Utama Unconventional oil and gas resources handbook : evaluation and development

Unconventional oil and gas resources handbook : evaluation and development

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Unconventional Oil and Gas Resources Handbook: Evaluation and Development is a must-have, helpful handbook that brings a wealth of information to engineers and geoscientists. Bridging between subsurface and production, the handbook provides engineers and geoscientists with effective methodology to better define resources and reservoirs. Better reservoir knowledge and innovative technologies are making unconventional resources economically possible, and multidisciplinary approaches in evaluating these resources are critical to successful development. Unconventional Oil and Gas Resources Handbook takes this approach, covering a wide range of topics for developing these resources including exploration, evaluation, drilling, completion, and production. Topics include theory, methodology, and case histories and will help to improve the understanding,integrated evaluation, and effective development of unconventional resources.
  • Presents methods for a full development cycle of unconventional resources, from exploration through production
  • Explores multidisciplinary integrations for evaluation and development of unconventional resources and covers a broad range of reservoir characterization methods and development scenarios
  • Delivers balanced information with multiple contributors from both academia and industry
  • Provides case histories involving geological analysis, geomechanical analysis, reservoir modeling, hydraulic fracturing treatment, microseismic monitoring, well performance and refracturing for development of unconventional reservoirs
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22 March 2018 (18:04) 
Unconventional Oil and Gas
Resources Handbook
Evaluation and Development
Edited by

Y. Zee Ma
Schlumberger, Denver, CO, USA

Stephen A. Holditch
Texas A&M University, College Station, TX, USA


Gulf Professional Publishing is an imprint of Elsevier

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List of Contributors
Aura Araque-Martinez
Weatherford International, Houston, TX, USA
Tom Bratton
Tom Bratton LLC, Denver, CO, USA
Jing Chen
College of Geosciences, China University of Petroleum, Beijing, China
Faruk Civan
Petroleum Engineering, University of Oklahoma, Norman, OK, USA
W.J. Clark
Schlumberger, Denver, CO, USA
Charles-Edouard Cohen
Production Operations Software Technology, Schlumberger, Rio de Janeiro, Brazil
Deepak Devegowda
Petroleum Engineering, University of Oklahoma, Norman, OK, USA
Zhenzhen Dong
Schlumberger, College Station, TX, USA
S. Ganpule
SPE Member, Denver, CO, USA
Janz R. Garzon
Schlumberger, Denver, CO, USA
E. Gomez
Schlumberger, Denver, CO, USA
Veronica Gonzales
Technology Integration Group TIG, Schlumberger, Denver, CO, USA
David Gray
Nexen Energy ULC, Calgary, AB, Canada
O. Gurpinar
Schlumberger, Denver, CO, USA
Jun Han
PetroChina Coalbed Methane Company Limited, Beijing, China
David Handwerger
Schlumberger, Salt Lake City, UT, USA
Shannon Higgins-Borchardt
Schlumberger, Denver, CO, USA
Stephen A. Holditch
Texas A&M University, College Station, TX, USA




T. Izykowski
Schlumberger, Denver, CO, USA
Yong-shang Kang
College of Geosciences, China University of Petroleum, Beijing, China; State Key Laboratory
of Petroleum Resources and Prospecting, China University of Petroleum, Beijing, China
P. Kaufman
Schlumberger, Denver, CO, USA
Olga Kresse
Production Operations Software Technology, Schlumberger, Sugar Land, Texas, USA
W. John Lee
University of Houston, Houston, TX, USA
Yu-peng Li
EXPEC ARC, Saudi Aramco, Dhahran, Saudi Arabia
B. Luneau
Schlumberger, Denver, CO, USA
Jingsheng Ma
Y. Zee Ma
Schlumberger, Denver, CO, USA
De-lei Mao
PetroChina Coalbed Methane Company Limited, Beijing, China
Domingo Mata
Technology Integration Group TIG, Schlumberger, Denver, CO, USA
W.R. Moore
Schlumberger, Denver, CO, USA
O.C. Mullins
Schlumberger-Doll Research, Cambridge, MA, USA
K.E. Peters
Schlumberger, Mill Valley, CA, USA; Department of Geological & Environmental Sciences,
Stanford University, Palo Alto, CA, USA
I. Pirie
Schlumberger, Denver, CO, USA
A.E. Pomerantz
Schlumberger-Doll Research, Cambridge, MA, USA
Guan Qin
University of Houston, Houston, TX, USA
J.S. Qin
Weatherford International, Houston, TX, USA


Kelsey Schiltz
Colorado School of Mines, Golden, CO, USA
Richard Sigal
Independent Consultant, Las Vegas, Nevada, USA
J. Sitchler
SPE Member, Denver, CO, USA
David Sobernheim
Schlumberger, Denver, CO, USA
Tim Sodergren
Alta Petrophysical LLC, Salt Lake City, UT, USA
K. Srinivasan
Schlumberger, Denver, CO, USA
V.P. Vaidya
Weatherford International, Houston, TX, USA
Chenji Wei
PetroChina Coalbed Methane Company Limited, Beijing, China
Yunan Wei
C&C Reservoirs Inc., Houston, TX, USA
Xiaowei Weng
H.Y. Wu
China University of Geosciences, Beijing, China
Yinghui Wu
China University of Geosciences, Beijing, China; Weatherford International, Houston,
X. Xia
PEER Institute, Covina, CA, USA, Current address: ConocoPhillips, Houston, TX, USA
Xinya Xiong
Gas Technology Institute, Chicago, IL, USA
John Xu
C&C Reservoirs Inc., Houston, TX, USA
Mei Yang
Weatherford International, Houston, TX, USA
Jian-ping Ye
CNOOC China Limited, Unconventional Oil & Gas Branch, Beijing, China
Chun-lin Yuan
College of Geosciences, China University of Petroleum, Beijing, China
Bing Zhang




Shou-ren Zhang
Y. Zhang
University of Wyoming, Laramie, WY, USA
X.P. Zhao
ExGeo, Toronto, ON, Canada
Qun Zhao
Langfang Branch of Research Institute of Petroleum Exploration and Development, PetroChina,
Hebei, China
Wentao Zhou
Production Product Champion, Schlumberger, Houston, TX, USA
R.J. Zinno
Weatherford International, Houston, TX, USA

While different definitions have been proposed for unconventional resources, we consider the
subsurface hydrocarbon resources that are tight and must be developed using large hydraulic fracture
treatments or methods to be unconventional reservoirs. These geological formations generally have
very low permeability or high viscosity; they include tight gas sandstones, shale gas, coalbed
methanes, shale oil, oil or tar sands, heavy oil, gas hydrates, and other low-permeability tight formations. On the other hand, conventional reservoirs are those that can be economically developed
generally with vertical wellbores and without the use of massive stimulation treatments or the injection of heat.
Before the recent downturn in the oil industry, development of unconventional resources had a
terrific run, especially in the North Americas. For example, the United States has been producing more
oil than it had since the 1970s as a result of oil production from unconventional plays. While crude oil
price went down dramatically in late 2014 and 2015, there may be a silver lining behind the recent
price plunge. Historically, crude oil price has been very volatile, increasing and then decreasing
suddenly, but the worldwide consumption of oil and gas keep increasing annually. The International
Energy Agency has recently reported that the world oil demand is expanding at a fast pace and it was
raising its estimate for demand growth in the long term. Also, the industry has historically been able to
improve the efficiency of extracting hydrocarbons when facing the challenges of low prices.
A downturn gives us some time to step back, review what has been done, and think about possible
improvements and innovations.
Efficiency can be fostered by an integrated, multidisciplinary approach and innovative
technologies for evaluation and development of unconventional resources. Large heterogeneity of
unconventional formations and high extraction costs lead to substantial uncertainty and risk in
developing these reservoirs. Multidisciplinary approaches in evaluating these resources are
critical to successful development. This multidisciplinary approach is why we have assembled
this handbook that covers a wide range of topics for developing unconventional resources, from
exploration, to evaluation, to drilling, to completion and production. The topics in this book
include theory, methodology, and case histories. We hope that these contents will help to
improve the understanding, integrated evaluation, and effective development of unconventional
Chapter 1 gives a general overview of how to evaluate and develop unconventional resources.
It briefly presents a full development cycle of unconventional resources, includes exploration, evaluation, drilling, completion, and production. General methods, integrated workflows, and pitfalls used
in these development stages are discussed.
Chapter 2 presents assessments of worldwide recoverable unconventional gas resources while
characterizing the distribution of unconventional gas technically recoverable resources by integrating a
Monte Carlo technique with an analytical reservoir simulator.




Chapter 3 deals with applications of geochemistry to unconventional resource evaluation. It
overviews the current developments in the use of both organic and inorganic geochemistry to identify
sweet spots in unconventional mudrock resource plays, and updates the understanding of kerogen
structure. It also discusses strengths and weaknesses of current measurements, workflows for integrating geochemical and geomechanical measurements, and uses of three-dimensional basin and
petroleum system models for characterizing unconventional plays.
Chapter 4 describes pore-scale characteristics of gas flow properties in shale by digital core
analysis. The characterization of flow properties for shale gas reservoirs is important, but complex
because of the tightness of pore space and the diverse chemical compositions of the matrix as well as
nonDarcy flow. This chapter summarizes recent work on digital core analysis for flow characterization
of shale gas.
Chapter 5 is an overview of wireline–log signatures of organic matter and presents lithofacies
classifications for shale and tight-carbonate reservoirs. It presents several methods for lithofacies
prediction using wireline logs. The classical petrophysical charts are integrated with statistical
methods for lithofacies classification, through which wireline log signatures of organic shale are
further highlighted.
Chapter 6 discusses the role of pore proximity in governing fluid Pressure-Volume-Temperature
(PVT) behavior and produced fluids composition in liquids-rich shale reservoirs. It describes the
shortcomings in conventional PVT models in predicting fluid behavior, and reviews the recent advances in describing the effects of pore proximity on storage and transport-related properties and the
implications for well productivity and drainage area calculations. It also discusses the impact of pore
proximity on fluid transport, well drainage areas, and long-term well performance.
Chapter 7 presents geomechanics for unconventional reservoirs. Geomechanical properties of the
subsurface greatly influence both the drilling and hydraulic fracturing of a well in an unconventional
reservoir. A mechanical earth model (MEM) is an estimate of the subsurface mechanical properties,
rock strength, pore pressure, and stresses, and can be used to quantify the geomechanical behavior of
the subsurface. These components in the MEM are described in this chapter, with an emphasis on
applications to developing unconventional reservoirs.
Chapter 8 presents an overview on hydraulic fracture treatments and optimization, including
fracture fluid selection, proppant selection, fracture design, estimating fracture properties, and
completion strategies. It also discusses production modeling, and compares analytical and numerical
modeling methods.
Chapter 9 first briefly presents the basic microseismic monitoring theory, data acquisition, and
processing methods, and then focuses on the application of microseismic monitoring in unconventional reservoirs. Advances in the microseismic methodology have added value to existing datasets
through interpretations of reservoir parameters. Microseismic data mitigates uncertainty during
completions evaluation in unconventional reservoirs, which is critical as the economic challenges of
development increase.
Chapter 10 discusses the impact of preexisting natural fractures on hydraulic fracture simulation. It
first presents a hydraulic fracture network model that incorporates mechanical interaction between
hydraulic fracture and natural fracture, and among hydraulic fractures. It then discusses fracture
simulations for various configurations of natural fractures, the impact of the natural fractures on the



