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Figure Captions
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The Ormen
Lange gas field is operated by Norsk Hydro (development) and Shell
(production). The Ormen Lange Field is situated 100 km off the west
coast of Norway (Figure 1) and has an areal
extent of 350 km2. Four gas wells and one dry well have been
drilled to appraise the gas discovery. The reservoir (Figure
2) is severely faulted by polygonal faults. The reservoir is at 2913
m at the deepest in south and approximately 2650 m at the shallowest to
the north.
Field
development (Figure 2) involves 24 subsea
production wells, two 8-slot templates from production start-up, and 2
more templates, if required, at a later date. The gas will be depleted
with compression added as required. At DG3, an alternative concept, with
clustered platform wells, was also evaluated.
Uncertainties and Risks
The main
subsurface uncertainties and risks anticipated since the
discovery (1997) have been:
1. GIIP uncertainty (lack of well control)
2. Reservoir quality: Related to the thinning and possibly increased
shale content in the turbiditic sandstones in flank, saddle, and distal
areas.
3. Fault properties: Approximately 700 polygonal-type faults have been
interpreted within, or close to, the reservoir-containing section.
Stepping of GWC is observed. The fault-sealing properties ( modeling )
are important for estimation of reserves and field development
considerations.
4. Rough seabed and varying water depths lead to challenging depth
conversion, and great efforts are required in planning the placement of
seabed installations (templates and pipelines).
5. High water production from formation or surrounding aquifer may lead
to hydrate problems.
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Decision and Management
Process
A decision
and management process was agreed to by the joint partnership
(1999):
1. A governance process divides the project into stages, milestones, and
decision gates with agreed-upon support documentation.
2. A risk assessment process supports the governance process. Risks and
opportunities are ranked in accordance with probability and consequence.
The highest ranked risks have high-occurrence probability or large
consequence. These are treated as management level issues. Lower ranked
risks are then technical or watch-list-level issues. Risks throughout
the governance process either will be resolved, through work, or
mitigated through the field development strategy.
3. A risk-based internal and external verification process is carried
out.
4. Technical and economical evaluations and approvals at each decision
gate, involving base, low and high cases and scenarios and uncertainty
evaluation at discipline and total project level.
5. Use of decision trees and value of information exercise to decide on
further investments.
History:
Objectives and lessons learned:
Lead Phase
The
flatspot was interpreted as a GWC on a single seismic line in late
1980's. 2D data (1992) supported initial observations, and in 1996 3D
seismic data were acquired and processed on board (Norsk Hydro). The
seismic interpretation (Figure 3) confirmed
early work. Mapping of interpreted flatspot and AVO (amplitude versus
offset), DHI (direct hydrocarbon indicator), showed that the 350-km2
field outline was likely gas-filled.
The
geological model was a turbidite sourced from the southeast with
potentially deteriorating reservoir quality north of the mapped DHI.
PL209 (Norsk
Hydro operated) and PL208 (BP operated) were awarded in early 1996.
6305/5-1
(NH 1997) was drilled high on the structure proving gas down to 2763
mMSL. The reservoir model (Figure 4) was
confirmed as an Upper Cretaceous and Lower Tertiary sand-rich,
high-density turbidite. The reservoir may be split into sand-rich
channel and channelized lobe facies and frontal splay and a distal
mudstone facies with thin interbedded sandstones. The main reservoir,
Egga RU, contains sand with a thickness of approximately 50 m, a net to
gross ratio of 90% and permeability approaching 500 md in average. The
water saturations are ranging from less than 20% to more then 40%. At
this stage the cause of the high water saturations was questioned. It
was found that the reservoir sands might be divided into a clean sand
(C-Sand) and a green shale-rich part (G sand). The C sand was split into
an upper (low Swi) and a lower (high Swi) part. The non-reservoir part
consists of shale and calcite (cemented) zones. This forms the building
blocks of the reservoir modeling work.
6305/7-1
(BP 1998) to the south proved a GWC at 2913 mMSL. The well successfully
tested and confirmed the good reservoir characteristics anticipated. No
faults were observed. Similar gas pressure was found as in 5-1. A 14-m
zone of residual gas was encountered below the FWL.
