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Modeling Deepwater Reservoirs J.N. Ezekwe, SPE, and S.L. Filler, SPE, Devon Energy Corp.

Copyright 2005, Society of Petroleum Engineers This paper was prepared for presentation at the 2005 SPE Annual Technical Conference and Exhibition held in Dallas, Texas, U.S.A., 9 – 12 October 2005. This paper was selected for presentation by an SPE Program Committee following review of information contained in a proposal submitted by the author(s). Contents of the paper, as presented, have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material, as presented, does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Papers presented at SPE meetings are subject to publication review by Editorial Committees of the Society of Petroleum Engineers. Electronic reproduction, distribution, or storage of any part of this paper for commercial purposes without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to a proposal of not more than 300 words; illustrations may not be copied. The proposal must contain conspicuous acknowledgment of where and by whom the paper was presented. Write Librarian, SPE, P.O. Box 833836, Richardson, TX 75083-3836, U.S.A., fax 01-972-952-9435. Abstract Deepwater reservoirs pose significant challenges worldwide to companies exploring and producing such reservoirs because of the high exploration, development, and production costs. Great uncertainty and risk attend the evaluation of deepwater reservoirs because of the environment, sparse well control, and lack of direct measurement of reservoir properties. Proper modeling of deepwater reservoirs provides companies with tools to evaluate these reservoirs and quantify risks associated with their development. There are five critical areas in the process of modeling deepwater reservoirs. These are geological and geophysical modeling, reservoir characterization, reservoir flow modeling, facilities/flow assurance, and uncertainty/risk analyses. This paper presents methodologies found useful by experience in the modeling of deepwater reservoirs. Actual field cases describe our experience in using systematic steps based on the five critical areas to model the Zia reservoir, a Manatee reservoir in the Troika Basin, and the Magnolia reservoirs using the Markov Chain Monte Carlo technique.

Introduction In this paper, we describe a general modeling process that improves reservoir understanding and performance forecasting. These factors are extremely important in the high cost, high risk deepwater provinces, where wrong decisions lead to expensive mistakes and can materially affect a company’s financial standing. In addition to the geological, geophysical, and engineering analyses usually conducted for offshore fields, total systems analysis and risk analysis are critical to assess the economic viability of a deepwater project. Several descriptions of reservoir modeling exist in the literature. Deepwater reservoirs have been subjects of several

studies. The integration of geological and geophysical data using geostatistical methods has been discussed in several papers 1-10. The sparse data available makes this step very important in deepwater reservoirs. This integration leads to better reservoir characterization by utilizing a comprehensive data set with low vertical resolution (the seismic data) conditioned to a limited data set with high vertical resolution (well data). Reservoir flow modeling involves upscaling from geocellular models with very large numbers of cells (in most cases) and use of fast software platforms to edit those models when new data becomes available. In addition, these models should be tied to systems models which include wellbore and surface facilities models to properly forecast the achievable rates and expected recoveries from deepwater reservoirs. Several authors have discussed both the reservoir model itself 2-4,12,14

or the reservoir model with integrated facilities 5,9,15-17. Flow assurance studies are crucial to the systems modeling effort as well, and flowline deposits can cause production interruptions and expensive interventions. Although not addressed in any detail in our paper, we note that this step is vital to a successful project. Inclusion of uncertainty can also be time-consuming for flow simulations, so techniques to reduce the time required to investigate the various interaction of reservoir variables as they relate to the uncertainty of the results are very important. Experimental Design (ED) or Design of Experiment (DOE) methods have been found to be very efficient for determining ranges of uncertainty of the results of flow simulations. Friedmann et al.11, Corbishley et al.12, and Portella et al.13 describe approaches using experimental design to reduce the large flow simulation workload. Other uncertainty reduction involves risk analysis to mitigate the risk inherent in development of extremely expensive wells and facilities in the deepwater arena. All of the experimental design discussions involved estimating risk profiles, as did Ghorayeb et al.15, Capeleiro Pinto et al.18, and Ring et al.19. Understanding the risk profile of any project leads to better decisions for deepwater projects. Generalized Procedures for Building Models The processes of building models for deepwater reservoirs were divided into five critical areas. These areas are:

1. Geological and Geophysical modeling- construction of geological framework,

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2. Reservoir characterization- distribution of geological units, rock and fluid properties,

