examples presented for

New Albany, Lower Huron and Bakken Shales. The natural

fractures in the shale contribute significantly to the

production as the main conduit for reservoir permeability.

Recent revival of interest in production from shale

formations can be attributed to multi-stage hydraulic

fractures. It is a known fact that success of these hydraulic

fracturing procedures is directly related to their ability to

reach and intersect the existing natural fractures in the shale

formation. Mapping of the natural fractures in the shale

formations have proven to be an elusive task. Even with

most advanced logging technologies one can only detect the

intersection of the natural fractures with the wellbore while

the extent of these fracture beyond the wellbore and how

they are distributed throughout the reservoir (between wells)

remains the subject of research.

Instead of modeling the discrete fracture networks and

mechanism of multi-stage hydraulic fractures, and then

trying to couple them, Top-Down Modeling attempts to

model the impact of hydraulic fractures and natural fractures

on the production from wells. While developing stochastic

realizations of natural fractures and their intersection with

the induced hydraulic fracturing are being studied using

stochastic and numerical reservoir modeling, TDM fills the

existing gap for a predictive model that can be built using

minimum amount of assumptions about the nature of the

reservoir and our understanding of its complexity. TDM

starts with a solid assumption that whatever the nature of the

natural fracture distribution and its interaction with the

induced hydraulic fractures may be, their impact is bound to

show itself in the amount of the hydrocarbon that each well

is able to produce. These signatures can be used in order to

build reservoir models, match the production history and

build a predictive model that can help us make reservoir

management decisions.

Top-Down Modeling (TDM) technology is an elegant

integration of traditional reservoir engineering methods with

pattern recognition capabilities of artificial intelligence and

TOP-DOWN MODELING OF OIL & GAS PRODUCING SHALE RESERVOIRS; CASE STUDIES 13

data mining. Advantages of this new modeling technology

include its flexible data requirement, short development

time and ease of development and analysis. Its shortcoming

is that it can only be applied to brown fields where

reasonable amount of data from the field is accessible. The

data requirements for the Top-Down Modeling necessitate a

field with about 35 to 40 wells and about 5 years of

production history. As number of wells increases, the

amount of required production history may be reduced.

Traditional reservoir simulation and modeling is a bottom-

up approach. It starts with building a geological model of

the reservoir followed by adding engineering fluid flow

principles (Diffusivity equation, Darcy's law, Fick's law of

diffusion …) to arrive at a dynamic reservoir model. The

dynamic reservoir model is calibrated using the production

history of multiple wells and the history matched model is

used to strategize field development in order to improve

recovery.

Top-Down Modeling approaches the reservoir simulation

and modeling from an opposite angle by attempting to build

a realization of the reservoir starting with well production

behavior (history). The production history is augmented

with core, log, well test and seismic data (upon availability

of each) in order to increase the accuracy and fine tune the

Top-Down Model. The model is then calibrated (history

matched) using the most recent wells as blind dataset.

Although not intended as a substitute for the traditional

reservoir simulation of large, complex fields, this novel

approach to reservoir modeling can be used as an alternative

(at a fraction of the cost and time) to traditional numerical

reservoir simulation in cases where performing traditional

modeling is cost (and man-power) prohibitive, specifically

for shale formations. In cases where a conventional model

of a reservoir already exists, Top-Down Modeling should be

considered a complement to, rather than a competition for

the traditional technique. It provides an independent look at

the data coming from the reservoir/wells for optimum

development strategy and recovery enhancement.

Top-Down Modeling provides a unique perspective of the

field and the reservoir using actual measurements. It

provides qualitatively accurate reservoir characteristics

maps that can play a key role in making important and

strategic field development decisions.

Accuracy and validity of TDM have been demonstrated

against numerical reservoir simulation models and details of

this technology have been published extensively in several

recent articles and therefore will not be repeated here

(Gaskari 2007 – Mata 2007 – Mohaghegh 2009b – Gomez

2009 – Khazaeni, 2010).