hydraulic fracture network geometry, and the impact of the fracture geometry on proppant distribution
in the fracture network.
Chapter 11 deals with an effective core sampling for improved calibration of logs and seismic data
for unconventional reservoirs. Core data are commonly used to calibrate reservoir parameters with
wireline logs. However, such a calibration is often suboptimal. This chapter discusses pitfalls in the
calibration, and proposes guidelines for core sampling and adequate calibrations between various data
in unconventional resource evaluation.
Chapter 12 discusses hydraulic fracture design and well performance analysis. The workflow
presented in the chapter includes basic hydraulic fracture design, dynamic calibration of the design
based on performance analysis, and completion optimization. The chapter gives an overview of
fracture design, and discusses the well performance analysis in relation to the effective fracture geometry, and the various factors affecting well productivity. It also discusses optimal completion
strategies and refracture treatments.
Chapter 13 discusses the impact of geomechanical properties on completion in developing tight
reservoirs. Geomechanical properties generally vary across a field and have significant impact on the
design of the completion. The chapter elaborates the impact of in situ stress on hydraulic fracture
initiation, growth, connectivity, drainage, and well spacing. In the study, the MEM is integrated with
hydraulic fracture and production modeling to optimize drainage strategy and reduce the development cost.
Chapters 14, 15, and 16 present case histories involving factors such as lithofacies, petrophysical
analysis, reservoir modeling, and the development of tight gas sandstone reservoirs. Tight gas sandstones represent an important unconventional resource. Two schools of thought are discussed
regarding the geologic control of tight gas sandstone reservoirs: continuous basin-centered gas
accumulations and gas accumulation in low-permeability tight sandstones of a conventional trap.
Differences and similarities between evaluating and developing tight-gas sandstone and shale reservoirs are also discussed.
Chapter 17 presents an overview of the evaluation and development of coalbed methane (CBM)
reservoirs, with an example from Qinshui basin in Northern China. CBM is generated from methanogenic bacteria or thermal cracking of the coal. Most methane and other gases are retained in coal by
adsorption, but free gas also occurs in fissures and pore systems. This chapter discusses CBM potential
and producibility of coal bearing strata as these are strongly affected by the hydrogeological regime of
formation waters and coal permeability.
Chapter 18 discusses the monitoring and prediction of steam chamber development in a
bitumen field. Steam-assisted gravity drainage (SAGD) is an in situ thermal recovery method used
to extract heavy oil and bitumen. The efficacy of SAGD depends on the presence of permeability
heterogeneities. This study demonstrates how the integration of compressional and multicomponent seismic data using neural network is used to build a predictive model for steam chamber
A glossary is presented, which contains the definitions of many terminologies commonly used in
evaluating and developing unconventional resources.
To ensure the scientific standard of this book, the chapters were peer-reviewed along with the
editorial review. We thank the reviewers for their diligent reviews as they have helped improve



Table 1 Reviewers of the Manuscripts
Du, Mike C.
Foley, Kelly
Forrest, Gary
Fuller, John
Hajizadeh, Yasin
Han, Hongxue
Handwerger, David
Higgins-Borchardt, Shannon
Liu, Shujie
Marsden, Robert
Moore, William R.

Psaila, David
Prioul, Romain
Royer, Jean-Jacques
Sitchler, Jason
Wei, Yunan
Weng, Xiaowei
Yan, Qiyan
Zachariah, John
Zhang, Xu
Zhou, Jing

the quality and depth of the manuscripts. Table 1 is a list of the technical reviewers. We also thank the
authors who have worked diligently with the editors to produce this volume.
August 2015
Y. Zee Ma, Denver, Colorado, USA
Stephen A. Holditch, College Station, Texas, USA



Y. Zee Ma

Schlumberger, Denver, CO, USA

Evaluating and developing unconventional resources without using a multidisciplinary approach is
analogous to the old tale about the blind men and the elephant. One grabs his long trunk, one touches
his large ears, one pats his broad side, and each comes away with a totally different conclusion.

Although some unconventional resources, such as heavy oil and oil sands, have been developed for
some time, the large-scale production of oil and gas from deep unconventional resources, such as shale
gas and shale oil, has occurred more recently. The success of the Barnett Shale in central Texas has led
the way for a number of other successful shale plays in North America (Bowker, 2003; Parshall, 2008;
Alexander et al., 2011; Ratner and Tiemann, 2014), including the Fayetteville, Haynesville, Marcellus,
Woodford, Eagle Ford, Montney, Niobrara, Wolfcamp, and Bakken. While shale plays have been in the
spotlight in the last decade or so, unconventional resources are much broader. It is worth systematically
reviewing and classifying various hydrocarbon resources based on reservoir quality.
Conventional hydrocarbon resources typically accumulate in favorable structural or stratigraphic
traps in which the formation is porous and permeable, but sealed by an impermeable layer that
prevents hydrocarbon from escaping. These favorable subsurface structures possess migration
pathways that link the source rocks to the reservoirs, and the formations have good reservoir quality
and generally do not require a large-scale stimulation to produce hydrocarbon. Unconventional
resources reside in tight formations, and are of lower reservoir quality and more difficult from
which to extract hydrocarbons. On the other hand, unconventional resources are more abundant in
the earth. The relative abundance of conventional and unconventional resources can be described by
a triangle (Masters, 1979; Holditch, 2013), illustrated in Fig. 1.1. Unconventional resources represent
a variety of geological formations, including tight gas sands, gas shales, heavy oil sands, coalbed
methanes, oil shales, and gas hydrates. In fact, any hydrocarbon resources that are not conventional
may be considered unconventional. For example, the tight basement formations that contain oil or gas
can also be considered to be unconventional resources as they have lower permeability than the
conventional reservoirs. Moreover, it is not always easy to classify a reservoir into a specific category.
Some studies consider gas and oil shales, coalbed methane, and gas hydrates as unconventional while
putting tight gas sandstones in the conventional category (Sondergeld et al., 2010). From the
Unconventional Oil and Gas Resources Handbook. http://dx.doi.org/10.1016/B978-0-12-802238-2.00001-8
Copyright © 2016 Elsevier Inc. All rights reserved.




Resource triangle. (Modified after Holditch. (2013).) The high to medium-quality reservoirs in the upper
region of the triangle are generally considered to be conventional resources; the other resources in the larger
part of the triangle are considered to be unconventional reservoirs.

standpoint of reservoir characterization methodology, tight gas sandstone reservoirs (perhaps other
tight formations as well) indeed share many characteristics of conventional reservoirs (Law and
Spencer, 1989; Ma et al., 2011); they can be termed conventional unconventional resources or
nonsource rock unconventional reservoirs, while shale reservoirs can be termed deep unconventional
resources or source-rock unconventional resources. The same ambiguity can be applied to a specific
reservoir; a shale formation may contain both oil and gas, or a hydrocarbon-bearing geological
formation may contain a variety of lithofacies, both shale and other coarser-grained lithologies.
Bakken oil-producing formations, for example, are mostly siltstones and other coarser-grained
rocks that are sandwiched between the upper Bakken Shale and lower Bakken Shale (Theloy and
Sonnenberg, 2013; LeFever et al., 1991).
The main characteristics of unconventional reservoirs include low-to-ultralow permeability and
low-to-moderate porosity. As a result, hydrocarbon production from these reservoirs requires different
extraction technologies than from conventional resources. An unconventional reservoir must be
stimulated to produce hydrocarbons at an acceptable flow rate to recover commercial quantities of
hydrocarbons. Permeability for unconventional reservoirs is mostly below 0.1 mD, and reservoirs with
permeability above 0.1 mD are generally considered to be conventional. But this is not clear cut in
practice because the permeability of a reservoir is never a constant value and the permeability
heterogeneity of a reservoir, both conventional and unconventional, is generally high.
In most conventional reservoirs, shales are considered to be source rocks or seals as they have low
permeability, but the Barnett Shale play has proved that shales can be important reservoirs. Shales are
the most abundant sedimentary rock formations on Earth, but not all the shales are hydrocarbon plays,
nor are all shale reservoirs made equal. There is no one unique geological or petrophysical parameter



