NotesNov-09NOTES:The papers listed here have been obtained by search SPE and IPTC papers post 2005 on the SPE's OnePetroThe papers relating to reservoir engineering have been catergorised for inclusion on the reservoirengineering.org.uk websiteThe affiiations searched were;Total No PapersReservoir Engineering RelatedBP551175Shell575279Chevron482238ConocoPhillips19168Marathon5537Total255129Schlumberger1130563Imperial College, London9553Heriot Watt University, Edinburgh235175(Anywhere in Article)Total35691717Total number of papers published post 2005 =10,00035% of papers published categorised

SHELLOrganisationSourcePaper No.ChapterSectionSubjectTitleAuthorAbstractSHELLIPTC11491CO2Corporate ProcessesShell's Carbon Capture ProjectReducing CO2 emissions by making cheaper CO2 capture technologies availableTheo Klaver, Shell Global Solutions International B.V.Abstract The Royal Dutch Shell Group (Shell) was one of the first energy companies to acknowledge the threat of climate change - to call for action by governments; our industry and energy users; and to take action ourselves. Shells strategy: to expand our alternative energies portfolio while investing in advanced CO2 solutions in order to improve our ability to manage emissions from our hydrocarbon business. Measures to manage future emissions will include developing new technologies to capture and store CO2 underground. The pursuit of Carbon Capture and Storage (CCS) technologies allows Shell to play an important and leading role towards addressing the need for an increasing worldwide demand for energy while at the same time dealing with the need to reduce global emissions. No single universal policy or technology will solve the CO2 challenge. Therefore various CCS solutions will need to be considered within a portfolio of measures to reduce global CO2 emissions while assisting a transition towards a low-carbon energy future. Shell seeks to position itself as part of the solution to the climate change issue. The United Nations Intergovernmental Panel on Climate Change (IPCC) has identified CO2 capture and storage (CCS) as the most promising for the rapid reduction of global emissions - by up to 55% by 2100. As the bridge to a more sustainable enerSHELLSPE109245CO2Corporate ProcessesShell's Carbon Capture ProjectReducing CO2 Emissions By Making Cheaper CO2 Capture Technologies AvailableTheo Klaver, Shell Global Solutions International BVAbstract The Royal Dutch Shell Group (Shell)[1] was one of the first energy companies to acknowledge the threat of climate change - to call for action by governments; our industry and energy users; and to take action ourselves. Shells strategy: to expand our alternative energies portfolio while investing in advanced CO2 solutions in order to improve our ability to manage emissions from our hydrocarbon business. Measures to manage future emissions will include developing new technologies to capture and store CO2 underground. The pursuit of Carbon Capture and Storage (CCS) technologies allows Shell to play an important and leading role towards addressing the need for an increasing worldwide demand for energy while at the same time dealing with the need to reduce global emissions. No single universal policy or technology will solve the CO2 challenge. Therefore various CCS solutions will need to be considered within a portfolio of measures to reduce global CO2 emissions while assisting a transition towards a low-carbon energy future. Shell seeks to position itself as part of the solution to the climate change issue. The United Nations Intergovernmental Panel on Climate Change (IPCC) has identified CO2 capture and storage (CCS) as the most promising for the rapid reduction of global emissions - by up to 55% by 2100[2]. As the bridge to a more sustainable energy system it is therefore a key solution for combating climate change - among a portfolio of solutions including renewable energies energy efficiency and biofuels In order to achieve deeper reductions in CO2 emissions there will need to be new technologies brought to the market to enable a Kyoto 2 type-agreement. Authorities such as the International Energy Agency the European Union Commission for Research and the US Department of Energy predict that new technologies will include hydrogen fuels cells clean coal technology and storage of CO2 below ground in deep saline formations or redundant reservoirs or for enhanced oil recovery. Considerable attention is being focused on CO2 storage with the desire to reduce the cost of capture and storage below 25 $/t CO2. Shell has a special team working on the CO2 Capture Project (CCP) Joint Industry Project. For the CCP Shell carries out studies manages projects and the team is involved evaluating opportunities for deployment of the technologies within Shell. Shell also provides the Vice Chairman for this initiative and has several key-players working on this project. CCP is an international collaboration among industry governments academics and environmental interest groups focused on developing technology for CO2 capture and geological storage. The CO2 Capture Team (CCT) conducts Shells participation in the CO2 Capture Project (CCP) and other external programs. CCT also works internally to apply external learnings and technologies within Shell businesses. Additionally Shells research and development funds and manages a separate CO2 storage program. Our goals are to: reduce the cost of capture by 60 to 80% and demonstrate that geological storage can be secure.SHELLSPE108161CO2Mechanism - Capillary and WettabilityFluid DescriptionCapillary Pressure and Wettability Behavior of CO2 Sequestration in Coal at Elevated PressuresWillem-Jan Plug, SPE, Horizon Energy Partners B.V., Saikat Mazumder, SPE, Shell; and Johannes Bruining, SPE, Delft University of TechnologySummary Enhanced coalbed-methane (ECBM) recovery combines recovery of methane (CH4) from coal seams with storage of carbon dioxide (CO2). The efficiency of ECBM recovery depends on the CO2 transfer rate between the macrocleats via the microcleats to the coal matrix. Diffusive transport of CO2 in the small cleats is enhanced when the coal is CO2-wet. Indeed for water-wet conditions the small fracture system is filled with water and the rate of CO2 sorption and CH4 desorption is affected by slow diffusion of CO2. This work investigates the wetting behavior of coal using capillary pressures between CO2 and water measured continuously as a function of water saturation at in-situ conditions. To facilitate the interpretation of the coal measurements we also obtain capillary pressure curves for unconsolidated-sand samples. For medium- and high-rank coal the primary drainage capillary pressure curves show a water-wet behavior. Secondary forced-imbibition experiments show that the medium-rank coal becomes CO2-wet as the CO2 pressure increases. High-rank coal is CO2-wet during primary imbibition. The imbibition behavior is in agreement with contact-angle measurements. Hence we conclude that imbibition tests provide the practically relevant data to evaluate the wetting properties of coal. Introduction Geological sequestration (Orr 2004) of CO2 is one of the viable methods to stabilize the concentration of greenhouse gases in the atmosphere and to satisfy the Kyoto protocol. The main storage options are depleted oil and gas reservoirs (Shtepani 2006; Pawar et al. 2004) deep (saline) aquifers (Kumar et al. 2005; Pruess et al. 2003; Pruess 2004) and unmineable coalbeds (Reeves 2001). Laboratory studies and recent pilot field tests (Mavor et al. 2004; Pagnier et al. 2005) demonstrate that CO2 injection has the potential to enhance CH4 production from coal seams. This technology can be used to sequester large volumes of CO2 thereby reducing emissions of industrial CO2 as a greenhouse gas (Plug 2007). The efficiency of CO2 sequestration in coal seams strongly depends on the coal type the pressure and temperature conditions of the reservoir (Siemons et al. 2006a 2006b) and the interfacial interactions of the coal/gas/water system (Gutierrez-Rodriguez et al. 1984; Gutierrez-Rodriguez and Aplan 1984; Orumwense 2001; Keller 1987). It can be expected that in highly fractured coal systems the wetting behavior positively influences the efficiency of ECBM recovery. It is generally accepted that the coal structure consists of the macrocleat and fracture system (>50 nm) and the coal matrix (30%). The new technologies aim for efficiencies above 85 % where efficiency is expressed as a percentage of hydrocarbon sales gas divided by the hydrocarbon feed stream. (Losses are due to fuel gas and hydrocarbons left in the contaminant stream.)SHELLSPE109011Reservoir ManagementSurveillenceDraugen FieldUnderstanding a Teenager: Surveillance of the Draugen FieldK. Langaas, SPE, A.D. Grant, N.A. Horvei, A. Cook, H.M. Klokk, and K.B. Flatval, SPE, Norske ShellAbstract Production from Draugen started in 1993. In its 14th year Draugen faces declining oil and increasing water production and is around halfway in its production life. The field development with water injection and relatively few wells has proved to be very successful. This paper shows how well and reservoir surveillance has been set-up in the Draugen field. Interesting features of this set-up are Structured and automatic data integration. Information providers for the important subsurface risks and uncertainties have been mapped which has resulted in a better understanding of the value of surveillance activities. Integration of laboratory data production well test results and model-based-rates are enablers for improved production allocation to individual wells. Data-to-information work processes have been mapped and automated as a natural part of the collaborative work environment between the platform and the offices. Use of time-lapse seismic. Draugen acquired seismic of excellent quality in 1998 2001 and 2004. The data has been a key enabler to understand the water flood and to manage the reservoir. This paper shows how visualisation methods and discipline integration have maximized the information of the 4D surveys and how this has been used to improve the reservoir simulation model. The paper provides clear examples of how surveillance is supporting reservoir management and production optimisation in the field. Introduction The Draugen Field is located 100 km offshore mid-Norway. The field has excellent reservoir characteristics that have enabled a field development with a minimal number of appraisal and development wells given the areal size of the field. The lack of well control for the structural mapping has resulted in a relatively high degree of uncertainty for both the top reservoir structure and the in-place volume. Therefore interpretation of the field structure is largely dependant upon 3D seismic surveys conducted in 1990 1998 2001 and 2004 and the associated seismic time to depth conversion work1. The latter three surveys included 4D seismic interpretations of the water movement through the reservoir; the most recent survey was acquired as a combined high-resolution and 4D survey. 