6305/1-1
(NH 1998) was drilled to the north of the mapped DHI gas effect. The
well had only gas shows in a silty and shaly sequence (less than 1 m of
sand). Reservoir pressure was 80 bar overpressured as opposed to the
normally pressure gas-filled Ormen Lange.
Appraisal Phase
PL250
(Shell operated) was awarded late in 1999. The Ormen Lange unit was
established with Norsk Hydro as operator for the development and Shell
for operation. It was decided to enter the concept selection phase. An
appraisal strategy was agreed to, with one firm and one optional well.
6305/8-1
(NH 2000) was drilled in the saddle area (Figure
4), considered to have uncertainties in reservoir quality. The well
proved good reservoir quality. A specially designed MDT water sample
proved fresh formation water, confirming previous high Swi calculations.
The high Swi was shown to be a function of pore throat size (sorting)
and clay content and type (coating or particles and smectite content).
The well proved a shallower contact (FWL of 2898 mMSL) than 7-1 and
penetrated a 2-m thick oil column and 7 m of residual oil at the base.
Seismic
modeling work was performed in 2000 to evaluate the influence of
residual gas on the interpreted flat spot. It was concluded that this
zone may influence the well tie.
In 2001,
after a period of testing and evaluation, the partnership approved
reprocessing of the seismic data focused on removal of seabed-generated
multiple energy, in combination with improved seismic imaging by
pre-stack depth migration (PSDM).
It was
decided to drill, and test, 6305/4-1 (NH 2002) prior to deciding on
concept (DG3). The well was designed to penetrate the reservoir north
of, and deeper than, the 5-1 well to disapprove a possible dynamic
aquifer fed from the north. The main objective was, however, to perform
a fault seal / depletion test in a closed and fault-bounded segment with
good reservoir quality. It was desired to drill the well in an area with
high seismic quality inside a clearly defined flatspot. Special efforts
were placed on planning the test design. A numerical simulation model
was required that could be used for evaluating test results. The grid
was designed to minimize numerical dispersion (local grid refinement).
The well successfully penetrated a gas-filled reservoir deeper than 5-1.
The result supported a reduced GIIP uncertainty acceptable to all
partners for approaching the DG4 stage. Furthermore, the test well (Figure
5) clearly showed faults seals in a limited 1x1x1-km u-shaped fault
intersection just west and 500 m to the east of the well. However, the
test disapproved depletion and also showed pressure support from outside
the mapped faults east of the u-shaped fault.
The PSDM-reprocessed
seismic data successfully improved the data quality in large areas,
increasing the confidence considerably of the seismic interpretation,
including fault definitions and well ties.
The
uncertainty evaluation has been built on a principle of system
development:
1. Get the owners and users involved.
2. Use a problem-solving approach.
3. Establish phases and activities.
4. Establish standards for consistent development and documentation.
5. Justify system as capital investment.
6. Don't be afraid to cancel.
7. Divide and conquer.
8. Design system for growth and change.
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An
Uncertainty Work Flow was set up early in the project, as follows:
a) Selection of input parameters--Task, milestones, and meetings on
inter- and intra-disciplinary level. Qualitative and quantitative
evaluations to limit parameters to be brought forward; i.e., uncertainty
in porosity estimates is small compared to depth map and water
saturation uncertainties.
b) Definition of input parameter levels--Parameter levels spanning the
range of possible outcomes were identified early, while probabilities
and weights to carry forward were not requisite until later in the
uncertainty workflow. The results were files or values that may be
combined in volume and dynamic models.
c) Experimental design was used to reduce the number of required dynamic
simulations to about 100 without loosing significant information. The
D-optimality procedure was used.
d) Calculations of response parameters were made by use of reservoir
modeling and simulation to evaluate parameters having an influence on
bulk, GIIP, reserves, production profiles and plateau.
e) Regression models were estimated for recovery factors and plateau
length. Based on statistical and visual quality controls, simulations
were added iteratively to achieve a satisfactory confidence level for
the model.
f) Distributions/correlations of input parameters were evaluated late in
the process when the technical work had sufficiently matured. The
benefit of the workflow is the possibility to change distributions and
individual parameters at any time during the uncertainty workflow.
g) Monte Carlo simulation was used to combine the regression models
including model uncertainty and the probability distributions of the
input parameters. 5000 iterations were run.
h) Analysis and recommendations--The results were analyzed by extracting
key statistics (P90/Exp./P10) from the data, plotting probability
distributions, and using tornado graphs to rank the input parameters
quantitatively.
