drilling risks

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A process-knowledge management approach for assessment and

mitigation of drilling risks

W.F. Prassl a,*, J.M. Peden a , K.W. Wong b

a Curtin University of Technology, Perth, Australia b Nanyang Technological University, Singapore

Accepted 20 May 2005

Abstract

The purpose of this paper is to impart the development of a Process-Knowledge Management System (P-KMS) designed to

investigate drilling in gas hydrate environments, identify potential well risks (well control, borehole stability and/or well integrity),

and assess mitigation of them due to alteration of drilling parameters and/or strategies. Since successful drilling in gas hydrate

environments is influenced strongly by numerous parameters and relations that are sometimes ill-defined, or not understood

properly, the P-KMS developed needs to permit users the ability to update the knowledge utilized, as well as handle uncertainty as

multidimensional phenomena. For this, traditional engineering simulations and uncertainty handling by soft computing have been

combined in a novel approach with knowledge management paradigms. This allows the inclusion of knowledge from multiple field

experts, and its combination with approved company best practices and guidelines.

In order to consider the uncertainties associated with knowledge, approximate reasoning by type-2 fuzzy logic is combined withrelevant numerical simulations (pressure and temperature fields, thermodynamic and kinetic gas hydrate behavior). This combi-

nation of knowledge is established by the P-KMS through the construction of individual reasoning blocks and a coherent reasoning

lattice. Because expert knowledge (expressed in rules and equations) and standard engineering calculations are combined in a

modular form, updating of knowledge, as well as well situation-specific applications of it, are assisted. The P-KMS can be applied

in two reasoning modes, either for dynamic process assessment (simulation mode) or static well situation analysis (parameter

inference mode). During reasoning, uncertainties employed are monitored constantly in such a way that knowledge gap

identification and system performance evaluation is supported. Consequently, it is anticipated that using such a P-KMS approach

will lead to (1) increase in well-site security, (2) reduction of overall well costs, and (3) creation of experience in development,

implementation and maintenance of a P-KMS for similar problem areas.

D 2005 Elsevier B.V. All rights reserved.

Keywords: Knowledge management; Expert system; Type-2 fuzzy logic; Uncertainty handling; Risk mitigation

1. Introduction

High oil and gas prices combined with a worldwide

increasing demand for energy cause that drilling and

production of hydrocarbons are carried out more fre-

quently in Deepwater and Arctic locations. Both loca-

tions have in common the fact that they establish

pressure–temperature conditions where solid gas

hydrates are stable. Gas hydrates are crystalline com-

plexes of water molecules that trap gas molecules of

suitable size inside their cage structure. Their are stable

at relatively high pressures (e.g. as established in water

0920-4105/$ - see front matter D 2005 Elsevier B.V. All rights reserved.doi:10.1016/j.petrol.2005.05.012

* Corresponding author.

E-mail address: [email protected] (W.F. Prassl).

Journal of Petroleum Science and Engineering 49 (2005) 142–161

www.elsevier.com/locate/petrol



depths larger than 400 m), when combined with rela-

tively low temperatures, the presence of gas hydrate

forming gases in excess of their solubility, presence of

adequate water, and allowance for sufficient time to

form. Depending on these parameters, gas hydrates

form different crystalline structures, namely structureI (sI), structure II (sII) and structure H (sH). Details

about conditions for gas hydrate formation and disso-

ciation, as well as relevant crystalline parameters are

given by Sloan (1998).

For the cases where drilling takes place in Deepwa-

ter or Arctic locations, gas hydrates may exist as

d Natural Gas HydratesT inside the formations, or they

may form inside the well-system.

Drilling activities often alter the pressure–tempera-

ture conditions inside the well adjacent formations.

This may cause the destabilization of natural gashydrates. When this occurs, substantial amounts of

gases can be released, which in turn may lead to

various drilling problems. When the well is shut-in

for a considerable time, the changing pressure–tem-

perature conditions inside the well can cause the

formation of solid gas hydrates, resulting in serious

well control problems.