that can determine high hydrocarbon production, but two categories of variables are important:
reservoir quality and completion quality (Miller et al., 2011; Cipolla et al., 2012).
Reservoir quality describes hydrocarbon potential, amount of hydrocarbon in place, and hydrocarbon
deliverability of the rock formation. Completion quality describes stimulation potential or the ability to
create and maintain fracture surface area. The important variables in reservoir quality include total organic
carbon (TOC), thermal maturity, organic matter, mineralogical composition, lithology, effective porosity,
fluid saturations, permeability, and formation pressure (Passey et al., 2010). Completion quality is highly
dependent on geomechanical properties (Waters et al., 2011), and mineral composition of the formation,
including rock fracturability, in situ stress regime, and the presence and characteristics of natural
fractures (Weng et al., 2015).
Because of the tightness of unconventional formations, developing these resources is quite different
from developing conventional reservoirs. Producing hydrocarbons from tight formations often requires
not only drilling many more wells than developing a conventional reservoir, but also modern technologies, including horizontal drilling and hydraulic fracturing. The combination of horizontal drilling
and multistage hydraulic fracturing has proven to be the key for economic production of hydrocarbons
from many shale and other tight reservoirs. The important variables and parameters in these new
technologies include horizontal well patterns, hydraulic fracturing design, stage count, and perforation
clusters. Various well-spacing pilots are commonly used, and various hydraulic fracturing operation
schemes are frequently tested for optimizing the well placement (Waters et al., 2009; Du et al., 2011;
Warpinski et al., 2014).
As in other field development projects, uncertainty in developing unconventional resources is
high because of the complex nature of subsurface formations. This is why more than ever, evaluating
and developing unconventional resources requires an integrated, multidisciplinary approach. A
multidisciplinary workflow can optimally integrate all the available information; geological, petrophysical, geophysical, and geomechanical variables can be incorporated into the workflow,
enabling better characterization of reservoir properties, ranking critical parameters, optimizing
production, and managing the uncertainty. An integrated workflow can be efficient in capturing the
main characteristics of unconventional resources, and offers a quantitative means to effectively
develop these fields. This requires study of many variables, including TOC, mineralogy, lithology,
pore and pore throat geometry, anisotropy, natural fracture network, rock mechanical properties,
in situ stress, structure and fault impact, and production interaction with the reservoir. Full characterization of these formations requires integrating cores, borehole images, well logs, seismic, and
engineering data.
Without a truly integrated approach, prospecting and drilling in these fields are often somewhat
subjective, with high uncertainty and risk. Three approaches can reduce the uncertainty in field
development: acquiring adequate data, using better technologies, and using scientifically sound
inference for interpreting various data and integrating different geoscience disciplines (Ma, 2011). The
importance of data and technologies for reducing and managing uncertainty in field development is
well known. Here we discuss the importance of inference in integrating various data for analyzing
production data and reservoir properties.
Reservoir quality, completion quality, and completion effort for developing unconventional resources
are multidimensional or composite variables that are made up of several other physical variables, such as
kerogen, TOC, porosity, fluid saturation, and permeability for reservoir quality; stress, mineralogy, and
mechanical properties for completion quality; and lateral length, proppant tonnage, and stage count



for completion effort. Although these variables all impact production, a weak or no correlation, or even a
reversed correlation, between production and such a variable, is frequently observed (Jochen et al., 2011;
Gao and Du, 2012; Zhou et al., 2014). For example, lateral length or stage length and production rate
may appear to have no, weak, or negative correlation (e.g., see Figures 12–17 in Miller et al., 2011).
Similarly, one principle in selecting a hydraulic fracturing zone based on completion quality is to target
the rocks that have a high Young’s modulus and a low Poisson ratio, but what about rocks that exhibit
simultaneously high Young’s modulus and high Poisson ratio as in some cases, the Young’s modulus
is positively correlated with the Poisson ratio. This counterintuitive phenomenon, typically ignored in
both evaluation and production data analyses, is related to a statistical phenomenon termed the
Yule–Simpson’s effect or Simpson’s paradox (Ma et al., 2014). The crux of Simpson’s paradox lies in
interactions among many variables in a complex system (Simpson, 1951; Paris, 2012; Pearl, 2010; Li
et al., 2013; Ma and Zhang, 2014); sensitivity analysis based on experiments often tests the effect of a
variable while keeping all the other variables constant, but often, all the relevant variables change
simultaneously and are correlated in real data. Without understanding the Yule–Simpson’s effect
in analyzing the relationships in a multivariate and nonlinear system, it is easy to make incorrect
interpretations and decisions. An integrated approach is to understand the interaction of all the
concerned variables in reservoir quality and completion quality so that the hydraulic fracturing
treatment (HFT) is designed optimally for achieving the best economics.
Therefore, the evaluation of reservoir quality and completion quality must include evaluations of
individual physical variables as many as possible. In practice, because of the limited availability of
data, it is quite common to use a proxy to estimate reservoir quality, often based on correlation
analysis. One of the pitfalls is to determine the overall reservoir quality using only a single or a limited
number of physical variable(s). Some scientists call this type of reasoning atomistic inference, or
seeing the forest from the trees (Gibbons, 2008; Lubinski and Humphreys, 1996). Although this type of
inductive inference is common, and sometimes may be the only way of reasoning, geoscientists should
attempt to mitigate the inferential bias. For example, does high kerogen content imply high reservoir
quality in a shale formation? Although kerogen is an important component for evaluating reservoir
quality for a shale reservoir, high kerogen content does not necessarily imply high reservoir quality. In
fact, for a given TOC, high kerogen content implies a low maturity, and a small amount of oil or gas
generated, as illustrated in Table 1.1. Conversely, low kerogen content may be a result of a significant
amount of conversion of the original kerogen into oil or gas.

Table 1.1 Composition of Organic Carbon Content
in Sedimentary Rock
Total Organic Carbon (TOC)
EOM carbon

Convertible carbon



EOM, extractable organic matter.
After Jarvie (1991).

Residual carbon



Another pitfall is somewhat opposite to the atomistic inferenceddrawing inferences for individual
reservoirs based on the aggregated statistics. For example, as multiple studies from different basins
show a positive correlation between kerogen content and reservoir quality in unconventional formations, researchers may use the kerogen content as a proxy for reservoir quality. This may lead to an
ecological inference bias (Robinson, 1950; Ma and Zhang, 2014). Although the kerogen content and
amount of in-place hydrocarbon may be positively correlated using aggregated data, they are causally
correlated inversely; as a matter of fact, when kerogen matures, the amount of kerogen in the formation
decreases, and in-place hydrocarbon increases (Table 1.1).
In short, no single physical variable determines the quality of an unconventional reservoir,
but many variables are important. Therefore, the evaluation of unconventional formations should
use an integrated and multidisciplinary approach that includes studies of all the important
parameters, whenever they are available, while considering their scales, heterogeneities, and
Unconventional resource development includes exploration, evaluation, drilling, completion, and
production. Several geoscience disciplines are used in these processes, including petroleum system
analysis, geochemistry, sequence stratigraphic interpretation, sedimentary analysis, seismic surveys,
wireline log evaluation, core analysis, geomechanical study, petroleum and reservoir engineering,
production data analysis, and many others. Table 1.2 presents important methods and parameters in
evaluating and developing unconventional resources, especially those suitable for developing shale
reservoirs. Development of some other unconventional resources, such as oil sands and tight gas
sandstones, may use different evaluation methods, but most methods and parameters listed in the table
can still be useful.

Exploration strategies may be different for different unconventional resources; but for the most part,
unconventional resources are found in basin-filling environments. In some cases, exploring a tight
sandstone reservoir may be somewhat similar to exploring a conventional clastic reservoir, wherein
source rock may or may not be proximal to the reservoir (Spencer, 1989; Law, 2002; Shanley, 2004).
For shale reservoirs, although it is easy to find shale formations, identifying reservoir-quality shales
can be difficult. Although shales are the most abundant sedimentary rocks on Earth, not all shales are
hydrocarbon plays. Judging whether a given shale formation has a sufficient amount of hydrocarbonbearing rocks requires detailed interpretations of geological, geophysical, geochemical, and engineering data, and integration of these disciplines. In particular, two fundamental analyses for
exploration are petroleum system analysis and prospect assessment by integrating various data of
different scales, including cores, logs, seismic data, and regional geological studies (Suarez-Rivera
et al., 2013; Slatt et al., 2014).

Petroleum system analysis can help understand the evolution of a sedimentary basin. Petroleum
system modeling sheds light on the source and timing of hydrocarbon generation, migration routes,
gas-in-place, maturation, and hydrocarbon type. These include kerogen quality and quantity,



Table 1.2 Methods and Important Parameters in Evaluating and Developing Shale and Other
Tight Reservoirs
Disciplines and

Issues and

Drilling and


Core, wireline
Seismic attributes,
seismic inversion

Horizontal and
vertical wells,
Geosteering, well
hydraulic fracturing

Production data
analysis and

Integrated studies

Drilling fluids

Fluid saturations
Natural fractures,
Pore pressure

Wellbore stability
Well spacing
Lateral length

reservoir and
drivers to
Artificial lift

Proppant type and


Stage count

Stacking patterns


Reservoir continuity

Young’s modulus

Perforation clusters
Fracture network


Sweet spots

Poisson ratio



Petroleum system
Regional geology,
stratigraphy, core,
Seismic survey

Vitrinite reflectance


Seismic attributes

Water supply

Feedback for
Feedback for
future drilling and


LWD, logging while drilling; MWD, measurement while drilling

hyrocarbon generation, timing, migration, retention, transformation, thermal maturation, and TOC.
Petroleum system analysis is important for both conventional and unconventional reservoirs to assess
exploration risk before investment, but it is especially important for unconventional reservoirs found in
a basin-filling environment.



Important processes in petroleum system analysis include the following (Momper, 1979; Peters,
1986; Peters et al., 2015):




Three main stages of evolution of sedimentary rocks include: (1) diagenesis in which no
significant thermal transformation of organic matters occurs; (2) catagenesis in which organic
matters are under thermal transformation and converted into hydrocarbon; and (3) metagenesis in
which organic matters are under thermal transformation and the converted hydrocarbon may be
overly cooked or destroyed.
Organic-rich source rocks are exposed to heat during burial.
Kerogen becomes partially converted to oil or gas.
Converting kerogen into hydrocarbon often causes increased porosity and pressure within the
source rock.
When the pressure significantly exceeds geostatic pressure, the rock ruptures, oil and gas are
expelled, the fractures close, and the source rock returns to near-pregeneration porosity and
The above processes can be repeated in the formations as the source rock passes through the oil
and gas-generation maturity.
Oil expulsion is inefficient and a large proportion of the generated oil stays in the source rock,
leading to shale reservoirs; the oil can be further converted first to condensate, then to wet gas and,
ultimately, to dry gas and graphite if heat source is sufficient and temperature continuously rises.
It is possible that the pressure of source rocks is higher than geostatic pressure, but not high
enough for oil and gas to expel from the source rock, which causes overpressure in the source rock

In shale gas and oil reservoirs, source rocks typically are also the reservoirs. Kerogen content is
often an important variable to analyze reservoir quality. However, kerogen is only the basic material
for generating hydrocarbons. Its conversion into hydrocarbon requires a long geological process for
thermal maturation, under high temperature and high pressure. The amount of gas or oil generated is
determined by the kerogen type and the heating rate, which can be estimated using kinetic modeling
(Tissot and Welte, 1984). In some cases, hydrocarbon saturation is highly correlated to kerogen
content, but not always, because low present-day kerogen content may either indicate low original
kerogen during deposition, or partial conversion of high kerogen content that has thermally matured.
These two cases can be distinguished with petroleum system modeling. Causally, the high kerogen
content would mean that source rock is good; but it requires thermal maturation for the organic matters
to enter the oil or gas window. Measuring kerogen, TOC, and maturity is necessary, and a number of
methods have been proposed (Herron et al., 2014; Peters et al., 2015).
Kerogen type and its evolution into hydrocarbon can be described by a van Krevelen diagram using
hydrogen and oxygen indices (Fig. 1.2(a)). Hydrocarbon generation takes place as kerogen is under
thermal transformation in burial conditions (Fig. 1.2(b)). In the thermal transformation of kerogen to
hydrocarbon, the rate of maturation depends on the type of kerogen, and heat and pressure related to
burial (Fig. 1.2(b)). Migration of hydrocarbon typically occurs in conventional formations. But mature
organic-rich shales that have not expelled all their oil are termed oil-bearing shales, such as the
Bakken, Monterey, and Eagle Ford formations. The organic-rich shales that are either more thermally
mature or of a different kerogen type contain gas instead of oil, such as the Barnett, Fayetteville, and
Marcellus formations.



