SPE 154347

Ekofisk 4D Seismic - Seismic History Matching Workflow Evgeny Tolstukhin, Bjarne Lyngnes, Hari H. Sudan, ConocoPhillips

Copyright 2012, Society of Petroleum Engineers This paper was prepared for presentation at the EAGE Annual Conference & Exhibition incorporating SPE Europec held in Copenhagen, Denmark, 4–7 June 2012. This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.

Abstract This presentation outlines an integrated workflow that incorporates 4D seismic data into the Ekofisk field reservoir model history matching process. Successful application and associated benefits of the workflow benefits are also presented. A seismic monitoring programme has been established at Ekofisk with 4D seismic surveys that were acquired over the field in 1989, 1999, 2003, 2006 and 2008. Ekofisk 4D seismic data is becoming a quantitative tool for describing the spatial distribution of reservoir properties and compaction. The seismic monitoring data is used to optimize the Ekofisk waterflood by providing water movement insights and subsequently improving infill well placement. Reservoir depletion and water injection in Ekofisk lead to reservoir rock compaction and fluid substitution. These changes are revealed in space and time through 4D seismic differences. Inconsistencies between predicted 4D differences (calculated from reservoir model output) and actual 4D differences are therefore used to identify reservoir model shortcomings. This process is captured using the following workflow: (1) prepare and upscale a geologic model, (2) simulate fluid flow and associated rock-physics using a reservoir model, (3) generate a synthetic 4D seismic response from fluid and rock physics forecasts, and (4) update the reservoir model to better match actual production/injection data and/or the 4D seismic response. The above-mentioned Seismic History Matching (SHM) workflow employs rock-physics modeling to quantitatively constrain the reservoir model and develop a simulated 4D seismic response. Parameterization techniques are then used to constrain and update the reservoir model. This workflow updates geological parameters in an optimization loop through minimization of a misfit function. It is an automated closed loop system, and optimization is performed using an in-house computer-assisted history matching tool using evolutionary algorithm. In summary, the Ekofisk 4D SHM workflow is a multi-disciplinary process that requires collaboration between geological, geomechanical, geophysical and reservoir engineering disciplines to optimize well placement and reservoir management. Introduction

The Ekofisk Field is located in the Norwegian Sector of the North Sea. It was discovered in 1969 and began production in 1971. The field is one of the largest fields on the Norwegian Continental Shelf with initial oil in place estimate of 7.1 billion STB of oil. The produced volumes are extracted from two fractured chalk formations. These reservoir formations are characterized by very high porosities and low matrix permeabilities. Formation productivity is enhanced by the natural fracture systems that allow commercial production from the field.

The first field development phase was natural depletion production. The first pilot water injection was initiated in 1981, and large scale water injection was initiated in 1987.

Expected recovery factor have increased from an initial estimate of 17% OHIP (Original Hydrocarbon In Place) to a current estimate of more than 50% OHIP through continuous improvements in field development plans, implementation of IOR, application of new technology and investments in new and existing facilities. It is also believed that a significant upside exists in further development optimization.

Future development plans at Ekofisk include an active drilling program. The program includes replacement of mechanically failed wells coupled with new infill wells to optimize recovery. Conducting a successful drilling programme in a mature chalk field is challenging. A single wellbore can experience large reservoir pressure and water saturation differences. Furthermore, compaction can alter the target interval depth, thickness, and reservoir properties as a function of time.

A full field reservoir simulation model is the primary tool used to identify and justify Ekofisk drilling targets. A reliable reservoir simulation model is therefore an important tool when optimizing ongoing Ekofisk development activities. Computer-assisted history matching is employed at Ekofisk to improve model reliability. The assisted history matching process employs

2 SPE 154347

generations of model realizations that reflect the range of uncertainty in model parameters. The model parameters are then perturbed in an iterative manner using a closed-loop system.

