The Math Forum

Search All of the Math Forum:

Views expressed in these public forums are not endorsed by NCTM or The Math Forum.

Math Forum » Discussions » sci.math.* » sci.stat.math

Notice: We are no longer accepting new posts, but the forums will continue to be readable.

Topic: JOB: UK, Statistics, Post-doctoral research, 3 years
Replies: 0  

Advanced Search

Back to Topic List Back to Topic List  
Peter Craig

Posts: 1
Registered: 12/18/04
JOB: UK, Statistics, Post-doctoral research, 3 years
Posted: Jun 24, 1996 11:30 AM
  Click to see the message monospaced in plain text Plain Text   Click to reply to this topic Reply

A postdoctoral research position is available for a three-year, EPSRC
funded, collaborative project in the Department of Mathematical
Sciences, University of Durham, England, entitled:

"Bayes Linear Forecasting and Decision-Making for Large-Scale Physical
Systems in the Petroleum Industry"

The objectives of this three-year research project, funded by the UK's
Engineering and Physical Sciences Research Council, are (i) to develop
a Bayes linear approach to forecasting and decision making using
complex, high dimensional models for physical phenomena such as
hydrocarbon reservoirs; (ii) to devise useful prior descriptions for
the input and output uncertainties in such models, synthesising
graphical elicitation tools for expert beliefs, theoretical analysis
and the study of simplified forms for the models; and (iii) to combine
(i) and (ii) to derive improved methods for asset management for
hydrocarbon reservoirs and for decision problems such as leak
detection for pipeline networks, and test these methods in a series of
case studies. The project is in collaboration with Scientific
Software-Intercomp (UK) Ltd, a leading software developer for the oil

This project follows on from a recent EPSRC funded project held by the
investigators which concerned strategies for matching hydrocarbon
reservoir simulator output to actual reservoir production history.

The project involves a combination of statistical, numerical and
computational skills and an ability to develop innovative solutions to
challenging practical problems. Desirable qualities include experience
of Bayesian methodology, modelling of complex stochastic systems and
fluent computing skills. The appointee will be expected to
collaborate closely with the investigators in all areas.

The position is for three years from 1st October 1996, or later by
negotiation, and initial salary will be up to 15986 UK pounds on the
RA(1A) scale. We welcome informal enquiries, which should be addressed
either to Professor Michael Goldstein (e-mail:, tel.: +44-191-374-2365) or to Dr Allan
Seheult (e-mail:, tel.:
+44-191-374-2371). Application forms and further particulars are
available from the Director of Personnel, Old Shire Hall, Durham DH1
3HP, England (e-mail:, fax:
+44-191-374-7253). Please quote reference A582. The closing date for
applications is 19th July 1996.

For more information about the statistics group, the Department of
Mathematical Sciences and about Durham, see the WWW page (URL, which also has
information about the project and a paper outlining a case study for
this problem.

---------- Further details about Durham and the project follow ----------


Durham is a beautiful city occupying a magnificent site in a meander
of the river Wear. Its cathedral (the finest example of Romanesque
architecture in Britain) and castle (which became the first college of
the university) are recognised as a supremely important part of our
national heritage. Durham lies on the main North-South rail network
(less than three hours by train from London), is just two miles from
the A1(M) motorway and thirty minutes drive from Newcastle
International Airport.


The University was founded in 1832 as the third university in England.
Durham is a collegiate university. Its twelve colleges and two
societies offer first class accommodation close to the academic
departments, and to facilities for sport, music and drama. They help
promote the University's very friendly atmosphere. The University of
Durham is one of the UK's leading centres of scientific
research. Mathematical Sciences (Applied Section), Physics, Chemistry
and Engineering were all awarded the highest grade (5) research rating
by the HEFCE in the last national research assessment.