Prassl (2004a,b) discusses details about potential

risks and problems to drilling posed by gas hydrates.

In practice, the most common approaches to mitigate

these risks and problems are to carry out drilling in such

a manner that dissociation of natural gas hydrates isminimized, and to add various thermodynamic and/or

kinetic gas hydrate inhibitors to the mud system. The

latter is done to prevent gas hydrates from forming

inside the well. In order to streamline and potentially

optimize these attempts of risk mitigation, as well as

allow testing of different drilling situations, a system-

atic reasoning method called dProcess-Knowledge

Management T is proposed in this paper. By this ap-

proach, the relationships between drilling activities

and parameters and gas hydrate induced risks and

problems are investigated via drilling scenarios andalternatives planning.

2. Process-knowledge management

2.1. General considerations

To assess the risks and problems to drilling due to

gas hydrates, the effects of drilling activities on the

temperature–pressure conditions in the well adjacent

formations and inside the well must be combined

with gas hydrate behavior models capable of predictingwhen gas hydrates form or dissociate, and at what rate.

Since some of these gas hydrate behavior models are

currently not well established and/or efforts of current

research, a system for such combination must allow

adaptation and/or exchange of them. Furthermore,

most of these behavior models and pressure–tempera-

ture simulations require knowledge about input para-meters that are associated with considerable

uncertainty. Consequently, uncertainty as multidimen-

sional phenomena must be included. In addition, a

significant amount of expert knowledge is available

often in-house that describes circumstances and

cause–effect relationships regarding drilling within

gas hydrate environments.

Considering these circumstances, the Process-

Knowledge Management System (P-KMS) developed

is constructed such that it permits the combination of

engineering simulations with expert knowledge under the inclusion of uncertainty, as well as provides basic

knowledge management capabilities (e.g. modification

and addition of knowledge). Knowledge included in

such a P-KMS and its potential applications are

sketched with Fig. 1.

2.2. Inclusion of uncertainty

As mentioned above, uncertainty is considered in-

side the P-KMS as multidimensional phenomena. For

this, the uncertainty dimensions imprecision , strife ,

randomness and vagueness , as characterized by Klir and Folger (1988), are taken into account. These un-

certainty aspects are illustrated briefly as:

! Imprecision: Missing of information or knowledge

for full description of the situation or problem. This

uncertainty aspect is sometimes called information/

knowledge deficiency or non-specificity.

! Strife: Conflicting information or knowledge, which

is substantiated either in the description of the situ-

ation or within the conclusions derived.

! Randomness : Determination of the value or meaningof a particular parameter or knowledge, when mea-

sured/provided multiple times under the same cir-

cumstances, leads to different results.

! Vagueness : Manifestation as non-unique description

of particular information or knowledge, for instance

when information or knowledge provided from, or

given to experts, is interpreted differently.

As demonstrated by Mendel (2001), the application

of type-2 fuzzy sets and with them constructed systems

is appropriate to consider these uncertainty aspectsaccording to their nature.

W.F. Prassl et al. / Journal of Petroleum Science and Engineering 49 (2005) 142–161 143



A fuzzy set, or to be more specific, a type-1 fuzzy

set, is fully described through its membership functionas:

f : X Y L ð1Þ

Here a function f describes the shape of a type-1

fuzzy set along its universe (universe of discourse) X , in

such that the values are within the interval L. For the P-

KMS develo ped, t he description interval L is restricted

to [0,1]. In Fig. 2 t wo type-1 fuzzy sets are sketched for

description of a fuzzy concept. Note that, as indicated

in this figure, multiple experts who have to define the

same concept would most likely provide different descriptions.

As seen in Fig. 2, for the incident values 1 and 3

both experts accord about the dagreement of the inci-

dent with the concept meaningT i.e. provide the same

membership value. A disagreement would be for the

incident value 2 where expert A would declare an

dagreement AT to the concept and expert B an

dagreement BT. A method to model multiple, and devia-

ting concept descriptions is by the application of so-

called type-2 fuzzy sets. Type-2 fuzzy sets were first

introduced by Zadeh (1975) and extended by Mizumot o

and Tanaka (1976). Fig. 3 depicts a general type-2fuzzy set.

