May 2009 The Geomodeling Network Newsletter


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May 2009 The Geomodeling Network Newsletter

  1. 1. The Geomodeling Network Newsletter May 2009 H as it really been 2 months since the last Geomodeling Network newsletter? Based on the number of (kindly) reminder emails I received in my inbox this morning asking where the May edition is, I think it must be! This month’s newsletter is one of the best yet and has contributions from E&P companies, software vendors as well as a great discussion taken from our online forum. Talking of emails, you may have spotted that this newsletter does not have the Blueback Reservoir watermark running through it. The reason for this is that a couple (2) of you recently contacted me requesting that this should be removed and thus making it easier to read. Never being one to shirk from my responsibilities, (especially when the elderly are concerned), I have removed the offending watermark – the jumbo-print version should be available for the next release :o) Anyway, for the next 20 or so minutes sit back, relax, grab a cup of coffee and enjoy the latest offering of the Geomodeling Network newsletter. Many thanks to those members who took the time to contribute the interesting articles contained in this version, it’s very much appreciated. And finally, as our network quickly approaches the 800 members mark, I hope some of you will take inspiration from the articles and discussions of this (and previous newsletters). If you do get the urge to make a contribution for future versions, drop me an email with your thoughts – the next one is not due out until the end of July 2009, so you have plenty of time! Mitch Sutherland Page 1 The Geomodeling Network – Sponsored by Blueback Reservoir
  2. 2. The Geomodeling Network Newsletter May 2009 Table of Contents 1. How “Good Looking” are your Faults? The first of a series of short articles that will look at faults and fault geometry, using straightforward structural geological principles. Titus Murray & Merrick Mainster - FaultSeal Pty Ltd Page 3 2. Rock types and flow zones Practical methods for defining rock types, their use in property models and flow zone characterisation. Steve Cannon – Senior Staff Geologist at DONG E&P UK Ltd Page 8 3. The Petrosys Plug-in for Petrel The Petrosys Plug-in for Petrel allows geoscientists and engineers utilizing Petrel to present their insight, integrated with information from many other data sources, through the Petrosys map interface. Scott Tidemann, Global Sales & Marketing Manager at Petrosys Page 18 4. What problems have you had using horizontal well data within your models? This was a question placed on the Geomodeling Network discussion forum which generated a fair bit of response from our members Brian Casey – Geological Consultant at Oxy Page 19 5. EAGE 2009 This year’s event is in Amsterdam. Page 26 6. The Blueback Toolbox (a Petrel plug-in) – update Page 27 Page 2 The Geomodeling Network – Sponsored by Blueback Reservoir
  3. 3. The Geomodeling Network Newsletter May 2009 Member Articles, Reviews & Questions The most exciting phrase to hear in science, the one that heralds new discoveries, is not 1. How “Good Looking” are your Faults? 'Eureka!' but 'That's Titus Murray & Merrick Mainster, FaultSeal Pty Ltd funny...' This is the first of a series of short articles that will look at faults and fault Isaac Asimov geometry, using straightforward structural geological principles. We aim to help your understanding by showing examples from our software application FaultRisk that we use on a daily basis when assessing fault seal capacity in our consulting business. Within a faulted 3D model it is important to understand the uncertainty related to the position and throw of the faults in the model. In many of the models we come across in consulting projects we see that the structure is “watertight” it is also in some cases geologically improbable. This is generally due to faults not being imaged in seismic but they are actually inferred from the absence of a reflector. Due to the inherent problems of seismic fault imaging, the following uncertainties arise in: Position of the footwall; Throw on the fault; Shape of the fault; Stratigraphic thicknesses; Growth across the fault; Tectonic inversion. The best way to define fault displacement is based on detailed well correlation but wells are generally only drilled on one side of the fault! Throw Profiles The displacement on a fault should vary systematically across and down the fault plane. Faults should have a point of maximum displacement with a zero throw at the tips of the fault, the throw diminishes radial from the Page 3 The Geomodeling Network – Sponsored by Blueback Reservoir
  4. 4. The Geomodeling Network Newsletter May 2009 maximum displacement. See the diagram below which shows the footwall and hanging wall of a faulted stratigraphic layer. In most faulted reservoir cases the lateral variation of displacement is the key factor and when looking at the displacement it is common to review these profiles. If the profile is not consistent is it likely that the fault is segmented or there is another problem with its interpretation in some way as shown in the diagram below. Gulfax Field Examples As an example of this type of analysis we will look at the Gulfax model that ships as a demonstration example set in Petrel. “The use of solar energy has not been opened up Fault polygons have been made from the Petrel grid and imported into because the oil industry FaultRisk™. The picture below shows the FaultRisk™ mapping interface, with a structure contour map loaded. does not own the sun.” Ralph Nader Page 4 The Geomodeling Network – Sponsored by Blueback Reservoir