Reservoir history matching task is an ill-posed problem because many parameter combinations can provide similar simulated reservoir pressure and production responses. The fundamental reason for this is that reservoir modeling spatially distributes uncertain geological or dynamic parameters (e.g. porosity, permeability, etc.) using localized well information from production data. Therefore the geological parameters in between the wells remain largely uncertain or unknown. At Ekofisk, 4D seismic data enables reduction of spatial uncertainty due to dense spatial data coverage. Ekofisk 4D seismic data have historically been employed for describing the spatial distribution of reservoir compaction and subsidence (Smith et al., 2002). The data have also been historically used to qualitatively guide reservoir simulation history match improvements. To summarize, the main objectives for using Ekofisk 4D seismic in history matching are to locate remaining oil and reduce drilling risk.

This paper details a Seismic History Matching (SHM) workflow employed to quantitatively update the Ekofisk reservoir simulation model using both production and 4D seismic data. Ekofisk development

The Ekofisk field reservoir is an elongated anticline covering 12,000 acres. The major axis (north-south) is about 6 miles long while the east-west axis extends roughly 4 miles (Figure 1). The large areal extend coupled with an oil column of 180 meters make the Ekofisk field one of the largest fields on the Norwegian Continental Shelf with 7.1 billion STB of original oil.-in-place.

The field is an example of a strongly compacted chalk reservoir. The produced volumes are extracted from two fractured chalk formations, the Ekofisk and Tor formations. These formations are characterized by high porosities (up to 40%) and low permeabilities (0.1 - 5 mD). Formation productivity is enhanced by the natural fracture systems allowing commercial production from the field. The fracture system forms the primary conduction path for produced and injected fluids (Hermansen, 2008).

Figure 1 A map showing the location of the Ekofisk field Figure 2 Conceptual sketch of fracture orientations and styles of fracture development in the centre of the Ekofisk field. Outflow of ground-water was observed at a zone of stacked hard-grounds in the Etretat chalk cliffs (France) (Modified after Thomas et al, 2010)

The major part of Ekofisk fractures are small shear fractures related to the tectonic activity. These planar features form

well developed parallel sets with high dip. Fracture intensity and spacing are highly variable throughout the formations both vertically and laterally. The length of the fractures can not be determined from core information. But the outcrop analogues suggest an estimate of fracture length ranging from a few centimeters to hundred of meters. The second set of fractures is related to stylolite seams. These fractures are small scale features and might create a pervasive network if they are combined with tectonic fractures (Figure 2).

The Ekofisk Field was discovered in 1969. Initial production was started in 1971 from the discovery well and appraisal wells using temporary production facilities. Development drilling was initiated in 1974 following the commissioning of permanent production facilities. The initial recovery mechanism was primary depletion with produced gas reinjection. Laboratory studies evaluating the waterflood potential at Ekofisk were launched in 1979. A waterflood pilot (using seawater) was initiated in 1981. Large scale waterflood started in 1987 following favorable pilot results.

SPE 154347 3

The reservoir responded rapidly to secondary recovery operations. Oil production increased from 70 MSTBO/D in 1987 to 300 MSTBO/D by mid 2000. The average field GOR during this period decreased from approximately 8000 SCF/STB to 1150 SCF/STB (Figure 3).

An active drilling program is integral to optimizing rate and recovery at Ekofisk. There have already been drilled over 320 production and injection wells, of which around 100 are still active. The average well life in the field is only 17 years, due to compaction induced mechanical wellbore failures. This relatively short mechanical well life provides a waterflood optimization opportunity. Replacement wells are repositioned, both vertically and areally, to optimize the waterflood using the full field model.

Figure 3 Ekofisk field historical production and injection data Ekofisk simulation model

The task of planning infill drilling in a mature field sets especially high requirements with respect to the precision of well targets and well trajectories, and reservoir simulation model predictability in general. The current Ekofisk reservoir model is a result of the shared effort of the PL018 license partners (Thomas et al., 2010). The goal of this effort has been to develop updated and comprehensive geological and simulation models that are in agreement with 40 years of production history. The long production history and the large number of wells represent an enormous dataset for both reservoir characterization and dynamic simulation.

