GB2398900A - Identification of best production potential oil wells and identification of drilling strategy to maximise production potential - Google Patents
Identification of best production potential oil wells and identification of drilling strategy to maximise production potential Download PDFInfo
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- GB2398900A GB2398900A GB0404261A GB0404261A GB2398900A GB 2398900 A GB2398900 A GB 2398900A GB 0404261 A GB0404261 A GB 0404261A GB 0404261 A GB0404261 A GB 0404261A GB 2398900 A GB2398900 A GB 2398900A
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- 238000004519 manufacturing process Methods 0.000 title claims abstract description 42
- 238000005553 drilling Methods 0.000 title claims description 21
- 239000003129 oil well Substances 0.000 title 1
- 238000000034 method Methods 0.000 claims abstract description 47
- 238000005259 measurement Methods 0.000 claims abstract description 12
- 230000035699 permeability Effects 0.000 claims description 8
- 230000015572 biosynthetic process Effects 0.000 claims description 7
- 239000011148 porous material Substances 0.000 claims description 4
- 238000004422 calculation algorithm Methods 0.000 description 10
- 238000011161 development Methods 0.000 description 4
- 238000011084 recovery Methods 0.000 description 4
- 238000013459 approach Methods 0.000 description 3
- 238000009826 distribution Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000004088 simulation Methods 0.000 description 3
- 230000001419 dependent effect Effects 0.000 description 2
- 230000001747 exhibiting effect Effects 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000000877 morphologic effect Effects 0.000 description 2
- 230000002776 aggregation Effects 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 230000003466 anti-cipated effect Effects 0.000 description 1
- 230000001174 ascending effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000001143 conditioned effect Effects 0.000 description 1
- 230000010485 coping Effects 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- HJUFTIJOISQSKQ-UHFFFAOYSA-N fenoxycarb Chemical compound C1=CC(OCCNC(=O)OCC)=CC=C1OC1=CC=CC=C1 HJUFTIJOISQSKQ-UHFFFAOYSA-N 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 230000002427 irreversible effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Classifications
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B43/00—Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B44/00—Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B7/00—Special methods or apparatus for drilling
- E21B7/04—Directional drilling
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- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Geology (AREA)
- Mining & Mineral Resources (AREA)
- Physics & Mathematics (AREA)
- Environmental & Geological Engineering (AREA)
- Fluid Mechanics (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Geochemistry & Mineralogy (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
A method for determining the best production potential wells in a reservoir, comprises determining a productivity proxy function for the reservoir, for example using gridblock values for a range of relevant parameters, proving the productivity proxy function for a variety of geological scenarios and solving the productivity proxy function to determine the best production potential wells. Two reservoir models can be combined by using gradual deformation. Also disclosed is a method of controlling the steering of a drill bit based upon obtained production parameter measurements.
Description
IDENTIFICATION OF DRILLING TARGET AND STEERING STRATEGY
FOR WELLBORES AND WELLBORE FIELDS
The present invention relates to the field of oil and gas wells More specifically, the invention relates to a method to determine a drilling strategy for
wellbores and wellbore fields.
It is desirable to be able to improve or maximise the production potential of a wellbore being drilled. The production potential of a wellbore is dependent upon a number of factors. For example, the production potential may be dependent upon the porosity, oil saturation, absolute permeability end pore pressure ofthe formation through which the wellbore passes.
It is a object of the invention to provide a steering strategy whereby the production potential of a wellbore to be formed is improved or even maximised.
According to one aspect of the invention there is provided a method for determining the best production potential wells in a reservoir, comprising: determining a productivity proxy function for the reservoir; proving the productivity proxy function for a variety of geological scenarios; and solving the productivity proxy function to determine the best production potential wells. À
The productivity proxy function may involve making use of gridblock values of a number of parameters. Suitable parameters include porosity, oil saturation, absolute permeability and pore pressure of the formation in which the wellbore is to be formed.
The invention also relates to a method for combining two reservoir models, comprising: providing two different reservoir model; and using gradual deformation to combine the two models.
According to another aspect of the invention there is provided a method for selecting the direction of well, comprising: obtaining production parameter measurements using a logging while drilling tool including a drill bit; and controlling the steering of the drill bit based on the production parameter measurements.
The manner in which these objectives and other desirable characteristics can be obtained is explained in the following description and attached drawings in which: Figure 1 is a flow chart showing one general step of a method in accordance with an embodiment of the prevent invention.
Figure 2 is a flow chart showing an algorithm for use in the combination of more than two models.
Figure 3 is a flow chart showing an algorithm for use in the steering of drill bits to optimise production.
