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ABSTRACT Recent developments in software are beginning to deliver the use of a single “toolkit” to store, visualize and synthesize geological, geophysical and engineering data – allowing, for the first time, the creation of “true” multidisciplinary Shared Earth Models (SEM).

There have been several hurdles to creating these models in the past: First, scale difference – pore scale for a petrophysicist, well scale for the driller, reservoir scale for the RE, and prospect or regional scale for the geoscientist have hindered tighter integration. Second, the wider E&P communities have viewed integration only in terms of their own team foci – partly due to the difficulty of amalgamating domain specific data – the geophysicist’s preferred domain being time, a driller’s being depth, and a reservoir engineer’s being temporal and spatial data. Third, we have limited integration to a subset of the E&P community with, for example, economic models not being an integral component of SEM. As a result, mistakes have commonly been made at interfaces between disciplines as one group hands off its output to another.

Using a 3D modeling software platform, a SEM of the Columbus Basin in offshore Trinidad was constructed. Scalability is a key component of the SEM – with support for individual prospects or regional scale. The relational framework facilitates an environment that can rapidly make computations across scales, allowing, for example, a casing design to be generated taking a reservoir scale model and translating it to the well scale. With the advent of 4D data handling, basin evolution, migration of fluids, pressure and other time variant properties are integrated into the SEM. This flexibility allows fluid migration studies to be better linked with prospecting.

Creating a unified and scalable model promoted wider ownership and engagement – going beyond the geoscience and reservoir disciplines to engage the drilling, facility, economic and strategy communities. The SEM environment facilitated better cross-functional inferences to be made – with

economic models being directly linked to the earth description and associated uncertainties. INTRODUCTION It takes time to collect, prepare, understand and make decisions based on data, one of our biggest assets. Today, not only is this workflow too often inefficient, high-risk business decisions are being made without full recourse to all the available data and, more importantly, without the full knowledge derived from all the data [1].

One of our key challenges is to ensure that each component in the E&P decision process is integrated and can add synergistic value. We should ensure that all data can be enhanced and used by all members of the E&P value chain, whether they are geologists, drillers, production engineers, geophysicists, or economist and business managers. By reducing the time and effort needed to build, maintain and continuously update these models, the “knowledge base” can be fully integrated and better utilized

Historically, use of shared earth models has been a means to ensure consistency between views of the reservoir held by people in different subsurface disciplines. The essential principle is that all the disciplines should participate in the definition and construction of a common numerical description of the reservoir, and should use this numerical model as the basis for their respective calculations and interpretations. The SEM is generally implemented as a fine-scaled cellular model held by a suitable geological modeling package containing rock properties, geophysical attributes, and reservoir characteristics, and with a close linkage, through upscaling, to a reservoir simulator [2].

However, expanding the SEM’s utility to a wider community, going beyond the traditional G&G disciplines, can provide synergistic benefits. By providing a more encompassing shared view, with scale and domain considerations important to each discipline captured, a wider ownership and engagement can be achieved – going beyond the geoscience and reservoir communities to engage the drilling, facility, economic and strategy communities.

Constructing a SEM with the aforementioned issues in mind requires novel considerations of scale and diversity of data. It requires developing the whole picture, whether on a well, a field, a play, or a province. It should allow for not only considerations important on a project scale, but also facilitate strategic level views.

We applied these principles in developing the Columbus Basin SEM in offshore Trinidad. First, the integration was performed principally using data on a regional scale. Second,

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A Basin-Scale Shared Earth Model for Prospecting to Drilling and Beyond A. Vittachi, BP Trinidad and Tobago, and S. Jones and K. McKenna, Earth Decision Sciences

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the entire geologic column was modeled, from surface to source rock depth. Third, both static and dynamic data and its associated properties and changes were captured. The following regional-scale data types were integrated: • Cultural data • Structural model • Stratigraphic model and facies • Well data • Drilling data • Prospect descriptions and polygons • Petroleum systems and migration model • Geochemistry • Petrophysical model and properties • Reservoir model

BUILDING A SHARED EARTH MODEL – COLUMBUS BASIN The Columbus Basin, forming the easternmost part of the Eastern Venezuela Basin, is situated along the converging margins of the Caribbean and South American plates. The two primary structural elements that characterize the basin are transgressive northeast-southwest trending anticlines and northwest-southeast oriented extension normal faults (fig. 1).