2 TOP-DOWN MODELING OF SHALE RESERVOIRS

Top-Down Modeling is part of a larger class of reservoir

simulation models that are referred to as AI-Based

Reservoir Models (Mohaghegh, 2011). AI-Based Reservoir

Models consist of models that are built using data generated

by numerical reservoir simulators, also known as Surrogate

Reservoir Models (Mohaghegh 2008, Mohaghegh 2009a)

and models that are built using field data, also known as

Top-Down, Intelligent Reservoir Models that are the subject

of this paper and will be referenced in more detail

throughout this manuscript.

Traditional reservoir simulation is the industry standard for

reservoir management. It is used in all phases of field

development in the oil and gas industry and is now being

used on some but not all of the shale formations. The

routine of simulation studies calls for integration of static

and dynamic measurements into the reservoir model.

Traditional reservoir simulation and modeling is a bottom-

up approach that starts with building a geological (geo-

cellular or static) model of the reservoir. Using modeling

and geo-statistical manipulation of the data the geo-cellular

model is populated with the best available petrophysical and

geophysical information. Engineering fluid flow principles

are added and solved numerically to arrive at a dynamic

reservoir model. The dynamic reservoir model is calibrated

using the production history of multiple wells in a process

called history matching and the final history matched model

is used to strategize the field development in order to

improve recovery. Characteristics of the traditional reservoir

simulation and modeling include:

1. It takes a significant investment (time and money)

to develop a geological (geo-cellular, static) model

to serve as the foundation of the reservoir

simulation model.

2. Development and history matching of a reservoir

simulation model is not a trivial process and

requires modelers and geoscientists with

significant amount of experience.

3. It is an expensive and time consuming endeavor.

A prolific asset is required in order to justify a significant

investment that is required for a reservoir simulation model.

Top-Down Intelligent Reservoir Modeling (TDM) can serve

as an alternative or a complement to traditional reservoir

simulation and modeling. TDM is a process that follows the

following logic:

a. Perform individual well analysis using all the

available reservoir engineering techniques.

i. Static analysis using well logs.

ii. Dynamic analysis using production data.

b. Use data-driven modeling to model interference

between wells using the impact of offset wells on

the production of each individual well.

c. Couple the reservoir engineering analysis of

individual wells with the interference modeling

using pattern recognition technology in order to

develop a cohesive full field model.

Top-Down Modeling (TDM) starts with the well-known

reservoir engineering techniques such as Decline Curve

Analysis, Type Curve Matching, History Matching using

single well numerical reservoir simulation, Volumetric

Reserve Estimation and calculation of Recovery Factors for

all the wells (individually) in the field.


Using statistical techniques multiple Production Indicators

(3, 6, and 9 months cumulative production as well as 1, 3, 5,

and 10 year cumulative production) are calculated. The

reservoir engineering analyses along with the statistical data

form the basis for a comprehensive spatio-temporal

database. This database represents an extensive set of snap

shots of fluid flow in the shale formation. It is expected that

all the characteristics that governs the complexity of fluid

flow in the naturally fractured reservoir to be captured in

this extensive spatio-temporal database.

This large volume of data is processed using the state-of-

the-art in artificial intelligence and data mining (neural

modeling, genetic optimization and fuzzy pattern

recognition) in order to generate a complete and cohesive

model of the entire reservoir. This is accomplished by using

a set of discrete modeling techniques to generate production

related predictive models of well behavior, followed by

intelligent models that integrate the discrete models into a

cohesive model of the reservoir as a whole, using a

continuous fuzzy pattern recognition algorithms.

The Top-Down, Intelligent Reservoir Model is calibrated

using the most recent set of wells that have been drilled in

the field. The calibrated model is then used for field

development strategies to improve and enhance

hydrocarbon recovery. Top-Down Models are used in

reservoir management workflows using the flowchart that is

shown in Figure 1.

Upon completion of the spatio-temporal database, which

proves to be one of the most important steps in development

of a Top-Down Model (TDM), the process of training and

history matching of the TDM is performed simultaneously.