  5. 5. The Geomodeling Network Newsletter May 2009 As the fault polygons are made from the Petrel grid it includes a set of XYZ coordinates that can be split into hanging wall (down-thrown) and foot wall (up-thrown) lines. This diagram shows either side of the fault as a set of points that we can edit or modify. “The past history of our globe must be explained by what can be seen to be happening now. No powers When reviewing the displacement profile in FaultRisk™ any potential anomalies in the profiles can easily be identified. In the case below a are to be employed that are “Bow Tie” displacement can be seen. not natural to the globe, no action to be admitted except those of which we know the principle.” James Hutton Page 5 The Geomodeling Network – Sponsored by Blueback Reservoir
  6. 6. The Geomodeling Network Newsletter May 2009 Looking at another fault from the Petrel model, segmentation along the fault’s length can be observed. “The oil can is mightier than the sword.” Everett Dirksen In this fault there are some anomalous cross cutting faults in the hanging wall Page 6 The Geomodeling Network – Sponsored by Blueback Reservoir
  7. 7. The Geomodeling Network Newsletter May 2009 One has to look out for engineers -- they begin with sewing machines and end up with the atomic bomb. Marcel Pagnol that generate a complex displacement profile. In an ideal world one would review the seismic data to look for fault linkages and branch lines. Pragmatically in a fault seal analysis the fault can be split into two or three segments to look for leak points from the compartment. This style of analysis is quick and easy to do and will greatly improve the quality, and accuracy of your 3D models and help you amend the Petrel model with good looking faults. When investigating the probability of fault leakage and/or across fault flow this analysis is a vital step in the workflow. The next article will look at throw length ratios and fault segmentation. If you have any queries on this article or fault seal issues please contact us at and/or visit our website Page 7 The Geomodeling Network – Sponsored by Blueback Reservoir
  8. 8. The Geomodeling Network Newsletter May 2009 2. Rock types and flow zones Steve Cannon, DONG E&P UK Ltd This article attempts to present some practical methods for defining rock types, their use in property models and flow zone characterisation. Rock typing is common practice in Middle Eastern carbonate reservoirs because they tend to be extensively cored with vast conventional and special core analysis datasets used for petrophysical interpretation. Each petrophysical data point will usually be associated with a petrographic description based on thin section analysis, and often SEM data as well: such comprehensive and consistent datasets are less common in clastic reservoirs. Such detailed studies can have their downside however, especially when an over-enthusiastic sedimentologist defines 85 lithotypes in a field where 40% are in non-reservoir sections and the rest just subsets of the about eight major petrophysical rocktypes; some simplification is required before they can be used for reservoir modelling. The list below attempts to define some of the nomenclature commonly in use to define different levels of description: Lithofacies/lithotype: the character of a rock described in terms of its visible components; structure, colour, mineral composition, grainsize, sorting etc: the smallest scale purely geological description of a rock. Facies: a mappable unit of rock that forms under certain conditions of sedimentation, reflecting a particular process or environment. A facies may be defined by the types of component lithofacies and is an interpretation rather than a description of any unit. Facies association: a group of facies that together define a sedimentary unit with a common depositional setting; again this is an interpretation based on an understanding of the different components and their process of deposition. The recognition of a specific facies and facies associations defines the depositional environment of a group of rocks. Rock type: a rock with a well defined porosity network leading to a unique porosity-permeability relationship and saturation profile: the Page 8 The Geomodeling Network – Sponsored by Blueback Reservoir
  9. 9. The Geomodeling Network Newsletter May 2009 porosity network is the result of a predictable depositional and diagenetic history. Petrofacies/petrotype: terms used to integrate petrophysical relationships with lithofacies/lithotype descriptions: petrophysics in a geological context. The term petrofacies is also used by many researchers to define only mineralogical/petrographical classes. Flow unit/flow zone: a mappable unit of the total reservoir volume within which geological and petrophysical properties that effect fluid flow are internally consistent and predictably different from other reservoir rock volumes. Other terms used are hydraulic flow unit and genetic hydraulic unit that attempt to standardise or map different petrotypes within discrete depositional environments. A flow unit, while ideally being related to geologically defined depositional package, may not correspond with discrete facies boundaries and may not be laterally and vertically contiguous. Essentially all these terms fall into two categories, either purely descriptive or largely interpretative: both are important to understand when characterising a reservoir, especially for dynamic modelling. But what does all this mean to the different subsurface disciplines? To a geologist, a flow unit is a discrete facies object such as a channel or a carbonate shoal; to a petrophysicist it is correlatable zone with similar petrophysical