The basin was filled throughout the Pliocene and Pleistocene by more than 40,000 ft. of clastic sediment supplied primarily by the Orinoco Delta system. The delta prograded eastward over a storm-influenced and current-influenced shelf during the Pliocene-Pleistocene, depositing marine and terrestrial clastic megasequences as a series of prograding wedges atop a lower Pliocene to pre-Pliocene mobile shale facies [3].

The strategy of the modeling was to re-create the basin in a numerical sense, so allowing for the understanding of the nature and dependencies of the basin's stratigraphic section: uncertainty in reservoir and seal prediction, hydrocarbon-generation mechanisms and migration, and pressure prediction, as well as many other variables involved in an integrated exploration solution.

Using a 3D modeling software platform, a SEM of the Columbus Basin was constructed (fig. 2). Scalability was a key component of the SEM – with support for individual prospects or regional scale. In considering these goals, there were several distinct steps to the construction of the SEM: • Size and scope of the model • Velocity model • Data loading and integration • Model construction • Model maintenance and updating Size and Scope of the Model Deciding on a proper scope for the SEM should balance the needs of geoscientists versus hardware performance and data management issues. In the case of this SEM, construction of a basin-wide geocellular model capable of modeling geologic properties such as porosity and pressure for the entire geologic column was a primary goal. However, this goal had to be balanced against hardware constraints that limited the resolution of such a model. Software used in model construction had to be flexible enough to construct geocellular

models at different scales – upscaling and downscaling on an as-needed basis to provide the appropriate model resolution. Building a Velocity Model Creation of a regional velocity model is one of the first steps in building a SEM. Considerations include: • Combination of velocity data from 2D and 3D seismic • Interpolation techniques • Spacing and orientation of velocity cube • QC against existing well data

Construction of the Columbus Basin SEM regional velocity model incorporated data from more than a dozen 3D surveys, numerous 2D seismic lines, and more than 100 wells. Interpolation of the velocity model provided a regional cube needed to successfully depth convert stratigraphic horizons that extended from the shallow shelf into areas of the shelf edge and deep water where well data was sparse or non-existent (fig. 3). The velocity model also allowed interpolation of horizon data between seismic surveys, providing a guide to interpreters and geologists in areas with no seismic interpretation or well data.

Data Loading and Integration Once the geographic and geologic scope of the model is decided, the next step is to decide on a set of procedures for the submission of data to the model. Issues include: • Geographic projection • Well-naming conventions • Well datum choices • Units of measure • Horizon-naming conventions • Well log naming conventions

Creating procedures for inputting the various data types prior to beginning construction of the model will ease integration problems during later updates and maintenance. Deciding on naming conventions for horizons ready for submission to the SEM will force some consolidation of horizon names from field to field, making the construction process easier.

Likewise, deciding on geographic projection, well datum, and units of measure (both aerially and vertically) will identify problems with the construction of the model early enough in the process that they can be avoided through proper workflow procedures during the data loading process. Model Construction Construction of the model begins with developing an integration strategy for the primary interpretation data from individual projects. This includes: • Import of horizon, fault, and well marker data • Depth conversion of horizons • Construction of regional horizons • Import of stratigraphic and facies data

Import of horizon data and the interpolation of that data across areas where control does not exist is one of the primary objectives. Also important is grid resolution and local refinement. Defining the key stratigraphic and structural features in the model is a consideration for local grid refinement. Figure 4 shows an example of local grid

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refinement, an important step in preserving the scalability of the model. Model Maintenance and Updating The model should have the flexibility to be updated and to propagate those updates to the products of the SEM. For example, if new wells within the project area are drilled, then the horizons of the structural model and the geocellular grid of existing property models must be updated accordingly without time-consuming regeneration of surfaces and grids.