4D seismic has been very important for the reservoir management on Draugen1 2 4. An overview map of the Draugen Field is shown in Figure 1. The reservoir is a low relief anticline with a maximum vertical closure of 50 m that trends north-to-south with an areal extent of 20 km by 6 km. The field comprises two Mid-to-Late Jurassic sand reservoirs (Rogn and Garn) separated by a shale (Lower Spekk) up to 10 m thick that pinches out to the west of the field. A structural saddle separates the main northern accumulation (Rogn Main) from the southern extension (Rogn South) see Figure 2. The main oil-bearing formation is the Upper Jurassic Rogn which is a generally high quality reservoir consisting of an upwards coarsening sand sequence. The Garn is oil-bearing to the west of the platform (Garn West) but is water-bearing over the rest of the field forming a regionally active aquifer see Figure 3. Both sands were deposited in a coastal predominantly shoreface setting. Porosity ranges from 28 to 32 %; permeability ranges up to 30 Darcy with an average of 5 Darcy. The Draugen oil is highly under-saturated with an initial hydrostatic pressure of 165 bar. All of the field producers except for A53 are horizontal with completed well lengths ranging from 370 m to 395 m. The Garn West and Rogn South wells are produced through sub-sea installations tied back to the Draugen platform. All platform wells and the Rogn South and Garn West subsea wells are currently on gas lift. The field has been water flooded since coming on production. Pressure has been maintained by water injection from the north (NWIT) and the south (SWIT) of the Rogn Main area and also from the active aquifer situated in the Garn. There are three northern injectors B1 B2 and B5 and two southern injectors C1 and C2. Due to a pipeline failure in July 2005 the northern injectors are currently shut in.SHELLSPE108651Reservoir ManagementSurveillenceProduction OptimisationThe Value of Surveillance and Advanced Logging Applications for Brownfield OptimizationBababola Akiode, Vikas Bhushan, and Robert Lee, SPE, Shell UK, and Parijat Mukerji and Zouhir Zaouali, SPE, SchlumbergerAbstract There has been a tremendous growth in the number of high-angle and horizontal wells in the past decade. Coupled with the increase in water cut from various brownfield environments these high angle wells present us with complex reservoir and production management challenges. Fit for purpose production logging technology is helping to provide a better understanding of fluid movement enabling higher confidence decision making leading to successful interventions. Production logging in high angle and horizontal wells that produce mixtures of fluid phases is challenging because of the associated complex flow regimes that radically change the physics and technology of measurement. Depending on the borehole deviation the velocity and fluid holdup of different phases can change dramatically for a given flow rate. We present examples that encompass various reservoir management objectives well optimization and flow profiling. Surveillance logs were acquired in these wells to obtain key inputs for production optimisation identifying bypassed oil and evaluating potential for additional perforations. Where necessary production logs were integrated with pulsed neutron capture and spectroscopy measurements to enhance our understanding. In one of the examples a compact integrated production logging tool comprising an array of spinners and holdup probes was conveyed with tractor in a horizontal well. Besides conveyance in this horizontal well the challenge involved detecting minor oil entries in a very high water cut scenario. Two examples relate to the effective use of pulsed neutron spectroscopy measurements reservoirs. Finally one of the examples involves oxygen activation for positive identification of water movement behind pipe. Creating value through surveillance is the common thread that binds these well intervention examples together. Introduction North Sea oil and gas production from mature basins face some particular challenges against a backdrop of production from platform and subsea wells in a challenging offshore environment. These offshore field developments often consist of long horizontal wells with subsea completions resulting in a high cost of well interventions and technical challenges in the conveyance of logging tools. The importance of surveillance for field management and further brownfield development cannot be underestimated even though the ultimate benefits are not always apparent in advance. These benefits include for example optimizing production reducing watercut improving well performance optimizing water injection and sweep efficiency and identifying unswept areas of the field to target with further infill drilling activities. In this paper we present four examples of reservoir surveillance activities in different mature North Sea reservoirs. Each example was selected for illustrating the application of a particular surveillance technology to meet a specific objective. Applying the appropriate measurement tools and techniques is essential to the success of the well intervention. Further in some cases the results can be surprising and lead to unexpected benefits as will be described in one of the following examples. Fluid flow regimes in vertical deviated and horizontal wells In a system with multiphase flow buoyancy causes the fluids to separate into different phases with a mixing layer in between. Gravity ensures that the lighter phase travels at a faster speed than the heavier phase. The difference in velocity between the phases is referred to as slip velocity. This also causes the downhole holdups to be different from the surface cuts.SHELLIPTC12344Reservoir ManagementThin Oil RimConcurrent Oil/Gas WellsConcurrent Oil & Gas Development Wells: A Smart Well Solution to Thin Oil Rim Presence in Gas ReservoirsSascha van Putten, SPE, and Marc Naus, SPE, Shell International Exploration and Production B.V.Abstract For a number of gas supply projects feeding LNG export schemes there exists a challenge that key gas reservoirs have associated underlying oil rims. Without due consideration to these oil rims regulator approvals to move ahead with the gas projects may be delayed and can erode project value. In order to optimize the development of both oil and gas hydrocarbon resources a novel concurrent oil and gas development concept is proposed. In this concept the gas cap and oil rim are produced simultaneously from the start of production through a single well conduit. As a result significant cost benefits can be realized (i.e. one concurrent smart well can potentially replace two conventional dedicated oil and gas wells). Reservoir simulation has demonstrated the ability of concurrent wells to enable simultaneous oil and gas production with minimal impact on oil recovery. The proposed concept can significantly impact the portfolio of available gas reservoirs by delivering a cost effective technology solution. Especially for reservoirs with water drive as the dominant drive mechanism (i.e. reservoirs with a strong aquifer) a concurrent oil and gas development is attractive. The main conclusions for this type of reservoir are: A relatively high gas offtake rates (up to 10% GIIP/year) can be achieved with limited reduction in oil recovery. Oil rim recovery is a function of how fast the oil rim migrates into the gascap itself mainly driven by gas offtake rate reservoir permeability and aquifer strength. Placing the horizontal leg close to the GOC is advantageous to maximize the time that the oil section of the well is exposed to the oil rim. Given the time dependency a relatively large tubing size improves oil recovery. This also enables the desired gas offtake rate to be achieved late in field life. Active monitoring and surveillance of the oil rim movement is essential to maximize recovery. For reservoirs with gas cap expansion as the dominant drive mechanism a concurrent oil and gas development is less attractive. It is however still feasible and should still be considered.SHELLSPE102310Reservoir ManagementWaterflood ManagementFracturesApplication of Smart, Fractured Water Injection Technology in the Piltun-Astokhskoye Field, Sakhalin Island, Offshore RussiaD.J. van Nispen, SPE, J. Hunt, SPE, A. Hartwijk, and A. Trofimov, SPE, Sakhalin Energy Investment Co.Abstract This paper discusses the application of new technologies and surveillance requirements with particular reference to fractured waterflood developments highlighting specific considerations for the Piltun-Astokhskoye field and the harsh and sensitSHELLSPE112940Reservoir ManagementWaterflood ManagementWell Placement OptimisationThe Omar Field (N.E. Syria) is Overcoming Its Mid-Life CrisisJ. Neidhardt, H. Farran, I. Gonzalez, and P. Vledder, Shell Syria; and Y. Doughry, Al Furat Petroleum CompanyAbstract With an approximate STOIIP of 760 MMbbls the Omar field is the largest field in Al Furat Petroleum Company's portfolio. The field located in the Euphrates Graben 45km SE of DeirEzZor - was discovered in 1987 and holds a maximum undersaturated oil column of more than 500m with two original oil-water contacts of 3750 and 3778 meters subsea. The oil production almost exclusively originates from two sandstone formations: the Cretaceous sheet-like shallow marine Lower Rutbah (RUL) and the Triassic coastal fluvial plane Mulussa F (MUF) formation. The Omar Field is formed by an elongated high relief tilted horst block which is internally compartmentalised. Originally the field produced naturally at a peak net oil rate of some 80kbpd but production declined rapidly because of the lack of any pressure support. Following the implementation of water injection from 1991 onwards a plateau production of around 60-70kbopd was achieved for some five years (1994-1997) declining to the current net oil production of 20 kbopd. Despite the structural complications the injector-producer connectivity in the laterally extensive RUL sands could be established rather confidently and recoveries in excess of 55% should eventually be achievable. Predicting water-flood efficiency in the Mulussa F 3D sand channel labyrinth turned out more complicated. As a matter of fact it was demonstrated that the resolution achievable by static reservoir modelling was not sufficient to predict the water-flood efficiency meaningfully. As a consequence a statistical infill campaign was launched with a focus to infill the existing major gaps between the MUF wells and secondly to establish a line drive waterflood pattern while investigating the merits of a dense five spot. The results of this infill drilling campaign (executed in 2005-2006) and a new 600-fold high-resolution seismic survey gave a multi-disciplinary team the challenge to improve in identifying more attractive targets while reducing the downside drilling results observed during the infill campaign. A combination of the new structural data with a regional geological well correlation fully and iteratively integrated with dynamic well information and production