The Ekofisk field is characterized by large variability in reservoir quality. The large amount of data permits deterministic mapping of the fracture network using static (e.g. fracture distribution) and dynamic observations (e.g. well tests, water breakthrough) as fracture network indicators. The effective permeability model is a combination of two properties: (1) fracture-enhanced matrix permeability and (2) fracture network permeability based on 14 different indicators (Figure 2). Fracture network indicators are mapped directly in the flow simulation grid. This deterministic 3D mapping forms the basis of a high-contrast single porosity and permeability model, which is used as input for dynamic simulation. Modeled porosity, effective permeability and fracture index are used to calculate 6 rock-types, each designed to model a specific behavior in terms of compaction and/or pressure rebound and relative permeability. Reservoir descriptions are also compared to outcrop analogues, which assisted development of concepts for the influence of mechanical stratigraphy and fault zones on reservoir flow.

The integration of geological mapping and definition of reservoir engineering parameters ensures both a credible history match and trusted field performance prediction. These two objectives are achieved by integrating static and production data in the permeability mapping and by using a loop between the dynamic flow simulation and reservoir characterization.

The described above Ekofisk reservoir model exhibits a very high quality of history match. On the field level, the difference between the observed and simulated cumulative production is 0.3% for oil, 0.2% for gas and 1% for water. The resulting work is now used for the complex task of planning infill drilling in a mature field with large pressure differences and many flooded sections.

4 SPE 154347

Figure 4 Examples of fracture network indicators mapping (a) and permeability map (b) in the Middle Ekofisk Formation. Numerous data types were used as fracture network indicators, such as well-tests, distinct water-breakthrough, tracers, PLT, fracture analysis of image log and 4D time-lapse data, etc. 4D Seismic at Ekofisk

Extensive rock physics and seismic modeling suggested that 4D seismic data could help in tracking the water front and indicate reservoir compaction in Ekofisk (Key et al., 1998). The first 3D seismic survey covering Ekofisk field was acquired in 1989. It coincided with the expanded areal water injection. ConocoPhillips acquired a repeat 3D seismic survey in 1999 to monitor the waterflood and waterflood-induced compaction by 4D seismic analysis. The encouraging results from this survey led to repeated time-lapse seismic surveys to be acquired in 2003, 2006 and 2008.

The crestal area of the field is seismically obscured due to presence of shallow gas in the overburden. The seismically obscured area comprises approximately one third of the field. The inability to rely on seismic data in the crestal area makes it difficult to map faults and hence complicates well placement efforts. However, the main horizons are mappable in the rest of the Ekofisk 3D seismic data. These horizons are the top chalk reservoir, interbedded layers, a tight zone separating the upper reservoir (Ekofisk formation) from the lower reservoir (Tor formation), and the base reservoir. The main 4D observations are time shifts of the main horizons and time-thickness changes between horizons (time-strain). In addition, reliable 4D amplitude differences have also been detected around some producers, mainly for the two most recent 3D streamer seismic surveys.

Figure 5 Top Ekofisk 4D time-shift maps for the time-lapse surveys. Blackened area in the middle is the Seismic Obscured Area (SOA) (Modified after Haugvaldstad et al., 2011)

There are a number of applications for qualitative and quantitative 4D seismic analysis on Ekofisk. For example, 4D

seismic helps perform reservoir compaction mapping (Smith et al., 2002). The Ekofisk reservoir compacts by two mechanisms (1) pressure reduction associated with the natural depletion phase, and (2) chalk water-weakening associated with the waterflood. Figure 5 shows the time-shift maps at top reservoir between the time-lapse surveys. Reservoir compaction results in large time-shifts between seismic surveys which are easy to detect. The general observations are that time-shift values are greater in the thick and porous crest, and are also mappable on the flanks, particularly in the vicinity of water injectors. The co-location of a strong 4D signal with thick, porous chalk is consistent with expectations because thicker and higher porosity

a b

SPE 154347 5

chalk compacts more strongly than thinner and lower porosity chalk. No 4D seismic signal is observed in the areas where no appreciable change in reservoir pressure or water saturation has occurred.