A type-2 fuzzy set defines at each element of its

primary universe ( X ) instead of one precise value

(membership value) a function. This function, defined

on U , is often called secondary membership function,

and its shape is used to name the type-2 fuzzy set.

Consequently, when the secondary membership func-

tion is an interval in [0,1], the set is called interval type-

2 fuzzy set, see Fig. 4.

Note that since the secondary membership function

of an interval type-2 fuzzy set is the interval [0,1], theset is declared fully by the so-called upper bound and

lower bound .

Mendel (2001) states that: bThe application of in-

terval type-2 fuzzy sets is generally sufficient to de-

scribe uncertain concepts Q and bThere are currently no

logical methods that would allow the determination of

the parameters of general type-2 fuzzy sets Q . Conse-

quently, the most complex concept description permit-

ted in the P-KMS is based on interval type-2 fuzzy

sets. How they are utilized for different concept types

Fig. 2. Sketch of two type-1 fuzzy sets describing a fuzzy concept.

Fig. 1. Inclusion of knowledge and potential applications of the Process-Knowledge Management System developed.

W.F. Prassl et al. / Journal of Petroleum Science and Engineering 49 (2005) 142–161144



(e.g. interval open to the left, interval open to the

right, centering interval and point-like concept) is

shown in Fig. 5.

2.3. Representation of knowledge

The knowledge representation scheme applied in

the P-KMS is founded on concepts expressed as

type-2 or type-1 fuzzy sets (a type-1 fuzzy set is

interpreted as special case of a type-2 fuzzy set),

type-1 fuzzy numbers and relations constructed with

them. In particular, the knowledge syntax used for thelinguistic expression of concepts and the declaration

of relations is:

! Concept : Variable is k. Modifier Identifier. For in-

stance: shut -in time is sometimes very long , utilizing:

Variable: shut-in time; Modifier(s): sometimes, very;

hence k = 2; and identifier: long. Note that such

linguistic description of concepts is equivalent to

their mathematical representation as fuzzy sets.

! Conditional rule: If m(Concept Connector) Then

n(Concept Connector) where m and n are positiveintegers and Connector is either an AND or an OR

fuzzy operation (i.e. t-norm or t-conorm, respective-

ly). An example of a conditional rule is: If shut-in

time is long and temperature is low or pressure is

very high then gas hydrate formation is substantial .

! Comparison rule: If m(variable Compare variable

Connector) Then n(Concept Connector) where

Compare is a fuzzy variable comparison operation

(e.g. dlarger thanT, dequalT and dsmaller thanT). An

example of a comparison rule is: If cell-temperature

is larger than gas hydrate stability temperature then gas hydrate is dissociating .

! Equations : Within equations fuzzy (type-1) and crisp

numbers can be combined to calculate variables. For

instance: gas hydrate temperature = 16[1.2]+9 * cell

temperature, where gas hydrate temperature and cell

temperature are fuzzy variables and 16 [1.2] is a

fuzzy number. The term [1.2] indicates the uncertain-

ty associated with the value 16, expressed as standard

deviation.1

In addition to such relational knowledge, knowl-

edge is embedded in the P-KMS as general proce-

dures, describing subsystems through their input– output relationships. Examples of such procedural

knowledge are the pressure and temperature field

simulations and the default models to predict the be-

havior of gas hydrates.

2.4. Knowledge utilization

Since concepts are defined in their most complex

form as type-2 fuzzy sets, rules constructed with them

need to be evaluated using type-2 fuzzy set theory. For

this, the approach by Mendel (2001) is utilized, whereas t-norm either the dminT or the dalgebraic product T

operation and as t-conorm the dmaxT operation are

available. Following the notation by Mendel (2001),

the Extended sup- star composition to calculate if–then

rules is given as:

lc B1 l yð Þ ¼ l

c A xoc R

yð Þ ¼ t x X lc R

x; yð Þ u lc A x

xð Þh i

ð2Þ

1

Note that all examples given are for illustration only and not part of the knowledge base created.