data indicates that the recovery in the MUF formation could well be optimized through a more deterministic instead of the previously adopted statistical infill drilling approach. Introduction The Omar field STOIIP of 760 MMstb is located for about 50% in the Cretaceous sheet-like shallow marine Lower Rutbah (RUL) and some 45% is contained in the Triassic coastal fluvial plane Mulussa F (MUF) formation. The field is an elongated high relief tilted horst block which is internally compartmentalized. The field is delimited by two main boundary faults and sealed by an erosional unconformity (BKU) which has removed the Rutbah reservoirs over the crestal part of the structure. A schematic cross section is shown in Figure 1. The Lower Rutbah consists of well-developed shallow marine and tidal channel sands with a high net-to-gross ratio (~73 %). The full Lower Rutbah thickness typically varies between 120 to 130 m in areas without BKU erosion. The Mulussa F1 and F2 (MUF1 MUF2) consist of fluvial channel sands and flood plain shales with a total thickness of up to 350m. The Mulussa F1 is characterised by a lower net-to-gross ratio (~22 %) than the Mulussa F2 (~35 %). Connectivity in this thinly bedded (single sand thickness is around 5m) 3D labyrinth-type reservoir is affected by extensive faulting with a multitude of throws below the seismic resolution of ~50 m. The Mulussa F3 formation below the Mulussa F2 has an even lower net-to-gross and is underlain by the Mulussa E carbonates.SHELLSPE105764Reservoir ManagementWaterflood Optimisationodelling - Adjoint Simulation MethodOptimal Waterflood Design Using the Adjoint MethodJ.F.B.M. Kraaijevanger, Shell Intl. E&P B.V.; P.J.P. Egberts and J.R. Valstar, TNO Built Environment and Geosciences; and H.W. Buurman, LogicaCMGAbstract We address the problem how to operate the injectors and producers of an oil field so as to maximize the value of the field. Instead of agressively producing and injecting fluids at maximum rate aiming at large short term profits we are after optimizing the total value (e.g. discounted oil volume) over the whole lifecycle of the field. An essential tool in tackling this optimization problem is the adjoint method from optimal control theory. Starting from a base case reservoir simulation run this extremely efficient method makes it possible to compute the sensitivities of the total (lifecycle) value with respect to all (time-dependent) well control variables in one go at a cost less than that of an extra reservoir simulation run. These sensitivities can be used in an optimization loop to iteratively improve well controls. We implemented the adjoint method and an associated optimization algorithm in our in-house reservoir simulator. In addition to conventional well control options based on the wells pressure or total rate we have also implemented smart well control options which allow the separate control of individual inflow intervals. Special adaptations of the optimization algorithm were required to allow the inclusion of inequality constraints on well control (pressure and rate constraints). We applied the optimization algorithm to a number of cases and found interesting non-trivial solutions to some optimal waterflood design problems that would not easily have been found otherwise. In this paper we also present a self-contained elementary derivation of the adjoint method which is different from but equivalent to the well-known derivation based on the Lagrange formalism. Introduction We focus on the problem of designing an optimal waterflood for an oil field. We limit ourselves to the situation where the well configuration and well types are given so the only degree of freedom left is the way the injector and producer wells are operated. The waterflood design we are looking for should be optimal in the lifecycle sense i.e. it should maximize the lifecycle integral Equation (1) where the integrand is a weighted sum of field rates Equation . Here the weights are denoted by the letter and the field rates by the letter . The subscripts and refer to oil and water while the superscripts prod and inj refer to production and injection respectively. We note that the weights which are given functions of time can have arbitrary sign. This makes it possible to combine oil revenues and water costs in the lifecycle integral. Well control is modeled with one or more time-dependent well control variables per well. For conventional wells there is just one control variable which can be tubinghead pressure (THP) bottomhole pressure (BHP) or a rate. For smart wells there are generally more control variables corresponding to the setting of downhole inflow or outflow devices which can be controlled independently. Well control is not completely free as it should take into account certain operational limits such as rate and pressure constraints. These operational limits correspond to inequality constraints either directly on the control variables or indirectly in terms of certain state variables of the well/reservoir system. Apart from constraints on individual wells there can also be global constraints dealing with several wells. Examples can be (equality or inequality) constraints imposed by surface facilities or constraints imposed by reservoir management considerations (e.g. voidage balance constraints). The mathematical optimization problem to be solved is to maximize the lifecycle integral by choosing the optimal well control while satisfying all constraints.SHELLSPE123563Reservoir ManagementWaterflood OptimisationOptimised SimulationOptimization of Smart Wells in the St. Joseph FieldG.M. van Essen, SPE, Delft University of Technology (TU Delft); J.D. Jansen, SPE, TU Delft and Shell International Exploration & Production (SIEP); D.R. Brouwer, SPE, and S.G. Douma, SPE, SIEP; and K.I. Rollett and D.P. Harris, Sarawak Shell BerhadAbstract The St. Joseph field has been on production since September 1981 under natural depletion supported by crestal gas injection. As part of a major redevelopment study the scope for water flooding was addressed using 'smart' completions with multiple inflow control valves (ICVs) in the wells to be drilled for the redevelopment. Optimal control theory was used to optimize monetary value over the remaining producing life of the field and in particular to select the optimal number of ICVs the optimal configuration of the perforation zones and the optimal operational strategies for the ICVs. A gradient-based optimization technique was implemented in a reservoir simulator equipped with the adjoint functionality to compute gradients of an objective function with respect to control parameters. For computational reasons an initial optimization study was performed on a sector model which showed promising results. Introduction St. Joseph is a mature oil field located 135km offshore Sabah Malaysia. The oil initially in place (STOIIP) is estimated at 630 MMstb of which 83% is located in the main reservoir package in the Northwest Flank. These reservoir units dip at an angle of approximately 20 degrees to the NW and have a strongly layered internal architecture with only limited (vertical) communication between layers. Sand porosities and permeabilities are good in the oil column generally deteriorating down flank into an aquifer which has a weak direct influx into the main reservoir see Walsh et al. (1996). The St. Joseph field has been on production since 1981. Until 1996 the recovery mechanism was natural depletion under gravity drainage. At the end of 1995 the field had produced 105 MMstb out of the total ultimate recovery estimated at 230 MMstb. Average pressure had fallen from 7.3 MPa to 4.1 MPa. Since May 1996 production has been supported by crestal gas injection. Gas is injected into the reservoir for two reasons: disposal of produced gas from St. Joseph and neighbouring fields and reservoir pressure maintenance. A feasibility study completed in the second quarter of 2006 concluded that water injection was not only feasible but also required to safeguard developed reserves and to realize additional oil recovery from the field. A large redevelopment project is planned to facilitate water flooding. The total scope of the project includes the installation of a new platform for offshore living quarters seawater treatment and injection facilities and the drilling of six horizontal water injectors and five infill producer wells. Treated seawater will be used as injection fluid at a total rate of 10 000 bbl/d for each injection well. First water is to be injected in 2010 however at the time this study was conducted start of injection was expected to be in October 2009. The horizontal water injectors require a high degree of zonal control because of the laminated nature of the reservoir and historical problems with controlling water and gas breakthrough in high permeability streaks. The selected concept for the water injection wells is a horizontal well injecting under fracturing conditions completed with multiple zones. It is intended that injection will be into two zones simultaneously alternating between zones several times a year. This concept is not feasible without the use of smart technology: each zone will be fitted with an inflow control valve (ICV) and dual downhole pressure gauges to allow remote control. At the time this study was conducted the number of zones was assumed to be limited to four as a result of physical and financial constraints. Currently however the injection wells are planned to be equipped a five-zone smart completion. There are significant benefits associated with a smart well completion over multiple zones. The increased control capability allows the potential optimization of the flooding process thus maximizing total oil recovery. The main objective of this waterflood optimization study is to determine the value of down-hole control in the planned water injectors in terms of incremental cumulative oil production. The maximum incremental oil production using downhole control is determined by finding the:SHELLSPE102650Reservoir Modelling4D SeismicIntgrated ModelSeismically Based Integrated Reservoir Modelling, Lunskoye Field, Offshore Sakhalin, Russian FederationLiz Ross and Kevin King, Sakhalin Energy Investment Co. Ltd.; Gerard Bodewitz, Hajo van Hasselt, Greg Stone, Wim Twigt, Wim Swinkels, Andrew James, and Tony Addis, Shell Intl. E&P B.V.; Chris Parsons, Robert Meij, Sarah Bell, Alexei Trofimov, Patrick Jackson, and Valery Cholovsky, Sakhalin Energy Investment Co. Ltd.; and Edwin Lamers and Syrie Crouch, Shell Intl. E&P B.V.Abstract The Lunskoye Field is a centrepiece of the Sakhalin II development one of Shell's most significant current projects. Demand for LNG in the nearby Asia-Pacific market and availability of a large (18.2 Tcf GIIP) gas resource underpins the investment decision in a challenging offshore environment. Robust suites of seismically-constrained integrated reservoir models were generated to evaluate short- medium- and long-term subsurface uncertainties. Previous quantitative reservoir mModelling efforts were hampered by the existence of