properties; to a reservoir engineer it is a layer in a model that has a consistent and predictable dynamic response to flow in the simulator. To a reservoir modeller it is all of these things! Petrophysicists and engineers still often think in terms of simplified zone average property models as the way forward in determining in-place volumes and reserve estimates, hence the concept of discrete zones rather than the more stochastic idea of reservoir objects with common petrophysical properties. Amaefule et al (1993) developed a method of reservoir description using core and log data to identify hydraulic flow units and predict permeability in un-cored intervals. This method has been used in many ways to define both rock types and flow units, and is based on well founded experimental methods developed over many years. The method is depends on understanding the pore geometry of a rock and relating this to the mean hydraulic unit radius of the pore throats: mean hydraulic Page 9 The Geomodeling Network – Sponsored by Blueback Reservoir
  10. 10. The Geomodeling Network Newsletter May 2009 “It puzzles me how they radius realtes porosity, permeability and capillary pressure know what corners are measurements. A similar approach was published by Kolodzie (1980) good for filling stations. based on work down by Winland of Amoco. Pore geometry is a function of the mineralogy and texture of a rock, which means that different Just how did they know lithofacies may have similar pore throat attributes: in this way different gas and oil was under facies may belong to the same rock type. there?” Dizzy Dean Theoretical background The basis for all rock typing is Darcy's Law and Pouseille's theory for capillary bundles under laminar flow: these are used to derive a relationship between porosity and permeability for a capillary bundle. 2 2 e r2 e r r e mh k 2 2 2 8 2 2 2 Equation 1 The mean hydraulic radius is function of grain surface area (Sgv) and effective porosity. 2 e 1 e S gv r 1 e rmh 1 e Equation 2 Carmen and Kozeny obtained the following relationship by substituting for mean hydraulic radius. 3 e 1 k 2 2 2 1 e Fs S gv Equation 3 where F is a shape factor, which for a circular cylinder is 2. In real rocks the Kozeny constant (Fsτ2) can vary between 5 to100. Because it is a quot;variable constantquot;, varying between hydraulic units in a reservoir a Page The Geomodeling Network – Sponsored by Blueback Reservoir 10
  11. 11. The Geomodeling Network Newsletter May 2009 further mathematical transformation is required: dividing both sides by effective porosity and taking the square root of both sides the following relationship is derived: k e 1 e 1 e F s S gv Equation 4 where permeability, k is in μm2. Presenting permeability in millidarcies, then a Reservoir Quality Index (RQI) can be defined: k RQI 0.0314 e Equation 5 e The Flow Zone Indicator (in μm) is related to RQI by the term z 1 e where φz is the ration between pore volume and grain volume. Thus Flow Zone Indicator, 1 RQI “Sometimes, I guess there FZI Fs S gv22 Equation 6 z just aren't enough rocks.” Forrest Gump On a log-log plot of RQI against φz (PhiZ) all samples with a similar FZI will lie on a straight line with unit slope; samples with other FZI values will lie on parallel lies. Samples that lie on the same straight line have the same pore throat attributes and therefore constitute an hydraulic unit, even though they may represent different facies. Permeability can also be calculated from this relationship using the appropriate hydraulic unit or FZI relationship: 3 2 e k 1041( Fzi ) 2 1 e Equation 7 Page The Geomodeling Network – Sponsored by Blueback Reservoir 11
  12. 12. The Geomodeling Network Newsletter May 2009 Corbett et al (2005) present an excellent case study of braided river sandstones which goes through the workflow and offers some interesting interpretations of the effect of grain-size and sorting on capillary pressure measurements. Workflow There are two main phases to the process; firstly analysis of core data to determine the various petrophysical relationships and secondly application within a reservoir modelling workflow where the relationships are integrated with log data. Data analysis 1. Routine core porosity and permeability data should be characterised in terms of an appropriate lithofacies or facies scheme. It is essential to have a consistent dataset, and any outlying data removed. To ensure data integrity later in the process it is important that depth correspondence between core and log data is accurate. The data should be plotted in the normal way and general porosity permeability relationship established for one or two dominant facies groups, if sufficiently different: these can be used to produce a permeability curve from the wireline log derived porosity. 2. In a spreadsheet, the flow terms PhiZ, RQI and FZI should be calculated for each core analysis point and sorted in order of decreasing values of FZI. As a first approximation the results can be plotted as two groups, greater than and less than a value FZI equal to one on a log-log plot of PhiZ against RQI: the better rock types will be greater than one. Any recognizable rock type variations will be apparent as parallel groups of data. Generally this will reveal the better rock types, and correspond with those that have previously been identified from the facies breakdown as better quality. 3. Plotting porosity against permeability classified by FZI will allow the generation of a predictive permeability relationship for each rock type. The quality of the predicted permeability can be compared with the original core permeability using a Q-Q plot: an organised plot of comparing the same points Alternatively the relation shown in Equation Page The Geomodeling Network – Sponsored by Blueback Reservoir 12