Figure 4 is a flow chart showing an algorithm for placing wells in a reservoir I to optimise production.
The present invention comprises a system and method for determining a drilling strategy for wellbores and wellbore fields based on production potential.
The invention involves using a heuristic method to identify reservoir regions that exhibit favourable production potential to facilitate selection of drilling target for a development or infill well. The method also permits assessing the impact of alternate steering strategies on well path and expected productivity.
The method is based on the use of a current model of the reservoir, i.e., a model that is conditioned to production data or history-matched. The method can cope with multiple models, in the event there is multiplicity of geological scenarios.
The method includes the following steps, each of which can also stand by itself.
( 1) Use of Production Proxy Function for General Well Planning For a single-model problem, a productivity attribute map is constructed based on a proxy function incorporating the gridblock values of porosity, oil saturation, absolute permeability, and pore pressure, as well as effects of neighbouring gridblocks and boundaries. The effectiveness of the proxy function is verified by correlating well performance obtained from flow simulations (within the operation envelope deemed for the well), as the well is placed in a multitude of locations spanning the feasible drilling domain. The Junctional form of the proxy function adheres to Darcy's law, but adjustments can be made to reduce the scatter of the correlation. Results of this approach can be compared to more exhaustive methods (e.g., stochastic optimization of well placement with genetic algorithms) and obtain reasonable agreement. The productivity proxy function aids in choosing horizontal well trajectories in a heterogenous reservoir to achieve maximum oil production based on the measured properties of the reservoir.
The productivity proxy function (PPF) is used to calculate "productivity proxy factors" for each gridblock in an Eclipse model. Discretised values of production proxy factors calculated at gridblock level based on the PPF should represent the production capacity of a reservoir. "Well production factor" calculated based on production proxy factors of the gridblocks where the wellbore passes through should show correlation with oil production for a horizontal well. When the correlation is established for a variety of possible geological scenarios, PPF can be assumed to represent the production potential of a given trajectory. When this assumption is proven, the trajectory of the best production potential wells can be obtained by calculating the PPF alone. Numerical simulations can be carried out in a limited selective search space to confirm the results and a decision on well placement can be made.
2. Aggregate Modelling of Distinct Geological Scenarios When distinct geological scenarios exist, an aggregate model is derived based on a methodology that has shown to yield target locations optimum in an overall sense. Therefore, a well positioned in the region highlighted by the aggregate model, exhibits a performance distribution with respect to the underlying models, possessing (by and large) properties of maximum mean and minimum variance.
Model aggregation is achieved by the method of gradual deformation and the results have been verified for models with small and large geological/morphological contrast in two-, three-, and four-model scenarios.
The methodology arrives at a well placement plan that is the overall optimum, given the reservoir may resemble a number of equally probable earth models. An optimum solution for the multiple-model problem is defined as one that attains the highest expected performance (cumulative production) and minimises the variance about this performance. The optimum placement is one that is optimum for a "blend" of the underlying reservoir models that captures the connected features of the models. The method of gradual deformation (geostatistics) was found to I produce blends of this characteristic. The finding was confirmed by analysing problems with two and more underlying models, as well as models with small and large "morphological"/geological contrast. Gradual deformation may be used to combine two models as well as a combination of models to arrive the overall! optimum.
In order to find the overall optimum well for two given reservoir models, several steps are performed as shown in Figure 1. In the first step 100, the original models are transformed to a Gaussian Normal distribution (nscore software can be used for this step). In the second step 102, the gradual deformed models are calculated for needed p constants by using the following equation: Z = cos (p)Z + sin(prt)Z2' where Z. and Z2 are two independent realizations in Gaussian Normal space, and Z is the new realisation which is also in Gaussian Normal space which honours the geostatistical parameters for any value of p (Gtrans software can be used for this step). Next, in the third step 104, the gradual deformed models are back transformed to original distribution (backtr software can be used for this step). The beck transformed models are unscaled in the next step 106 (upscaler i software can be used for this step). The files from GSLib co-ordinated are then converted to Esclipse co-ordinates in the next step 108 (gel 2ecl can be used for this step). In the next step 110, the optimum well is found for each combined model (MLOPT software can be used for this step). Using ECLIPSE in the next step 1 12, the performance of each well from the previous step can be checked on the two original models. In the final step 114, the well is chosen which performs best according to selection criteria.
In order to combine more than two models, the algorithm shown in Figure 2 is performed. First, Model Z' is combined with Model Z2 to provide Model Z'.
Next, Model Z3 iS combined with Model Z' to provide Model Z". This algorithm can be extended to more than three models.