For many years, mapped seismic faults have been used in Ekofisk flow simulation to define pathways and barriers to flow inferred from interference tests and production history. 4D seismic data provides direct information on the distribution of water and the effects of fault compartmentalization on reservoir fluid flow. 4D seismic is also used to introduce new faults or adjust transmissibility factors along existing faults to improve the history match. Similar findings and studies have been published in the literature for other fields (Benguigui et al., 2009).

Another application for 4D seismic analysis is “4D Seismic as Production Logging Tool” (Folstad et al., 2008). During production, reservoir pressure is reduced around perforations when new wells are put on production. As a consequence, reservoir compaction is largest around the best performing perforations. This is demonstrated in Figure 6a, where measurements from a production logging tool (PLT) show that 90% of the oil production from a well coincides with the compacted area observed in the 4D seismic data. Similar observations are illustrated in Figure 6b for an injection well.

Figure 6 Time-shift maps at Top Ekofisk between years 2003 - 2006. 4D Seismic PLTs shown for (a) producer well and (b) injector well (Modified after Folstad et al., 2008)

At Ekofisk, 4D seismic data are being used extensively for planning of new Ekofisk wells. The interpretation results for the observed overburden travel time and time-lapse reservoir amplitude differences are used to optimize placement of new producers. These results are of great value especially in areas with high risk of encountering water-swept reservoir.

The most reliable 4D seismic attribute parameters at Ekofisk are amplitude, time-shift and time-strain. The time-strain attribute (Landrø et al., 2009) was selected for incorporation into the history-matching workflow for two main reasons. First, unlike the time-shift, the attribute contains layer-based information and has higher vertical resolution. Second, this attribute includes both layer thickness change (compaction) and velocity change within the layer. The latter fact is of particular value in the Ekofisk field, since the chalk is compacting, and compaction contributes significantly to time-lapse anomalies. The attribute provides an additional source of information for geomechanical simulations.

These previously described observations and similar observations from other time-lapse surveys in 1999, 2003, 2006 and 2008 formed the basis for qualitative and quantitative updates to the Ekofisk flow simulation and geomechanical model. Production-induced changes that take place in the reservoir are revealed in space and time through 4D seismic differences. Inconsistencies between predicted 4D differences (calculated from reservoir model output) and actual 4D differences are therefore used to identify reservoir model shortcomings. Seismic History Matching workflow

In order to ensure the model predictability, the reservoir model needs to incorporate all the observed heterogeneities at all scales in the effective properties model. Moreover, the reservoir model needs to honor both static and dynamic data. In the classical approach, reservoir engineering methods for history matching generate changes in the distribution of dynamic parameters directly in the simulation model. This is often done without considering the realism of the changes with respect to the original geological concept and model. In order to overcome this bottleneck and preserve the consistency between the static and dynamic models, a fully integrated workflow is employed that dynamically couples all elements of the seismic to simulation workflow, including 4D Seismic, the rock physics forward model, the geological model, and reservoir simulation model building using a computer-assisted history matching process.

The Ekofisk SHM workflow includes the following steps: (1) prepare and upscale a geologic model, (2) simulate fluid flow and associated rock physics using a reservoir model, (3) generate a synthetic 4D seismic response from fluid and rock-physics forecasts, and (4) and update the reservoir model to better match actual production and injection data and/or the 4D seismic response.