It must be noted that a rigorous blind history matching is

required in this step of the process to ensure the robustness

of the Top-Down Model. Using the design tool that is part

of the TDM process, field development strategies are

planned and then using the history matched model (in

predictive mode) the plans are tested to see if they fulfill the

objectives of reservoir management. This process is

repeated, iteratively (by planning new wells to be drilled

and predicting their performance), until the reservoir

management objectives are met. Once the objective is

accomplished, the plan is forwarded to operation for

implementation.

Figure 1. Reservoir management workflow using Top-Down

Modeling.

The Top-Down Model, like any other reservoir model,

needs to be updated regularly, as shown in the flow chart in

Figure 1. It is noteworthy to mention that most of the work

presented in this paper has been performed on publicly

available data. Only for Lower Huron shale some

completion data was acquired from one of the operators in

the region.

In the following sections some of the results that have been

achieved from application of TDM to three shale formations

are briefly presented.

3 APPLICATION OF TOP-DOWN MODELING TO

LOWER HURON SHALE

While the details of the Top-Down Modelling application to

Lower Huron Shale can be found in a recently published

SPE paper (Grujic 2010) some new information on this

study are presented here. The Lower Huron Shale that is

sometimes referred to as the “Devonian Shale” is located in

extreme southeast Ohio, West Virginia and Northeast

Kentucky. It is a part of the Appalachian Basin which links

Chattanooga Shale and Marcellus Shale. The Lower Huron

Shale has a wide range of thickness, ranging from 200 to

2000 ft thick.

Figure 2. Shale distribution in Kentucky.

Thickness of the Lower Huron Shale in Kentucky is shown

in Figure 2, identifying the deeper and thicker shale

formations. Depth, formation thickness and porosity

distribution of the portion of the field that is the subject of

Top-Down Modelling is shown in Figure 3.

Following the flow chart that was presented in Figure 1, the

TDM for the Lower Huron Shale is trained and history

matched. During the TDM training and history matching

process usually the tail-end of the production is removed

from the model building process and is used as blind test in

order to check the validity of the reservoir model. The

quality of the TDM is usually judged based on its capability

to predict the part of the production history that has not been

used during the reservoir model training.

Figure 4 shows the strategy that was incorporated during the

Top-Down Model training, history matching and blind

history matching for the Lower Huron Shale. Production

history was available for this field from 1982 to 2008. The

Top-Down Model was trained and history matched with

data from 1982 to 2004 and production history from 2005 to


2008 was left out to be used as validation of the model in

the form of blind history matching.

Figure 3. Formation Depth, Pay Thickness and Porosity

distribution used in the TDM.

Figure 4. Strategy used during the training and history matching of

the Top-Down Model for the Lower Huron Shale.

Figure 5 shows the result of training and history matching of

the Top-Down Model for Lower Huron Shale when applied

to the production history of the entire field (this study

included a portion of a field with 75 wells). Top-Down

Model is built (trained and history matched) on a well by

well basis and then is integrated as was explained in the

previous section of this article.

In Figure 5, both production history from the field and the

trained and history matched model using TDM are shown

for the entire field. In this figure result of TDM is compared

with the actual production history from the field in monthly

production rate versus time as well as cumulative field

production versus time.

Figure 5. Results of the TDM (monthly rate and cumulative

production) as applied to the production from the entire field.

Figure 6. Results of the Top-Down Modeling (monthly rate and

cumulative production) as applied to the production history from

wells KH1184 and KF 1638.

This figure shows the accuracy of the TDM. While TDM

results capture the trend of the monthly production for the

entire field the TDM model‟s match of the cumulative gas

production from this field is remarkably accurate. It should


also be noted existence of several outliers (monthly

production) in the production data has not adversely

impacted the results of TDM and the major production

trends have been detected and modelled.

Furthermore it must be noted that data shown in Figure 5 is

the result of summation of the TDM model results for

individual wells. In other words, TDM is trained, history

matched and validated at the well level and not for the field

as a whole. Upon completion of the modelling process, the

results of individual wells are summed in order to see the

model results for the entire field.