  13. 13. The Geomodeling Network Newsletter May 2009 quot;The box said 'Required 7 can be used to predict permeability and again be compared with the Windows 95 or better'. So, original data. I installed LINUX.quot; Application 4. Within the reservoir modelling package calculate the FZI terms using the log derived porosity and permeability. Using a property calculator develop a series of logical statements to classify each of the rock types according to the core defined scheme. Visually check the results against cored intervals to ensure reasonable correspondence and consistency: anything over an 80% correspondence is acceptable; 60 to 80% is a common result. The key is to ensure that the extremes are captured, especially low permeability layers that could form barriers/baffles, and high permeability streaks that might dominate flow in the reservoir. 5. Block the data to grid scale and check that the detailed description of rock types is retained in the upscaled well data. 6. Either use the rock types directly to populate a model zone or build a detailed facies model and populate each facies/object with the appropriate rock type. Reservoir properties, porosity, permeability and water saturation can then be distributed according to the rock type relationship defined in the analysis stage. When modelling S w, a direct link to core-based capillary pressure data can be established with respect to height above a local or regional free water level. The value of SCAL data cannot be over-emphasised; capillary pressure measurements should be representative of the different rock types recognised. A data base of as little as ten samples can be sufficient to characterise a series of reservoir rocktypes; fifty is even better! Other applications In an attempt to standardise all possible lithofacies in terms of hydraulic flow zones, Corbett & Potter (2004) used the Amalaefule methodology to create 10 Global Hydraulic Units (Figure 1) that utilised fixed FZI lower boundaries against which they plotted core derived porosity and permeability from different depositional environments. This is an alternative to the clustering method described above and relies on Page The Geomodeling Network – Sponsored by Blueback Reservoir 13
  14. 14. The Geomodeling Network Newsletter May 2009 mapping these predetermined classes on the porosity-permeability crossplot; they coined the term petrotyping. Because the rock types in the petrotyping FZI Global Hydraulic Unit approach are quot;globalquot; in the sense they are predetermined, the base map can be used 48 10 to determine whether a reservoir 24 9 comprises one or more rock types. 12 8 Different reservoirs can be compared 6 7 quickly using this method as well as a rapid 3 6 technique for screening and selecting 1.5 5 samples for further analysis. This method 0.75 4 can also be used to decide the appropriate 0.375 3 scale of cells needed to capture the 0.1875 2 porosity and permeability distribution in a 0.0938 1 reservoir model; ideally at the smallest scale, each grid block should contain an individual global hydraulic unit. Using an example data set from a series of braided river deposits, Corbet et al (2005) demonstrated the workflow output. Figure 2 shows the results of the PhiZ:RQI log-log plot and the four hydraulic units indentified with the corresponding FZI values. Figure 3 recasts the data in terms of a familiar Phi:K plot with the predictive relationships calculated for each hydraulic unit or rocktype. Figure 4 shows the result of grainsize and sorting analysis for each hydraulic unit/rock-type: grain size shows a clear contrast whereas sorting has little impact. Conclusions Rock-typing can be a challenging process, but ultimately very satisfying: engineers would much rather talk about a rock-type than a lithofacies because it infers some sort of numerical consistency, even when it is directly related to depositional unit! But these methods must be used Page The Geomodeling Network – Sponsored by Blueback Reservoir 14
  15. 15. The Geomodeling Network Newsletter May 2009 quot;If at first you don't with care; a detailed understanding of what rock-types might be expected succeed; call it version and how they can be grouped is required. Rock-typing works when the 1.0quot; geologist is in control of the input and the output, especially when that output is going to be used to populate a static model. References Enhanced Reservoir Description: Using Core and Log Data to Identify Hydraulic (Flow) Units and Predict Permeability in Uncored Intervals/Wells: Amaefule et al, SPE 26436 (1993) Use of Flow Units as a Tool for Reservoir Description: A Case Study: Guangming et al, SPE 26919 (1995) Permeability Prediction by Hydraulic Flow Units – Theory and Application: Abbaszadeh et al, SPE 30158 (1996) Early Interpretation of Reservoir Flow Units Using and Integrated Petrophysical Method: Gunter et al, SPE 38679 (1997) Petrotyping: A basemap and atlas for navigating through permeability and porosity data for reservoir comparison and permeability prediction: Corbett & Potter, SCA2009-30 (2004) (Society of Core Analysts) The geochoke test response test response in a catalogue of systematic geotype well test responses: Corbett et al, SPE93992 (2005) Page The Geomodeling Network – Sponsored by Blueback Reservoir 15
  16. 16. The Geomodeling Network Newsletter May 2009 Figures Figure 1: Global hydraulic units as applied to a shallow marine sandstone Plot of RQI vs. Phi(z) for well X2 10 HU-1 FZI = 2.509 HU-2 FZI = 1.233 HU-3 FZI = 0.685 HU-4 FZI = 0.323 1 RQI 0.1 0.01 0.01 0.1 1 Phi(z) Figure 2: Log-log plot of PhiZ against RQI used to define different hydraulic units Page The Geomodeling Network – Sponsored by Blueback Reservoir 16
