07 - Increasing Oil Recovery by Advanced Reservoir Management
Submitting InstitutionsHeriot-Watt University,
University of Edinburgh
Unit of AssessmentGeneral Engineering
Summary Impact TypeTechnological
Research Subject Area(s)
Mathematical Sciences: Numerical and Computational Mathematics
Information and Computing Sciences: Artificial Intelligence and Image Processing, Computation Theory and Mathematics
Summary of the impact
ERPE research into uncertainty quantification for oil reservoir
modelling, described in this case study, has led to 3 impacts in the
- An additional 3 million barrels of oil (corresponding to $330M of
additional income at an oil price of $110/barrel) obtained by a single
application of ERPE algorithms to optimising oil recovery from one of
BP's North Sea assets;
- Incorporation of ERPE developed code in BP's internal code base;
- Formation of a spin-out company (Epistemy) providing 3 full-time and
3 part-time jobs, with £267k of software sales since 2012.
The uncertainty quantification (UQ) group in ERPE has carried out a
programme of EPSRC, TSB and industry funded research into uncertainty
quantification for oil reservoir modelling since 2000. The key researchers
involved were: Prof Christie (ERPE, director of the UQ group), Prof Corne
(Computer Science (CS)), A Abdollahzadeh (ERPE postdoc), A Reynolds (CS,
postdoc), A Gripton (CS, postdoc), Dr Vasily Demyanov (ERPE, lecturer), Dr
D Arnold (ERPE, postdoc and Royal Society of Edinburgh Enterprise Fellow).
The aims of this research programme have been (i) to develop and
demonstrate algorithms that offer a significant performance gain over the
algorithms routinely deployed in the oil industry, (ii) to develop
techniques to ensure that forecast uncertainty ranges are robust and not
significantly affected by sampling algorithm or model resolution, and
(iii) demonstrate the value of geological realism in constraining model
The impacts claimed here have largely been underpinned by research into
the use of stochastic optimisation algorithms for history matching (model
calibration), uncertainty quantification and optimisation. References [1 -
5] give examples of this research. The research has been driven by a
combination of simplified test problems, used for developing ideas and
algorithms, and real examples usually donated by oil companies. The
purpose here is to develop algorithms that are capable of deployment on
real-world problems (with all the problems of large models, extensive run
times etc, that real-world problems bring).
The research underpinned these impacts in the following ways:
- the codes implemented by BP (BOA, MOBOA, ) in their internal code
base were developed in ERPE in a joint activity with Computer Science at
Heriot-Watt, including novel extensions (GuPSO, ) to already
- for the spin-out company, the algorithms (single and multi-objective
versions of Particle Swarm Optimisation (PSO), Differential Evolution
(DE), Bayesian Optimisation Algorithm (BOA) ) coded up in the
commercial code were those that had been extended and exhaustively
evaluated for effectiveness in the UQ group in ERPE. These tests
included demonstration that the codes show a 2 - 5 fold speed up over
existing, highly-tuned implementations.
References to the research
The references identified with * are the ones which best indicate the
quality of the underpinning research.
 * L Mohamed, M Christie, V Demyanov "Comparison of Stochastic
Sampling Algorithms for Uncertainty Quantification", (2010)
SPE-119139-PA, SPE Journal, 15(1), 31-38. DOI:10.2118/119139-PA.
41 Google Scholar (GS) citations
This research showed that use of an evolutionary algorithm (such as PSO)
combined with a post-processing step based on a Gibbs Sampler yields an
equivalent assessment of uncertainty as applying the `gold standard'
Markov Chain Monte Carlo. This paper underpins the decision to implement
evolutionary algorithms with an appropriate Gibbs sampler based
post-processor in the commercial software Raven developed by Epistemy
 * A Abdollahzadeh, A Reynolds, M A Christie, D Corne, G William &
B Davies, "Estimation of distribution algorithms applied to history
matching" Jun 2013 SPE Journal. 18, 3, p. 508-517 DOI:10.2118/141161-PA
13 GS citations
 M Christie, D Eydinov, V Demyanov, J Talbot, D Arnold, V Shelkov, "Use
of Multi-Objective Algorithms in History Matching of a Real Field"
(2013), Society of Petroleum Engineers Reservoir Simulation Symposium,
2013, The Woodlands, Texas SPE 163580, DOI:10.2118/163580-MS
Papers ,  also arise from the TSB funded project [G1] aimed at
developing significant improvements in history matching capability. 
describes the use of the BOA, and shows that it performs better than
highly developed internal BP code.  demonstrates that multi-objective
algorithms can offer speed advantages over the equivalent single objective
algorithms. As a result of the research described in these papers (and
other unpublished TSB work), BP has taken the developed code and coupled
it with their TDRM proprietary code, and used the algorithms for
optimising well placements.
 * Y Hajizadeh, V Demyanov, L Mohamed, M Christie, "Comparison of
Evolutionary and Swarm Intelligence Methods for History Matching and
Uncertainty Quantification in Petroleum Reservoir Models", in
Intelligent Computational Optimization in Engineering: Techniques &
Applications, 2010 DOI: 10.1007/978-3-642-21705-0_8
This (refereed) book chapter demonstrates that (applied effectively),
modern swarm intelligence algorithms can provide reliable and robust
history matching and estimates of future uncertainty. The study showed
that both DE and PSO can converge faster than other algorithms tested, and
this led to their selection for implementation in Epistemy's commercial
 A P Reynolds, A Abdollahzadeh, D W Corne, M Christie, B Davies, G
Williams "Guide Objective Assisted Particle Swarm Optimization and its
Application to History Matching". In Parallel Problem Solving from
Nature-PPSN XII (pp. 195-204), 2012. DOI:10.1007/978-3-642-32964-7.