To demonstrate the results of TDM for the individual wells

two examples are presented in Figure 6. This figure shows

the results of TDM model training and history matching

(blind portion of history matching is shown in different

color) for two wells, namely well KF1184 and KF1638

(both monthly production and cumulative production).

These figures demonstrate the predictive capability of TDM

in Lower Huron Shale.

As demonstrated in the flow chart of Figure 1, TDM

includes a design module. The objective of the design

module is to assist engineers in performing reservoir

management tasks such as identifying which portion of the

reservoir has been depleted. By identifying reservoir

depletion as a function of time (which is a reflection of

pressure draw down in the field) and by cross referencing

that with the original hydrocarbon in place, an indication of

remaining reserves in the field (as a function of time and

well placement) will emerge.

TDM design tool uses Fuzzy Pattern Recognition in order to

identify the portions of the shale formation that has

contributed the most to the production during the first three

month, 3, 5 and 10 years as shown in Figure 7. Details of

this Fuzzy Pattern Recognition process has been covered in

several previously published papers (Gomez 2009 –

Kalantari 2009 – Kalantari 2010 – Mata 2007 – Mohaghegh

2009c). In this figure the reservoir is delineated into several

RRQIs (Relative Reservoir Quality Index) shown in

different colors. The portion of the reservoir that is shown

with the darkest color represents RRQI of 1. This is the

portion of the reservoir that has made the largest

contribution to production followed by RRQI 2, 3, and 4.

The colors of other RRQIs gradually get lighter until the

region for RRQI 5 become almost white.

The contribution of the delineated RRQIs to production is

calculated taking into account the number of wells that are

included in each of the RRQIs. Furthermore, these regions

refer to depletion in the shale formation since locations that

have the highest amount of production are, relatively

speaking, the most depleted parts of the reservoir. For

example RRQI 1 is shown with dark red (almost black)

color in this figure. This region is small in the 3 months

FPR map (top-left) and increases in size as a function of

time until it occupies a much larger region in 120 months

FPR map (bottom-right). This change in size of RRQI 1

corresponds to an increase in contribution of north and

south central parts of the reservoir to production as a

function of time (since depletion is obviously a time

dependent variable). Cross referencing these maps with

similar maps that are developed (as part of this modeling

technology) for remaining reserves can aid engineers in

identifying the best infill locations in this asset. If

permeability values are available for a given asset they also

play a role in identifying optimum infill locations. It goes

without saying that the optimum infill location (relatively

speaking) is where the depletion is at minimum, remaining

reserve is at maximum, and there is good permeability.

Figure 7 shows that in this field the central part is the most

depleted portion of the reservoir with more depletion shown

in the north and south parts of the field. Furthermore, it

shows that as time progresses the most depleted central

portion of the reservoir expands toward east and west.

Figure 7. Results of the TDM‟s Fuzzy Pattern Recognition

showing reservoir depletion as a function of time.

The design tool in the Top-Down Modeling that is powered

by Fuzzy Pattern Recognition technology is used to support

reservoir management decisions such as identifying infill

locations in the field.

For instance, in order to calibrate the location of the

reservoir quality separator lines in Figure 7 the latest drilled

wells in the field (wells drilled in 2008) are removed from

the analysis and the reservoir delineations is performed

using wells prior to 2008. Then the production indicator for

the wells that are drilled in 2008 are compared with the

RRQI that they are located in to find out if the pattern

recognition analysis (reservoir delineation into different

RRQIs) is valid.

Figure 8. Testing the validity of identified RRQIs in Lower Huron

Shale.


This exercise is performed on the first year cumulative

production of wells completed in the Lower Huron Shale.

Figure 8 shows that the first year cumulative production of

wells drilled in RRQI(2) should be between 27.3 and 39.9

MMSCF and the first year cumulative production of wells

drilled in RRQI(3) should be between 18.7 and 27.3

MMSCF . The averaged first year cumulative productions

of wells drilled in RRQI (2) in 2008 were 33.9 MMSCF

while the averaged first year cumulative production of wells

drilled in RRQI (3) in 2008 were 22.0 MMSCF, both within

the predicted range.