(a) van Krevelen diagram that describes kerogen types in relation to hydrogen and oxygen indices, and various
types of hydrocarbon (oil, wet gas, dry gas), (adapted from Allix et al. (2011)). (b) Thermal transformation of
kerogen into different types of hydrocarbon, (adapted from Boyer et al. (2006)).

Marine and lacustrine kerogens (Type I and II) tend to generate oil, but when the kerogen is
overmature, gas is generated instead, especially for Type II (Passey et al., 2010; Peters et al., 2015).
Type III kerogen, mostly from plant-generated organic materials, is more prone to gas or condensate
generation. Type IV kerogen generally represent residual organic matters with low hydrogen index and
little capacity for hydrocarbon generation.
Petroleum system modeling can aid the estimation of hydrocarbons in place. Kerogen contains
carbon and other elements. As kerogen matures, carbon concentration increases. TOC is an important
indicator for hydrocarbon resource base, and TOC from kerogen conversion can be estimated by the
following equation (Tissot and Welte, 1984):

Kerogen dk


where TOC and kerogen are in weight percentage, dk is the kerogen density, db is the bulk density,
and Kvr is a maturity constant that can be represented by vitrinite reflectance. The latter depends on the
type of kerogen and stage of maturation (diagenesis, catagenesis, and metagenesis; see, for example,
Tissot and Welte, 1984). Another important parameter for the kerogen-hydrocarbon conversion is
the transformation ratio (TR), which differs from vitrinite reflectance in terms of kinetic response
(Peters et al., 2015).

Exploring for unconventional resources includes studies of regional geology and integrated analysis
using core, log, seismic and geological data. In exploring shale reservoirs, quality screening is important
since all the source rocks are not reservoir-quality plays (Evenick, 2013; Wang and Gale, 2009).



Table 1.3 Early Resource Play Assessment: Disciplines, Methods, and Parameters

Methods or Properties

Parameters and Descriptions


Source rock
Seal and migration

Richness, thickness
Vitrinite reflectance
Formation age, timing of maturation
Seal thickness, capillary entry pressure, migration
pathway, and network
Hydrocarbon trapping mechanism
Was the generated oil or gas preserved? Possible
conversion of oil into gas? Migration into neighboring
Shoreface, channelized, bar-dominant, estuary,
lacustrine, marine, and others
Mineralogical composition, lithofacies proportion,
especially nonclay proportion
Average porosity, porosity by lithofacies, pore radius,
nanopores, interparticle pores, intraparticle pores,
organic-matter pores/kerogen-lined pores
Water saturation, wettability type
Matrix permeability, fracture permeability, relative
permeability, Klinkenberg permeability, nonDarcy flow,
permeability under stress
Pore pressure, hydrostatic pressure, lithostatic pressure
(overburden pressure), pressure gradient, normal
pressure, overpressure, underpressure.
Regional stress, orientation, local stress, principal
stresses, anisotropy, stress contrast
Young’s modulus, Poisson ratio, mineral composition
Formation bedding, faulting
Natural fracturing network, fracture permeability, DFN
(discrete fracture network)
Target zone depth from surface


Reservoir properties

Depositional environment
Lithology, lithofacies

Fluid saturation, wettability

Mechanical, physical


Stress field
Structural complexity
Natural fractures
Formation depth

Common source rock evaluation errors during prospect or play appraisals has been discussed
elsewhere (Dembicki, 2009). One of the main objectives in exploration is the prospecting for
hydrocarbon resources and their assessment. Important play elements include trap, charge access,
reservoir storage and fluid properties, and other physical or mechanical properties. Table 1.3 presents
methods and important parameters in early resource-play assessments. An integrated assessment
process should integrate multidisciplinary data and present an overall play chance of success. It should
enable transforming play fairway maps into a play chance map, and provide a basis for comparing
geological risks, concessions, leads, and prospects. Understanding the limits of the elementary plays
and how they contribute to the overall play chance enables ranking different resource opportunities
and investment decisions. Figure 1.3 illustrates an example of combining elementary play properties
into an overall assessment of play chance.



Example of play chance mapping by integrating multiple elementary play chance maps.
Adapted from Salter et al. (2014).

Seismic surveys are important tools for resource assessment; they are conducted to delineate the
potential reservoir, and to interpret the structure and stratigraphic surfaces of the formations. Seismic
analysis can help identify structural complexity, recognize analogous shale play systems, and
determine possible sweet spots (Glaser et al., 2013). Seismic data can be used to identify faults and
karsts (Du et al., 2011), helping assess the vertical connectivity between the hydrocarbon zone and the
base formation. They can also be used for fault and stress analysis for a regional-scale interpretation.
High-quality seismic data can be used to describe the reservoir properties through inversion, and
attribute extraction and analysis.

In developing both conventional and unconventional resources, the importance of evaluation lies in
leveraging science and technology from geological, geophysical, petrophysical, and engineering
studies to optimally produce the hydrocarbons from the subsurface. However, one misperception
in developing unconventional resources is that shale reservoirs are “statistical” plays, meaning
that the integrated studies based on various scientific disciplines are not useful to develop these
types of reservoir, but the strategy should be to drill in uniformly or randomly selected locations.
Schuenemeyer and Gautier (2014) compared the production costs between random drilling and
smart drilling in the Bakken and Three Forks formations, and showed significant advantages of smart
drilling based on the knowledge of the subsurface. In fact, the “statistical” play is simply a trial and
error process, often leading to a large number of false positives (i.e., dry holes and mediocre producers; Ma, 2010), and false negatives (i.e., missing good prospects or prolific locations). In contrast,
a comprehensive evaluation using a multidisciplinary approach can lead to fewer dry and mediocre



wells (reduced false positives), and good locations are likely to be drilled (reduced false negatives),
which increases the proportion of prolific wells and the overall productivity of the wells.
Evaluating unconventional resources is a complex process that involves the interpretation of
seismic data, formation evaluation using wireline logs, core interpretation, abductive inference based
on analogs, and integrated analyses using the available data and various geoscience disciplines.
Important physical parameters or variables for unconventional reservoirs include sequence stratigraphy, fault system, kerogen content, TOC, mineralogy, lithology, pore structure and connectivity,
natural fracture networks, rock mechanical properties, and in situ stress regime, among others. The
main purpose of evaluation is to identify the sweet spots, and to assess the reservoir quality and
completion quality. A good unconventional resource typically requires a certain level of reservoir
quality and completion quality, as hydrocarbon production of an unconventional resource project
depends on both (Suarez-Rivera et al., 2011). The questions to be answered in evaluation include:

How much oil or gas is there?
How producible are the resources?
Is the reservoir easy to complete?
What is the estimated ultimate recovery potential?

As conventional resources reside in high-quality rocks, petrophysical investigations have focused on
sandstone and carbonate facies, their porosity, fluid saturations, and permeability for the last century or
so. In some unconventional formations, such as tight sandstones and carbonates, reservoirs are not the
source rock; lithofacies interpretation and analysis based on cores and/or wireline logs often is an
effective way for reservoir characterization and modeling (Ma et al., 2011). Although mineralogy
plays an important role in understanding the lithofacies and rock types (Rushing et al., 2008) and the
lithofacies may govern the fluid storage and flow, a detailed breakdown of mineral compositions is
generally not necessary. In shale reservoirs, however, a description of their mineral composition is very
Shales can be mineralogically described using a ternary diagram with three dominant mineral
componentsdsilica, clay, and carbonates. Geochemical analysis, along with core and well log data,
can help determine the position of a specific shale sample or reservoir on such a ternary plot.
Figure 1.4(a) relates some of the well-known unconventional reservoirs to this mineralogical
composition framework. These known reservoirs generally have less clay content than the average
shale, which is calculated from all shales that include both organic and inorganic rocks. Notice also
that these reservoirs have different mineral and lithological compositions, but they have been successful hydrocarbon projects, implying that mineralogy is not the only determining variable for
reservoir quality. Conversely, other formations may have similar lithological compositions to some of
these reservoirs, but they may or may not be good reservoirs depending on other variables. Moreover,
for a given shale reservoir, mineralogical composition may vary widely as a function of vertical and
lateral locations, as shown by the Barnett and Eagle Ford plays (Fig. 1.4(b)). These variations simply
reflect the mineralogical heterogeneity of the formations.
An accurate mineralogical model is highly important for formation evaluation of shale
resources. Individual shale reservoirs and even different locations in the same reservoir can vary



(a) Ternary plot of mineral compositions and relating several known shale reservoirs to this mineralogycompositional triangle. (Adapted from Grau et al. (2010)). Notice that some of these reservoirs were projected
on the plot based on limited data, and may not be accurate. For example, new data have shown that the
Bakken is more calcareous (Theloy and Sonnenberg, 2013). (b) Heterogeneity in mineral composition in two
shale-gas reservoirs. (Adapted from Passey et al. (2010)). Notice that the means on the ternary plot for these
reservoirs in (a) and (b) are not necessarily in the middle of the areas because of the asymmetric frequency
distributions. (c) Vclay and GR relationship from Marcellus Shale formations. The two variables have a weak,
negative correlation, as opposed to a strong, positive correlation in a conventional reservoir (Ma et al., 2014).

considerably in mineralogy (Fig. 1.4). Understanding the mineralogical mix of shale formations is
critical to analyze both reservoir quality and completion quality. Good organic shales have clay
content that is significantly lower than that of conventional shales, and they have less clay-bound
water. Most well-known productive shale reservoirs are highly siliceous, but a few known shale
reservoirs have high calcareous content (e.g., Eagle Ford and Niobrara). When a shale reservoir has a
relatively high clay content, such as the Bazhenov, it generally needs to have other favorable factors,
such as a more intensive fracture network, to compensate for the negative effect of the high clay