3. Reservoir flow modeling- upscaling (or upgridding) and flow modeling,

4. Facilities/flow assurance- fluid flow dynamics in pipes and vessels, and

5. Uncertainty/risk analyses- quantification of uncertainty and risks.

Geological and Geophysical Modeling: This process begins with the creation of a geological framework of the structure and reservoir architecture from seismic data and well logs (if the structure has been penetrated by wells). In this process, horizon maps of the top and base of the structure are generated from seismic data tied to well markers, if available. Additional horizons should be added as needed between the top and base horizons to aid in the vertical layering of the model, especially in areas that indicate contrasting lithologies. The main goal in geological and geophysical modeling is to represent all major geological features, such as faults, flow barriers, compartments, pinch-outs, etc., that are likely to affect the connectivity of the reservoir. For instance, in constructing geological models for deepwater reservoirs, the presence of faults in the structure should be investigated very thoroughly. Specific questions that should be addressed through fault modeling are the type of faults, nature of the faults (sealing or non-sealing), fault displacement, and any other fault characteristics that may affect fluid flow in the reservoir. Since deepwater reservoirs are typically developed with few wells, the presence of sealing faults could change development plans, and, in severe cases, this compartmentalization may render the project uneconomic. Geological modeling should be conducted on a software platform that can be readily updated. In most cases, initial geological maps created from seismic data are most likely to be changed depending on the quality and processing of the seismic data. As wells are drilled into the reservoir and additional data are obtained, the geological maps are updated. Choosing a platform that allows for quick updating of the geological model with new maps and data will greatly aid the entire process of geological and geophysical modeling. Most geological (static) models are eventually converted into flow (dynamic) models. Consequently, initial work on the construction of the geological model should bear the requirements of the conversion process in mind. Care should be exercised in the selection of both the areal and vertical gridding system. Areal grids for the geological model should be selected to capture and retain important geological features that exist across the structure. Vertical grids should at minimum be at the resolution of the well logs depending on the nature of the vertical heterogeneities that are represented. The key in the choice of grid systems at this stage is to capture key geological features, retain these geological features in the flow models, and minimize distortion or elimination of the geological features during the upscaling (or upgridding) process. Reservoir Characterization: Reservoir characterization “is a

process of integrating various qualities and quantities of data in a consistent manner to describe reservoir properties of interest at inter well locations”20. For deepwater reservoirs, reservoir characterization is the process of integrating seismic data of various qualities and well data of limited quantities in a consistent manner to assign reservoir properties to a large extent of the reservoir using the seismic data. This is the main difference between characterizing reservoirs with many well penetrations, such as onshore reservoirs, and deepwater reservoirs with few well penetrations. Consequently, in characterizing deepwater reservoirs, seismic data with their uncertainties are relied upon heavily in the assignment of properties to areas where no other data exist. Geostatistics is especially useful in the characterization of deepwater reservoirs because it is a technique that enables the propagation of reservoir properties in a manner that is statistically coherent and consistent. It allows the application of concepts such as trends and variability of properties as well as subjective interpretation in the description of deepwater reservoirs. The basic inputs in the reservoir characterization process are the geological framework, well log data, core data, seismic amplitude data, acoustic impedance data, well test data and any other data which can be correlated to rock properties, such as porosity, permeability, saturation, thickness, and lithofacies. Each data source must be examined carefully to ascertain the quality of the data. Erroneous and spurious data must be eliminated from the data set so as not to amplify and propagate such errors in the geostatistical process. Careful preparation of data during the data analysis process is the key to successful application of geostatistics in characterizing deepwater reservoirs. The data analysis processes are typically concluded with the generation of histograms, trends, correlation coefficients, and spatial and vertical variograms. Generally, well log data, well test data, and core data are treated as ‘hard’ data while seismic data such as amplitude and acoustic impedance data are considered to be ‘soft’ data. In reality, any data set that was not measured directly or whose origin has a high degree of uncertainty is typically treated as ‘soft’. In propagating rock properties through geostatistics, ‘hard’ data at well penetrations are matched exactly while ‘soft’ data are used to constrain or guide property distributions at other locations away from the wells. If the correlation between ‘hard’ and ‘soft’ data is high at well locations, the same degree of correlation is applied at other areas where only ‘soft’ data exist. This concept is very useful in characterizing deepwater reservoirs because of the limited number of well penetrations that are usually available in these reservoirs. The simplest geostatistical methods applicable to deepwater reservoirs are the sequential simulation techniques21,22. These are Sequential Gaussian Simulation (SGS) and Sequential Indicator Simulation (SIS). SGS is generally used to distribute continuous properties such as porosity, permeability, saturations, net-to-gross ratios or net thickness, gross thickness, etc. There are many variations of SGS, such as SGS with external drift, SGS with collocated cokriging, SGS with