  17. 17. The Geomodeling Network Newsletter May 2009 Crossplot of (k vs. Phi) for different Hydraulic Units, Well X2 1000 100 10 1 k, mD 0.1 0.01 0.001 0.0001 0 0.05 0.1 0.15 0.2 0.25 Phi, frac. Figure 3: Porosity-permeability cross-plot broken down by hydraulic units Grain Size and Sorting for each HU 4 3.5 Grain Size and Sorting 3 2.5 , Phi Units 2 1.5 1 0.5 0 G7HU1 G7HU1 G7HU2 G7HU3 G7HU4 G7HU5 Hydraulic Units Figure 4: Impact of grainsize (violet) on definition of hydraulic units Page The Geomodeling Network – Sponsored by Blueback Reservoir 17
  18. 18. The Geomodeling Network Newsletter May 2009 When NASA first started sending up astronauts, they 3. The Petrosys Plug-in for Petrel Accelerate exploration, improve productivity. discovered that pens Get collaborative mapping results more easily. would not work in zero Scott Tidemann, Petrosys gravity. To combat this problem, NASA The Petrosys Plug-in for Petrel allows geoscientists and engineers utilizing Petrel to present their insight, integrated with information from many scientists spent a decade other data sources, through the Petrosys map interface. This enables asset and $12 million teams to accelerate decision making through consistent use of Petrosys developing the ball point mapping and surface modelling as their focus moves from the regional pen that writes in zero 8 8227 2799 > Americas: 1888 PETROSYSthe reservoir scale. 6555 > Calgary: +1 403 537 Australasia: +61 overview through the field to > Europe: +44 141 420 gravity, upside>down, 5600 Web: Harness the power of the Petrosys plug-in for Petrel to: underwater, on almost any surface including Start Petrosys mapping, surface modelling or 3D viz from icons in the glass and at Petrel application. temperatures ranging Effectively map and present from below freezing to opportunities by directly over 300C. incorporating Petrel 3d seismic horizons and 3d model grids using When confronted with Petrosys map colorfill and 3D viz displays. Compute and map the same problem, the contours for the structures. Russians used a pencil. Integrate decision making, using a range of other Petrosys display options to overlay geoscience and Your vital information comes together, with cultural data from OpenWorks, both applications working side by side to support collaborative workflows and GeoFrame, ArcSDE, SMT, PPDM and understanding. many other data sources directly accessible through Petrosys. Map in many coordinate reference systems (CRS); the underlying CRS of maps can be switched to effectively map surfaces in regional interpretation situations. Page The Geomodeling Network – Sponsored by Blueback Reservoir 18
  19. 19. The Geomodeling Network Newsletter May 2009 Use Petrel seismic data as a direct input data source in Petrosys gridding workflows. Effectively combine 2d & 3d interpretive workflows, using Petrosys surface modelling functions such as volumetrics and well tie. Use direct data inputs and efficient import/export facilities, while creating repeatable workflow processes. Import faults, model grids/horizons and seismic data to Petrosys. Export Petrosys grids directly into Petrel projects. Petrosys effectively and efficiently handles the * Petrel is a mark of Schlumberger. mapping of Petrel models, including faults, colorfill display and posting of surface values. 4. What problems have you had using horizontal well data within your models? Taken from a discussion posted on the Geomodeling Network discussion forum. Brian Casey, Oxy Many modern fields are dominated by horizontal wells, but modelers are reluctant to use this data due to: - Zonal bias - Imprecise tops - Imprecise well path locations - Other? Page The Geomodeling Network – Sponsored by Blueback Reservoir 19
  20. 20. The Geomodeling Network Newsletter May 2009 We handle zonal bias through debiasing workflows. Imprecise well paths and imprecise tops can be managed through careful data selection and the use of Zone Log. Do group members have other modeling issues and solutions for the use of horizontal well data in their models? Brian Li bin – geologist, Tiandi Energy The well path locations are relatively reliable, the tops could be corelated using chosed logs. the reservior quality and facies also could be analysed and evaluated. all these information can be used in modeling Samir Benmahiddi – production geologist, Sonatrach the way I see it, regardless the zonal bias, continuous lateral data sampling from a decent number of horizontal wells, well distributed throughout the reservoir, should help to check / adjust the main reservoir attributes anisotropy and variograms, should provide hints on lateral heterogeneity and deposits architecture, particularly if you come to run imaging tools (which is quite difficult I agree but still less than coring, and always worth to try) with intent to collect some imagefacies and dipmeter data allowing much better facies mapping at least. But still it should be framed with a robust sedmentary and stuctural conceptual model and avoid mixing fractures/faults with simply a lamination of high contrast steeply dipping on image log ! Otherwise, tops issue is only important when target is a tiny window and you add up uncertainties on depth because the cable length stretch and such ...which is less likely to happen. Anders Ørskov Madsen –Senior Consultant, Blueback Reservoir I use horizontal well data regularly for building reservoir models. Intead of using the whole horizontal well I have in some cases simply cut out a section where I had high confidence to which zone/stratigraphic unit it was drilled through. In other case I have made a pseudo trajectory for the well due to the depth uncertainty for long horizontal wells, in order to place it correctly in the model. Petter Abrahamsen –Research Director, NCC We are curently making a software for getting the surface consistent with the zonation in wells. This assumes the zonation is correct. A (vertical) correlated uncertainty on the well path location is possible to include but hasn't been prioritized so far. The easy part is to ensure that surfaces cross the well trajectories at the correct locations. The hard part is to ensure that they do NOT cross the trajectories at the wrong locations. Page The Geomodeling Network – Sponsored by Blueback Reservoir 20