4 APPLICATION OF TOP-DOWN MODELING TO BAKKEN SHALE

While the details of Top-Down Modelling application to

Bakken Shale can be found in a recently published SPE

paper (Zargari 2010) some new information about this study

is presented here.

The Upper Devonian-Lower Mississippian Bakken

Formation is a thin but widespread unit within the central

and deeper portions of the Williston Basin in Montana,

North Dakota, and the Canadian Provinces of Saskatchewan

and Manitoba. The formation consists of three members: (1)

lower shale member, (2) middle member (consist of 90%

Silty-Calcareous limestone-dolomite mixture with about

10% sand), and (3) upper shale member. Each succeeding

member is of greater geographic extent than the underlying

member. Both the upper and lower shale members are

organic-rich marine shale of fairly consistent lithology; they

are the petroleum source rocks and part of the continuous

reservoir for hydrocarbons produced from the Bakken

Formation.

The middle sandstone member varies in thickness,

lithology, and petrophysical properties, and local

development of matrix porosity enhances oil production in

both continuous and conventional Bakken reservoirs. Using

a geology-based assessment methodology, the U.S.

Geological Survey estimated mean undiscovered volumes of

3.65 billion barrels of oil, 1.85 trillion cubic feet of

associated/dissolved natural gas, and 148 million barrels of

natural gas liquids in the Bakken Shale Formation of the

Williston Basin Province, Montana and North Dakota.

Top-Down Modelling and analyses were performed for both

Upper and Middle Bakken. A summary combination of both

of these studies is presented here. Figure 9 shows the

portion of the field with wells that have been completed in

Upper and Middle Bakken. As part of the TDM, Voronoi

polygons have been generated for the wells in this field.

Figure 10 shows the strategy that was incorporated during

the Top-Down Model training, history matching and blind

history matching for the Bakken Shale. As shown in this

figure data from April 2006 to January 2010 was used to

train and history match the TDM for the Bakken shale and

the historical production data from February to July 2010

were used as blind history match data to validate the Bakken

Shale TDM.

Figures 11 and 12 show the distribution of some of the

reservoir characteristics in Upper (Figure 11) and Middle

Bakken (Figure 12). In Top-Down Modelling a high-level

static model of the reservoir is developed based on well logs

and all other available reservoir characteristics. Since TDM

is an AI&DM-based reservoir simulation and modelling

technology it does not require a static model in the form that

is common and customary for the numerical reservoir

simulation models.

Figure 9. Voronoi polygons identified for the wells in Upper

Bakken and Middle Bakken.

Figure 10. Strategy used during the training and history matching

of the Top-Down Model for the Bakken Shale.

The static model that is developed during the Top-Down

Modelling process uses only the available (and preferably

measured) data. The objective of this measurement-based

geological (static) model is to refrain from interpretations,

as much as possible. The TDM static model represents the

reservoir characteristic indications (well logs, results of core

analysis and well tests, seismic attributes) that is associated

with each well and relates them with similar reservoir

characteristic indications from the offset wells. By


performing this for all the wells the reservoir characteristic

indications of each portion of the reservoir is sampled

multiple times, once as the main well and several times as

offset to the neighbouring wells.

Figure 11. Distribution of Pay Thickness and Porosity in the

Upper Bakken Shale.

Figure 12. Distribution of Pay Thickness and Deep Resistivity in

the Middle Bakken.

Figures 13 and 14 show the performance of the Top-Down

Model after training and history matching for both Upper

Bakken Shale and Middle Bakken. In each of these figures

examples of four wells are shown. From these figures it can

be concluded that TDM has captured the essence of fluid

flow in naturally fractured shale reservoirs and can model

(in predictive mode) the performance of wells in such

formations.