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trend, etc. These are modifications of the SGS procedure. The user is advised to choose a particular procedure after careful review and clear understanding on how applicable that procedure is to the data set. In our experience, straight-forward SGS will work quite well in most situations. SIS is used in modeling discrete properties such as facies or rock types. Also, there are other variations of SIS such as SIS with cokriging. In most cases, simple SIS will be adequate. Many deepwater reservoirs were deposited as channel sands. If the patterns of these channel sands can be defined geologically, such reservoirs are best modeled with object-based techniques8. Object modeling is gaining wide popularity, especially for channel sands, and should be the preferred method for applying geostatistics to such reservoirs. Object modeling is a very powerful tool and consequently can easily be misapplied. Before object-based methods can be applied reliably, it is important that the pattern and distribution of the channel sands be mapped deterministically on the basis of other data such as seismic inversion data. Reservoir Flow Modeling: Reservoir flow modeling converts the static geocellular model created through the process of geological modeling and reservoir characterization into a dynamic model with fluid properties and flow parameters. The first step in transforming the static geocelluar model into a dynamic model is determining the size of the geocelluar model that can be handled in the dynamic environment, given the limitations of hardware, software and manpower. This is the upscaling (or upgridding) process. Upscaling is the process of reducing a geocellular model with a large number of cells into a model with a much smaller number of cells while retaining key geological heterogeneities and features. In an ideal case, upscaling will not be necessary. The geocelluar model is used directly in the flow simulation mode. In practical cases, especially for large geocellular models, some degree of upscaling is usually necessary. The key here is to minimize the amount of upscaling that has to be done. This is achieved principally during the geological modeling phase by selecting a grid system that is transferable to the flow modeling phase while capturing and retaining key geological features. A practical approach is to select an areal (x, y) grid system for the geological model which is retained for the flow model while upscaling only in the vertical (z) direction as the geological model is transformed into a flow model. This approach is essentially an upgridding process in which vertical layers with similar reservoir properties are combined together by averaging techniques to create a single representative vertical layer. There are several upscaling methods that can be used ranging from simple averaging techniques (as described earlier) to more sophisticated methods, including streamline simulations23,24. Each method has its advantages and disadvantages 23,24. Irrespective of the upscaling method used, it is good practice to thoroughly compare the original model to the upscaled version to ensure that key geological features are not ‘lost’ or ‘smoothed-out’ during the upscaling process. To maintain geological integrity, the upscaled model should retain key geological features such as faults, permeability

barriers, lithologies, and vertical heterogeneity as described in the original geological model. In our experience, upscaling a geocelluar model should be conducted as a joint exercise that involves the geologists, geophysicists, petrophysicists, engineers, etc., who contributed to and participated in the building of the original geocellular model. This team should collectively review the upscaling process and ensure that the upscaled model is the ‘most realistic’ representation of the original model. Reservoir models of deepwater reservoirs are used primarily for three purposes:

1. Evaluation of an exploration prospect 2. Development planning and economic appraisal of a

discovery 3. Reservoir management of producing assets

Reservoir models of deepwater prospects are relatively uncomplicated. The geological modeling data input are based on seismic data. No detailed reservoir characterization is possible at this point. Most of the input data are based on empirical correlations and data from analogous reservoirs. These models are used to provide ranges of potential reserves and justification for investing in the exploration well. After discovery of a deepwater reservoir and successful drilling of one or more appraisal wells, a decision must be made on whether or not to sanction the project. In most cases, this is at least a multi-million dollar decision and may be a multi-billion dollar decision. A decision to sanction a deepwater project depends on the potential reserves that are expected to be recovered from the reservoirs. If the estimate of potential reserves recovery is erroneous or over-stated, the total cost of the project could be devastating to the company. Deepwater reservoir models can be used as effective tools in the development planning and economic appraisal of deepwater projects. If constructed properly, deepwater reservoir models can provide wide ranges of potential reserves recovery and optimized development plans. Optimization of development plans generally involves evaluation of well types, placement of wells, number of wells, production rates, assisted recovery strategies, future recompletions, etc. All these alternatives can be evaluated with reservoir models at a small fraction of the cost of an actual project. But for these models to be useful, extreme care must be used in their construction. It is important that the known geology of the reservoir is captured in the models, the models are properly characterized and upscaled, and predictions from the model are adjusted as necessary, given the unknown subtleties of reservoirs and inherent optimism of reservoir modeling. Deepwater reservoir models are being used more as real-time reservoir management tools. This advance results from the installation of real-time down-hole monitoring tools that constantly provide data on reservoir performance. Consequently, deepwater reservoir models can be updated quickly and used to predict the impact of changes of operating strategies or to predict the potential outcome of observed changes in reservoir performance. This is the ultimate application of deepwater reservoir models: as tools for