  21. 21. The Geomodeling Network Newsletter May 2009 Here is a link with some more details: “It is only when they go The introduction in the manual (pdf) gives a good overview. wrong that machines remind you how powerful We essentially use kriging to interpolate the well picks. The challenge is to use the additional constraints from the horizontal wells in a consistent they are” manner so that we can provide realistic uncertainty description in terms of simulations (Monte Carlo) and prediction errors. Clive James Thorbjorn Pedersen –Chief Geoscientist, Oxy Interesting to see Madsen's approach using a pseudo trajectory to place the horizontal well quot;correctlyquot; in the model. What is the depth accuracy of the model in the first place? Do you have vertical well tops near the toe end of the Horizontal trajectory? Or is the depth model for horizons constrained by depth converted seismic horizons? In that case what is bigger, the depth uncertainty to the depth conversion or the depth to the toe end of the horizontal well? Holger Rieke –Principal Geologist, StatoilHydro Peter, Could you please elaborate why you choose kriging for the interpolation between well picks? Thickness of reservoir zones or the depth surface to well tie are not accurately computed using kriging unless you select a large variogram i.e. at least half the distance of well spacing. The kriging result then resembles convergent or global b-spline (depending on which software you prefer). Those algorithms are in my opinion more suitable to extrapolate these types of data. Petter Abrahamsen –Research Director, NCC The reason we using kriging is to be able to quantify uncertainty. The uncertainty is (indirectly) described by the shape of the variograms and the standard deviations (sill). The depth uncertainty is quantified by prediction error maps (kriging error) or by a set of simulated realizations depending on usage. The shape of the variograms can be chosen so that the result is similar to spline interpolation. This is visually appealing but rarely realistic in natural phenomena. Moreover, the variogram shapes and the sill can be estimated from well picks so that we can confirm consistency between interpolation method and data. We never use zone thickness data directly since these are only available in vertical wells. We always use surface depth data (well picks) and Page The Geomodeling Network – Sponsored by Blueback Reservoir 21
  22. 22. The Geomodeling Network Newsletter May 2009 “Geologists don't wrinkle, constraints on the surfaces in the horizontal sections. This is to avoid the use of pseudo-data that are hard to make and even harder to justify. they show lineation” Finally note that we consider many surfaces, and the intervals (zones) between them, simultaneously. Variograms for all interval thicknesses are specified. This can amount to 20 or more different variograms. The big advantage of considereing all surfaces simultaneously is that well data influence surfaces below and above. In particular horizontal sections in thin zones lock surfaces above and below very accurately. I hope this didn't obscure things rather than clarified them :-). Thorbjorn Pedersen –Chief Geoscientist, Oxy I like Petter's approach assessing all surfaces and their associated variograms at once. This allows for a holistic look into the relationship between surfaces, their controlling data and subsequently the total structural/stratigraphic architecture. However, dependent upon the well density and geologic setting, I would still maintain that from time to time usage of zone thickness data may be required to maintain a morphology that is in line with the respective sedimentological setting defined by core data or infered from regional context. These cases generally tend to be used in models of fields in their early stages of development. Tim Wynn –Senior Reservoir Geologist, AGR-TRACS We have built several models with a large number of horizontal wells and found problems with the use zone logs option in Make Zones (zone logs not honoured, random spikes etc). In a large 'layer cake' stratigraphy reservoir we used pseudo tops shifted by the requisite isochore thickness, this was quite successful, particularly a there was no seismic data constraint. However, care had to be taken around the faults so it was quite time consuming. We have considered using psuedo well paths (polygons) clipped to the required zones and shifted up by an arbitrary amount. These polygons could then used as part of the input data for the surface. These would only be required where the surface cuts a well where it shouldn't Both these options are quite time consuming and result in pseudo data so they are not perfect but they do ensure the surfaces honour the zone logs. Page The Geomodeling Network – Sponsored by Blueback Reservoir 22