Fig. 3. Sketch of a general type-2 fuzzy set.




In Eq. (2) the applied operators join (t, Eq. (3)) and

meet (u, Eq. (4)) are defined as:

lc A

xð Þ t lc B

xð Þ ¼

Z u J u x

Z w J w x

f x uð Þ1 g x wð Þ

u|wð3Þ

lc A

xð Þ t lc B

xð Þ ¼

Z u J u x

Z w J w x

f x uð Þ1 g x wð Þ

u}wð4Þ

where u and w are the variables for the respective

secondary membership functions f x (u) and g x

(u).

Note that since interval type-2 fuzzy sets are utilized,

after combining the individual rules, a procedure named

typeÀreduction (Mendel, 2001) is required. By type-

reduction, the interval type-2 fuzzy set describing the

conclusion is reduced to an interval along its universe

of discourse. A description of these intervals and how

they are translated to permit further reasoning is de-

tailed later.

For the construction of generic conditional rules, theuse of concept modifiers is allowed. Since concept

modifiers are generally defined for type-1 fuzzy sets

only, selected modifier definitions (e.g. the modifiers

dveryT and dmore or lessT) have been extended so they

can be applied for interval type-2 fuzzy sets. For illus-

tration of their effects on a center-based interval con-

cept, see Fig. 6.

See that for the crisp incident value applied in this

illustration, both dveryT and dmore or lessT modification

result in reduced fuzziness (agreement ranges are closer

to total and non-agreement, respectively), and reduced

uncertainty ranges (max-agreement–min-agreement).

The extensions necessary to define modifiers for inter-

val type-2 fuzzy sets have been based on Eq. (5), where

v m determines modification according to Zadeh (1972)

Fig. 4. Interval type-2 fuzzy set, together with its upper and lower bound.

Fig. 5. Utilization of interval type-2 fuzzy sets for concept description, as used in the P-KMS developed.




and um modification via translation. For individual

parameters applied, see Prassl (2004a).

um : 0; 1½ Â 0; 1½ v m : X YY ð5Þ

When knowledge is declared as fuzzy equation,

Gaussian shaped type-1 fuzzy numbers are applied for

fuzzy arithmetic based on a-cuts (Buckley and Eslami,

2002). Since the multiplication and division of fuzzy

numbers, carried out with the help of a-cuts or through

the extension principle (Zadeh, 1975), distort their

shape type (i.e. Gaussian shape), an approximation

method for multiplication and division of Gaussian

shaped type-1 fuzzy numbers is proposed. At this ap-

proximation interval arithmetic is applied first for an a-cut at the points of inflection (l x ¼ mFr ¼ e

À12 ), see

Eq. (6). Note that 1 stands for multiplication or divi-

sion, respectively. Afterwards, the uncertainty of the

resulting fuzzy number, expressed through its standard

deviation rc, is declared using Eq. (7), and the mean of

the resulting fuzzy number mc is calculated by Eq. (8).

c1; c2½ ¼ ma À ra; ma þ ra½ 1 m b À r b; m b þ r b½ ð6Þ

rc ¼c2 À c1

2ð7Þ

mc ¼ ma1m b ð8Þ

Fig. 7 shows a comparison of the results from this

approximation method (C corr ) with the results obtained

by the extension principle (C ), when applied for fuzzydivision.

2.5. Acquisition of knowledge

Knowledge can be entered manually into the P-

KMS via Graphical User Interfaces (GUI), which

ensure that knowledge syntax requirements are ful-

filled. As alternative, knowledge can be acquired

automatically through Data-Pair analysis based on

the Wang–Mendel algorithm (Wang and Mendel,

1991, 1992). To embed this algorithm some modifi-cations were required to accommodate interval type-2

fuzzy sets. While Data-Pair analysis, a set of data

points, describing a multiple-input–single-output rela-

tionship, is analyzed and fuzzy concepts and rules

constructed. Fuzzy concept description is founded on

static concept declaration, utilizing pre-defined con-

cept distributions as dequally spacedT, dcenter value-T,

dminimum value-T, or dmaximum valueT oriented, or

dynamic concept declaration. When dynamic concept

declaration is selected, concept centers are computed

either via fuzzy clustering, using the fuzzy c-meansalgorithm (Dunn, 1992), and shown in Fig. 8, or

Fig. 6. Modifiers very and more or less for center fuzzy interval.




through histogram analysis, see Prassl (2004a), and

depicted in Fig. 9.