Data loading and integration procedures during an update share the same needs as the initial data loading and construction process. Ideally, a procedure for loading and updating the model should preserve information associated with the origin of the data, allowing later users of the SEM to locate the original inputs to the model. Such auxiliary information should include: • Original interpreter • Date of the update(s) • Geographic projection of source data • Velocity model used (for depth converted data) • Originating project name

Clear workflows should exist for periodic updates and QC of data. These workflows facilitate an accurate and up-to-date model for all users (fig. 5). Finally, the workflows must contain a QC step consisting of visual inspection, summary statistics, or other methods to ensure that the updates are loaded correctly into the model.

Without these steps, the SEM exists in a vacuum – a one-time construct that lacks continuity. A consequence of constructing the model without adequate QC procedures is propagation of errors. Without being able to quickly identify and correct the source of an error in the SEM’s data, the value of the entire model may be compromised. CULTURAL DATA AND WELLS The industry has traditionally concentrated on well data when integrating cultural data in shared earth models. However, we contend that all assets in the value chain should be considered. These include platforms, pipelines, lease and geographic boundaries to name a few.

For the Columbus basin model, we included these data types, including over one thousand wells containing trajectory, marker and log data (fig. 6). We used newly developed data-sharing half-links to quickly move data from the corporate datastore into the SEM environment. Without access to the data-sharing links, the process would have been cumbersome and very time consuming. The interoperability is therefore critical for model building, maintenance and updating, and should be incorporated into the SEM workflow.

Using the cultural data types mentioned along with subsurface data, we can simultaneously view multi-attribute/multivolume datasets. Being able to access this information simultaneously allows exploration strategy and development to be linked. As a result, the economic value of exploration prospects can be quickly ranked against development potential. Being able to look at this for an entire basin can help form exploration strategies which are more closely linked with development.

STRUCTURE, STRATIGRAPHY AND EARTH PROPERTIES Structural Model

For the Columbus Basin SEM, fault picks from the different interpretation projects were combined to develop a regionally consistent fault model. Correlating fault picks, from field-specific projects, provided insight on inconsistencies that were generated by individual interpretations. These were rationalized into a regionally consistent fault model, which was then refined to provide fault-horizon intersections needed to build the geocellular model.

The regional structural model was a direct benefit to the interpretation of areas where data was sparse. Using structural balancing techniques, areas of poor seismic resolution were re-interpreted for a more consistent structural model. A refined structural interpretation also provided a depth model for improving the regional velocity model.

Visualization of the structural model with ancillary information such as geochemical data revealed areas where traditional interpretation could be aided by this multidisciplinary approach to understanding the basin.

Using results from structural restoration modeling, 4D realizations of structure and stratigraphy could be integrated and stored in the model. A series of structural reconstructions can be used to provide input to basin modeling applications. Stratigraphy The understanding of depositional environment, sequence stratigraphy, and facies are key elements in defining the constitution of the model to be built (for example: horizons, layers and faults). Some considerations for building the stratigraphic model are: • Structural element maps and chronostratigraphic surfaces • Basin classification schemes and gross depositional

environment maps (GDEs) In areas where detailed knowledge of the above is lacking,

more rudimentary models can still be built using a basic geological model.

In the stratigraphic model assembly phase, contradictions and inconsistencies in sub-regional work may become more apparent, and strategies in merging disparate interpretations will have to be formulated (exactly the intent of SEMs). Where components are incompatible, alternative hypotheses are sketched which may be confirmed by a re-interpretation of the original data. This again emphasizes the importance of bidirectional data transfer – allowing regional chronostratigraphic surfaces to aid interpretation projects while interpretation projects continually provide inputs to refining the SEM.

A consistent model with chronostratigraphic surfaces defined with associated GDEs is the goal. Each of the GDE facies has an assigned descriptor, allowing linkage with other elements within the SEM. Figure 7 shows a geological model of the Columbus Basin. A geocellular earth framework is constructed within the SEM to emulate its properties.