  23. 23. The Geomodeling Network Newsletter May 2009 To the optimist, the glass Anders Ørskov Madsen –Senior Consultant, Blueback Reservoir Answer to Thorbjorn question about 'pseudo trajectory' is half full. To the pessimist, the It depends on the well density. In cases where I have shifted the horizontal well trajectories it has usually been on fields with a bunch of glass is half empty. vertical or deviated wells near the hz wells highlighting the depth To the engineer, the glass uncertainty of the horizontal wells. In many cases the hz wells have been more than 25000 ft MDRT with an depth uncertainty of +/- 50 ft TVD at TD is twice as big as it needs (e.g. chalk wells in the Danish North Sea), so it has been neceassry to to be shift these wells in order to use them as input in a 'base case' structural depth model. Petter Abrahamsen –Research Director, NCC Note that we can use trends for the zone thicknesses so we can impose interpreted sedimetological trends. The trends can be globally adapted to data (well picks and trajectories) or kept untouched. The simplest trend is of course a constant (e.g. 20m). The kriging essentially interpolates the difference between the trends and the observed data. Trends can also include velocity fields, travel times, anomalies, pinch outs and all kinds of weird geological features but thats another story. As Tim Wynn comments, the biggest challenges are really areas close to faults where simple layer cake models can fail. Normal faults will squeze zone thicknesses to zero and this requires special care to avoid opening up the faults. Reverse faults are even worse since this requires multi-z values at surface locations near the fault. This is currently not handled but we are discussing how to integrate surface and fault models in a proper and efficient way. Brian Casey –Geological Consultant, Oxy Further to Anders comments, it is not just extremely long reach horizontal wells with + 50 ft TVD error that should concern us. Horizontal well placement is relative to our grid dimension, so both the geologist and simulation engineer should be concerned. Even if no static properties are attributed to a horizontal well, dynamic performance must still be matched. If the well is mis-located in the grid the engineer will make the adjustments. Better to be pro-active… Thank you to Petter and everyone else for contributing to this discussion. We may wish to test some of these processes for placing horizontal wells “correctly” in the model. Also, I had not previously considered Petter’s approach toward horizontal well placement uncertainty. I can see a lot of applicability, in both the static and dynamic modeling. There is considerable support for using the static properties of horizontal Page The Geomodeling Network – Sponsored by Blueback Reservoir 23
  24. 24. The Geomodeling Network Newsletter May 2009 wells, and as Samir observed, these wells provide information on lateral heterogeneity and depositional architecture. [that we might not observe in the vertical wells.] How we handle that lateral reservoir data may need further discussion. Petter, perhaps you would like to further address handling multi-z values in the case of reverse faults (and recumbent folding), and the proper integration of fault and surface data. I would be very interested in your thoughts and approach, but do not wish to bury that discussion within one about horizontal well placement. It deserves its own topic. Juan Cottier –Subsurface Manager, Blueback Reservoir 2 ideas to add in here of a purely pragmatic nature: Firstly, I have built all sorts of models (in PETREL) using horizontal wells (West African, UK, Danish and Norwegian producing fields) and have found that from quot;get the job donequot; approach that if one takes the MAKE HORIZONS and MAKE ZONES steps slowly and try to achieve incrementally improving results then a decent job can be done in most cases. In PETREL I tend to turn off the quot;use in geomodelingquot; option for most horizontal wells and repeat and repeat layer by layer slowly turning on each well or even each individual well top. This allows simple QC of the results and the quot;problem childrenquot; can be more easily identified. Secondly, those of us who have been around more than 10 years have seen how deviation surveying has changed and how so much more confidence can be put upon surveys. However, MWD surveys still do not get close to a wire/slickline survey in terms of accuracy so it is worth checking where you survey comes from. Also if drilling we'll often get a MWD survey and then some days later there maybe a wireline survey ... so has the project trajectory been updated? Anoother little wrinkle I came across was the elipses of uncertainty on MWD surveys in particular. As they are based upon Hall's Effect the uncertainty varies in geographical regions and azi/inclination. In the Ivory Coast we were drilling horizontal wells to the south, running along the earth's magfield and the lateral uncertainty was huge. Similar thing for UK north sea t 60 degrees (ish) Right ... I'm dragging on a bit, I'll stop now. Keith Milne –Petroleum Geologist This subject has generated a lot of discussion because we regularly come across fields with a mixture of vertical and horizontal wells. Regarding the point about producing a sensible zonation, lets not forget Page The Geomodeling Network – Sponsored by Blueback Reservoir 24