One of the capabilities of Top-Down Model is its ability to

perform fast track analysis. As part of such analyses TDM is

capable of developing type curves for each of the wells in

order to quantify (in predictive mode and for new wells) the

uncertainties associated with parameters that are used as

input to the model. Such parameters can be reservoir

characteristics or operational constraints that are imposed on

the well during production. If parameters involved in the

hydraulic fracturing such as number of stages or amount of

proppant injected are part of the input parameters of the

TDM, they can also be used during such analyses.

Figure 15 shows an example of such analysis that can be

performed routinely once a Top-Down Model is trained and

history matched for a shale formation. In this figure

production rate is plotted against time for a given well while

the formation thickness (on top) and Porosity (on bottom)

are changed. TDM shows the expected changes in

production behaviour in each of these wells as formation

thickness and porosity are modified. Curves displayed in

Figure 15 can be referred to as type curves that can be

generated for each well, for groups of wells and/or for the

entire field very quickly. Such type curves can help

engineers during analysis of the reservoir performance.

When these type curves are developed for parameters that

are involved in hydraulic fracturing (such as number of

stages, amount of proppant or fluid that are injected and the

injection rate) they can serve as a guide for optimum design

of future hydraulic fracturing procedures for new wells.

Similar to the analysis that was presented for the Lower

Huron shale, the TDM design tool can be used in order to

analyze the depletion of hydrocarbon in the shale reservoir

and to identify the remaining reserves as function of time.

The remaining reserves as well as other parameters can

serve as guide for placing infill wells.

Figure 16 shows the contribution of different part of Upper

Bakken shale to production as a function of time. It can be

seen that during the first 3 years of production contribution

to production is concentrated on the south-eastern part of

the field while as times goes on the south-western and

western part of the field starts to contribute more and more

to the production until they become the dominant

contributor to the production by the end of the tenth year of

production.

As these contributions to the production (that correspond to

the depletion) are cross referenced with the original oil in

place, a qualitative picture of remaining reserves in the field

starts to emerge. Figure 17 shows the remaining reserves in

this part of the Upper Bakken Shale as of January of 2010.

Remaining reserves maps such as the one shown in Figure

17 can be generated for different dates. Such maps can show

the impact of continuous production (with and/or without

new infill wells) on the reservoir depletion and eventually

on the remaining reserves. By identifying the new locations


for infill drilling, one can refer to the predictive model

(trained, history matched and validated in the previous steps

of TDM) to forecast the production of these infill wells and

consequently estimate the impact of the infill wells on the

depletion of the reservoir. Once this process is performed in

an iterative manner, a strong reservoir management tool

emerges from this exercise.

Therefore, maps such as the one shown in Figure 17 can

play an important role in reservoir management decisions

that are made in Bakken Shale.

Figure 13. Training and history matching of Top-Down Modeling

in Upper Bakken Shale.

Figure 14. Training and history matching of Top-Down Modeling

in Middle Bakken.

6 APPLICATION OF TOP-DOWN MODELING TO NEW ALBANY SHALE

Detail of Top-Down Modelling application to New Albany

Shale can be found in a recently published SPE paper

(Kalantari 2009).

The New Albany Shale is predominantly organic-rich black

shale that is present in the subsurface throughout the Illinois

Basin. It has a continuous 100 foot thick pay zone of shale,

capped by a very thick, dense, gray-green Borden Shale.

New Albany Shale encompasses some 53,000 square miles

and is estimated to contain 86 trillion cubic feet (Tcf) of gas.

The Shale is shallow, biogenic and thermogenic shales that

lie at depth of 600-5,000 feet and are 100-200+ feet thick.

Natural fractures are believed to provide the effective

reservoirs permeability and gas is stored both as free gas in

fractures and as absorbed gas on kerogen and clay surfaces


(Kalantari 2011). Figure 18 shows the location of the New

Albany Shale and the portion of the formation that was used

in the Top-Down Modelling along with well locations, the

Cartesian and the Voronoi grid that was used to identify the

Estimated Ultimate Drainage Area (EUDA) for each well.