After declaring concept means ( x1 to x5 and y1 to y5in Fig. 8, and M1 to M3 for X and Y in Fig. 9), the

concept spreads are computed utilizing an user defined

doverlapping factor T or d percentage spread factor T, re-

spectively. Finally, the P-KMS investigates whether

merging of individual concepts is recommended, con-

sidering the location of the individual concepts, their

description parameters and the two merging criteria

defined with Eqs. (9) and (10):

lDMR ¼ 1 xV0:75exp À 1

2 xÀ0:60:175

À Á2h i

x N 0:75&

ð9Þ

lDMCN ¼0

1þ

0:1

2þ

0:25

3þ

0:5

4þ

0:75

5

þ0:9

6þ

1

7þ

1

8þ

1

9ð10Þ

Fig. 8. Clustered Data-Pair for approximate reasoning analysis.

Fig. 7. Comparison of approximation of fuzzy division with the results obtained by the extension principle.




soning blocks are translated into Gaussian shaped type-

1 fuzzy numbers. The translation applied is defined by

Eqs. (11) and (12), and illustrated with Figs. 11 and 12.

m ¼Int-max þ Int-min

2ð11Þ

r ¼ Int-max À Int-minð Þ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiÀ 1

8ln 0:5ð Þs

ð12Þ

In Fig. 11, an interval type-2 fuzzy set (doriginal

fuzzy set T) is type reduced resulting in the interval [Int-

min, Int-max]. Applying Eqs. (11) and (12), the dtype

reduced fuzzy set T is declared. As seen in this figure,

this type reduced fuzzy set can be considered as a

dreasonable type-1 interpretationT of the original inter-

val type-2 fuzzy set. A more realistic example is

sketched in Fig. 12. Here, an interval type-2 fuzzy

Fig. 11. Example for type-reduction and translation of the reasoning result.

Fig. 12. Interval transformation for realistic reasoning block output.




conclusion B1, as obtained by the combination of two

rules that fired at different strength, its type reduced

interval and the fuzzified result are shown.

2.7. Missing of information

Knowledge acquired about drilling in gas hydrate

environments can be utilized within the P-KMS in two

distinct reasoning modes. One reasoning mode allows

the investigation of particular drilling parameters for a

given drilling scenario (Question/Answering mode),

and the other permits a dynamic simulation of the

well construction process. In either reasoning modes,

while reasoning is carried out, all parameters (input

and reasoned) are monitored about their uncertainty.

By this, one type of knowledge gap known to the

system is identified, namely an unexpectedly highaccumulation of uncertainty. The other type of knowl-

edge gap recognized by the P-KMS is lack of knowl-

edge or information to either answer the question

posed to the system (i.e. compute the drilling para-

meters selected), or to complete the drilling simula-

tion-based reasoning circle. To mitigate the second

problem and to enhance the usability of the P-KMS,

a method to temporarily continue simulation while

information is missing, is included. For this, either

the last value of the missing parameter is applied, an

average of the last n values used, or one calculated

based on trend prediction from the last m values

recorded. Note that all three options assume that

values for the missing parameter have been obtained

before missing of information occurred (e.g. failure of

sensor). While predicting the missing parameter, itsassociated uncertainty is increased constantly. Within

the P-KMS developed this increase of uncertainty can

be modelled as either linear (Eq. (13)) or exponential

behavior (Eq. (14)).

rn ¼ r base þstandard deviation increaseð Þ

timeð13Þ

rn ¼ r base þ100d r baseð Þk d time

100ð14Þ

Note that for an exponential increase of uncertainty,an arbitrary factor k (k z0) is applied which deter-

mines the extent of uncertainty increase over time.