In the first step, advanced parameterization techniques are used to capture the uncertainty in geological and dynamic parameters, and model updating techniques are utilized to populate an ensemble of reservoir geological model realizations

a b

6 SPE 154347

with local updates. The fine-scale geological reservoir model is then upscaled and prepared for the forward reservoir simulation. After the forward reservoir simulations, the workflow employs forward rock physics modeling to develop a simulated 4D seismic response. Inconsistencies between the predicted 4D differences (calculated from reservoir model output) and observed 4D differences are used to identify potential reservoir model shortcomings. Then the uncertain geological parameters are updated in an optimization loop through minimization of a misfit function that comprises both production misfit and 4D seismic misfit values. The workflow is an automated closed loop system, and optimization is performed using an in-house computer-assisted history matching tool using an evolutionary algorithm.

Several techniques have been developed for such closed loops (see Gosselin et al., 2003, Caers, 2003, Mezghani et al., 2004, Castro et al., 2006, Stephen et al., 2006, Roggero et al., 2007, Skjervheim et al., 2007, Jansen et al., 2008, Jin et al., 2008, etc.). However, the reservoir history matching task is still an ill-posed problem with an essential lack of information. There is a possibility to reduce the ill-posedness of the problem, or reduce the degrees of freedom for solutions to problem, by introducing additional constraints on the range of uncertain parameters and outcomes in the process, like incorporating realistic geological modeling and/or 4D seismic.

Generally, there are two main approaches to incorporate 4D seismic data into history-matching and model updating processes. The first approach, presented by Kretz et al., 2002, is to match 4D seismic data simultaneously with production data. This approach requires time-lapse forward rock-physics modeling and comparison of the modeled and observed 4D data. Here the misfit function includes both production and 4D seismic misfit terms. This approach is utilized for a large portion of the 4D SHM problems on Ekofisk. The success of this approach strongly depends on the quality of the seismic data and the rock physics model. Despite the theoretical, experimental and calibration efforts that have been undertaken (Key et al., 2002, Walls et al., 1998), forward rock physics modeling and 4D seismic interpretation on Ekofisk is a complex problem. The complexity is due to a combination of pressure, saturation and compaction effects on the modeling response that leads to non-unique interpretation. There are areas of the Ekofisk field that exhibit different level of 4D data and rock physics model quality, and therefore the first approach can be utilized only for certain regions of the field.

The second approach in seismic history matching considers 4D data as an additional constraint in the 3D geological modeling process (Castro et al., 2006). 4D seismic data is included in the geological modeling process through a spatial probability of reservoir facies occurrence. In such a case, the goal of the process is to generate a range of possible facies realizations rather than an exact match of 4D seismic observations. In certain areas of the Ekofisk field where high quality 4D seismic data is not available, the second approach, or a combination of two approaches, is utilized. An integrated map of Ekofisk 3D/4D seismic quality has been constructed in order to distinguish the areas of applicability for the first and the second approaches.

It is important to note that the main goal of Ekofisk SHM in this study is not to further improve the history match of the existing full-field model, or get the global history match, but to improve the particular decision that is to be taken based on the reservoir simulation model predictions. For example, the full knowledge of the entire field is usually not required during the infill well planning phase. However, it is essential to control the limited number of parameters that dominate the fluid flow in the area of interest.

The following sections describe the mentioned above steps of the SHM workflow in more detail. Model parameterization and perturbation

Fluid flow in the Ekofisk field is dominated by chalk compaction, faults and fractures. These static and dynamic parameters, along with many others, are included in the uncertainty studies and history matching exercises.

For example, let’s consider the technique to update the permeability map in a history matching loop. The permeability map is a combination of reservoir characteristics and parameters such as matrix permeability, fracture properties, fracture density and fracture network pattern. Usually, the fracture network properties have the most significant impact on the production forecast. The model updating method on Ekofisk therefore breaks down to two main sub-problems: description and parameterization of the fracture distribution, and updating of the model so it honors both production and 4D seismic data constraints without losing the geological realism.