Recommendations for further work and focus were given. A rapid modeling
update approach was used in order to be able to run the project in
parallel within the time constraints at each decision gate; it was
decided to: 1) Prototype the work flow, 2) Use programming scripts to
initiate, close, and monitor the different modeling and simulation
tools in an automated way, and 3) Encourage, in practice, a flexible
approach in which both fully or semi-automated workflow is allowed. This
preserves available deterministic models and scenarios and simulations
that would be tedious and unnecessary to rerun in a loop. As example, in
DG2 all models were simple, while the time was constrained. As such, a
fully automated loop was first prototyped, then updated, and simulation
finally rerun overnight. Later studies involved more complex and slower
models, and a more semi-automated process was implemented. Automation
was used to fill gaps in simulations run. The methodology proved
flexible concerning iterations with a possibility to modify continuously
probability distributions, change the number of input parameters,
including additional simulation results and have late updates of input
data.
A success
factor for projects with constantly new data and access to more
sophisticated procedures is to establish a framework where the
individual elements easily can be replaced. New ideas, possible
refinements, simplifications or tests of work around to save time were
planned and, if possible, prototyped prior to full implementation. New
methods or tools require backup solutions.
All
parameters from geology to production technology that may influence
reserves, well layout, and production profiles, including uncertainty in
well performance and pressure calculations, were considered. The results
were directly used as input to the economical and field development
evaluation.
Results/Analysis
The results
were analyzed and used as input to the governance process. The relative
ranking of the input parameters, as shown in the example tornado graph
in Figure 6, indicates which parameters
should be the focus in the next phase. GIIP, fault properties, and
pressure available at well head are found to be of high importance for
reserves estimates.
Simple
process and data-modeling techniques were used to divide the tasks and
uncertainties into individual workflow, as exemplified in
Figure 7 and Figure
8.
In the
example, bulk volume uncertainties related to geophysical
interpretation, velocity models, and depth maps were performed using a
Monte Carlo based reservoir model loop, with results later integrated
into the main study.
The
technical level of the fault modeling has increased several times since
1999 when all faults were distributed stochastically in the geological
model. In 2000 the largest faults were interpreted as deterministic and
the smaller faults were included stochastically. In 2001 the location
and throw of the smaller faults were estimated from dip maps. The fault
sealing properties were estimated based on throw classes. In the latest
models, all faults are deterministic, and the sealing properties are
estimated from throw and amount of shale (Vsh).
Aquifer
mapping, sensitivities, analogy studies, and analytical evaluation were
used to study water production. Only small volumes of water are
expected. A water handling strategy was established with a plan to
reduce gas rates or shut-in off-water-producing wells. Need for
non-saline water-measuring equipment was noted as a risk mitigating
requirement.
Special
care was taken, using company guidelines, to honor all input data
whether soft (interpretation) or hard (wells); i.e., input and out put
data must be matched by inspecting statistical data and log data
(blocking of wells) and comparing the conceptual models against
reservoir modeling results. The upscaling from geological model to the
reservoir simulation model was tested with alternative algorithms, and
numerical and grid size issues were sufficiently tested. As an example,
visual inspection showed that use of a grid that was too coarse led to
severe fault sampling (alias) errors with individual faults linked-up in
the grid.
An
integrated methodology for uncertainty evaluation has been developed.
Results have been used in risk assessment and management, appraisal
strategy, governance process, and selection of field development
concept.
The authors
like to thank the Ormen Lange partners bp, petoro, Shell, ExxonMobil,
Statoil, Norsk Hydro, and NPD, for permission to publish and support and
the Ormen Lange project team for contributions.
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