Durham University Library contains an excellent collection of books,
monographs and conference proceedings in statistics and other subject
areas, and a good selection of current periodicals. In addition, it
provides access to and training opportunities for a range of
electronic information resources. These include catalogues of other
libraries, bibliographic data sources such as BIDS and OCLC
FirstSearch, and access to the rapidly expanding range of information
(bulletin boards, online journals and others) available through the

The Department of Mathematical Sciences also has its own research
library, the Collingwood Library, which contains a smaller collection
of books and collected works primarily for research, and an
undergraduate library.

The University and the Department of Mathematical Sciences together
offer excellent computer facilities for calculation, document
preparation and communication via the Internet and with external
computing services. The facilities are provided on both UNIX
workstations and PCs.


Members of the research group in statistics and applied probability

FPA Coolen (Bayesian reliability, software testing, foundations of statistics)
PS Craig (spatial statistics, dynamic models, applied Bayes linear methods)
M Goldstein (Bayes (linear) methods, decision theory, foundations)
IM MacPhee (stochastic decision processes, applied probability)
MD Penrose (geometric probability, percolation, interacting particle systems)
AH Seheult (robust analysis of designed experiments, applied Bayesian methods)
DA Wooff (Bayes linear methods, graphical models)

The interests of the group range over a wide range of topics
associated with statistics and probability. These include:

Bayesian methods:

Bayes linear methodology offers a systematic way of analysing
uncertainty, based on the combination of statistical data and a linear
analysis of limited aspects of expert judgements. Similar in spirit to
other Bayesian approaches, it is often more straightforward to apply
to complex problems. The approach addresses fundamental practical and
philosophical issues about learning-based on partial knowledge. To
implement the rich mathematical theory underlying this methodology, we
have developed a general purpose programming language, which handles
large practical applications by using graphical models to analyse and
display information flow. You can find out about more about our
development on the World Wide Web by accessing the Durham statistics
home page (URL

Applied statistics:

We are particularly interested in substantial research problems,
arising from our contacts with other academic departments and with
industry. Current projects include: computer experiments for history
matching for hydrocarbon reservoirs; applications of Bayes linear
methods to problems in oil and gas pipeline technology; sales
forecasting in large competitive markets; industrial experimentation
for quality control; collaborations with engineers developing hip
replacements; with archaeologists using spectrometry to determine
object composition; with medical physicists on measurement problems in

Robust analysis of designed experiments:

Anomalous behaviour in data from designed experiments is common and
can seriously affect their classical analysis. Robust methods are
tailored to detect, highlight and accommodate any such unusual

Other areas of methodology:

These include: modelling and inference for spatial phenomena; time
series analysis and forecasting; statistics in the earth sciences;
Bayesian reliability theory; software testing; expert judgements and
uncertainty; and statistical selection.

Applied probability:

Stochastic decision processes are used to model and guide decision
making for systems with incomplete information. The use of semi-Markov
decision processes together with modern optimisation methods has led
to many successful applications to fields as diverse as admission to,
routing within and collision resolution for various telecommunication
networks; machine replacement and maintenance problems; various search
and resource allocation questions.

Percolation and geometric probability:

Some rather easily described spatial random systems have deep
properties, the analysis of which is relevant to the modelling of
biological systems such as epidemics, and in statistical physics, and
also to certain statistical tests. There is scope both for analysis
and for computer simulation in this field of research.


We have regular seminars in statistics and probability, in conjunction
with the University of Newcastle upon Tyne. Additionally, we have a
more informal, internal seminar series. We have an expanding role in
statistical consultancy, and recently established the University of
Durham Statistics and Mathematics Consultancy Unit, which offers
statistical and mathematical advice and analysis for all kinds of
industrial, commercial, academic and institutional clients.


The investigators are statisticians Michael Goldstein, Allan Seheult,
Peter Craig, David Wooff and numerical analyst Alan Craig. The
statistics group in Durham University are leaders in the theory and
applications of the Bayes linear methodology underpinning the proposed
research. It is also a leading statistics group working on history
matching and related problems in the oil industry. Our industrial
collaborator, Scientific Software-Intercomp (UK) Ltd (SSI), is a
leading software developer for the oil industry.