Dif ferent k -factors and their influences are illustrated

in Fig. 13.

3. Process-knowledge management for drilling

within gas hydrate environments

To equip the generic P-KMS outlined above and

construct a realization that handles ddrilling within

Fig. 13. Exponential increasing of standard deviation applying different k values (r base=0.1).




gas hydrate environmentsT, selected engineering simu-

lations and default gas hydrate models must be em-

bedded. These built-in procedural subsystems

concern:

! Numerical temperature field simulation;! Numerical pressure field simulation (due to the

assumptions applied, see Prassl (2004a,b), t he sim-

ulation of the pressure field is reduced to a simula-

tion of pressure profiles inside the drillstring and

along the annulus); and

! Default gas hydrate behavior models. In particular,

gas hydrate models are embedded for:

! Gas hydrate thermodynamic stability: Sloan

(1998);

! Gas hydrate dissociation within formations: Holder

(1985);! Gas hydrate formation inside the well-system:

K ashchiev and Firoozabadi (2002a,b, 2003); and

! Gas hydrate dissociation inside the well-system:

K im et al. (1987) and Clark and Bishnoi (2000).

For numerical simulations of the temperature field

and the pressure profiles, a simulation grid is de-

clared that considers the sequence of activities per

well phase, individual circulation information and

actual well geometries. Note that this simulation

grid is modified dynamically as the well progresses,

either during simulation or while well construction.Hence, for P-KMS application as real-time well ob-

server actual geometries and drilling parameters are

considered. Fig. 14 sketches thereto how the simula-

tion grid is modified vertically during depth-changing

well operations (e.g. from simulation step n to step

n +1).

The temperature field simulation is founded on theapproach by Marshall (1980), which has been extended

to consider potential dheat sourcesT such as the bit

(during drilling) and cement hardening. Prassl (2004a)

discusses details of equations applied for temperature

field and pressure profile calculation.

While drilling, the temperature inside the well adja-

cent formations may increase above the corresponding

gas hydrate stability temperature. When this occurs,

natural gas hydrates begin to dissociate. This situation

is sketched in Fig. 15.

As shown in Fig. 15, within the P-KMS it isassumed that the cell temperature stays constant

while natural gas hydrate dissociation. The rate of

dissociation is a function of heat flowing into the

particular simulation cell, and modelled based on

the approach by Holder (1985). After all gas hydrates

are dissociated in this simulation cell, additional in-

flow of heat causes the cell temperature to continue

to rise.

Note that dissociation of natural gas hydrates is

assumed to result in an increase of gas inside the

well, i.e. mud-system. Furthermore, it is anticipated

that this increase in mud-gas can be detected as gas-reading at the surface. Consequently, increased surface

Fig. 14. Sketch of the vertical modification of the simulation grid.




vals, see Fig. 19 and Prassl (2004a). Note that the

dotted lines indicate uncertainty of minimum and

maximum points, respectively.

Once a gas mixture is described through its

components, declared as vague amounts, the proce-

dure estimates their most likely combination under

Fig. 18. Illustration of the model applied to estimate gas hydrate formation inside the well.

Fig. 17. Illustration of the possibilistic model applied for changing well conditions during shut-in periods.




the constraint that the total has to be 100%. For

this, all type-1 fuzzy intervals are added, applying

fuzzy arithmetic as defined through the dmin–max

extension principleT, and the result is defuzzified

based on dcenter-of-gravityT defuzzification. After-

wards, it is analyzed where the defuzzified sum is

located in respect to 100%. With this information,

which also declares the extent of amount-modifica-

tion necessary, the type-1 descriptions of the indi-

vidual components are updated accordingly. Note

Fig. 19. Illustration of vague amount words utilized inside the P-KMS.

Fig. 20. Main GUI of the P-KMS.




that fuzzy arithmetic is non-linear, thus updates are

found through iteration.