3. Use of Production Proxy Function for Well Steering Having localised the region exhibiting greatest production potential, the steering problem is analysed by generating the well path that is obtained within a defined drilling corridor (e.g., point of entry, overall alignment, planned length, and curvature constraints) for alternate steering methods (e.g. based on single or multiple parameters measured in an LWD operation). This step formulates a possibility of steering a well path based on production parameters. The aim is to find a proper combination of a parametric group that gives a probable high potential representation of the reservoir, and an algorithm is developed to steer the well path based on the difference oftwo successive gradients in the direction ofthe well. The direction of change in gradients signifies the possible path for the well.
An algorithm using the gradient of the productivity proxy Function (as defined/described) is developed to steer the well from a defined starting point, along a prescribed orientation, respecting curvature and total length constraints. The algorithm computes the gradient using LWD measurements (such as processed values of porosity, oil saturation, and permeability) behind the bit, and model- provided parameters ahead of the bit. The algorithm can assess the impact of the mismatch between the model and the reservoir on well trajectory and productivity.
The method is used to quantify the impact of an additional LWD measurement (e.g. permeability) on resulting well path and performance. ! In the course of drilling, LWD measurements can be used to update the geostatistical description of the reservoir. Eigen-reconstructors reduce the bewildering multiplicity of possible reservoir states to a few fundamental building blocks, arranged in rapidly decaying sequence. The primary building blocks are what matter in determination of optimum well bath. The optimum well path is determined using an elegant technique in operations research, which determines the globally optimum well path. This path represents the global maximum of the productivity proxy function, within the constraint that drilling is irreversible. The i bit moves on within prescribed drilling constraints and the game theoretic reasoning recurs. The process continues until the drilling termination criterion is met.
Figure 3 then shows a flow chart of this drilling technique. First at 200, measurements are taken using LWD. At step 202, measurements are used to update the geostatistical description. Next at 204, the optimum well path is determined (as previously described). At 206, drilling is continued in the chosen direction. At 208, if the drilling criteria has not been met, the process returns to step 200. If the drilling has been met at step 208, then the process ends at step 210.
it was determined that well productivity is most governed by target location and secondarily by traversed path.
In addition, the impact of well placement strategy on field performance can also be assessed in terms of anticipated field production profile (build-up, plateau, decline) and recovery factor within prescribed field development parameters - well type, number of wells, production constraints, and planned project duration or field life. This step provides a scheme by which to steer horizontal wells in regions with high productivity in order to improve the oil recovery efficiency of the entire reservoir.
In one embodiment, the method comprises defining well placement strategy for field development that numerical investigations prove to be superior to conventional or intuitive approaches.
An ordered well placement strategy from lower or small-standoff horizons to higher horizons, as is common field practice, can also be used to investigate and compare to a variable spacing and alignment strategy of populating ascending horizons. Well spacing and alignment in this method was determined by a productivity proxy technique that accounts for formation permeability, oil saturation, and pressure within feasible drainage areas ofthe horizon under development. The area exhibiting maximum potential was chosen for the next well, yielding in general a variable spacing and alignment pattern. For the problem investigated, however, this technique yields a longer plateau compared to the ordered placement scenario (and exceeds this plateau), producing an incremental recovery factory of 4% of original oil-in-place.
In other words, a noticeable gain in field recovery factor (4%) is achieved when a field is developed following a variable-spacing well placement strategy dictated by productivity criteria, compares to a fixed spacing strategy. In both cases the wells are placed first in the layer closest to the aquifer, then layers with greater standoff are developed (the "field-proven" approach). It was determined that, in early field life, well productivity is driven by permeability, whereas in late field life it is driven by oil saturation. A parametric group has been formulated that captures this dynamic.
Consistent application of this technique requires that prior to each drilling operation, an updated map of the formation properties (required in the proxy function) be generated from a current model ofthe reservoir. This map is then used! to highlight the drilling target and preferred orientation. Target delineation, therefore, requires frequent application of production data inversion techniques.
Subsequent to target delineation is the drilling and steering problem, which due to the inherent uncertainties of subsurface models and their coarseness, requires I robust method of coping with high-frequency formation property variations. A framework of employing LWD measurements suggested by the productivity proxy function is presented to rapidly assess well productivity potential for feasible drilling courses. Active steering can be implemented within this framework, yielding distinct paths commensurate with the operator's attitude towards risk.