This project is concerned with problems of forecasting and
decision-making based on information derived from large physical
models, with particular application to the petroleum industry. These
models are implemented as pieces of computer software known as
simulators. A simulator takes as input a complete physical
description of the system and instructions on system operation.
However, some aspects of the physical description are not known. Even
for the best possible choices, there will be differences between
computed model behaviour and real system behaviour. Predictions and
consequent decisions should take into account such uncertainties, but
in many applications a single choice of physical description is made,
with predictions and decisions based solely on that choice, perhaps
supported by a simple sensitivity analysis. The choice is made by
`history matching', where it is attempted to make simulated production
match the historical record, by running the simulator a number of
times with different inputs. More careful treatment, for example
using a full Bayes analysis, is usually infeasible as the models are
very high dimensional and slow to run.

Bayes linear strategies for history matching of hydrocarbon reservoirs
were the subject of a previous EPSRC grant held by four of the
applicants. A successful approach to history matching was developed,
incorporating the various sources of uncertainty about the physical
system by using computer based elicitation tools and the analysis of
simplified versions of the simulators. This approach suggests a
practicable real time methodology for forecasting and decision-making,
as the simplified models are sufficiently fast that we may account for
model uncertainties using repeat runs of these models to produce prior
judgements as to forecast outcomes and good decisions, which are
updated by selected evaluations on the full model. To develop this
methodology, we must (i) construct detailed spatio-temporal prior
descriptions relating the uncertainties in the models and the physical
system, (ii) derive Bayes linear methodology for forecasting and
decision-making using such high dimensional descriptions, and (iii)
review the various elements of our history matching strategy, given
our requirement to seek matches which give reliable forecasts or
identify good decisions.

We shall apply the methodology to reservoir simulators and pipeline
network simulators: two important types of model in the petroleum
industry. The methodologies will be tested in case studies concerning
asset management for oil reservoirs and real time decision problems
such as leak detection for pipeline networks, as supplied by our
industrial collaborator Scientific Software-Intercomp (UK) Ltd (SSI).

The petroleum industry will benefit from improved forecasting and
decision-making methods for reservoirs and pipelines. Methodologies
developed should generalise to a wide class of physical phenomena for
which computer simulators are used to assist prediction and
decision-making. Finally, there is general statistical and scientific
value in developing Bayes linear methodology for such high dimensional
decision problems, as the ideas will be transferable to many other
areas where good decision-making requires careful use of expert
judgements but the problem is too complex readily to allow a full
Bayes solution.


The work involves two related themes: (1) developing informative prior
specifications, and (2) developing the Bayes linear approach to
forecasting and decision-making for such complex models. These strands
will proceed in parallel for the first eighteen months. As our
approach builds strongly upon our previous work on history matching,
we will also use this opportunity to review, critically, the various
elements of that strategy, in the light of our requirement to seek
matches which give reliable forecasts or identify good decisions.
While our intention is to produce methodology which is widely
applicable to general problems using physical models, the theory and
models will be tested during this development upon trial problems in
reservoir and pipeline management, suggested by SSI, for which it will
be possible to compare our results with objective solution criteria.

The second eighteen month period will be devoted largely to two case
studies in which we apply the methodology to selected problems for
real physical systems, and to critical feedback to allow the
development of the theory to react to case study results. The first
case study will involve applying the methodology to a number of
genuine oil reservoirs. The second will concern gas and liquid
pipeline networks, including the problems of batch arrival forecasting
and fast automatic leak detection and location systems, based on our
experience of using dynamic linear models to monitor pipeline
networks. The case study will be based on actual oil and gas networks
owned by international petroleum companies collaborating with SSI.

| E-mail: Telephone: +44-91-3742376 (Work) |
| Fax: +44-91-3747388 +44-91-3846041 (Home) |
| |
| WWW: |
| |
| Snail: Peter Craig, Dept. of Math. Sciences, Univ. of Durham, |
| South Road, Durham DH1 3LE, England |

Point your RSS reader here for a feed of the latest messages in this topic.

[Privacy Policy] [Terms of Use]

© The Math Forum at NCTM 1994-2018. All Rights Reserved.