4. System realization

The P-KMS has been developed as prototype, using

the Matlabn R13 programming environment. In its

current state, it can be presented to clients (companies

interested in its application) and customized further to

accommodate individual requirements. For illustration

of some of its features, selected GUIs are presented and

explained briefly below. A detailed description of the

prototype and how to interact with it, is given by Prassl

(2004a).

Fig. 20 shows the main interface of the P-KMS,

appearing after program start. As seen, the P-KMS is

Fig. 21. Illustration of the geothermal temperature profile, defined as fuzzy graph.

Fig. 22. GUI to add/modify knowledge declared as fuzzy concepts.




Fig. 25. GUI to declare method and parameters for automatic knowledge creation via Data-Pair analysis.

Fig. 26. GUI to interact with the P-KMS while reasoning is conducted as drilling simulation.




heat capacity and thermal conductivity, as well as the

mud density profile are declared as fuzzy graphs or

fuzzy points, respectively.

GUIs to declare knowledge as individual concepts,

conditional rules and fuzzy equations are shown in

Figs. 22–24, respectively. Note that knowledge displayed in these figures is for illustration only, and is

not part of the knowledge base constructed. In the

current P-KMS realization, the maximum number of

concepts linked to a particular variable is set to nine.

This limitation is founded on the fact that humans are

normally not capable of distinguishing more than seven

grades (variations) of one variable.

Once relevant concepts are defined, rules and equa-

tions can be constructed with them, see Figs. 23 and 24.

Notice that whilst rules and equations are declared, they

are displayed as text for visual verification carried out by the user.

To initiate Data-Pair analysis essential parameters

must be declared by the user. As seen in Fig. 25,

these parameters are:

! Variable names of the inputs and output, defining the

data-points;

! Method of analysis;

! Number of sets to be constructed per variable;

! Overlapping factor or percentage factor defining the

concept spread; and

! Range of data values expected normally.

By stating the latter, the effects of extreme data-

points on the creation of concepts and rules are reduced.

For the cases that reasoning is carried out as drilling

simulation, intermediate simulation results are shown in

order to monitor the changes of parameters. For in-

stance, Fig. 26 shows a drilling situation as anticipated

after 43 simulation steps, at which the well has a current

well depth of 1051 m and ddrilling aheadT takes place.

5. Discussion

The in this paper proposed P-KMS aims to utilize all

available information and knowledge for the drilling

scenario at hand. The knowledge acquired, expressed as

embedded simulations and expert descriptions, relates

to the considerations necessary for drilling in gas hy-

drate environments. Hence, the anticipated outcomes of

this P-KMS approach are:

! Basis for intelligent risk assessment is formed; and

!

Better-informed decision making and consequentlyimproved risk mitigation is realized.

To achieve these objectives, the P-KMS combines

diverse fields of current research (e.g. knowledge man-

agement systems, fuzzy systems, uncertainty handling,

gas hydrate stability, formation and dissociation descrip-

tion, as well as drilling parameter simulations). Some

methods and approaches that have been developed with-in these research areas as reported in the literature are

embedded in the system. Others are modified according

to requested functionalities, and are proposed so that

they offer more flexibility, or fit better into the system.

In addition, it has been sometimes necessary to formu-

late new procedures so that a coherent framework for the

system is established. Whilst developing this system, it

has been found that some methods and procedures

applied require further improvements, whereas for

others additional investigations are needed so that their

applicability is verified, or alternative approaches can beselected. Some of these findings and identified short-

comings are discussed below.

5.1. General

Since the P-KMS has been developed as prototype,

knowledge handling, visualization, verification, change

tracking and scenario-based application at some occa-

sions are not explicitly user oriented. Nevertheless, it is

expected that by transferring the prototype into a de-

velopment version most of these shortcomings will be

addressed, and subsequently removed.While testing the system, it has been found that

faster, more efficient algorithms are needed for drilling

and gas hydrate behavior simulations (subsystems). It is

anticipated that such efficiency enhancements can be

achieved by re-assessing:

! Description of the individual drilling activities;

! Implementation to populate simulation equations

and matrices;

! Approach to solve defined simulation equations and

matrices;! Processing method of fuzzy inference subroutines;

! Procedure as to how information that defines the

drilling situation at the particular simulation step is

modified to describe the next conditions;

! Linkage of the individual subroutines;

! Methods to observe uncertainty accumulation; and

! Goal searching, and goal achieving verification.