shallow gas over the crest of the structure which masked the seismic response in the crestal area of the reservoir. This impacted the evaluation of structure and reservoir properties from seismic. To address this a new 3D-survey was acquired in 2003 in an east-west direction to undershoot the shallow gas deposits. The Pre-Stack Time Migration and Pre-Stack Depth Migration seismic results show the reservoir image is greatly improved and there is improved confidence in the seismic signal that allows it to be used for the definition of rock property distribution. In parallel to the seismic interpretation a detailed evaluation of velocities core and well-log information was undertaken to provide input into an in-house probabilistic model-based seismic inversion (1). This fundamental work was used to: construct an integrated and fully consistent structural and stratigraphic hierarchical framework in the lateral and vertical domain; update the view of the depositional setting taking into account variations in provenance and relative sea-level; define the petrophysical parameters for the rock property model which underpins the inversion. The results of the model-based inversion formed the basis of the detailed static models and analysis of key subsurface uncertainties. Results of the improved property models were upscaled and fed into the well inflow models and dynamic models to improve productivity estimates long-term reservoir performance prediction and to support development planning and reservoir management. This paper summarises all of the refinements undertaken as part of the new model building including the improvement in the seismic data the insight into the stratigraphic changes the hierarchical framework the rock property model the probabilistic inversion the well inflow modelling and the dynamic simulation. Introduction The Lunskoye Field is located in the Sea of Okhotsk offshore Sakhalin Island (Figure 1). Sakhalin Island is a product of a northward propagating transpressive strike slip fault system. This generated island uplift affecting sediment supply to the east Sakhalin shelf from the late Miocene onwards. At the crest of the Lunskoye anticline top reservoir (Daghinsky Formation) is at a depth of ~1650 m tvdss (Figure 2). The field can be divided into six major structural blocks and is penetrated by seven wells. All fault blocks except Block VI are penetrated by at least one well (Figure 1).SHELLIPTC12550Reservoir ModellingAdjoint Based SimulationAssisted HMReservoir Simulation Model Updates via Automatic History Matching With Integration of Seismic Impedance Change and Production DataYannong Dong, Shell International Exploration and Production Inc., and Dean S. Oliver, University of OklahomaAbstract Automatic history matching can be used to incorporate 4D seismic data into reservoir characterization by adjusting values of permeability and porosity to minimize the difference between the observed impedance change and the predicted impedance change while remaining as close as possible to the initial geological model. To perform the history matching efficiently an adjoint method is used to compute the gradient of the data mismatch and a quasi-Newton method is used to compute the search direction. Compared to other approaches that use the time-lapse seismic data to infer saturation and pressure directly this method uses a finite-difference black-oil reservoir simulator to ensure that the material balance and flow equations are honored. Two ancillary issues were important in obtaining a match. First it was necessary to convert the map of change in reflection coefficients to a map of change in impedance. Second it was necessary to characterize the noise in observed seismic impedance change data to prevent overmatching of the data. All the procedures are illustrated with an application to a reservoir in the Gulf of Mexico. Introduction The objective of automatic history matching is to obtain a reservoir simulation model that honors observed production history and is geologically plausible. Although dynamic data from the wells such as pressure gas oil ratio (GOR) and water oil ratio (WOR) provide useful information for reservoir characterization in traditional history matching the resolution of an estimate obtained from this type of data is typically poor. The only way to improve the resolution in such cases is to integrate additional data that can provide more constraints especially at the areas far from wells. Seismic data have much better spatial coverage than production data. We have chosen to history match the change in seismic impedance that is computed from the time-lapse seismic data. The change in seismic impedance is highly sensitive to changes in saturation and pressure in the reservoir; these changes can then be related to the porosity and permeability1. Automatic history matching problems are typically formulated as the minimization of the difference between actual and predicted data. Thus the choice of an effective minimization algorithm is critical. In a recent comparison of several gradient-based minimization methods Zhang and Reynolds2 showed that the limited memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) method3 is relatively efficient for large history matching problems. Thus in this study the LBFGS method is used for history matching of both production and seismic impedance change data.SHELLSPE105797Reservoir ModellingAdjoint Based SimulationWell Placement OptimisationAdjoint-Based Well-Placement Optimization Under Production ConstraintsM.J. Zandvliet, SPE, M. Handels, SPE, G.M. van Essen, SPE, Delft University of Technology; D.R. Brouwer, SPE, Shell International E&P; and J.D. Jansen, SPE, Delft University of Technology and Shell International E&PSummary Determining the optimal location of wells with the aid of an automated search method can significantly increase a projects net present value (NPV) as modeled in a reservoir simulator. This paper has two main contributions: first to determine the effect of production constraints on optimal well locations and second to determine optimal well locations using a gradient-based optimization method. Our approach is based on the concept of surrounding the wells whose locations have to be optimized by so-called pseudowells. These pseudowells produce or inject at a very low rate and thus have a negligible influence on the overall flow throughout the reservoir. The gradients of NPV over the lifespan of the reservoir with respect to flow rates in the pseudowells are computed using an adjoint method. These gradients are used subsequently to approximate improving directions (i.e. directions to move the wells to achieve an increase in NPV) on the basis of which improving well locations can be determined. The main advantage over previous approaches such as finite-difference or stochastic-perturbation methods is that the method computes improving directions for all wells in only one forward (reservoir) and one backward (adjoint) simulation. The process is repeated until no further improvements are obtained. The method is applied to three waterflooding examples. Introduction Determining the location of wells is a crucial decision during a field-development plan because it can affect a projects NPV significantly. Well placement is often posed as a discrete optimization problem (Yeten 2003) (i.e. involving integers as decision variables). Solving such problems is an arduous task; therefore well locations often are determined manually. However several automated well-placement optimization methods are available in the literature. They can be classified broadly into two categories. The first category consists of local methods such as finite-difference-gradient (FDG) (Bangerth et al. 2006) simultaneous-perturbation-stochastic-approximation (Bangerth et al. 2003 Spall 2003) and Nelder-Mead simplex (Spall 2003) methods. The second category consists of global methods such as simulated annealing (Beckner and Song 1995) genetic algorithms (Montes et al. 2001 Gyagler et al. 2002 Yeten et al. 2003) and neural networks (Centilmen et al. 1999). The first category is generally very efficient requires only a few forward reservoir simulations and increases NPV at each iteration. However these methods can get stuck in a local optimal solution. The second category can in theory avoid this problem but has the disadvantages of not increasing NPV at each iteration and requiring many forward reservoir simulations. A rather different approach is proposed by Lui and Jalali (2006) where standard reservoir models are transformed to maps of production potential to screen regions that are most favorable for well placement. In this paper we present a gradient-based method that is distinct from those previously mentioned. The adjoint method used in optimal-control theory has been used previously for optimization of injection and production rates in a fixed-well configuration (Ramirez 1987 Asheim 1988 Sudaryanto and Yortsos 2001 Zakirov et al. 1996 Virnovsky 1991 Brouwer and Jansen 2004 Sarma et al. 2005 Kraaijevanger et al. 2007). In these applications the parameters to be optimized are usually well-flow rates bottomhole pressures (BHPs) or choke-valve settings. Because these are not mixed-integer problems gradient-based methods are used commonly to solve them and the adjoint method efficiently generates the required gradients. We propose to use the adjoint method for well-placement optimization. An example of well-placement optimization using optimal control theory has been proposed previously by Virnovsky and Kleppe (1995). Our approach however is significantly different. Moreover two further applications of adjoint-based well-placement optimization were published recently (Wang et al. 2007 Sarma and Chen 2007.) The outline of our paper is as follows: First the effect of production constraints on optimal well locations is investigated. Then an adjoint-based well-placement-optimization method is presented. Finally the benefits of this method are demonstrated by three waterflooding examples.SHELLSPE101880Reservoir ModellingAnalogue ModellingHistory matchingHistory-Matching Considerations of an Analogue Reservoir Model (ARM)E.T. Montague, SPE, Curtin U. of Technology*; D.H. Sherlock, SPE, CSIRO Petroleum; and E. Santoso, SPE, Curtin U. of Technology**Abstract This paper describes a research program at the Australian Resources Research Centre (ARRC) to establish and use an analogue model to gain insight into issues of uncertainty in numerical reservoir simulation. Reported in this paper are the initial findings from history matching the production response of an Analogue Reservoir Model (ARM) and its numerical representation in a finite difference simulation model. The ARM is a large-scale physical model comprising two intersecting synthetic sandstone channels encased within an impermeable acrylic matrix. The initially oil-saturated model was waterflooded via the upper channel and the injected water was dyed blue to allow the displacement process to be recorded on video. Production rates pressures water cuts etc were