As described above, the current Ekofisk simulation model is a deterministic model. This adds complexity on the model updating exercise. There are two alternative methods to model the fracture networks in this case. The first is to use object modeling functionality to generate fracture network realizations. In this case, the fracture properties are addressed directly. The model perturbation routine updates the distributions of such parameters like fracture orientation, length, width, fracture density, fracture and fault conductivity.

The second approach is to use Multiple-Point Statistics (MPS) modeling (Strebelle, 2000, Caers, 2003). In this case, a training image provides a basis for generation of more geologically realistic fracture networks. The fracture network pattern uncertainty, or different geological scenarios, can be conveniently addressed through a set of training images. The Ekofisk fracture network is characterized by a number of non-stationary features. In order to mitigate any effect of these features, the field was divided into the number of regions that represent different geological settings. Each of the regions was supplemented with the dedicated training image. The well observations and the number of fracture indicators with high rank of certainty are considered to be the hard data that constrains the stochastic algorithm. Information such as 3D/4D seismic, fracture pattern characteristics (e.g. orientation, length and density) are included in the modeling as soft constraints. The soft constraints are

SPE 154347 7

subject to uncertainty and history matching studies. Figure 7 illustrates the results of the MPS method application at Ekofisk. The stochastic realizations honor well data and geological features of the fracture pattern.

Both methods described provide opportunities for integrating model perturbation functions into the 4D Close-The-Loop applications. The current reservoir simulation model serves as the starting point for the model updating process. The fracture network is perturbed in a hierarchical way. At the initial phase, the fractures that correspond to the fracture network indicators (see Figure 4) with the lowest rank of certainty are perturbed. Then, if a satisfactory match is not achieved, the fractures with the next level of certainty are updated simultaneously with the lowest certainty fractures. In the SHM workflow, the model parameters are updated in an optimization loop through minimization of a misfit function until the desired level of history match is achieved.

Figure 7 Application of the MPS method for modeling of fracture network indicators. Figure illustrates modeling results for one layer in the Ekofisk formation: (a) the original fracture network indicators model, (b) stochastic realizations of fracture network indicators, (c) the posterior fracture probability map based on all the MPS realizations Forward Rock Physics Modeling (Sim2Seis)

The Ekofisk Rock Physics Model is a set of quantitative relationships that link rock elastic properties with rock porosity, composition, pore pressure, and fluid saturation. The quantitative relations give confident prediction of changes in elastic properties that occur with production induced changes in reservoir properties. The relationships are based on empirical correlations of the effective medium theory and are calibrated to well data (Wells et al., 1998, Guilbot et al., 2002, Janssen et al., 2006).

The Ekofisk rock physics modeling process consists of the following stages (Wells et al., 1998). First, compressional and shear log velocities are converted to bulk and shear modulus using standard elastic equations, and the log velocity is then transformed to dry rock conditions. Mineral volume and the Voigt-Reuss-Hill average are used to determine solid mineral moduli. Batzle and Wang equations are utilized for pore fluid properties predictions under reservoir conditions. The primary seismic attributes associated with Ekofisk production are travel time changes and amplitude changes. Travel time changes between repeated seismic surveys when the velocity of overburden and reservoir rocks change in response to thickness, stress or porosity changes. Compaction provides the dominant effect on seismic response due to reservoir porosity loss and consequent increases in velocity and impedance, and through stress changes in the overburden causing velocity decrease and seismic travel time delays. The Ekofisk RPM uses the modified Upper Hashin-Shtrikman model to describe velocity behavior in Ekofisk chalk as a function of porosity, and the effect of chalk compaction is introduced with the help of porosity and compressibility dependence on stress conditions. Additionally, the Hertz-Mindlin contact theory is employed to model a velocity response to pressure.

Time-lapse forward RPM, or 4D RPM, uses the reservoir simulator outputs for time-steps corresponding to seismic surveys dates to develop a simulated 4D seismic response.