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management of assets that produce at very high rates with very rapid changes in data (such as GOR, WOR, pressures, etc.) used for monitoring reservoir performance. Facilities/Flow Assurance: Deepwater reservoir models should include the entire flow system, from the reservoir through the wells and subsea pipelines and risers to the host facility. In many cases, depending on the composition of the fluids, flow through the subsea systems could be the controlling factor for producing rates and ultimate recovery as much as fluid flow through the reservoir25. Deepwater production involves flow in pipelines and risers that are submerged in water with temperatures often colder than 40 oF. The low temperatures have large effects on the phase behavior of fluids as well as their transport properties. Severe reduction of fluid temperatures within the subsea flow systems may cause precipitation of paraffins, asphaltenes, and other hydrocarbon deposits, as well as the formation of gas hydrates26. These deposits can reduce flow and ultimately plug the pipelines. Deepwater production systems require heat-preserving measures to deliver the fluids to a host facility. These measures range from non-insulated pipes that depend on high flow rates and chemical treatment to heavily insulated pipes with regulated chemical treatment. Other, more exotic measures have been tested recently, including electrical heating, but a majority of subsea pipelines are insulated. Flow assurance is a technical discipline of enormous importance. It must be conducted for deepwater projects to ensure that the subsea flow systems will deliver the fluids at projected rates under prevailing conditions. It has been noted in this paper as a reminder that the results of these engineering studies should be included in flow models of deepwater reservoirs. Uncertainty/Risk Analyses: To a greater degree than most other operational environments, deepwater reservoirs have a very high degree of uncertainty and associated risks because of scarcity of reservoir data and the high costs of development. The wide ranges of reservoir data that could have significant impact on estimates of in-place hydrocarbon volumes, productivity, reservoir continuity, drive mechanism and reserves recovery, etc., require that the uncertainties associated with these data be examined in a systematic manner. In addition, the costs associated with well types, number of wells, completion costs, production systems, etc., are very uncertain. Because of the large number of variables that have significant impact on the forecasts and economic viability of deepwater projects, it is necessary to analyze the impact of each of these variables in a consistent and organized manner. One of the methods widely used in the industry is termed Experimental Design (ED) or Design of Experiment (DOE)11,27-31. The ED (or DOE) method allows a host of variables to be evaluated systematically and the results summarized in statistical terms. With this approach, variables that could significantly impact the project are identified and evaluated in sufficient detail. In addition, ED reduces the amount of work required to evaluate the impact of these important variables and includes the effect of each of the variables on the others. In some cases, the work load to adequately determine the impact using individual

simulations with all variations of parameters would be prohibitive. We recommend using this methodology to analyze all deepwater projects. Field Examples Three field examples are presented to illustrate the processes described in this paper for modeling of deepwater reservoirs. The examples have been arranged in terms of levels of complexity starting with simple to very complex procedures. The first example on a Zia field reservoir is a very straight forward methodology that illustrates retention of key geological features in the geological model and application of geostatistical techniques for reservoir characterization. The second example, based on a Manatee field reservoir, introduces the incorporation of seismic inversion data (acoustic impedance data) in reservoir characterization. The last example, based on a Magnolia field reservoir, uses the Markov Chain Monte Carlo (MCMC) method to combine geological and seismic data to generate multiple realizations of reservoir properties. These field examples were modeled by investigating and analyzing most of the critical areas we have discussed. Some uncertainty in reservoir properties was incorporated implicitly in the geostatistical analyses performed. We have not to date performed the experimental design method on these cases because of time constraints. Ongoing studies are continuing for other fields and will incorporate experimental design methods. Zia Reservoir The Zia reservoir is located in Mississippi Canyon Block 496, Gulf of Mexico. It is a structural trap on the south flank of an upper slope basin. Sand deposits consist of amalgamated turbidite sheet sands that were deposited during the Late Miocene period. The Zia field consists of two main sand reservoirs, the AA1A (or 1A) and AA1B (or 1B) Sands. The 1A sands overlie the 1B sands, and the AA2 sands are the deepest reservoirs encountered, as shown in Fig. 1. The models described here were built for the 1A and 1B sands. Geological Modeling: The basic data input for the geological model were:

1. Top and base structure maps for 1A and 1B sands 2. Net sand maps for 1A and 1B sands 3. Fault polygons for all major faults 4. Processed log data for five well penetrations

The top and base structure maps were digitized and transformed into surfaces, as shown in Fig. 2 for the top of 1A sand and base of 1B sand. The net sand maps were also transformed into surface maps shown in Figs. 3 and 4 by the same process. The main geological feature in this field is a system of faults (shown in Fig. 5) that divides the field into eastern and western sections. The fault system, as well as other smaller faults, was modeled as vertical fault surfaces as shown in Fig. 6. Reservoir Characterization: The net sand maps were used to assign net sand values to corresponding grid cells. Reservoir properties, such as initial water saturations, porosities, and

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permeabilities, were upscaled into model cells along the well paths from processed well logs. Porosity values were distributed across the model using Sequential Gaussian Simulation (SGS) with collocated cokriging on net sand data. Similarly, water saturation and permeability were distributed with SGS with collocated cokriging on distributed porosity data. Fig. 7 is an example of the geological model showing the porosity distribution created by this process. Reservoir Flow Model: The geological model was directly loaded into the reservoir simulator. There was no upscaling of the geological model. The grid system of the model is 91 X 104 X 34 for a total of 321,776 grid cells. The reservoir flow model has 168,300 active cells. The 1A sand was represented with 10 layers while the 1B sand was represented with 20 layers. The shale interval between the two sands was represented with the remaining 4 layers. Fig. 8 is a cross-section through the major fault that separates the eastern and western parts of the reservoir. Fig. 9 is the representation of the same fault in the reservoir model. By representing this major fault accurately in the reservoir model, a major feature of reservoir geology was captured and preserved in the reservoir model. This geological structure was crucial in understanding the observed performance of this reservoir. The resulting flow model is shown in Fig. 10. Facilities/Flow Assurance: An extensive study was conducted by an outside contractor on flow assurance prior to installation of this project. The results from this study were incorporated in the hydraulic data that was part of the dynamic modeling process. Manatee Reservoir The Manatee reservoirs are located in Green Canyon Block 155, Gulf of Mexico, in the same basin as the Troika and Angus reservoirs (Fig. 11). The Manatee reservoirs consist of S10A1 Sands, S10A2 Sands, and S10B Sands. These are turbidite sand deposits. The work described in this paper was performed on the S10A2 Sands. Geological and Geophysical Modeling: The geological framework was created from top and base horizon maps of the S10A2 Sands derived from seismic data. A net pay map was also constructed from net pay counts from three well penetrations and pre-stack seismic inversion volume. The top and base horizon maps were used to create top and base surfaces for the geologic model. The net pay map was used to constrain the acoustic impedance volume as described under reservoir characterization. Reservoir Characterization: The main data input were seismic data and well log data. Sidewall core data from Angus field, which is located in the same minibasin, was used to help construct the porosity-permeability transform. An acoustic impedance volume was created from the seismic data and shifted vertically to match time horizons. The acoustic impedance volume was then converted into a net sand volume by performing fluid substitution analyses on well data to distinguish between ‘pay’ and wet seismic response. The resulting net sand volume, subdivided into reservoir and