recorded and used to history match the reservoir simulation model. The simulation model was built using Roxars Nextwell software and populated with the rock and fluid properties of the ARM. The initial history matching attempt did not yield a good match. The model was unable to match the preferential flow of water along the lower channel. It was discovered later that the lower channel had become inadvertently fractured during fabrication of the model. Although incorporating the fracture system into the simulation model significantly improved the quality of the history match the character of the water cut development was dependant upon the relative permeability curve used. It has been well documented that different relative permeability curves are used to model fluid flow through porous media as opposed to fracture and fracture-matrix flow This ARM provides a unique opportunity to test the applicability of published fracture relative permeability models by using these in history matching the production response between the reservoir simulation model and the physical model. Introduction The ARM project is a unique experimental program between CSIRO Petroleum and Curtin University of Technology that has been devised to investigate issues relating to uncertainty in reservoir simulations of channelised fields and their seismic expression1. The ARM program is designed to integrate aspects of seismic and reservoir engineering through the construction of a laboratory-scale model of sand bodies with flow characteristics that can be monitored and modelled and that can be scaled to reflect fluid flow behaviour on a field-scale. The increase in costs associated with the push towards deepwater reservoir production means that risks in exploration and development remain significant despite continual technological advances. The understanding of risk and uncertainty in these frontier environments presents a major challenge to the industry. In these environments permeability architecture and reservoir connectivity are key uncertainties which lead to differences in dynamic reservoir performance and estimates of ultimate recovery. This is particularly important in deep-water reservoirs where the understanding of complex channelised geological systems is restricted by the limited resolution of seismic data and by fewer well penetrations because of the high cost of drilling. Accordingly the project sponsors (Chevron and Woodside Energy Ltd.) requested that the ARM be designed to mimic a turbidite channel system i.e intersecting sinuous channels with varying degrees of connectivity. This paper presents the reservoir simulation modeling component of the ARM project and discusses the various methods and results achieved during the process of history matching the ARM to the measured production data. This reservoir simulation work follows up on the geophysical modeling which has been published previously1. Analogue Reservoir Modelling (ARM) ARM is based around a synthetic cementation technique that allows scaled analogue representations of reservoir systems to be conducted in the laboratory. The cementation technique known as Calcite In-situ Precipitation System (CIPS) allows sandstones to be fabricated with predetermined physical properties such as porosity permeability and impedance2. Laboratory tests have shown that CIPS sandstones closely reproduce the acoustic and mechanical properties of natural sandstones3.SHELLSPE106170Reservoir ModellingAssisted HMSingular Evolutive Interpolated Kalman FilterA Singular Evolutive Interpolated Kalman Filter for Rapid Uncertainty QuantificationBaosheng Liang, U. of Texas at Austin; Faruk O. Alpak, Shell Intl. E&P Inc.; and Kamy Sepehrnoori and Mojdeh Delshad, U. of Texas at AustinAbstract Inherent data and model uncertainties render the history-matching inverse problem extremely non-unique. Therefore a reliable uncertainty quantification framework for predicting reservoir dynamic performance requires multiple reservoir models that match field production data. It has been previously demonstrated that the ensemble Kalman filter technique can be used for this purpose. In this technique an ensemble of reservoir models is evolved by means of a stochastic nonlinear filtering procedure to agree with the observed production data. An efficient variant of the ensemble Kalman filter namely Singular Evolutive Interpolated Kalman Filter (SEIKF) is applied to the multi-model history-matching problem in this work. This novel technique operates in three steps: resampling forecasting and assimilation. Unlike the ensemble Kalman filter where the members of the model ensemble are operated by forecasting and assimilation in SEIKF the members of the model ensemble are selected in the main orthogonal directions of a functional space described by an approximation to the error-covariance matrix. This enhanced sampling strategy embedded into the resampling step improves the filter stability and delivers rapid convergence. SEIKF is applied to a three-dimensional proof-of-concept waterflooding case where reservoir permeability is calibrated to production data. Accuracy and convergence of history match as well as the uncertainty of dynamic predictions yielded by the final model ensemble are used as criteria to evaluate the performance of SEIKF. The outcome of the proof-of-concept studies quantitatively demonstrates that SEIKF exhibits rapid convergence in the domain of model parameters. In terms of accuracy and uncertainty reduction SEIKF performs comparable to a conventional ensemble Kalman filter. SEIKF promises a rapid and reliable framework for history matching and naturally lends itself to uncertainty quantification. Introduction The optimazation of a reservoir development strategy is measured by its robustness under the influence of uncertainty. In addition to economic unknowns uncertainties in reservoir characterization constitute a large component of the financial risk. The practice of forecasting hydrocarbon recovery performance through dynamic reservoir modeling is therefore an integral component of risk analysis and uncertainty reduction strategies. Emerging technologies such as geophysical reservoir monitoring (i.e. permanent sensors 4D seismic) and optimal reservoir management (i.e. smart completions) also heavily rely on dynamic modeling. In this perspective future forecasts of reservoir performance are used to optimize reservoir management decisions. The quality of the oil reservoir model is therefore of essential importance for performing robust and accurate predictions of recovery potential and in turn making decisions on correct premises. It is very desirable to constrain the dynamic model to all available data and reduce uncertainties. The most direct information about the physics of fluid flow in the dynamic model is embedded in the production data. The type of production data in turn is a function of the recovery mechanism. More precisely flowing phases and injection/production constraints associated with a given recovery mechanism determine the types of available production data. An arbitrary combination of water oil and gas production rate and wellbore pressure may constitute the individual components of a production data set. As a direct measure of the reservoir response integration of production data to dynamic reservoir models is the primary driver for history-matching. In a history matching exercise model parameters are adjusted in such a way that the dynamic simulation response reproduces the historical production record as accurately as possible. This is achieved either by manually adjusting the parameters of the dynamic model or permitting an automatic process to propose adjustments.SHELLSPE119030Reservoir ModellingAssisted HMStochastic FrameworkStochastic History Matching of a Deepwater Turbidite ReservoirFaruk O. Alpak, SPE, Florian van Kats, and Detlef Hohl, SPE, Shell International Exploration and Production Inc.Abstract A novel stochastic framework is described that facilities automatic history matching and uncertainty quantification workflows. The underlying algorithm of the framework combines Design of Experiments (DoE) and Markov Chain Monte Carlo (MCMC) inference techniques. A generic singular-value decomposition technique creates a Response Surface Model (RSM) from a DoE-based selection of simulations. Ensuing RSM serves as a rapid proxy to full-physics reservoir simulation. The data integration and uncertainty quantification framework extends current working practice by sampling Bayes' posterior parameter probability distribution with a rigorous MCMC method using the Metropolis-Hastings algorithm. This improves uncertainty estimates in the history-matching mode and provides a unified platform for scenario management and analysis in the forecast uncertainty quantification mode. The stochastic framework is tested for robustness and efficiency on a real field case. A history matching study is carried out for a complex deepwater turbidite reservoir involving multiple geologic realizations. Within the context of the study the stochastic framework delivered (a) a complete description of the high-impact model parameters dominating the quality of the match (b) multiple models honoring the historical production data (c) reduced uncertainty ranges for the history-matching parameters and (d) a quantitative measure of reliability for different measurement types. The outcome of the multi-realization history matching study reveals a better reservoir connectivity and an increased net-sand volume compared to the maximum a-posteriori (MAP) prediction attained by a commercial assisted history-matching software. A well-by-well analysis of the most probable model reveals that the match is comparable or superior to the ones delivered by the commercial tool. The field test outcome demonstrates the reliability expediency rigor and computational efficiency of our stochastic history-matching and recovery forecasting framework. Introduction Motivation. Optimalilty of a reservoir development strategy is measured by its robustness under the influence of uncertainty. Uncertainties around the subsurface model constitute a significant component of the financial risk along with economically rooted factors. The practice of forecasting recovery performance through dynamic modeling is an integral component of risk analysis and uncertainty reduction strategies. When rigorously calibrated to geologic geophysical and historical production information reservoir performance forecasts facilitate optimal reservoir management decisions on correct premises. History matching the calibration of the reservoir model to dynamic (production) data using numerical simulators has been one of the longstanding challenges of forecasting. Conventional history matching is a manual exercise whereby a few reservoir parameters are varied until a satisfactory match is achieved. Under time constraints the manual trial-and-error approach often leads to a single so called best" history-matched model. Often by reason of the ad hoc nature of the entire process only very little quantitative