The risk of placing wells in the reservoir with productivity steering is done automatically as shown in the flow chart in Figure 4. First initialise the input data file for Eclipse, then run Eclipse till the time to put a new well. Calculate the current productivity field from recorded pressure and oil saturation data in Eclipse RSM file. Based on the productivity field, compute nodal value using moving average, and rank the nodal locations by descending value. Pick an optimum well location, and optimise well trajectory within the depletion area. Then check if the proposed well overlap with other wells' drainage area. If so, pick another optimum well location; if not, accept the new well and record into data file. When there are not enough number of wells in the reservoir as designed, the program will continue and the data file will be modified for re-starting Eclipse simulation for the next production period until time to put another new well. So on and so forth until all the wells are drilled, the program will run Eclipse to a designed production stopping; time and generate a final production report.
For each of these steps and methods, the calculations, models, and solutions I can be performed by a computer which stores all of the relevant information. The computer may also be connected to the relevant downhole tools (i.e. LWD tools) in order to provide the real-time capability of the method.
Although only a few exemplary embodiments of this invention have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the exemplary embodiments without materially departing from the novel teachings and advantages of this invention. Accordingly, all such modifications are intended to be included within the scope ofthis invention.
Although separate methods/techniques have been described hereinbefore, it i will be appreciated that some or all of the methods/techniques may be used in conjunction with one another.
Claims (9)
1. A method for determining the best production potential wells in a reservoir, I comprising: ^' IT "rxv function for the reservoir; determining a proaurlvl., a, proving the productivity proxy function for a variety of geological scenarios; and solving the productivity proxy function to determine the best production potential wells.
2. A method according to Claim 1, wherein the productivity proxy function is calculated using gridblock values of a plurality of parameters of a formation in Am is lo be formed. ; whic l a wel u".
3. A method according to Claim 2, wherein the parameters include at least one of the porosity, oil saturation, absolute permeability and pore pressure of the formation.
4. A method for combining two reservoir models, comprising: providing two different reservoir models; and using gradual deformation to combine the two models.
5. A method according to Claim 4, wherein the two models are gradually deformed using the equation: zl = cos (p)Z, + sin (P)Z2 Where Zl, Zl and Z2 are Gaussian normal space realisations of the combination of the models and the two models, respectively.
6. A method according to Claim S. further comprising a step of gradually deforming the result of the first gradual deformation with a third model.
7. A method for selecting the direction of well, comprising: obtaining production parameter measurements using a logging while drilling tool including a drill bit; and controlling the steering of the drill bit based on the production parameter measurements.
8. The method of Claim 7, wherein the controlling step comprises: updating a geostatistical model with the measurements; determining the optimum well path; and continuing drilling in the optimum well path.
9. The method of Claim 7, wherein multiple wells in a reservoir are placed in the reservoir to optimise production from the wellbore.
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US45048303P | 2003-02-27 | 2003-02-27 |
Publications (2)
| Publication Number | Publication Date |
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| GB0404261D0 GB0404261D0 (en) | 2004-03-31 |
| GB2398900A true GB2398900A (en) | 2004-09-01 |
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| Application Number | Title | Priority Date | Filing Date |
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| GB0404261A Withdrawn GB2398900A (en) | 2003-02-27 | 2004-02-26 | Identification of best production potential oil wells and identification of drilling strategy to maximise production potential |
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2006065915A2 (en) * | 2004-12-14 | 2006-06-22 | Services Petroliers Schlumberger | Geometrical optimization of multi-well trajectories |
| US20070179767A1 (en) * | 2006-01-31 | 2007-08-02 | Alvin Stanley Cullick | Methods, systems, and computer-readable media for fast updating of oil and gas field production models with physical and proxy simulators |
| FR2923855A1 (en) * | 2007-11-21 | 2009-05-22 | Inst Francais Du Petrole | Drill hole trajectory optimizing method for underground tank exploration and production field, involves determining that if trajectory of drill hole is modified using geological evolution schema, and modifying trajectory if required |