To summarize, the Ekofisk SHM workflow employs rock physics modeling to quantitatively constrain the reservoir model and develop a simulated 4D seismic response. The RPM helps link seismic amplitudes, velocities, time-shifts, time-strains and reservoir simulation results to quantify and distinguish between pressure change, compaction and fluid substitution effects.

Figure 8 shows an example of a comparison between the time-strain attribute derived from the observed 4D seismic between 2006 and 2008 (Figure 8a), and the synthetic attribute calculated using the 4D Ekofisk forward RPM (Figure 8b). The

a b c

8 SPE 154347

time-lapse RPM calculation is based on outputs of the current reservoir simulation model. The injector well Inj 2 started water injection in the Ekofisk formation in September 2006, between the two time-lapse surveys. A positive 4D signal around the injector well is caused by injected water front propagation and increased reservoir pressure. In this case, there is a good match between the observed and modeled data which confirms quality of the reservoir model prediction in this region.

Figure 9 illustrates another example of the Ekofisk RPM application. The injector well Inj 3 started water injection in the middle and lower Ekofisk formations in November 2006. The injector was drilled in the vicinity of the Fault 1. Similar to the previous example, the positive 4D signal around the injector (Figure 9a) was caused by injected water front propagation and increased reservoir pressure. On the other hand, a negative signal in the eastern fault block shows pressure depletion and/or reservoir rock compaction in the area. The application of 4D RPM on the simulation model outputs (Figure 9b) immediately reveals the mismatch between the observed and simulated 4D response. In fact, the Fault 1 was assumed to be conductive in the original reservoir simulation model, but 4D interpretation indicated that the fault serves as a pressure baffle or barrier. A history match study was initiated, and transmissibility multipliers along the fault have been adjusted in order to improve the match. Figure 9c illustrates the 4D RPM predictions that are based on the updated reservoir simulation model output. The updated model is in agreement with the observed seismic data.

Figure 8 Examples of 4D Forward Seismic Modeling (Sim2Seis) using the Ekofisk RPM: (a) observed time-strain change in Ekofisk formation, (b) synthetic time-strain change calculated using RPM and reservoir simulator output. Red circle outlines time-lapse signal related to water injection from well Inj 2, started in September 2006

Figure 9 Examples of 4D Forward Seismic Modeling (Sim2Seis) using the Ekofisk RPM: (a) observed relative time-strain change in Ekofisk formation, (b) synthetic relative time-strain change calculated using RPM and original reservoir simulator output, (c) synthetic relative time-strain change calculated using RPM and reservoir simulator output based on updated model. Red circle outlines time-lapse signal related to water injection from well Inj 3, started in November 2006. Based on 4Ddata interpretation, Fault 1 serves as pressure barrier

As mentioned above, 4D seismic data can be used in reservoir engineering tasks when seismic data provides an additional constraint in the 3D geological modeling process, and when production data is history matched simultaneously with 4D

a b c

a b

SPE 154347 9

seismic data. The first way to use 4D seismic data was illustrated in the previous section when 4D seismic data was used as soft constraint in MPS geological modeling at Ekofisk. The second approach can be classified in two main types: (1) use of 4D seismic attributes or impedance inversion driven by rock physics model to derive reservoir properties (e.g. pressure, saturation), and (2) use of simulated 4D seismic response to quantify the misfit between observed and synthetic data that highlights reservoir model shortcomings. The successful application of the first methodology depends on the quality of seismic data and the rock physics model, and the performance of the inversion technique utilized (Landrø, 2002). The first approach can be considered only for certain regions of the Ekofisk field. Moreover, inversion results will lead to non-unique interpretation of the impedance change due to an additional unknown variable from compaction. At Ekofisk, the combination of pressure, saturation and compaction variables make the inversion procedure an ill-posed problem.

Therefore, the choice for the Ekofisk SHM workflow is to use the forward RPM in the optimization loop instead of the inversion procedure. In this case, simulated 4D seismic response is additionally constrained by the reservoir simulator output. Assisted history matching

An in-house technology has been developed to provide the integrated optimization system for the 4D Close-The-Loop Seismic History Matching workflow.