One commonly used, continuously described, composition in formation evaluation is the volume of
clay (Vclay). In conventional formations, Vclay is often estimated from the gamma ray (GR), but in shale
reservoirs, GR is often weakly correlated to, and sometimes even negatively correlated to Vclay, which is
often related to a statistical phenomenon called Simpson’s paradox (Ma et al., 2014, 2015), as shown in
Fig. 1.4(c). This often indicates a significant presence of organic matter in the shale, especially for shales
deposited under marine conditions (Passey et al., 2010). A weak correlation could also reflect an
anomaly of GR caused by a high concentration of radioactive minerals that are not necessarily associated
with clay in tight gas sand reservoirs (Ma et al., 2011, 2014). Thus, estimating Vclay independently from
GR is extremely important in evaluating shale reservoirs, and sometimes tight sandstone reservoirs as
well. Geochemical logging provides basic mineralogical compositions of the rock, such as clay, quartz,
carbonate, and pyrite, and can be an important tool for evaluating shale reservoirs for this reason.
Moreover, for an accurate evaluation, clay should be further subdivided into its components, such as
illite, chlorite, and smectite (smectite can causes expansion, leading to fracture closures). Similarly,
carbonates should be subdivided into calcite, dolomite, and phosphate so that their compositions can be
used to assess rock quality, such as to calculate the rock brittleness (Wang and Gale, 2009).
Quantification of TOC is very important to the evaluation of organic shales as TOC represents the
weight percentage of carbon in the rock. TOC can be evaluated as part of evaluating the mineral
composition or independent from it using various methods (Passey et al., 1990; Charsky and Herron,
2013; Gonzalez et al., 2013; Joshi et al., 2015). All these methods have some advantages and limitations; it is often useful to use several methods to reduce the uncertainty in the evaluation. One
advantage of evaluating TOC as part of mineral composition is the so-called closure, implying all the
components add to one when they are represented by fractions (Aitchison, 1986; Pawlowsky-Glahn
and Buccianti, 2011).

1.3.2 PORES
Pore characterization is one of the most basic tasks in formation evaluation for both conventional and
unconventional plays, because pore represents the storage capacity for hydrocarbon resources. In
conventional formations or nonsource rock unconventional reservoirs, pore space mainly includes two
types of pores: interparticle pores and intraparticle pores. In organic-rich shales, organic-matter pores
or pores associated with kerogen often play an important role in the pore network (Loucks et al., 2012;
Saraji and Piri, 2014), and as a result, porosity is often highly correlated to TOC (Alqahtani and
Tutuncu, 2014; Lu et al., 2015). An organic-rich shale is typically composed of clay and nonclay
mineral matrix, and contains pore space between these mineral components (Ambrose et al., 2012).
Rocks with high TOC often have high porosity because the transformation from kerogen to
hydrocarbons often leads to an increase in pore volume. When TOC is high and significant amounts of
kerogen transforms to hydrocarbons, more pores are generated and they tend to be interconnected,
which explains why TOC is often highly correlated to permeability as well. Intra and interparticle
pores may decrease as maturity increases, and they may be water-filled pores. Organic-matter pores
increase as maturity increases; they should be mostly hydrocarbon-filled pores.
The porosity is often measured using a volume displacement or gas-filling method after crushing
the rock to a specific particle size (Luffel et al., 1992; Bustin et al., 2008; Passey et al., 2010; Bust
et al., 2011). Total porosity represents the pore space in the mineral matrix and kerogen (if present),
including the space occupied by hydrocarbon, mobile and capillary-bound water, and clay-bound water.



Illustration of total and effective pore space, and other components in an organic shale rock (synthesized after
several publications: Eslinger and Pevear, 1988; Eslinger and Everett, 2012, and others).

Effective porosity does not include clay-bound water (Fig. 1.5). Determining porosity in shale
reservoirs is complicated by very small pores and the presence of smectite in low-maturity clays that
contain interlayer water. Passey et al. (2010) noticed that there were significant differences in quantifying porosity in shales among various labs because of different definitions of porosity and measuring
Porosity measured in the absence of reservoir stress is generally different from porosity in reservoir
conditions. This has a significant impact on estimating hydrocarbon saturation and in-place resources
(Passey et al., 2010).

Pore space in rock contains oil, gas, or water; evaluating fluid saturations is an integral part of
analyzing reservoir quality. In tight gas sandstone reservoirs, water saturation is often negatively
correlated to porosity (Ma et al., 2011). In shale gas reservoirs, gas-in-place includes adsorbed and free
gas. In general, the free gas saturation should be positively correlated to effective porosity as it represents gas-filled porosity. The adsorbed gas is a function of pressure, TOC content, and isotherm
(Boyer et al., 2006). Direct quantification of adsorbed gas is difficult; an isotherm measured from core
samples can be used. Adsorption isotherm measurements enable the evaluation of the maximum
adsorption capacity of gas by organic matter as a function of pressure (Boyer et al., 2006). The
Langmuir isotherm model is one of the earliest models for adsorption, in which a monolayer and flat
surface of pores are assumed, among other assumptions (Ma, 2015). Figure 1.6 shows the Langmuir
isotherm function that enables computation of the adsorbed gas based on the pressure. However, the
assumptions in the Langmuir isotherm model may not be always realistic for adsorption in shale gas
reservoirs. Other heterogeneous multilayer adsorption models have been proposed (Ma, 2015).

Permeability is very important to the producibility of hydrocarbon from the subsurface formation for
both conventional and unconventional reservoirs (see e.g., Frantz et al., 2005). In tight gas sandstone



Langmuir isotherm or adsorbed gas as a function of pressure.
Adapted from Boyer et al. (2006).

reservoirs, permeability is highly dependent on lithofacies and generally is highly correlated to
effective porosity (Ma et al., 2011). In shale reservoirs, both the kerogen type and maturity have an
impact on the permeability and deliverability of the source rock. In thermally mature shale reservoirs,
porosity and permeability have been likely enhanced by the kerogen conversion into oil or gas. The
permeability to gas in kerogen-lined pores should be much greater than pores within the inorganic
matrix. The presence of connected, kerogen-lined pores is the key to producing hydrocarbon from the
shale. These systems are permeable to gas and should not imbibe water from hydraulic stimulation.
Maturation of kerogen causes shrinkage and develops a connected hydrocarbon–wet pore system. This
is why a good correlation between permeability and the amount of TOC is commonly observed.
Natural and drilling-induced fractures are important to the production of gas as they enhance hydraulic
fractures. It is also important to understand the concept of wettability and pore connectivity. Separating
porosity and permeability created through the diagenesis and catagenesis of kerogen from matrix
porosity and permeability can help better characterize the pore systems in organic shales. Kerogen and
kerogen-related porosity are oil-prone wetting, and facilitate the flow.
Fracture permeability is also important for developing tight reservoirs. Natural fracture systems
can be modeled using a discrete fracture network (DFN) and the reservoirs may be modeled by a single
porosity and dual permeability method or a dual porosity and dual permeability method (Du et al.,
2010, 2011). Natural fractures can be reactivated or enhanced further by hydraulic fracturing treatment
(Weng et al., 2015).



Completion quality for unconventional reservoirs describes the rock quality for completing an oil or
gas well, notably for hydraulic fracturing. It is concerned with selecting the best zone in the formation
in which the rock can be adequately fractured hydraulically and the fractures can stay open after the
pressure is reduced. For the most part, completion quality is driven by rock mechanics and mineralogical composition; geomechanical analysis is an integral part of interpreting completion quality
(Cipolla et al., 2012; Chong et al., 2010; Warpinski et al., 2013). Some of the important variables in
completion include fracture containment, facture conductivity, fluid sensitivity, ability of fractures to
retain the surface area, and rock mechanics for stimulated reservoir volume (SRV) (Zoback, 2007;
Mayerhofer et al., 2008). Hydraulic stress gradients impact the amount of pressure to open and close a
fracture, and in situ stress and anisotropy impact the fracture geometry, orientation, and containment
The type and amount of clay minerals are important variables for completion quality as they are
related to the brittleness of the rock (Jarvie et al., 2007; Wang and Gale, 2009; Slatt et al., 2014). Stress
is high where the clay content is high, especially if significant quantities of smectite are present. In
general, quartz-rich rocks tend to be brittle and are more conducive for completion; clay-rich rocks are
more ductile as they generally have a high Poisson ratio, a low Young’s modulus, and a high fracture
closure stress. For example, the Barnett shale tends to be brittle as it is rich on silica and has a high
Young’s modulus and low Poisson ratio (King, 2010, 2014). Carbonate-rich rocks tend to be
moderately brittle with limestone being less brittle than dolostone (Wang and Gale, 2009). Brittleness
index can be defined as (Jarvie et al., 2007; Wang and Gale, 2009):
Q þ C þ Cl


Q þ Dol
Q þ Dol þ Lm þ Cl þ TOC


BI ¼
or more accurately
BI ¼

where BI is brittleness index, and all other variables are the rock components: Q is quartz, C is
carbonate, Cl is clay, Dol is dolomite, Lm is limestone, and TOC is total organic carbon.
It is also possible to use an average of a normalized Young’s modulus and Poisson ratio to represent
the BI (Rickman et al., 2008). However, as noted earlier, an occurrence of Yule–Simpson’s
phenomenon will likely invalidate this method because Young’s modulus and Poisson ratio may be
positively correlated in aggregated data, but negatively correlated in disaggregated data (for causal/
physical analysis, see e.g., Vernik et al., 2012).
A slight variation in clay content and clay type can also create significant vertical segmentation of
the organic shale, and segmentation for the completion. Apart from clay content, there are other
properties of the geologic formation that impact the fracturing. For example, beddings in the formation
are natural weaknesses that impact the hydraulic fracture height containment (Chuprakov and Prioul,
The in situ stress dramatically impacts the fracturing geometry, including hydraulic fracture
propagation and containment (Han et al., 2014). A high minimum horizontal stress contrast leads to



Impact of stress and mechanical properties on hydraulic fracture geometry. (a) Illustration of the effect of
minimum horizontal stress on fracture geometry. Higher stress contrast yields greater fracture lengths and
containment, while lower stress contrast yields shorter fractures. (b) Impact of stress anisotropy on fracture
growth (adapted from Sayers and Le Calvez (2010)); while high stress anisotropy tends to create narrower
fracture fairways, and low stress anisotropy creates wide fracture fairways.

a deeper fracture (Fig. 1.7(a)); similarly, a high Young’s modulus or low Poisson ratio tends to give
a deep fracture. Moreover, the anisotropy of the in situ stress also controls the fracture growth,
including the geometry and size of the fractures (Fig. 1.7(b)).