This process is facilitated by first projecting GDE maps to the respective stratigraphic layer. All well data can be viewed against the backdrop GDEs to aid in the delineation of

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appropriate facies. With each stratigraphic layer in the SEM being populated with GDE maps, a volumetric representation of facies is achieved to give a geologically robust 3D model. The Columbus Basin model combines the paleogeographic model of the basin with the structural model. Property Model

The critical link between geology and rock physical properties is at the heart of the earth model, as interpretational models will ultimately depend on our understanding of how physical properties relate to the geological description. An effective property model should characterize a full property description of the entire geologic column, not just the reservoir intervals which are typically of interest to the subsurface community.

The overburden is of obvious concern to the driller and knowing where geohazards may be encountered is of particular importance. By using 3D seismic amplitude, regions of geohazards can be highlighted within the SEM. Alternatively, geohazard interpretations performed externally using another software package could be integrated. For geologists, the ability to visualize trajectory uncertainties is also a definitive plus. Integrating information above the reservoir could also help reduce geological uncertainties, further optimizing the representation.

The physical rock properties are defined using the following methodology: • Rock-fabric facies are used for filling the geologic

framework with earth properties • Where appropriate, properties are calculated using

regional trends or basin modeling simulations • Petrophysical properties are created using conditional

stochastic simulation constrained by facies Properties from petroleum systems basin modeling

simulations form an ideal basis for populating the earth model’s rock physical attributes. Basin modeling tools are traditionally used to study basin development, source rock maturation, petroleum generation and expulsion. The emphasis is very much on understanding processes at the regional to semi-regional scale [2].

The advantage of using basin modeling as a foundation for creating SEMs is that they share many similarities, such as full geologic column description and paleogeographic information. The regional nature of basin models also makes them an ideal source of properties for populating a SEM. The creation of the 3D basin model of the Columbus Basin was performed using an external basin modeling application (per methodology described by Schneider, Swarbrick, Osborne and Duppenbecker).

Following the basin modeling simulations, a 3D property description of pressure, temperature, lithology, total porosity, petroleum migration footprint and charge access was integrated into the SEM (fig. 8). These inputs also made it possible to calculate secondary properties such as fracture pressure, effective stress and expected column heights. While we did not build the basin model from the SEM, it is anticipated that future workflow will entail a basin model construction starting from a SEM. This will further improve

the efficiency of basin model building by combining the two workflows.

Petrophysical Properties One of the key issues in the construction of the property model is scalability. Given current hardware and software constraints, modeling an entire aerial extent comparable in size to the Columbus Basin is possible only at coarse-scale. However, a scalable property model can downscale a course model to the reservoir level on an as-needed basis. This allows small-scale variations seen in seismic and well log properties to be captured at the reservoir scale while preserving regional trends on the course scale.

There were several steps used to create the Columbus Basin petrophysical property model. First, well log based porosity for individual wells were used to create ‘property regions’ (fig. 10). These regions store the relational dependency between porosity, facies, and depth. The regions then allowed for the determination of porosity characteristics in each facies type.

Second, as shown in figures 11a and 11b, porosity histograms were rapidly generated for each facies type for use with the stochastic simulation. Using the statistical distribution of the histograms, population of the shared earth model with porosity was accomplished by writing ‘petrofacies-based’ porosity equations in the model’s calculator. Stochastic simulation allowed for modeling statistical distribution in each facies type, and captured the finer scale property variations found at the reservoir scale. Figure 12a shows the statistical variation in porosity for each facies. Figure 12b shows the porosity trend calculated for the basin. Using the relationship between porosity, depth and facies, porosity for any spatial position in the SEM is described.

It is important, prior to populating the model layers with petrophysical parameters, to generate several possible realizations which capture the heterogeneity as well as possible structural features such as faults and unconformities [7]. PROSPECT INVENTORY The SEM should incorporate all prospects as soon as they are generated by the project teams. This will require having a workflow procedure in place to update the SEM as new prospects are generated. Once prospects are loaded in the SEM, they are integrated by placing the prospect outline in its proper geometric position, in 3D. Properties associated with each prospect, such as reserves and risk, are imported into the SEM and stored.