information could be captured about the uncertainties around the reservoir model. Automatic and semi-automatic techniques have been developed to address the difficulties associated with history matching and assist reservoir engineers. Optimal simulation workflows require conventional assisted history-matching tools to go beyond their core functionality of history matching and serve as comprehensive uncertainty management platforms. An integrated platform should facilitate forecast scenario generation analysis and optimization in the absence of production data (greenfield activities). As the focus shifts from the appraisal to the development phase and dynamic information becomes available the platform should permit rapid integration of production data to the dynamic model in addition to scenario forecasting and optimization capabilities (brownfield activities). A well-planned and easily extendable software architecture is a critically important success factor for such an integrated uncertainty management platform as it must facilitate the above-described complex tasks without compromising user friendliness and computational performance."SHELLSPE108307Reservoir ModellingConvective Heat TransferLaminar flowConvective Heat Transfer for Laminar, Steady-State Flow of Bingham and Power Law Fluids between Vertical, Parallel PlatesYildiz Bayazitoglu, Rice University; Paul R. Paslay, Manatee Inc.; and Paul Cernocky, SPE, Shell Int. E & P Inc.Abstract This paper explains how to model the convective heat transfer of Bingham and Power Law fluids across parallel plates. The analysis enables specification of the fluid properties necessary in order for Bingham or Power Law fluids to prevent or reduce convection and thus minimize Wellbore heat transfer. The paper gives the analytical convective heat transfer flow solution for the Bingham material and Power Law fluids and uses this to determine example Nusselt numbers. The analysis is similar to the analysis by Batchelor for linear viscous fluids. Applications are the reduction of heat transfer to minimize trapped annular pressures or to reduce heat loss from production fluids containing hydrates The most important conclusion is that the influence of gel strength on convective heat transfer rate in oil industry applications is quite strong. Another conclusion is that the temperature differential required to initiate flow can be appreciable in practical applications and this means the study indicates only a small gel strength should be sufficient to prevent convection. The gel strength does not need to be so large as to interfere with pumping. Another conclusion is a formulation for the way in which Power Law parameters influence convective heat transfer of the fluid and hence identification of the parameters which need to be measured in order to characterize the convection of the fluid. 1 Introduction For several decades packer fluids have been available with varying degrees of insulation against heat transfer. Usually this is done either to prevent heat loss from building large collapse pressures in drilling fluids trapped between casing strings; to prevent cooling of shut-in production fluids containing hydrates and paraffins; or to keep injection fluids hot (early steam well applications). Achieving reliable insulating performance often is critical to the longevity and economic viability of wells that need such fluids. The insulation usually is achieved by reducing or eliminating the packer fluids natural convection which would otherwise amplify heat transfer across the fluid. For a non-insulating fluid the convection amplification factor the Nusselt number can be as large as ten or more. Insulating also is achieved to a lesser degree by reducing the static thermal conductivity of the fluid when possible (e.g. changing from water based to oil based). In order to achieve partial or complete reduction of convection insulating packer fluids have either a high yield point or very high viscosities at low shear rates. Historically this has presented several problems relating to the unknown thermal and mechanical character of these non-Newtonian fluids. In the case of Newtonian fluids the solutions to the convection and mechanical performance of the fluids are readily available. For non-Newtonian packer fluids one problem is that the free convective heat transfer of the fluid (the amplification due to convection) has not been available in terms of a correlation model that depends on laboratory-measurable fluid properties. This is because of the non-Newtonian nature of the fluids. Consequently the heat transfer has been estimated only from correlations for Newtonian fluids or from very large convection cell experimental correlations of well performance. Absence of a conventional correlation model means that the fluids heat transfer properties cannot be engineered and quantified based on small laboratory measures of fluid properties such as yield point and shear rates. In the past this has made design and implementation of such fluids somewhat a hit or miss proposition. It should be noted that the annulus where the convection occurs is usually modeled by parallel plates3. In this paper only the parallel plate solution is considered.SHELLSPE102467Reservoir ModellingCoupled Fracture/Reservoir ModelInduced Water Injection fracturesInduced Fracturing in Reservoir Simulations: Application of a New Coupled Simulator to a Waterflooding Field ExampleB. Hustedt, SPE, D. Zwarts, H.-P. Bjoerndal, SPE, R. Masfry, SPE, and P.J. van den Hoek, SPE, Shell International Exploration and ProductionSummary Water-injection-induced fractures are key factors influencing successful waterflooding projects. Controlling dynamic fracture growth can lead to largely improved water-management strategies and potentially to increased oil recovery and reduced operational costs (well-count and water-treatment-facilities reduction) thereby enhancing the project economics. The primary tool that reservoir engineers require to guarantee an optimal waterflood field implementation is an appropriate modeling tool which is capable of handling the dynamic fracturing process in complex reservoir grids. We have developed a new modeling strategy that combines fluid flow and fracture growth in one reservoir simulation. Dynamic fractures are free to propagate in length and height-direction with respect to poro- and thermoelastic stresses acting on the fracture. A prototype simulator for contained fractures was tested successfully. We have extended the coupled simulator to incorporate noncontained fractures. The new simulator called FRAC-IT handles fracture-length and -height growth by evaluating a fracture-propagation criterion on the basis of a Barenblatt (1962) condition. The solution of the 5D problem is computed by use of a tuned Broyden (1965) approach. We demonstrate the capabilities of the coupled simulator by showing its application to a complex reservoir-simulation model. The fracture modeling is used to history match an injectivity test in a five-spot injection pattern using produced water. The coupled-simulation results and the field-data interpretation show a very good match. The outcome of the injection test led to an appropriate waterflood-management strategy adapted to the specific reservoir conditions and in terms of production to a net oil-production increase of 50 to 100%. The field example shows how the coupled-simulator technology can be used to achieve optimized waterflood-management strategies and increased oil recovery. Introduction Waterflooding is often applied to increase the recovery of oil in mature reservoirs or to maintain the reservoir pressure above bubblepoint in the case of green fields. Even though often unnoticed water injection frequently is taking place under induced-fracturing conditions. The rock fracturing has a strong influence on the water injectivity and the areal distribution of the fluids in the reservoir. A qualitative example of the impact of the fracture orientation on the areal sweep is demonstrated in Fig. 1. We show streamlines in two different water-injection-pattern configurations for two fracture orientations (i.e. line-drive and five-spot geometry and fracture oriented toward the producer and away from the producer. The density of the streamlines indicates that the fracture orientation changes the areal sweep. In order to achieve optimized water-injection management dynamic fracture propagation needs to be estimated properly before the injection controlled during operations and monitored to ensure predictions and reality do not deviate significantly. The tools commonly used to study fracture growth numerically are analytical fracture simulators which often are based on a single-well model in a simplified reservoir formation. Generally reservoir heterogeneity is reduced to a number of horizontal layers with homogeneous properties and a laterally infinite extent. Fracture propagation is described using a pseudo-3D description (van den Hoek et al. 1999). For many field developments under waterflooding fracture propagation is estimated with acceptable error bars using these or similar tools. The major drawbacks are Areal reservoir heterogeneity is not accounted for. Varying poro- and thermoelastic stresses along the fracture are neglected. Injection pressures have large error bars because the reservoir response is not properly captured. Nearby wells influences (e.g. pattern flood) are not captured. In the past many attempts have been made to address these issues. Common approaches can be grouped into fully implicit simulators (Tran et al. 2002) where both fluid-flow and geomechanical equations are solved simultaneously on the same numerical grid and coupled simulators (Clifford et al. 1991) where a standard finite-volume reservoir simulator is coupled to a boundary-element-based fracture-propagation simulator. To our knowledge both approaches are not standard and currently not used in the industry because Models need to be purpose built (i.e. reservoir models from standard reservoir simulator cannot be used). Fracture propagation is oversimplified. Numerical stability is questionable. We have developed an extension to an existing reservoir simulator to circumvent these shortcomings. We use a coupled-simulator approach based on a two-way communication strategy between the fully numerical reservoir simulator and the half-analytical geomechnical-modeling part. The new simulator enables the modeling of fluid flow and dynamic fracture propagation in a combined way. We have applied the tool to field applications for waterflooding projects in which injector/producer shortcuts are a potential risk (pattern floods) and also to environments in which fracture containment and estimating accurate injection pressures are the main concerns. In this paper we briefly review the coupled-simulator approach and discuss the application to a waterflooding field example.SHELLSPE115204Reservoir ModellingCoupled Fracture/Reservoir ModelInduced Water Injection fracturesDynamic Induced Fractures in Waterflooding and EORP.J. van den Hoek, B. Hustedt, M. Sobera, H. Mahani, R.A. Masfry, J. Snippe, and D. Zwarts, SPE, Shell International Exploration and Production B.V.Abstract It is well established within the Industry that water injection mostly takes place under induced fracturing conditions. Particularly in low-mobility reservoirs or when injecting contaminated water (e.g. PWRI) large fractures may be induced during the field life. This paper presents a new modeling strategy that combines fluid-flow and fracture-growth (fully coupled) within the framework of an existing standard reservoir simulator. We demonstrate the coupled simulator by applications to a model five-spot pattern flood model and to a number of actual field cases (waterfloods produced water disposal) worldwide. In these field cases validity checks were carried out comparing our results with available surveillance data. These applications address various aspects that often play an important role in waterfloods such as short-cut of injector and producer vertical fracture containment and reservoir sweep. We also demonstrate that induced fracture dimensions can be very sensitive to typical reservoir engineering parameters such as fluid mobility mobility ratio 3D saturation distribution (in particular shockfront position) positions of wells (producers injectors) and geological details (e.g. flow baffles faults). The results presented in this paper are expected to also apply to (part of) EOR operations (e.g. polymer flooding). 