Hydrocarbon production is highly impacted by reservoir pressure. Overpressure can be an important
production drive mechanism for tight formations (Meckel and Thomasson, 2008). Overpressure is
theorized to be the result of hydrostatic isolation of a volume, thus limiting equalization of the pressure
gradient of the reservoir with that of the surrounding rocks. Isolation can be caused by lithological
anisotropy, low porosity and permeability, and a restricted fracture or fault network. Other events that
can generate abnormal pressures include changes in fluid type, kerogen maturity, and tectonic
compression. When the source rock generates a large amount of gas in tight formations, gas cannot
escape easily and cause overpressure in the system.
Pore pressure prediction is important for drilling in tight formations because an accurate prediction of pore pressure can improve drilling efficiency, more easily deal with borehole adverse
conditions, and avoid blowouts (Zhang and Wieseneck, 2011). Pore pressures are difficult to
measure and estimate, especially for shale reservoirs because of the low porosity and permeability.
In some cases, they can be modeled using mud log and seismic velocity data or estimated using
rock physics (Bowers, 1995; Sayers, 2006). Pore pressure field in unconventional petroleum systems
can also be modeled.

The importance of integration for developing unconventional resources was briefly discussed earlier.
Here several specific aspects of integration in evaluating unconventional resources are discussed,
including the composite or aggregated variable problem (especially, ecological inference bias or



Simpson’s paradox; Robinson, 1950; Ma, 2009), nonlinearity, spurious correlation, and weak or
reversed correlation in analyzing and integrating various data.
For most reservoir evaluation projects, logs are the most readily available data, and core data are
much sparser, but can provide insights about the important drivers for hydrocarbon production.
Porosity, fluid saturation, and permeability derived from core are used to calibrate log responses. Core
descriptions can be used to validate petrophysical, geomechanical, and facies properties (Slatt et al.,
2008). Pressure tests and well production analysis should also be integrated with logs, seismic data,
and reservoir descriptions. Integration of petrophysical log data, completions, and seismic data is
valuable for selection of drilling locations and production optimization (Chong et al., 2010; Du et al.,
2011; Glaser et al., 2013; Hryb et al., 2014).
Variables that describe reservoir quality discussed earlier are generally correlated, but the
magnitudes of correlation may be quite variable. In analyzing and quantifying the relationships
among kerogen, TOC, porosity, fluid saturation and permeability, in situ stress, clay content, and
geomechanical properties, there are many pitfalls and it is easy to draw an inaccurate inference.
These include the use of correlation as causation, incorrect appreciation of the relationships
among three or more variables, and ignoring the correlation. For example, because of lack of
data, one often draws inference of a reservoir property from its relationships with other properties.
A related statistical paradox is that people generally think that when variables A and B are
correlated positively, and variables B and C are correlated positively, then variables A and C
must be correlated positively. But this is not necessarily true (Langford et al., 2001). In a system
affected by multiple variables, such as reservoir quality, all the influencing variables and their
relationships must be analyzed. The relationship between two variables may be affected by other
interfering variables, which may lead to a spurious correlation or other forms of Simpson’s paradox
related to a third variable’s effect (Ma et al., 2014). As an example, the resistivity and maturity
of organic matter in shales are generally correlated positively, but in some Niobrara formations,
the relationship between maturity and resistivity can be reversed to be negatively correlated because
of the wettability change in conjunction with development of petroleum-expulsion fractures or
other mechanism in transition from wet gas to dry gas (Al Duhailan and Cumella, 2014; Newsham
et al., 2002).
It is useful to distinguish completion quality from reservoir quality in general, but it is also very
important to analyze their correlation as they may or may not be highly correlated. Sometimes,
geomechanical parameters are indicative of not only completion quality, but also reservoir quality.
Britt and Schoeffler (2009) showed an example of separating prospective shales from nonprospective
shales using static and dynamic Young’s modulus correlation, yet Young’s modulus is often used as a
criterion for completion quality. In other cases, reservoir and completion qualities are not significantly or even inversely correlated, how to couple them is highly important for completion
Table 1.4 lists some of the most important parameters in evaluating shale reservoirs. A commonly
used cutoff of TOC for a source rock is 2% in weight. Effective porosity should be more than 4%
for shale gas reservoirs. Obviously, the higher the better; but it is uncommon for gas shales to
have effective porosity more than 10%. Marcellus shales have relatively high porosity compared
to most gas shales, but they also have higher clay content. Water saturation (Sw) should be less than
45%, and the lower the better. Higher Sw may be related to more water-wet pores within the inorganic



Table 1.4 Important Parameters in Evaluating Shale Reservoirs

Critical or Desired Values

Data Sources

Thermal maturity

>2% (Weight)
Oil window: 0.5 < Ro < 1.3
Gas window: 1.3 < Ro < 2.6
Clay <40%, quartz or
carbonate >40%
>100 nanoDarcy

Leco TOC, Rock-Eval
Vitrinite reflectance, Rock-Eval

Average porosity
Average water saturation
Average permeability
Oil or gas-in-place

Formation lateral continuity

Gas: free and adsorbed
gas > 100 Bcf/section
Moderate to dense, and
contained in the target zone
Oil-prone wetting of kerogen

Hydrocarbon type
Reservoir temperature
Young’s modulus
Poisson ratio

Oil or thermogenic gas
Overpressure is preferable
>230  F
<2000 psia net lateral stress
>3.0 MM psia

Natural fracture

X-ray diffraction, Spectroscopy, log-based
Core, logs
Core, Capillary pressure, log-based
Mercury injection capillary pressure,
nuclear magnetic resonance, gas expansion
Log-based, integrated evaluation
Seismic, image log
Special core analysis
Sequence stratigraphy with core, logs,
and regional data
Geochemistry, Rock-Eval
Log-based, seismic
Drill stem test
Logs, image log, seismic
Acoustic logs, cores
Acoustic logs, cores

Expanded and synthesized from a number of shale reservoir studies (Boyer et al., 2006; Sondergeld et al., 2010).

mineralsdpores that may not contain producible hydrocarbon. Analysis of both reservoir quality and
completion quality is important for successfully developing an unconventional reservoir.
It is possible to assign a favorability score for each of the reservoir and completion–quality
parameters and then compare the reservoir to analog reservoirs. In practice, only a small number of
these parameters may be evaluated because of limited data. These reservoir and completion–quality
parameters cannot be linearly combined; it is not straightforward to derive a composite score of
quality. For example, a reservoir with 10% porosity and 1% weight TOC may not be as good as a
reservoir with 5% porosity and 2% weight TOC. Radar plots provide a useful way to visually analyze
qualities and compare them with analog reservoirs. Figure 1.8 shows an example of ranking reservoir
and completion qualities based on the availability of data using radar plots. As more quality parameters
become available, the ranking becomes more complex, but analysis of more parameters should be
helpful to make a more informed decision. The comparison with a well-known analog reservoir using a
radar plot can also be useful to identify strengths and weaknesses, and subsequently helps the
completion design (Fig. 1.8(d)).



Radar plots for ranking reservoir and completion qualities of shale reservoirs with 6-level scores (0–5 from low
to high, decimals can be used as well). (a) 5-parameter ranking; (b) 6-parameter ranking; (c) 7-parameter
ranking; (d) 7-parameter ranking; and analog comparison.

As a result of developing unconventional resources, an increasing number of horizontal wells have
been drilled. In the US, for example, more than 70% of the rigs were drilling horizontal wells in
2013 versus only 30% in 2005 (RigData, 2014). This growth comes mainly from shale plays
and is related to hydraulic fracture stimulation because horizontal wells generally have more
contact with the target formation, which is especially important for increasing production from
source rock formations (Warpinski et al., 2008). By increasing the lateral length in a horizontal well,
the well can stay in the targeted geological zone and thus increasing its contact with the formation
(Fig. 1.9).
Well spacing regulations, reduced environmental footprint, and increased reservoir contact are
some of the main reasons why extended laterals are becoming more common. Not only multiple wells
can be drilled from a single pad, the directional S-shaped wells can be drilled using specially designed
rigs with a skid system, which allows substantial reduction of footprint and optimization of surface



Illustration of hydraulic fracturing from a horizontal well drilled into a subsurface source-rock zone.
Adapted from Allix et al. (2011).

facilities (Pilisi et al., 2010). Drilling technology, including interactive three-dimensional (3D) well
path design and rotary steerable systems, helps drill longer laterals with greater accuracy. Longer
laterals have generally improved the production performance, but longer laterals are more costly;
optimal lateral length and number of stages can be found based on the optimization of net present value
(NPV) incorporating the production and costs (details are discussed in the next section). After a
number of years of production in several known reservoirs, such as the Barnett, Fayetteville, and
Haynesville formations, an optimal lateral length along with stage count can be found using the
production history with various completion designs. In some cases, the lateral length is pushed beyond
the limits of coiled tubing.
Other issues related to drilling include challenge in multiwell pads, staying in the target geological
zones or optimal well placement (Kok et al., 2010), wellbore stability (Higgins-Borchardt et al., 2015),
drilling fluid selection, lost circulation kicks and blowouts (Pilisi et al., 2010; Zhang and Wieseneck,
2011). Subsurface formations are heterogeneous and shale reservoir quality can be highly variable in a
relatively narrow vertical or horizontal section. Horizontal or directional wells can be challenging
because the geological layers are not always horizontal and often difficult to track; many formations
are distorted, displaced, or undulated, making it difficult for the lateral to stay in the zone. Real-time
measurements, such as logging while drilling (LWD) and measurement while drilling (MWD), can
help depict the subsurface formation (Baihly et al., 2010). A more accurate image of the subsurface
formation can be depicted by coupling LWD with the 3D seismic surveys, allowing the placement of
the laterals in the correct geological layers. An accurate mapping of the subsurface is like a global
positioning system for well placement as it enables optimal landing and steering the lateral in the
right zone.
Drilling direction is important as breakdown will happen more frequently if a horizontal well is
drilled in the direction of maximum horizontal stress. High-pressure, high-temperature reservoirs can
be problematic for drilling. Advanced drilling fluids provide borehole strengthening and limit lost
circulation while adhering to environmental regulations. The right fluid can also help drill faster with
ease. Optimal drilling fluids vary from one play to another. Other important drilling considerations for
tight reservoirs are listed in Table 1.5.