Placing prospects in the proper spatial context allows for the property model’s data to be transferred and assigned to each prospect. With each prospect now inheriting the earth model’s properties, we are able to extract additional value. For example, pressure and temperature can be evaluated for each prospect, allowing each to be classified according to whether they are HPHT or not. This allows the drillers to quickly evaluate costs based on whether conventional or high pressure equipment is required. Further, results for each prospect can then be synthesized with other data such as platform positions, well locations, geochemical, geological and geophysical

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properties (fig. 13). This allows the following post-analyses to be performed: • Economic analysis • Risk management • Strategic planning • Ranking of fairways, blocks and prospects

Using these analyses, we can quickly delineate the economic value of the prospect inventory, including any upside, and better assess the tail of the inventory.

DRILLING Because the SEM is a regional model, it is an ideal tool for making strategic decisions that depend largely on a broader view. In order to do this effectively, the toolkit should facilitate rapid planning of wells and platforms. Since the SEM contains information about facilities in addition to subsurface information, exploration and development strategies can be formulated to maximize performance and efficiency.

To plan wells with platforms, targets are first picked to access prospects in the SEM. Platform specifications (number of slots, spacing, etc.) are determined and then platform location is optimized automatically based on the target locations. Well paths are then automatically generated between targets and platforms based on constraints given in the well program (fig. 15). Once the well paths and platforms are generated, they can easily be moved, added to, and edited while being updated automatically in real time. The process is iterative; a cost model, automatically generated for all wells and platforms, is updated automatically when edits are made.

Well planning of this kind is not meant to replace that done by drilling engineers. Certain constraints may be incorporated into the well planning process to make sure planned wells are not unrealistic. However, the emphasis is on being able to plan targets on prospects and create wells and platforms quickly for strategic planning decisions.

The ability to rapidly evaluate different scenarios for platform locations and their associated cost implications makes the SEM an effective tool for exploitation, development, planning and evaluation. E&P management can use the SEM to evaluate the relative impact of developing one prospect versus another, or one drilling strategy with another, interactively. The process allows teams to “forward model” a basin’s exploration and development strategy – encouraging participation between all disciplines involved. RESERVOIR MODEL In mature fields, remaining reserves could be looked at in terms of reserves density: the hydrocarbon pore volume per drainage area. Using the SEM, we consider the problem of remaining reserves with new access opportunities by displaying the 4D reserves density data from the reservoir model, with the prospect inventory’s reserves density (fig. 14).

As seen in the figure, a 4D representation of reserves density from the reservoir model is captured in fixed time steps, ranging from present day through life of field. Then a comparison is made between the reservoir model and prospect inventory, over the same time period. The integration allows the strategic planner to determine when new prospects should

be accessed, and which prospects to access over time. Existing infrastructure such as available platform slots can be integrated with this analysis.

CONCLUSIONS

The Columbus Basin shared earth model has allowed us to: 1) generate high quality subsurface interpretations consistent with scale 2) reduce cycle time of integrating new data 3) increase the effectiveness of the integration process and 4) make better decisions on exploration and development projects.

The relational framework of the shared earth model allows the synthesis of data and information to be made at different scales, and in different domains. Representing multi-domain regional data in a cohesive model has improved cross-functional decision making. Integration of subsurface discipline data, from overburden to reservoir, has improved reservoir and seal prediction, hydrocarbon-generation modeling, migration analysis, and pressure prediction, as well as many other elements involved in an exploration solution.

The SEM environment allows for co-visualization and interdisciplinary data manipulation, and field development planning – impacting on our ability to make better strategic decisions on basin exploitation.

Other benefits are better incorporation of regional data at the project scale and improved communication within exploration teams and management. ACKNOWLEDGEMENTS We thank Bp Trinidad and Tobago (BpTT) for permission to publish this paper. We also thank the BpTT’s exploration team for their contribution REFERENCES 1. Trayner, P.M., “Defining Business Critical Workflows for

Integrated Optimization”. SPE paper 39576. SPE International, New Delhi, Feb. 1998.