1. Introduction Water injection will generally result in rapid injectivity decline unless it takes place under induced fracturing conditions (e.g. 1 2). Important risks associated with waterflooding under induced fracturing conditions are related to potential unfavorable areal and vertical sweep. These risks can be managed if one has a proper understanding of dynamic induced fracture behaviour as a function of parameters such as injection rate voidage replacement reservoir fluid mobility and reservoir / injection fluid mobility ratio3. In order to enable building and using such an understanding as part of field development planning and of reservoir management we developed an add-on fracture simulator to our existing in-house reservoir simulator4. In the past several attempts were made to address the coupled problem of reservoir simulation and induced fracture growth. Common approaches can be grouped into fully implicit simulators (Tran et al.5) where both fluid flow equations and geomechanical equations are solved at the same time on the same numerical grid and coupled simulators (Clifford et al.6) where a standard finite-volume reservoir simulator is coupled to a boundary-element based fracture propagation simulator. Both approaches are not standard and currently not used in the industry mainly because reservoir models need to be purpose-built and numerical stability is questionable. Our approach as briefly described in 4 uses a standard reservoir simulator thereby enabling reservoir engineers to model induced fracturing around injectors using their standard reservoir models (sector full-field). Moreover our specific methodology of coupling induced fractures to the reservoir via special connections 4 helped to eliminate most of the numerical instabilities that are generally encountered in the coupled (reservoir flow)-(fracture growth) problem. The current paper presents an important application of coupled reservoir flow and induced fracture growth. The focus is on demonstrating how dynamic fracture growth around injectors is largely driven by reservoir engineering parameters. It is shown that the degree of induced fracture growth / shrinkage in waterfloods depends strongly on oil-water mobility ratio and can vary strongly with time because of changing reservoir saturation distribution (e.g. shockfront position). For example induced fracture growth in an injector can be strongly accelerated at the moment of water breakthrough in nearby producers. Once water has broken through the induced fracture shrinks again. These results imply that an optimized waterflood strategy will generally require variable injection rates over the field life in order to prevent jeopardizing sweep by excessive induced fracture growth.SHELLSPE110316Reservoir ModellingCoupled Well/Reservoir ModelFeasibilityAn Investigation Into the Need of a Dynamic Coupled Well-Reservoir SimulatorE.D. Nennie, G.J.N. Alberts, S.P.C. Belfroid, SPE, and E. Peters, TNO, The Netherlands, and G.J.P. Joosten, Shell International E&PAbstract Within the research framework of the Integrated System Approach Petroleum Production (ISAPP) knowledge center of TNO TU Delft and Shell the necessity of taking the interaction between dynamic reservoir and dynamic well behavior into account when optimizing a producing asset is investigated. To simulate dynamic phenomena in the well and in the reservoir a dynamic multiphase well simulation tool (OLGA) and a dynamic multiphase reservoir simulator (MoReS) have been used. Both simulators have been coupled using an explicit scheme. The dynamic well simulator the dynamic reservoir simulator and the coupled dynamic well-reservoir simulator have been used to simulate a realistic test case which consists of a horizontal well with three inflow sections located in a thin oil rim. A number of scenarios are investigated that play a crucial role during different stages of the wells lifetime: naturally occurring phenomena e.g. coning and production dynamics e.g. shut-in. The results of dynamic well simulations dynamic reservoir simulations and coupled well-reservoir simulations are presented and an overview is given of the cases where the results of the coupled simulations are significantly more accurate in comparison to stand-alone well or reservoir simulations. For gas coning it is shown that the coupled simulator has much faster pressure transients after gas breakthrough than the dynamic reservoir simulator. Therefore the coupled well-reservoir simulator should be used to simulate gas breakthrough and to optimize production using gas coning control. For small time scale phenomena order of less then one day the well and reservoir transients overlap. Simulations show that the coupled simulator is essential for an accurate prediction of the well-reservoir interaction during these small time scale phenomena. Introduction Production instabilities are undesirable and play a crucial role in the production lifetime and ultimate recovery of any reservoir. These instabilities can arise from or be governed by the interaction between the well and the reservoir.1 Production instabilities can be subdivided into two groups. Firstly the naturally occurring dynamical phenomena such as coning and slugging. Secondly the production dynamical phenomena such as shut-in clean-up and gas lift heading. Figure 1 displays the time and spatial scales for different naturally occurring and production dynamical phenomena. The values of the time and spatial scales are indicative and based on experience. There are several phenomena which have a certain amount of overlap. In these areas it is expected that the well dynamics are strongly influenced by the reservoir dynamics and visa versa. Simulations are widely used to predict oil and gas production. The current status of these simulations is to either use a dynamic well model combined with some analytical reservoir model2 or to use a dynamic reservoir model combined with either lift tables or a steady state well model.3 4 The disadvantage of these models is the fact that they underestimate the pre-mentioned well-reservoir interactions and therefore give non-realistic production forecast in cases where well-reservoir interactions play a crucial role.SHELLSPE118173Reservoir ModellingCoupled Well/Reservoir ModelThins Oil RimUsing a Dynamic Coupled Well-Reservoir Simulator to Optimize Production of a Horizontal Well in a Thin Oil RimE.D. Nennie, SPE, G.J.N. Alberts, SPE, and E. Peters, TNO, The Netherlands, and E. van Donkelaar, Shell International Exploration and ProductionAbstract Stabilization and optimization of production are the key challenges for smart well control. In order to compare the effectiveness of different control strategies a simulation environment can be used. To study and control the effects of for instance slugging gas coning and wax deposition both reservoir dynamics and the dynamics of multi-phase well flow have to be taken into account in such a simulation environment. In this paper we use a dynamic coupled well-reservoir simulator to identify instabilities of a field case and test a control strategy to mitigate gas coning and wax deposition. The dynamic simulation environment has been developed within the research framework of the 'Integrated System Approach Petroleum Production' knowledge center of TNO TU Delft and Shell. This simulator has been validated with field data of a horizontal well located in a thin oil rim suffering from gas coning and containing a crude which has a high tendency to wax. With the help of the simulation environment it was possible to understand the production instabilities observed in this well and to determine a control strategy to stabilize and optimize its production. The results of the coupled well-reservoir simulations are presented and the phenomena that are most likely to cause the production instabilities observed in the field are identified. In production optimization the current status is to use separate dynamic well and reservoir models. In case of significant reservoir-well interaction this approach gives a non-realistic production forecast. This project gives a clear indication that when using a coupled simulation on a real field case a better understanding of the instabilities and therefore a more accurate production forecast and a better control strategy can be designed. Introduction With increasing knowledge and new technologies more complex reservoirs with respect to location and dimensions can be explored. This brings new challenges in e.g. exploration drilling and production. Furthermore existing reservoirs require new insights to be able to increase its ultimate recovery. Dedicated simulation software tools can offer these new insights by helping to understand production instabilities and test new control strategies to avoid them and to optimize production. The field under investigation has most of its wells drilled with long laterals in a thin oil rim which have a strong tendency to gas-cone. Gas coning is a phenomenon where the gas oil contact of a reservoir slowly moves towards a well as a result of oil drawdown see Figure 1. At a certain moment in the production life of the well the gas oil contact will reach the well and the well will experience high influx of free gas. A second important issue which is causing production limitations is wax deposition. Wax deposition is a known problem for reservoirs located in cold areas or deep water but also for reservoirs containing a crude which has a large tendency to wax. Wax precipitates from the crude when the temperature of the crude drops below the critical wax solubility temperature. The crude produced