aquifer regions, is shown in Fig. 12. The modified net sand volume with distinct reservoir and aquifer regions was used to generate a modified acoustic impedance volume as shown in Fig. 13. Two correlations were developed from well log data for transforming acoustic impedances into porosities for the reservoir and aquifer regions. The two correlations are shown in Fig. 14. The transformed porosity distributed volume is shown in Fig. 15. A correlation (based on the Angus field core data) was developed to transform the porosity values into permeability values. This is shown as Fig. 16. The permeability distribution generated with the correlation is shown in Fig. 17. A geological model consisting of net pay, porosity, and permeability volumes was created from this process and used for flow simulation. Reservoir Flow Model: The geological model was converted into a flow model with 20,670 (53 X 39 X 10) grid blocks. Figs. 18 and 19 show porosity and oil saturation distributions for Layer 5 of the model. This flow model was used to evaluate the productive potential of the S10A2 Sands. Magnolia Reservoir The Magnolia reservoir is located in Garden Banks Blocks 783 and 784, Gulf of Mexico. It developed as Plio-Pleistocene turbidite sands on the north flank of a salt structure. The reservoirs consist of silt sized sediments that formed a complex series of mostly fining-upward channel/levee deposits. An apparent permeability barrier divides the structure into eastern and western sections with different oil/water contacts. The western section contains oil and the eastern section is a condensate reservoir. Reservoir properties in the western section are different from those in the eastern section. A map view of the Magnolia reservoir is shown in Fig. 20. Geological Modeling: Extensive data sets were used to construct the geological model of the Magnolia reservoirs. These include:

1. Core data (porosity, permeability, saturations, etc.) 2. Well log data (porosity, permeability, saturations,

etc.) 3. Geological structures (major horizons, permeability

barriers, fluid contacts, etc.) 4. Seismic data

The permeability barrier that apparently divides the reservoir into western and eastern sections was made a part of the geological model. Other major faults were included in the geological model. Even though wireline formation tester data indicate the existence of compartments in the western section of the reservoir, no attempt was made to physically include these compartments in the geological model at this stage. The reservoir characterization technique used in the process was accepted as sufficiently rigorous to capture any compartments that may exist in the western section of the reservoir. Reservoir Characterization: Five lithofacies were assigned to Magnolia rocks on the basis of core descriptions and log-derived shale cutoffs. The five lithofacies are:

1. Oil Sands- reservoir rocks in the western section 2. Western Wet Sands- wet sands in the western section

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3. Shale- non-reservoir rocks in western and eastern sections

4. Gas Sand- reservoir rocks in the eastern section 5. Eastern Wet Sands- wet sands in the eastern section

These five lithofacies were used in the characterization of the reservoir. The core data, well log data, geological structures, and seismic data were integrated in a geostatistical inversion process that used the Markov Chain Monte Carlo (MCMC) technique. The MCMC technique generates a “statistically correct random sample from a complex probability distribution”30. The products of the geostatistical inversion process are a lithology volume and a corresponding acoustic impedance volume. These volumes are co-simulated to generate additional volumes with distributed reservoir properties such as porosity, permeability, and water saturation. The net sand volumes were generated from transformation of the geostatistical outputs. The entire process could be altered to generate multiple realizations of the same reservoir property that are statistically consistent. Figs. 21 through 24 are cross-sectional views of the Magnolia reservoir showing a realization of reservoir lithology and corresponding realizations of reservoir properties of porosity, permeability, and water saturation. The application of MCMC technique in the characterization of Magnolia reservoir was presented in this paper simply to illustrate the use of this process to characterize a very complex reservoir. Detailed report on the work can be obtained from McCarthy et al.32. Reservoir Flow Model: One of the realizations of the geological model was selected by inspection and critical evaluation of the project team. This was the geological model that was converted into a reservoir flow model. The western section of the Magnolia reservoir will be modeled with a black oil simulator, while the eastern section will be modeled with a compositional simulator. Summary Five critical areas to be considered in the modeling of deepwater reservoirs are geological and geophysical modeling, reservoir characterization, reservoir flow modeling, facilities/flow assurance, and uncertainty/risk analyses. Modeling of three field examples in a systematic approach using the five critical areas as guidelines were presented. Acknowledgements We would like to acknowledge the work done by John Brand and Peter McCarthy on the Zia project, James Ten Eyck, Dick Willingham, and Mark Durkee on the Manatee project, and the authors of McCarthy et al.32 on the Magnolia project. We thank Gary Achee for his expert assistance in preparing the document and figures for this paper. We would also like to thank our partners in these fields for their permission to use the data in this paper. References 1. Slatt, R. M., Browne, G. H., Davis, R. J., Clemenceau, G. R.,

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18. Capeleiro Pinto, A. C., Guedes, S. S., Bruhn, C. H. L., Gomes, J. A. T., de Sá, A. N., and Fagundes Netto, J. R.: “Marlim Complex Development: A Reservoir Engineering Overview,” paper SPE 69438 presented at the 2001 SPE Latin American and Caribbean Petroleum Engineering Conference, Buenos Aires, Argentina, 25-28 March.