2. Gawith, D.E., Gutteridge, P.A., Tang, Z., “Integrating Geoscience and Engineering for Improved Field Management and Appraisal”, SPE #29928, SPE International, Nov. 1995.

3. Wood, L.J., “Chronostratigraphy and Tectonostratigraphy of the Columbus Basin, Eastern Offshore Trinidad," by the author in AAPG Bulletin, v. 84, no. 12 (December, 2000), p. 1905-1928.

4. Schneider, F., “Basin modeling in Complex Area: Examples from Eastern Venezuelan and Canadian Foothills”, Oil and Gas Science and Technology, Rev. IFP, Vol 58, 2003

5. Richard E. Swarbrick, “Developing Pressure Histories Through Basin Modeling”, AAPG Bulletin, Vol. 86 (2002), No. 13. (Supplement), AAPG Annual Meeting. Houston, Texas March 10-13, 2002.

6. Duppenbecker, J.S., Illife, J., Osborne, M.J., Harrold, T., “The Role of Multi-Dimensional Basin Modeling in Integrated Pre-Drill Pressure Prediction”. AAPG/SEPM Conference on Basin Modeling. 2004.

7. Nieto, J., Corrigan, K., “Shared Earth Modeling – A New Role for Petrophysicists”, SPWLA 45th Annual Symposium, June 6-9, 2004.

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FIGURES

Figure 1. Southwest-northeast well log cross section across the Columbus Basin, illustrating the typical gamma-log (left) and resistivity-log (right) signatures associated with depositional facies that make up the prograding megasequences. Environments of deposition are based on interpretation of integrated biostratigraphic data, well log motifs, seismic facies, and regional paleogeography [3]

Figure 2: Area of the Columbus Basin SEM (black rectangle). Regional data from outside this area was also used.

Figure 3: Areas of anomalously high velocities associated with interpolation technique and distribution of well control

Figure 4: Area of high resolution gridding incorporated into regional surface grid. Observe the relatively smooth contacts surface of the grid compared to the jagged, coarse features encountered outside the grid area.

Figure 5: Automatic updating of the SEM's geocellular model to account for new well information. . Here a new well marker (disk on well path) is used to thicken the upper layer while keeping the lower interval constant.

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Figure 6. Cultural data: pipelines, platforms, wells and lease boundaries.

Figure 7. Paleogeography of the lowstand Orinoco Delta in the Pliocene and Pleistocene. Low-sloping broad fluvial distributary plain feeds line-source, wave-modified strand-plain shoreline systems. These systems in turn feed line-sourced slope and fan deposits. Rising shale diapirs at the toe of the slope helped focus slope and basin floor deposition and ponded thick sediments on the basin floor in toe-of-slope sediment sinks [3].

Figure 8. Cross-sections showing the SEM’s properties. Geocellular framework emulates the paleogeology of the Columbus Basin as seen in Fig. 7. The model is built with chronostratigraphic surfaces and GDEs. Shading illustrates the different facies types.

Figure 9. Pore pressure computed from basin modeling simulation is integrated into the SEM. The shading shows regions of overpressure in the basin.

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Figure 10. Wells with computed porosities are used to create ‘property regions’ as shown by the shaded area along the well path. Porosity relationship to facies can then be examined.

Figure 11. Statistical distribution of porosity for different facies types.

Figure 12a. Porosity calculated using stochastic modeling, constrained by facies type. Left panel shows the facies, right panel is computed porosity. Note how porosity variation is constrained in each facies type.

Figure 12b. Porosity trend calculated for the basin using the relationship between porosity, depth and facies. Porosity for any spatial position in the SEM is hence described.

Figure 13. Migration data with faults and geochemical data. Data can be displayed together to optimize the migration model.

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Figure 14. 4D Reservoir model’s reserves density compared with new prospect inventory. New access opportunities are assessed as reserves are depleted in producing reservoir.

Figure 15. Platform and wells built from target set on prospect. Platform location was optimized based on target location.