Table 1.5 Important Considerations in Drilling and Completion of Tight Reservoirs
Variables and
Well spacing

Lateral length
Stage count
Fracture spacing

Specifications, Relevance, and Effects


Impacts the production; spacing should be
determined together with other variables, such as
reservoir quality and fracture design.
Impacts the contact area between wellbore and
formation, highly influential to production.
Directly impacts the contact area between wellbore
and formation, highly influential to production.
Fracture spacing impacts the production; it should be
optimized with several variables, including reservoir
quality, completion quality, proppant effect, and
mechanical interaction.

Malayalam et al. (2014); Warpinski
et al. (2014); Yu and Sepehrnoori
Zhou et al. (2014); Malayalam
et al. (2014)
Aviles et al. (2013); Zhou et al.
(2014); Malayalam et al. (2014)
Cheng (2009); Roussel et al.
(2012); Morrill and Miskimins
(2012); Jin et al. (2013); Wu
and Olson (2013); Sanaei and
Jamili (2014)
IIseng (2005); Nelson and Huff
(2009); Darbe and Ravi (2010);
Daneshy (2011); Ermila et al.
(2013); Pavlock et al. (2014).
Palisch et al. (2008); Teodoriu
Waters et al. (2009); Baihly et al.
(2010); Tollefsen et al. (2010);
Ketter et al. (2006)

Cased and
cemented versus
open hole

Cased and cemented completion enables isolation of
fracture stages, and development of complex
fractures. Openhole completion has a cost advantage.

Tubular selection

Need to ensure pumping slickwater at a high rate and
ensure HFT with an adequate number of stages.
Division of the wellbore into equally spaced zones is
a common way to select fracture initiation points, but
it may be suboptimal because of the heterogeneity of
the formation. Mud logging for gas composition;
LWD, MWD for formation stratigraphy; sonic logs
for stress variation.
Sequential fracturing may include acid stage, pad
stage, prop sequence stage, and flushing stage;
simultaneous fracturing requires close monitoring of
equipment, and multiple large pads.
Fluids are used to create fractures in the formation
and to carry proppant into fractures to keep them
from closing up. Slickwater is the most common one;
other types include cross-linked gel, gas-assisted,
and hybrid fluids. Choice depends on whether it
meets proppant placement need, and increases
fracture contact area.
Common additives include friction reducer, oxygen
scavenger and scale inhibitor. Special additives may
include enhanced oil recovery chemicals for
facilitating conductive pathways, recovering water,
and minimizing damage from water blocking.
Natural fractures include open and closed fractures;
both of them impact HFT, fracture geometry and
complexity, and SRV.

Selection of
fracturing point

versus sequential
Fracturing fluids


Hydraulic fracture
interaction with
natural fractures

Mutalik and Gilbson (2008);
Waters et al. (2009); Olsen and Wu
(2012); Wu and Olson (2013);
Viswanathan et al. (2014)
Palisch et al. (2008); Britt et al.
(2006); Britt and Schoeffler (2009);
Vincent (2011); Montgomery
(2013); McKenna (2014)

Kaufman et al. (2008); Holditch

Gale et al. (2007); Du et al. (2011);
Aimene et al. (2014); Huang et al.
(2014); Busetti et al. (2014); Smart
et al. (2014); Weng et al. (2015)



Table 1.5 Important Considerations in Drilling and Completion of Tight Reservoirsdcont’d
Variables and


Drilling fluids

Specifications, Relevance, and Effects


Placement efficiency; perforation clusters/fracture
stage, interval length, distance between clusters,
shot density, charge type.
Type and amount of proppant impact the flow
capacity in the fractures; it could prevent flowback.
The common proppant is sand with various
specifications. Small mesh bauxite and ceramic can
also be used. Proppant design has a significant
impact on fracture conductivity, allowing open
channels in the proppant pack improves the fracture
Oil-based, water-based

Wutherich and Walker (2012);
Kraemer et al. (2014)
Cipolla et al. (2009); Olsen et al.
(2009); Gillard et al. (2010);
d’Huteau et al. (2011); Gao and Du
(2012); McKenna (2014); Greff
et al. (2014); Mata et al. (2015)

McDonald (2012); Guo et al.
(2012); Stephens et al. (2013);
Young and Friedheim (2013)

Tight reservoirs commonly require hydraulic fracturing to be productive; for shale reservoirs, the
combination of horizontal well and HFT is often the key for commercial production. HFT is a
process of creating or restoring fractures in a geological formation using fluids and proppants to
stimulate production from oil and gas wells. The completion of hydraulically fractured wells involves many processes and variables, including completion type (cased and cemented or open hole),
stage design, lateral landing, hydraulic fracture geometry, fracturing fluid, proppant type/size/
schedule, hydraulic fracture monitoring, optimizing the hydraulic fracture treatment, stimulation of
reservoir volume, and production analysis (Mayerhofer et al., 2008; Chong et al., 2010; King,
2010, 2014; Gao and Du, 2012; Agrawal et al., 2012; Manchanda and Sharma, 2014). A good
completion should include generating, maintaining, and monitoring of fracture networks, and
optimally placing the perforation clusters. This requires understanding the geological and
geomechanical properties, and natural fractures of the formation, heterogeneity in the reservoir,
and optimized stimulation.
An accurate mechanical earth model (MEM) is important for developing shale reservoirs. The
MEM should include the distribution of mechanical properties and stress field of the subsurface
formations that include not only the targeted reservoir zone, but also the layers above and below it
because the stresses of the adjacent bounding formations impact the fracture height and length. The
modeling process integrates data from logging, core, drilling, and completions, and facilitates the
understanding of the mechanical properties and the stress state throughout the stratigraphic column.
A good MEM enables the understanding of the distribution and possible reactivation of natural
fractures during the hydraulic stimulation, and a reasonable prediction of hydraulic fracture geometry
(orientation, length, height growth, and aperture).



The completion design in developing unconventional reservoirs should be based on a combination
of geomechanical and reservoir properties. It is important to distinguish completion effort from
completion quality. While completion quality describes the properties of the rocks that make
completion straightforward or complicated, completion effort describes the various completion
methods and tools used in the completion. The combination of completion quality and completion
effort is the completion efficiency.
Completion effectiveness can be improved when hydraulic fracturing and perforating operations
take into account the anisotropies in stress and other reservoir parameters. Evenly fracturing all the
clusters in a heterogeneous zone may not be optimal as the lateral penetrates a heterogeneous reservoir.
Sequenced fracturing in combination with an effective fluid can increase the effective fracture length
(Kraemer et al., 2014). Typically, without knowledge of heterogeneity of the formation, a uniform
design for perforation clusters is used, but detailed knowledge of the lateral heterogeneity can help
place the perforation clusters in optimal locations, and enhances the hydrocarbon production of
perforated clusters (d’Huteau et al., 2011).
Other important variables in completion design include fracture spacing, cased and cemented
versus open hole, tubular selection, selection of fracturing point, simultaneous versus sequential
fracturing, fracture initiation points, proppants, fracturing fluids, additives, hydraulic fracture
interaction with natural fractures, and perforation strategies (King, 2010, 2014). These variables
impact the fracture complexity, SRV, and effectiveness of a fracturing treatment. Table 1.5
lists important considerations in well completion of a tight formation. Three topics are discussed

Some of the most important considerations in fracture design include: (1) the fracture azimuth and
orientation are mainly determined by the state of in situ stress; (2) rocks with lower Young’s modulus
tend to have greater hydraulic fracture widths; (3) stress contrast is critical for fracture containment
and length; (4) planar hydraulic fractures occur more likely without presence of natural fractures;
(5) transverse hydraulic fractures will likely occur if a horizontal well is drilled in the direction of
minimum horizontal stress; (6) effective fracture length is limited by the ability of the fracturing fluid
to transport the proppant over long distances; and (7) high injection rates generally improve proppant
transport and fracture length.
Rock properties impact vertical height and horizontal shape of fractures, including width, length,
and fracture complexity index (Daniels et al., 2007; Cipolla et al., 2008). Hydraulic fracture height is
governed mainly by the in situ stresses (Han et al., 2014), and can be also affected by the fracturingfluid viscosity and injection rate. Once a propagating fracture reaches a horizon where the stress
contrast is greater than the net pressure in the fracture, it will stop. There is a strong relationship
between net pressure and fracture width. Generally high injection rate increase fracture width.
Fracture width is important as narrow fracture width can result in early screenouts. A gradual
increasing net pressure after the initial growth period often indicates a vertical fracture extending in
length. Effective fracture length is often limited by the ability of the fracturing fluid to transport
proppant over long distances. Fracture-height growth and fracture width affect propped-fracture
half-length for a given treatment size. Generally high injection rate improves proppant transport
and increasing the effective fracture length.



(a) Illustration of a hydraulic fracture geometry. (b) Illustration of different levels of fracture complexity (some
of them can be natural fractures or reactivated natural fractures, synthesized from several publications, Fisher
et al., 2002; Cipolla et al., 2008).

Fractures can be simple or complex (Fig. 1.10(b)), and the stress anisotropy and the nature of a
natural fracture system are the key drivers to hydraulic fracture complexity (Sayers and Le Calvez,
2010; Weng et al., 2015). The interaction between the hydraulic fractures and the preexisting natural
fractures is an important consideration in hydraulic fracture design. Moreover, when the horizontal
maximum and minimum stresses have similar magnitude, fractures tend to grow in various directions
and fracture network may be complex. Fracture complexity has a strong impact on the production
(Warpinski et al., 2008, 2013; Waters et al., 2011; Mata et al., 2014), but notice that although fracture
complexity increases the reservoir contact, it may pose challenge in creating a durable proppant pack
with sufficient hydraulic continuity (Vincent, 2013).