Figure 15. Results of the Top-Down Modeling (monthly rate and

cumulative production) as applied to the production history.

Figure 16. Depletion in the Upper Bakken Shale as a function of

time using the Fuzzy Pattern Recognition of Top-Down Modeling.

The limitations imposed on this study included the extent of

the publicly available data (some production history along

with well logs for a subset of wells). Once the TDM static

model was constructed, the initial gas in place was

calculated and mapped. As mentioned in the prior sections,

this is an important first step in development process of

Top-Down Models. This figure (Figure 18) also shows the

permeability and the initial gas in place distribution

determined using the type curve matching and volumetric

calculations in the Top-Down Modelling workflow.

Figure 17. Remaining Reserves as of January 2010 in the Upper

Bakken Shale.

Figure 18. Applying Top-Down Modeling to New Albany Shale

(NAS). Steps involved in preparing the model.


The permeability distribution in this part of New Albany

Shale was calculated using a history matching process that

involved dynamic modelling of the production from some of

the wells using a stochastic discrete fracture network model.

Details of this procedure have been covered in a previously

published SPE paper (Kalantari 2009).

Figure 19 shows the Remaining Reserves as a function of

time from 2006 to 2040 (if no new wells are drilled in this

part of the field). Estimation of the Remaining Reserves is

one of the last steps in the Top-Down Modelling work flow.

Remaining Reserves along with other outputs from the Top-

Down Modelling provides means for identifying the

optimum locations for infill (new) wells. Using the trained

and history matched model that is developed during one of

the earlier stages of Top-Down Modelling, production from

infill wells can be predicted (estimated) and the Remaining

Reserves under new circumstances may be calculated and

mapped similar to those in Figure 19.

7 CONCLUSIONS

Share of shale formations to overall hydrocarbon production

in the United States and in the world is increasing rapidly.

As the interest in production from shale increases so does

the interest in managing shale reservoir and consequently

building predictive reservoir simulation model for shale

formations. Modelling shale reservoirs is a complex

process. Contribution of concentration gradient dependent

diffusion along with fluid flow through discrete natural

fracture networks become even more complex with multi-

stage hydraulic fractures that are used for completing wells

in shale reservoirs. All these factors make reservoir

modelling of shale formation particularly difficult and

challenging.

Probably the most challenging part of the shale reservoir

modelling is our quest for accurate representation of the

natural fracture network and the intersection of the induced

fractures with these networks. In this paper we summarized

a novel approach for modelling hydrocarbon producing

shale reservoirs by concentrating on production history and

any and all available reservoir characteristics measurements

and operational constraints. In this approach we follow the

philosophy of doing the best with the available information

and trying to stay away from assumptions about our

understanding of the details of what actually has happened

in the formation. In this modelling approach, instead of

starting from first principle physics, we let the actual

physics impose itself on the final model through data.

This data driven modelling technology concentrates on

measured data rather than assumptions. In this paper we

demonstrated the application of Top-Down, Intelligent

Reservoir Modelling (TDM) to gas producing Lower Huron

Shale and New Albany Shale and oil producing Bakken

Shale.

We showed the results of trained and history matched

models in matching actual production from the field

including blind history matches. We reviewed that

application of the design tool that is offered by TDM in

identification of infill location as well as depletion in the

reservoir and remaining reserves.

Figure 19. Assessing Remaining Reserves as a function of time

Using Fuzzy Pattern Recognition, NAS.


Top-Down, Intelligent Reservoir Modelling (TDM) is a

technically viable alternative to numerical reservoir

simulation that can be performed at a fraction of the cost

and man power in order to help engineers and geoscientists

learn more about shale formations and try to manage such

reservoirs.

8 ACKNOWLEDGEMENTS

Authors would like to acknowledge and thank U.S.

Department of Energy and National Energy Technology

Laboratory (NETL) through their RUA program, Gas

Technology Institute and Research Partnership to Secure

Energy for America (RPSEA) for their support of

application of Top-Down Modelling to shale formation in

the United States.

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