from the field under investigation has a maximum pour point temperature which is slightly lower than the reservoir temperature which implies possible wax deposition during production.SHELLSPE104580Reservoir ModellingDual Permeability SimulationTransfer FunctionsVerification and Proper Use of Water-Oil Transfer Function for Dual-Porosity and Dual-Permeability ReservoirsA. Balogun, SPE, Shell E&P, H. Kazemi, SPE, E. Ozkan, SPE, M. Al-Kobaisi, SPE, and B. Ramirez, SPE, Colorado School of MinesSummary Accurate calculation of multiphase fluid transfer between the fracture and matrix in naturally fractured reservoirs is a very crucial issue. In this paper we will present the viability of the use of a simple transfer function to accurately account for fluid exchange resulting from capillary and gravity forces between fracture and matrix in dual-porosity and dual-permeability numerical models. With this approach fracture- and matrix-flow calculations can be decoupled and solved sequentially improving the speed and ease of computation. In fact the transfer-function equations can be used easily to calculate the expected oil recovery from a matrix block of any dimension without the use of a simulator or oil-recovery correlations. The study was accomplished by conducting a 3-D fine-grid simulation of a typical matrix block and comparing the results with those obtained through the use of a single-node simple transfer function for a water-oil system. This study was similar to a previous study (Alkandari 2002) we had conducted for a 1D gas-oil system. The transfer functions of this paper are specifically for the sugar-cube idealization of a matrix block which can be extended to simulation of a match-stick idealization in reservoir modeling. The basic data required are: matrix capillary-pressure curves densities of the flowing fluids and matrix block dimensions. Introduction Naturally fractured reservoirs contain a significant amount of the known petroleum hydrocarbons worldwide and hence are an important source of energy fuels. However the oil recovery from these reservoirs has been rather low. For example the Circle Ridge Field in Wind River Reservation Wyoming has been producing for 50 years but the oil recovery is less than 15% (Golder Associates 2004). This low level of oil recovery points to the need for accurate reservoir characterization realistic geological modeling and accurate flow simulation of naturally fractured reservoirs to determine the locations of bypassed oil. Reservoir simulation is the most practical method of studying flow problems in porous media when dealing with heterogeneity and the simultaneous flow of different fluids. In modeling fractured systems a dual-porosity or dual-permeability concept typically is used to idealize the reservoir on the global scale. In the dual-porosity concept fluids transfer between the matrix and fractures in the grid-cells while flowing through the fracture network to the wellbore. Furthermore the bulk of the fluids are stored in the matrix. On the other hand in the dual-permeability concept fluids flow through the fracture network and between matrix blocks. In both the dual-porosity and dual-permeability formulations the fractures and matrices are linked by transfer functions. The transfer functions account for fluid exchanges between both media. To understand the details of this fluid exchange an elaborate method is used in this study to model flow in a single matrix block with fractures as boundaries. Our goal is to develop a technique to produce accurate results for use in large-scale modeling work.Permeability ReservoirsSHELLSPE128350Reservoir ModellingExperimental Design MethodReviewModelling Subsurface Uncertainties with Experimental Design: Some Arguments of Non-ConformistsKazeem A. Lawal, SPE, Shell Nigeria Exploration & Production CompanyAbstract As a consequence of limited capability for the acquisition analysis and interpretation of subsurface data uncertainties pervade the Exploration and Production (E&P) business. To minimise investment risks robust development plans premised on adequate understanding of uncertainties are critical. Experimental Design (ED) complemented with Response Surface Method (RSM) which uses a statistical proxy equation to model the response (dependent variable) as a function of independent variables (uncertainties) is a common method for studying subsurface uncertainties. In this paper current applications of ED to subsurface modelling are evaluated from fundamental principles- mathematical and physical consistencies of the proxy equations as well as robustness in modelling uncertainties. Within the context of modelling and mitigating subsurface uncertainties major shortcomings of the ED and their implications for decision-making are highlighted. These include inconsistency and non-uniqueness of proxy models violation of basic theoretical physics non-preservation of the correlation between variables that are known to be inherently related non-controllability of input variables under-estimation of the impact of uncertainties and the challenge of constructing (interpolating) realistic simulation models from an ED output. Although ED is consistent with statistical principles its description of reservoir physics is not satisfactory. In its present form reservoir complexities are apparently too overwhelming for reliable modelling or optimisation by the proxy models. Consequently it is recommended that the application of ED be limited to situations where a simple understanding of the effect of a controllable variable on a dependent variable is required or where the range of uncertainties is well known within a narrow interval. These include production/injection management model-based control algorithms for intelligent completions business planning and similar areas of the E&P business characterised by continuous data and where the independent variables could be engineered for the desired objectives.SHELLSPE101070Reservoir ModellingFractional Flow AnalysisWaterfloodRecapturing the Value of Fractional Flow Analysis in a Modern Day Water FloodH. Milln and A. Parker, SPE, Brunei Shell Petroleum Co. Sdn. Bhd.Abstract The Champion Field is located 40 km offshore Brunei Northern Borneo in a water depth of 10 45 m. It has been on production since 1972 and secondary recovery by treated seawater injection commenced in some reservoirs in 1984. In this paper we present an analytical approach for estimating relative permeability end-points from production/injection data as a starting point for sensitivity studies on the applicability and value of expanding water flooding to the rest of the Champion field. A significant volume of special core analysis data is available however the representativeness of this data is limited due to differences between reservoir and laboratory conditions. The cores might also have changed properties such as wettability depending upon coring method cleaning etc. In order to screen the value of expanded water flooding a performance review of the Champion water flood reservoirs was carried out using reservoir engineering analytical techniques to generate reservoir fractional flow. The fields performance to fractional flow was matched using a set of Water-Oil relative permeability curves generated by Corey exponents and Buckley-Leverett / Welge theory. This match leading to an estimation of the likely uncertainty range in end point relative permeabilites of Water-Oil curves mobility ratio and to calculate the expected production profile and ultimate oil recovery. Combined with the available SCAL data and a good understanding of past flood performance an ability to forecast future production was obtained. The work presented forms the first phase in the assessment of the value in expanding water flooding throughout the Field. In essence the work recaptures the value of analytical methods within the workflow of present-day project developments. Introduction The Champion Field is structurally and stratigraphically complex containing over 500 separate Miocene reservoirs within elongated fault block structures (Figure 1). The Miocene reservoirs were deposited in a shallow-marine to costal-plain environment in the Champion delta system. The coastal-plain and shallow-marine reservoirs include tidal-channel and upper to lower shoreface sands. Figure 2 shows the Champion field stratigraphic summary. The field was discovered in 1970 and went on production in 1972. Based on the fault structure and reservoir properties the Champion area is traditionally delineated into 3 main areas: CPSE Block 13/14 and CP Main (Figure 1). Water injection in part of the Champion Main area commenced in 1984 while extension to Block 13/14 and CPSE areas was planned for 2003 and beyond. A successful drilling campaign of highly deviated/horizontal wells has resulted in a dramatic increase in withdrawal rates from the Champion field and a corresponding decline in reservoir pressure due to limited aquifer support. As a result there is a need to implement an IOR scheme. This study is part of the Champion Waterflooding Study project and is focused upon estimating a range of relative permeability end-points as a starting point for sensitivity studies. This paper presents the performance review of the Champion water flood reservoirs using reservoir engineering analytical techniques with particular emphasis on Buckley-Leverett displacement theory and Welge displacement efficiency calculations.1-2 These two analytical techniques were applied to generate from the field production data the reservoir fractional flow relative permeability end-points mobility ratios and to predict the oil recovery in each of the blocks analyzed in this review. It was possible to obtain a reliable understanding of the water flood performance.SHELLSPE90320Reservoir ModellingGas PotentialGas-CondensateA Semianalytical Method To Predict Well Deliverability in Gas-Condensate ReservoirsNitin Chowdhury, SPE, Ravi Sharma, Gary A. Pope, SPE, and Kamy Sepehrnoori, SPE, The University of Texas at AustinSummary A fine-grid simulation is needed to capture the buildup of a condensate bank near wells operating below the dewpoint pressure. However full-field simulations with a sufficiently fine grid will often not be feasible or will require very long computational times. A semianalytical method has been developed that can be used to predict the gas- and condensate-production rates from such wells accurately and that has some advantages over the pseudopressure approach. The semianalytical method includes the effects of capillary number (high velocity) and non-Darcy flow. The new method has been implemented in a compositional-reservoir simulator and verified with fine-grid compositional simulation results for both lean and rich gas-condensate fluids. Pressures saturations relative permeabilities viscosities and densities calculated with the semianalytical method are in excellent agreement with the results of fine-grid compositional simulations. Coarse-grid simulations with gridblock sizes on the order of 200 ft coupled with the semianalytical meth