  25. 25. The Geomodeling Network Newsletter May 2009 that the well picks and position of the horizontal well takes more quot;If GM had kept up interpretation that a vertical well and there may be more than one with technology like the alterative. I do not expect any software to be able to solve the problem computer industry has, without some additonal data points to control the zonation, especially if seismic control is poor or if the borehole passed through a fault. If logs we would all be driving have been run that enable dip to be estimated, then this will assist in $25 cars that got 1000 determining the zones along the well. One approach is to make a cross MPG.quot; section before trying to model - this is what would be typically done during actual drilling of the well, using all available data sources. Bill Gates Knut Midtveit –Sales Manager, Roxar I have seen many attempts to solve the problem of handling horizontal wells and getting the model to honour the well data including zonelog in horizontal wells. This range from manual thus very tedious approach to clever scripting methods, to what I feel is a more holistic approach that Peter Abrahamsen talk about, so I look forward to that. Roxar has been including adjust model to zonelog functionality for a couple of years, and we are now seeing it being successfully use on some pretty large fields with many horizontal wells. I find it interesting to observe that some companies focus a lot on this and spend lot of time getting the model right in order to plan wells optimally, and others accept that no one has a good solution. Furthermore some companies regularly shift well positions manually though, and others object strongly to the concept of shifting the well position. Personally I hope that we will get a solution where you consider all your data with a certainty and allow both seismic envelopes and wells to be changed according to the uncertainty of each data type. Until we have a such a solution try the adjust to zone log in RMS, and yes it can handle faults to. Ahmad Nazhri Mohd Zain –Geological Modeler, Saudi Aramco How do we handle the large upscaling issue with regards to the horizontal wells?. I assume that having a 25m x 25m grid lateral grid dimensions is acceptable to take into account the horizontal section of the wells, thus for a 1km horizontal section, you will have around 40 grid cells. I work with Ghawar and I cannot have anything smaller than 250m x 250m grid spacing otherwise my static model will be in the hundreds of millions cells. How do I handle 6000 data points (6 inch sampling rate) in a 1km horizontal section? That 1km will be blocked into 4 grid cells only. You will Page The Geomodeling Network – Sponsored by Blueback Reservoir 25
  26. 26. The Geomodeling Network Newsletter May 2009 not be able to compare the blocked wells to the raw dataset as the horizontals will introduce a severe bias due to the amount of samples in the horizontal sections. This is a major issue at the moment in our modeling group. Petter Abrahamsen – Research Director, NCC For surface modelling this is not a big problem in practice since the well geometry is very regular. Sampling the well at approximately the grid spacing is sufficient in our experience. We can go back and check all the data points although this is hardly necessary (in our experience). We have tested our approach on Troll (North Sea) which is a giant field. But working with Ghawar is of course an even greater challenge due to the volume of data. It would be nice to do a practical test on such a huge field... Upscaling for petrophysics is of course a different issue since comparing e.g. permeability on plug scale and on modelling scale is non-trivial. 5. EAGE 2009 – Amsterdam 8th to 11th June I am sure that a lot of you will have attended, exhibited and indeed presented at previous EAGE’s. In the past, these conferences have been held in fantastic cities such as Rome, Vienna and Madrid, as well as Leipzig. The event organizers have chosen another great city to host this year’s event, with Amsterdam being the chosen one for 2009. There are many things that pop into my mind as I think about Amsterdam (notably canals and tulips and a certain brewery). However, distractions aside, the EAGE is shaping up to be quite an event, not least for Blueback Reservoir. Throughout the entire conference, Blueback will be exhibiting at stand #2538 where we will have a number of staff available to discuss geomodeling consulting opportunities, Bridge and the Blueback Toolbox, software development on the Ocean framework, as well as the Geomodeling Network itself. Page The Geomodeling Network – Sponsored by Blueback Reservoir 26
  27. 27. The Geomodeling Network Newsletter May 2009 If any of you are attending this event then please feel free to swing by our booth for an informal chat and some Blueback hospitality. 6. The Blueback Toolbox (a Petrel plug-in) Our Toolbox has been widely available for a month or so now and already we are seeing a great take-up with this FREE software. Indeed the reception we have received for the Toolbox is such that users are already seeing the benefits to using plug-in technology to supplement their existing Petrel workflows. A few users have already requested additional plug-in suggestions which we are planning to have ready for the next free release of the Blueback Toolbox – these suggestions include: - Facies Maps - Shift Well Log - Merge Seismic Cubes - Cube flattening seismic volume attribute - Make empty seismic cube If you would like access to the Blueback Toolbox, then please refer to the March 2009 edition of the Geomodeling Network newsletter on how to download the software and request a license. Or drop an email to and he will get back to you with information on what you need to do. Requests for the newsletter No6 The next newsletter is planned for a July 2009 release, so please send articles to me at the following email address for inclusion (mitch.sutherland@blueback- Finally, please take advantage of the Geomodeling Network discussion board on LinkedIn to initiate comments on any Geomodeling subject of interest to you, or to respond to any of the articles in this newsletter – all I ask is that you respect other people’s opinions. Fin Page The Geomodeling Network – Sponsored by Blueback Reservoir 27