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20. Kelkar, M.: “Application of Geostatistics for Reservoir Characterization-Accomplishments and Challenges”, J. Cdn. Pet. Tech. (2000) 39, No. 7, 25.

21. Deutsch, C. V. and Journel, A. G.: GSLIB: Geostatistical Software Library and User’s Guide, Oxford University Press, New York (1998).

22. Kelkar, M. and Perez, G.: Applied Geostatistics for Reservoir Characterization, SPE, Dallas, TX (2002).

23. Christie, M.A.: “Upscaling for Reservoir Simulation”, JPT (November 1996) 1004.

24. Christie, M.A.: “Tenth SPE Comparative Solution Project: A Comparison of Upscaling Techniques”, SPEREE (August 2001) 308.

25. Langli, G., Masdal, S.I., Nyhavn, F., and Carlsen, I.M.: “Ensuring Operability and Availability of Complex Deepwater Subsea Installations: A Case Study”, paper OTC 13002 presented at the 2001 Offshore Technology Conference, Houston, Texas, 30 April – 3 May.

26. Monger-McClure, T. G., Tackett, J.E., and Merrill, L.S.: “Comparisons of Cloud Point Measurement and Paraffin Prediction Methods”, SPEPF (February 1999) 4.

27. Kabir, C.S, Chawathé, A., Jenkins, S.D., Olayomi, A.J., Aigbe, C., and Faparusi, D.B.: “Developing New Fields Using Probabilistic Reservoir Forecasting”, SPEREE (February 2004) 15.

28. White, C. and Royer, S.: “Experimental Design as a Framework for Reservoir Studies”, paper SPE 79676 presented at the 2003 SPE Reservoir Simulation Symposium, Houston, Texas, 3-5 February.

29. Friedmann, F., Chawathé, A., Larue, D.K.: “Assessing Uncertainty in Channelized Reservoir Using Experimental Designs”, SPEREE (August 2003) 264.

30. Narahara, G.M., Spokes, J.J, Brennan, D.D., Maxwell, G., Bast, M.: “Incorporating Uncertainties in Well-Count Optimization with Experimental Design for the Deepwater Agbani Field”,

paper SPE 91012 presented at the 2004 SPE Annual Technical Conference and Exhibition, Houston, Texas, 26-29 September.

31. Li, B. and Friedmann, F: “Novel Multiple Resolutions Design of Experiments/Response Surface Methodology for Uncertainty Analysis of Reservoir Simulation Forecasts”, paper SPE 92853 presented at the 2005 SPE Reservoir Simulation Symposium, Houston, Texas, 31 January – 2 February.

32. McCarthy, P., Brand, J., Paradiso, B., Ezekwe, J., Wiltgen, N., Bridge, A., Willingham, R., and Bogaards, M.: “Using Geostatistical Inversion of Seismic and Borehole Data to Generate Reservoir Models for Flow Simulations of Magnolia Field, Deepwater Gulf of Mexico”, to be presented at the 2005 SEG International Exposition and Seventy-Fifth Annual Meeting, Houston, Texas, 6-11 November.

8 SPE 95066

Figure 1. Zia field reservoirs.

Figure 2. Top and Base Horizon Maps for Zia field.

Figure 3. Net sand map of 1A Sand, Zia field.

Figure 4. Net sand map of 1B sand, Zia field.

Figure 5. Zia fault system.

Figure 6. Zia fault surfaces.

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Figure 7. Porosity distribution in Zia reservoir.

Figure 8. Cross-section through Zia main faults.

Figure 9. Zia field flow model fault cross-section.

Figure 10. Zia flow model.

Figure 11. Manatee field area.

Figure 12. Manatee S10A2 net sand volume.

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Figure 13. Manatee S10A2 AI volume.

Figure 14. Manatee S10A2 porosity transforms.

Figure 15. Manatee S10A2 porosity volume.

Figure 16. Manatee S10A2 porosity-permeabilty transform.

Figure 17. Manatee S10A2 permeability volume.

Figure 18. Manatee S10A2 porosity flow model.

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Figure 19. Manatee S10A2 initial oil saturation model.

Figure 20. Magnolia field.

Figure 21. Magnolia field lithology.

Figure 22. Magnolia field porosity.

Figure 23. Magnolia field permeability.

Figure 24. Magnolia field water saturation.