This paper shows a novel way to improve the formulation of PSO for
large-scale problems where prior knowledge of parameter influence can be
exploited. The algorithm has been coded up for the next release of Raven.
[G1] TSB, #100729, BP, ERPE, £808k, "A Novel Approach to Uncertainty
Quantification and Risk Assessment in Petroleum Reservoir Developments",
[G2] Joint Industry Project, £2.8M funding from industry partners, Uncertainty
Project Phases I, II, III, IV. ERPE, Anadarko, BP, BG, Conoco, DTI,
Eni, JNOC/JOGMEC, Norsk Hydro, Shell, Statoil.
[G3] AWE, £420k, 3 years postdoc funding & AWE William Penney
Fellowship — M Christie.
[G4] EPSRC, GR/R63578/01, £125k, M Christie (PI), "JREI:Use of
Massively Parallel Simulation for Uncertainty Quantification in
Reservoir Engineering", 2002-2005.
[G5] EPSRC, GR/T24838/01, £237k (pre-FEC), M Christie (PI), "Error
Models for Sub-Grid Phenomena in Flow in Porous Media", 2005-2008.
[G6] EPSRC, EP/K034154/1, £2.04M, M Christie (Co-I), "Enabling
Quantification of Uncertainty for Large-Scale Inverse Problems (EQUIP)",
Programme Grant with Stuart (PI), Roberts and Girolami (University of
Details of the impact
The research has led to 3 principal impacts:
- application of the algorithms on a North Sea oil field, leading to an
additional 3 million barrel recovery (equivalent to $330M additional
income at a current oil price of $110/barrel) over their original
development plan [S3];
- implementation of ERPE multi-objective optimisation algorithms by BP
in their internal `TDRM' code base and
- formation of a spin-out company to commercialise application of the
algorithms developed in the uncertainty group to the oil industry [S1].
The first impact is an additional 3 million barrels of oil obtained by BP
using the ERPE multi- objective algorithms. BP has applied the new code
base to the task of optimising locations of additional injection and
production wells in one of their North Sea fields. The use of the multi-
objective algorithms discovered a new well arrangement yielding an
additional 3 million barrels over the optimised development plan produced
by their engineer. In this case, multi-objective optimisation was of
tremendous value as it allowed them to optimise both short-term oil (to
recoup the cost of drilling the new wells), and long-term oil (to maximise
the profitability) of the project. BP's comment on the project was "The
collaboration between ERPE and BP has resulted in a step-forward in
practical, multi-objective optimisation capability for the industry"
The second impact is that BP has taken the ERPE multi-objective
algorithms, developed in the TSB project [G1] and incorporated them in
their internal history matching and optimisation code during 2012 and
2013. The incorporation of the algorithms was assisted by their
recruitment of Asaad Abdollahzadeh, one of the ERPE researchers, into BP
from Jan 2013.
The third impact was the formation of spin-out company, Epistemy
(SC365481) in September 2009 providing 3 full-time and 3-part time jobs.
The company has made £267k in sales of its Raven history matching and
optimisation software since 2012. It has recently started attending trade
shows and generating significant interest (10 strong leads, with one trial
set up and one booked for Oct 2013) [S1]. The stochastic optimisation
algorithms in the latest release of Epistemy's Raven code (those developed
in [G1]) have already been deployed in history matching of real field data
by engineers in Japan Oil, Gas and Metals National Corporation (JOGMEC) (DOI:10.2118/164817-MS).
comment on the software was: "The software is a practical and simple
tool for history matching real field data and optimising field
developments; it has given us a significant speed improvement over our
previous algorithm, and has made it possible to deploy the advanced
techniques in Raven within our team" [S2].
The beneficiaries of all 3 impacts are: practicing reservoir engineers
who use the techniques in history matching reservoir models to data (see
for example  for application to a Russian oil field, and  for
application of BOA to history matching a BP oilfield, demonstrating a
significant performance improvement), and the impact on the UK economy of
3 new full-time and 3 part-time jobs created since 2009 [S1].
Additionally, industry support of £2.8M has been received from 10
international oil companies over 4 three-year phases of a Joint Industry
Project [G2]. As a result of the research published, we have also received
£420k funding from AWE [G3] to transfer ideas from the oil industry to
AWE. The interaction with AWE has led to improvements in their capability
[see example in S4], and has underpinned their decision to support EPSRC
Programme Grant [G6] by continuing Christie's William Penney Fellowship
Sources to corroborate the impact
[S1] Founder/Senior Executive, Epistemy. To confirm the establishment of
Epistemy (SC365481) was in September 2009 and that it provides 3 full time
and 3 part time jobs. The company has made £267k in sales of Raven history
matching and optimisation software since 2012. www.epistemy.com,
software sold through www.useraven.com.
[S2] EOR Division Director, Technical Department Oil & Gas Upstream
Technology Unit Japan Oil, Gas and Metals National Corporation will
describe how JOGMEC use the software, and what advantage it gives them
over previous tools.
[S3] Manager, Advanced Reservoir Performance Prediction, BP will confirm
that BP has applied the new code base to the task of optimising locations
of additional injection and production wells in one of their North Sea
fields, producing and additional 3million barrels of oil over the
optimised development plan.
[S4] An example of improvements in AWE's capability through the ERPE
research described above can be found in the document at this link. http://www.awe.co.uk/Contents/Publication/68e180bAWE_Discovery_22.pdf,