| WO2009079160A1 (en) * | 2007-12-14 | 2009-06-25 | Schlumberger Canada Limited | Optimizing drilling operations using petrotechnical data |
| GB2467214A (en) * | 2009-01-22 | 2010-07-28 | Schlumberger Holdings | Selecting an optimal well-bore trajectory while drilling |
| US8352226B2 (en) | 2006-01-31 | 2013-01-08 | Landmark Graphics Corporation | Methods, systems, and computer-readable media for real-time oil and gas field production optimization using a proxy simulator |
| GB2539739A (en) * | 2015-06-23 | 2016-12-28 | Petrochina Co Ltd | Method and apparatus for performance prediction of multi-layered oil reservoirs |
| US11680480B2 (en) | 2021-05-25 | 2023-06-20 | Saudi Arabian Oil Company | Multi-layer gas reservoir field development system and method |
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| US6101447A (en) * | 1998-02-12 | 2000-08-08 | Schlumberger Technology Corporation | Oil and gas reservoir production analysis apparatus and method |
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| US20020032550A1 (en) * | 2000-06-29 | 2002-03-14 | Ward Steven B. | Method for modeling an arbitrary well path in a hydrocarbon reservoir using adaptive meshing |
| EP1264961A1 (en) * | 2000-02-23 | 2002-12-11 | Japan Oil Development Co., Ltd. | Method of producing petroleum |
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| US6106561A (en) * | 1997-06-23 | 2000-08-22 | Schlumberger Technology Corporation | Simulation gridding method and apparatus including a structured areal gridder adapted for use by a reservoir simulator |
| US6101447A (en) * | 1998-02-12 | 2000-08-08 | Schlumberger Technology Corporation | Oil and gas reservoir production analysis apparatus and method |
| EP1264961A1 (en) * | 2000-02-23 | 2002-12-11 | Japan Oil Development Co., Ltd. | Method of producing petroleum |
| US20020013687A1 (en) * | 2000-03-27 | 2002-01-31 | Ortoleva Peter J. | Methods and systems for simulation-enhanced fracture detections in sedimentary basins |
| US20020032550A1 (en) * | 2000-06-29 | 2002-03-14 | Ward Steven B. | Method for modeling an arbitrary well path in a hydrocarbon reservoir using adaptive meshing |
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2006065915A3 (en) * | 2004-12-14 | 2006-08-03 | Schlumberger Services Petrol | Geometrical optimization of multi-well trajectories |
| US7460957B2 (en) | 2004-12-14 | 2008-12-02 | Schlumberger Technology Corporation | Geometrical optimization of multi-well trajectories |
| WO2006065915A2 (en) * | 2004-12-14 | 2006-06-22 | Services Petroliers Schlumberger | Geometrical optimization of multi-well trajectories |
| US8352226B2 (en) | 2006-01-31 | 2013-01-08 | Landmark Graphics Corporation | Methods, systems, and computer-readable media for real-time oil and gas field production optimization using a proxy simulator |
| US20070179767A1 (en) * | 2006-01-31 | 2007-08-02 | Alvin Stanley Cullick | Methods, systems, and computer-readable media for fast updating of oil and gas field production models with physical and proxy simulators |
| FR2923855A1 (en) * | 2007-11-21 | 2009-05-22 | Inst Francais Du Petrole | Drill hole trajectory optimizing method for underground tank exploration and production field, involves determining that if trajectory of drill hole is modified using geological evolution schema, and modifying trajectory if required |
| GB2488460B (en) * | 2007-12-14 | 2013-02-27 | Geco Technology Bv | Optimizing drilling operations using petrotechnical data |
| GB2468812A (en) * | 2007-12-14 | 2010-09-22 | Geco Technology Bv | Optimizing drilling operations using petrotechnical data |
| GB2488460A (en) * | 2007-12-14 | 2012-08-29 | Geco Technology Bv | Optimizing drilling operations using petrotechnical data |
| GB2468812B (en) * | 2007-12-14 | 2012-11-07 | Geco Technology Bv | Optimizing drilling operations using petrotechnical data |
| WO2009079160A1 (en) * | 2007-12-14 | 2009-06-25 | Schlumberger Canada Limited | Optimizing drilling operations using petrotechnical data |
| US9638830B2 (en) | 2007-12-14 | 2017-05-02 | Westerngeco L.L.C. | Optimizing drilling operations using petrotechnical data |
| GB2467214B (en) * | 2009-01-22 | 2012-04-18 | Schlumberger Holdings | Selecting optimal wellbore trajectory while drilling |
| GB2467214A (en) * | 2009-01-22 | 2010-07-28 | Schlumberger Holdings | Selecting an optimal well-bore trajectory while drilling |
| GB2539739A (en) * | 2015-06-23 | 2016-12-28 | Petrochina Co Ltd | Method and apparatus for performance prediction of multi-layered oil reservoirs |
| GB2539739B (en) * | 2015-06-23 | 2020-04-01 | Petrochina Co Ltd | Method and apparatus for performance prediction of multi-layered oil reservoirs |
| US11680480B2 (en) | 2021-05-25 | 2023-06-20 | Saudi Arabian Oil Company | Multi-layer gas reservoir field development system and method |
Also Published As
| Publication number | Publication date |
|---|---|
| GB0404261D0 (en) | 2004-03-31 |
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