5.2. Knowledge description and utilization

Concepts are defined in their most complex form asinterval type-2 fuzzy sets that are constructed using




Gaussian base shapes. Gaussian shapes have been se-

lected since they offer smooth transfer from non to total

membership, apply only one equation for description of

fuzzy numbers, and require only two parameters for

their declaration. The latter causes that minimum effort

is necessary for declaration, but also leads to reducedtunability. This reduced tunability can be overcome by

applying dquasi-intervalsT, intervals at which the core of

the concept (membership value is 1) is restricted to one

point. In spite of these benefits, it is recognized that

more and different concept base-shape types would

improve the ability to model knowledge. Hence, they

are suggested for implementation in the development

version. From a similar perspective, it is assumed that

the availability of more and different t-norms, t-con-

orms and implication operations would increase the

flexibility of the P-KMS.At the current P-KMS implementation, individual

reasoning blocks may consist of conditional rules or

comparison rules, of equations or procedural knowl-

edge. For the development version, it is anticipated that

combinations of these knowledge types within individ-

ual reasoning blocks, as well as more complex rule

structures are of benefit.

To acquire knowledge automatically, Data-Pair anal-

ysis based on the Wang–Mendel approach is implemen-

ted. To construct more flexible automatic knowledge

creation procedures, which can also work on for in-

stance plain text or tables, additional tools and proce-dures need to be embedded. Utilization of knowledge is

founded on reasoning with type-2 fuzzy sets and appli-

cation of the reasoning lattice constructed. It has been

identified that the current realization of this reasoning

lattice could be enhanced by introducing an additional

dknowledge layer T that acts as strategic knowledge.

Such strategic knowledge could handle when and

how to apply a particular solution/model for the drilling

situation at hand, as well as help to pursue user-de-

clared ddrilling objectivesT. Furthermore, through inclu-

sion of some form of dDefault knowledgeT, or dDefault reasoningT, handling of knowledge gaps and/or con-

flicts can be improved.

5.3. Drilling knowledge embedded

To construct drilling and gas hydrate behavior simu-

lations, various assumptions have been made, for

details see Prassl (2004a). Consequently, for the devel-

opment version it is strongly encouraged to re-assess

and possibly replace some of the subsystems embedded

with better, more realistic models. In particular, geolo-gical modelling and the simulation of dissociation of

natural gas hydrates are most likely to be updated with

latest findings, models, and/or knowledge.

6. Conclusion

By developing the P-KMS discussed as prototype, it has been proven that engineering simulations can be

combined with expert knowledge under consideration

of uncertainty. By such combination, various types of

knowledge, obtained from different sources, is consid-

ered for the situation at hand. As system actuation for

ddrilling within gas hydrate environmentsT, different

drilling scenarios can be investigated and potential

risks and problems assessed. Consequently, the benefits

expected by applying such a system are in particular:

!Potential increase in well-site security;

! Overall reduction of well costs by enhanced plan-

ning and risk avoidance;

! Reduced downtime and better understanding of the

processes defining drilling risks and problems in

these environments;

! Demonstration of criticality of data and knowledge

when drilling is carried out in gas hydrate

environments;

! Identification of current knowledge gaps in model-

ling gas hydrate kinetics, especially in different for-

mation types and inside the well;

! Establishment of a base-case in construction such asystem, as well as experience in its development,

embedding and maintenance; and

! Initiate development of further P-KMS applications

for similar domains.

Acknowledgements

The research reported in this paper has been co-

funded by Curtin University of Technology, Perth,

Australia and Commonwealth Scientific and Industrial

Research Organisation (CSIRO), Perth, Australia.

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