Fracturing fluids are additives or chemicals that are used to treat the subsurface formation in order to
stimulate the flow of oil or gas. The commonly used types of fracturing fluids include water-based
fluids (including slickwater, linear gels and cross-linked fluids), oil-based fluids, foamed fluids and
viscoelastic surfactants (polymer-free fluids).
Fracturing fluid should have stable viscosity during pumping, and needs to be broken at the end
of fracturing job. They should have a certain level of viscosity for proppant transport and controlling
the fracture net pressure. The viscosity of the fracturing fluid also impacts the fracture geometry.
Moreover, mineralogy impacts the hydraulic fracturing fluid selection. For example, the formations
that have more than 50% clay are difficult for completion and the fluid selection is more delicate.
The use of proppants in HFT is to support the opening of the fractures so that the reservoir and the
wellbore are connected for hydrocarbon flow. Naturally, the type and amount of proppant impact the
flow capacity of the fractures and production (Gao and Du, 2012; McKenna, 2014; Greff et al., 2014).
The proppant used in most shale reservoirs are sands with various specifications, including similarity
in grain size, roundness in shape, and crush resistance. Small mesh bauxite and ceramic are also used.
The main principle of using and selecting proppant for hydraulic fracturing is to prop the fractures



while not blocking the flow between the reservoir and the wellbore. Important considerations in
selecting proppants should include closure stress, conductivity, and the bottom-hole flowing pressure.
Proppant distribution in the fracture network is highly important for the effectiveness of hydraulic
fractures (Cipolla, 2009). When the proppant is concentrated in a primary planar fracture, the effective
fracture length is limited even when the hydraulic fracture may be large. When fracture growth is
complex, the average proppant concentration tends to be low and the proppant may not materially
impact well performance.

Longer laterals and more stages have generally increased the production in shale plays (Fig. 1.11(a)),
which is why the stage count has gone up for many shale plays. For example, in Marcellus and Eagle
Ford, the average fracturing stage count was less than 10 in 2008, and was increased to more than 16 in
2012; in Bakken, it was around 14 in 2008, and was increased to more than 27 in 2012 (RigData,
2014). However, because the complexity and cost of completion increase as the stage count increases,
a large stage count does not always increase the economic value (Fig. 1.11(b)). In some plays, such as
the Barnett, Fayetteville, and Haynesville, the optimal stage count has been well studied. In other
plays, operators are still trying to test longer laterals and increased stage count.

One main concern in hydrocarbon production from tight formations is sustainability. The wells may
begin producing at very high rates, then they fall off very sharply, and finally they level off at a much
lower rate. On the other hand, when wells are drilled in high reservoir-quality zones, shale wells can
last a very long time when the hydrocarbon-bearing deposition is relatively continuous. Maximization
of the recovery factor for each well is important for economic success of the overall project. This

(a) Illustration of the impact of lateral length and stage count on production. (b) Illustration of the impact of
lateral length and stage count on NPV while incorporating the completion cost.
Adapted from Malayalam et al. (2014).



requires production data analysis, diagnosis, and optimization. Production surveillance and forecasting
can help optimally produce hydrocarbons and enhance the ultimate recovery.
Because of the lack of detailed knowledge of the subsurface formation, the most common practice
is to drill and complete a well in the same way along the lateral, but the heterogeneity in the rock
properties often leads to highly uneven production from different perforation clusters. It is suboptimal
when wellbores are treated as if the rocks are the same all along the lateral. The production from the
wells can be evaluated, and changes can be made to improve the future wells. Production logging can
be conducted to identify where the gas or oil is coming from; it may show that only some of the
perforation clusters contribute to the production, while other perforation clusters produce little (Baihly
et al., 2010). Sometimes more than 40% of the perforations do not contribute to production (Miller
et al., 2011). By comparing the producing clusters with the rock strength log, it is possible to identify
the areas of low stress and high brittleness in the formation, and locating the fracture positions and
analyzing their geometries. Some fracturing techniques can sequentially isolate fractures at the
wellbore to ensure every cluster is fractured and contributes to the well performance. Comprehensive
production workflows reduce the time to analyze the data, help identify problems, and optimize
Important processes in production include analysis of production drivers, calibration of drivers to
actual production, refracturing, artificial lift, flowback management, and water management, among

Production drivers include variables in reservoir quality, completion quality and effort; these variables
are related to charge access, structural complexity, natural fractures, pore space, fluid properties,
pressure, geomechanics, and well placement. Table 1.6 gives a list of common production drivers and
their relevance to production. In practice, calibrating the reservoir quality and completion quality
drivers to production is critical to understand and enhance the production.

Production from shale reservoirs is highly variable as a function of the location in a field, and there are
many causes for the very heterogeneous productions (Baihly et al., 2010; Miller et al., 2011). Reservoir
quality, completion quality, and completion effort all impact the production. To understand the impact
of these variables, it is important to analyze individual physical variables, such as kerogen, TOC,
porosity, fluid saturation, and permeability for reservoir quality; stress, mineralogy, and mechanical
properties for completion quality; and lateral length, proppant tonnage, and stage count for completion
effort. Causally, all these variables impact production, a weak or no correlation, or even a reversed
correlation, between production and such variables is frequently observed (Jochen et al., 2011; Gao
and Du, 2012; Gao and Gao, 2013; Zhou et al., 2014). For example, lateral length or stage length and
production rate may appear to have no, weak, or negative correlation (Miller et al., 2011).
An example of negative correlation between oil production and stage length from a shale oil reservoir
is shown in Fig. 1.12(a). Without insightful analysis, one may conclude that the longer the lateral
length, the worse the production! In fact, this phenomenon is a manifestation of Simpson’s paradox
in complex systems with interaction of multiple variables (Pearl, 2010, 2014; Ma and Zhang, 2014).



Table 1.6 Production Drivers for Well Performance

Parameters and Variables


Petroleum system and
charge access

Kerogen types and content, TOC,
maturity (thermal maturation), fluid
properties, pore pressure

Structure, stratigraphy,
diagenesis, depth

Stacking patterns, formation (lateral
and vertical) continuity, net thickness,
faults, natural fractures, karsts

Static reservoir quality
Flow-related reservoir
Completion quality

Mineralogy, porosity, saturation,
Matrix permeability, fracture
Stress field, mechanical properties
(Young’s modulus, Poisson ratio,
brittleness), mineralogy
Pore pressure, overburden pressure,
temperature, wettability
Well placement, geosteering, LWD,
hydraulic fracture geometry, all the
parameters in Table 1.5

TOC is a measure of organic richness
or capacity for hydrocarbon
generation; maturity is a measure of
the conversion of kerogen into
hydrocarbon; kerogen type impacts
the fluid type and quality
Lateral borehole staying in the best
zone ensures more contact among
wellbore, fractures, and the
productive zone
Storage, oil or gas-in-place
Fluid mobility and deliverability from
formation to wells
Hydraulic fracturing containment,
fracture complexity, wellbore stability

Pressures, temperature,
and fluid properties
effort and effectiveness

Impact on drilling and hydrocarbon
productivity, cementing
Stay in zone, maximum contact of
well and fractures to the productive
zone, enhanced SRV

The lateral length per stage that is negatively correlated to production is caused by the third variable
effectdfor a given total lateral length, a longer lateral length per stage implies a smaller stage count;
stage count is generally positively, highly correlated to production, as discussed in the previous section
(also see Zhou et al., 2014), but exceptions occur (Miller et al., 2011; Gao and Gao, 2013) because of the
effect of other variables.
When a weak or reversed correlation between production and “reservoir quality” is observed,
people are perplexed with the fact that “the better the reservoir quality, the lower the hydrocarbon
production!” Two possible causes for this occurrence include an inaccurate definition of reservoir
quality and suboptimal completion. For example, when the reservoir quality is only partially
represented by the static component, such as bulk volume of hydrocarbon (i.e., porosity times
hydrocarbon saturation), the dynamic component, such as permeability, may be more highly
correlated to production, and leads to a negative correlation between the static reservoir quality and
the production. This occurs frequently in a single porosity and dual permeability system.
Figure 1.12(b) shows such an example, wherein the correlation between the static reservoir quality
and one-year oil production is 0.231. The negative correlation is caused by the dual permeability
that includes the matrix and fracture components. The dual permeability system can be interpreted as
two different reference classes (Ma, 2010; Ma et al., 2011); when the matrix and fracture static
reservoir qualities are separately analyzed, the correlation between production and the static
reservoir quality is high for both the systems, 0.748 for matrix reservoir quality, and 0.992 for
fracture reservoir quality.



(a) Negative correlation, 0.438, between the production and stage length in a tight oil reservoir. (b) Cross
plot between production (y-axis) and static reservoir quality (x-axis) in a dual-permeability system. Red (gray
in print versions) dots represent fracture properties. (c) Cross plot between production (y-axis) and reservoir
quality (x-axis). Color represents different wells.

It is also possible to generate a composite reservoir quality by combining the static and dynamic
ones. Then the composite reservoir quality will be highly, positively correlated with the production
(Fig. 1.12(c)). Conversely, Yule–Simpson’s effect (reversal or not) occurs in a dual permeability
system when the fracture permeability is not explicitly incorporated in the reservoir quality.
A reversal of correlation between reservoir quality and production can be also caused by a suboptimal completion. When the completion efficiency is the same for all the wells, production should
have a positive correlation with reservoir quality. When completion efficiency is not the same for all
the wells, the correlation between the production and reservoir quality is reduced or reversed. For
example, a reversal of correlation between production and reservoir quality may occur when the



Cross plot between the production and reservoir quality (combined static and dynamic qualities) overlaid with
completion efficiency (C_Efficiency). Notice that some wells have reservoir quality (RQ) greater than 0.8, but
their productions are significantly lower than some other wells with RQ less than 0.5. The former wells are
underperformers and should be candidates for recompletion.

completion efficiency is stronger for a moderate reservoir-quality zone while the completion efficiency
is lower for a high reservoir-quality zone.
Therefore, it is possible to identify ineffective completions and optimize them. Figure 1.13 shows a
cross plot between the production and reservoir quality overlaid with the completion efficiency. For
very low reservoir-quality rocks, production is low regardless of the completion efficiency, but for
moderate to high reservoir-quality rocks, completion efficiency determines the production. Completion efficiency includes two components: completion quality (CQ) and completion effort. It is very
important to distinguish them because the completion efficiency is impacted by both the quality of
rock for completion (i.e., CQ) and the intervention or effort (i.e., HFT).
In summary, it is very important to understand Simpson’s paradox in analyzing the relationships
between a potential driver and the production by considering third variables’ effects. Discerning
Simpson’s paradox can help screen and rank the importance of drivers and identify inefficient completion.

Microseismicity is a low-magnitude earthquake with frequencies in the order of 0.1–10 kHz, and it is a
geophysical remote-sensing technique that enables the detection of hydraulic fracturing activities and
imaging h