The following workflow steps are taken using this technology. The first step is to define the geological calibration parameters and identify the uncertainty ranges. These parameters are then updated in an optimization loop through minimization of a misfit function that combines both production and 4D seismic data. The ill-posed history matching problem is then split into several sub-problems. First, sensitivity analyses are performed (one parameter at a time) on a set of runs to identify the parameters that have the highest impact on outcomes. This provides a first-level screening of the calibration parameters based on their impact on the individual misfit functions for production and seismic data. Exploratory runs using Latin Hypercube design are additionally performed for the purposes of validating the parameterization and to initialize the weight-factors among the different misfit evaluation functions.

The next stage is to run the automated Close-The-Loop system to perform the assisted seismic history matching process. This process is done using the Particle Swarm Optimizer (PSO). This optimizer is a population-based stochastic optimization technique inspired by social behavior of bird flocking, and it has been applied successfully in various fields. PSO models the exploration of the parameter space by a population of particles that fly through the search space by following the current optimum particles. The particle’s history of success impacts their own exploration/exploitation pattern along with their peers. The search is focused towards promising regions by biasing each particle’s velocity vector toward their own remembered best position as well as the best position from the entire swarm (Eberhert et al., 1995). It is worth noting that this optimization process also requires careful consideration to adjust the optimizer settings in order to control the exploration vs. exploitation in the solution space.

The evaluation revealed certain advantages of the Particle Swarm Optimization when compared to alternative methods in terms of finding multiple solutions while converging in a reasonable time-frame. Similar conclusions have been addressed in the recent publication (Jin et al., 2011) where the authors compared the performance of different stochastic optimization algorithms.

During the optimization stage, several cycles of seismic history matching process are attempted by re-normalizing the misfit evaluation functions based on the optimization trends. The experimental runs the results are then filtered based on predefined acceptability criteria for production and seismic misfits. Even though 4D seismic misfit term provided the crucial spatial information, there still could be found many combinations of parameters that give very similar simulated production responses.

The final stage in the process is to perform data-mining using K-means Cluster Analysis to identify the most different calibrated models. These selected models give us a fair representation of the uncertainty in the multidimensional solution space, while limiting the number of models that are taken forward for model prediction. Field application example

This section illustrates an application of the Ekofisk SHM workflow to a region within the Ekofisk field. The example shows successful application of the methodology to the reservoir history matching task. The studied region is located in the western part of the field, and it is of interest due to an active drilling programme. Additionally, a full understanding of the reservoir production mechanisms in the region could help to improve future well targets and optimize well trajectories.

For the considered period of time, 1999-2008, three producers were active and two injectors were perforated in the Tor formation (Figure 11). A producer Prod 3 was perforated in early 2000 and exhibited sustainable oil production while a producer Prod 4 was perforated in 1998 and collapsed in 2002. Water injector Inj 4 provided pressure support from late 1998 and within the considered period of time. Injector Inj 5 experienced mechanical integrity issues, and therefore the well injectivity was reduced.

All the available time-lapse seismic data exhibit good quality of 4D time-shift and time-strain attributes at the Top Tor horizon. The data could be used for both qualitative and quantitative analysis. The quality of the reservoir simulation model predictions is illustrated for selected wells in Figure 10. Figure shows normalized historical and simulated oil production rate and water cut profiles for the wells. In particular, the original model predicted premature water breakthrough in the well Prod

10 SPE 154347

3, and underpredicted oil production in the well Prod 4. Noticeably, the reservoir simulation model predicts no water production in the well Prod 4, contrary to the well observations.

Negative time-shift signals observed within the region are interpreted to be caused by chalk compaction due to pressure depletion and water weakening. The consecutive time-lapse surveys allow tracking injection water front propagation towards producers in this area. The negative signals in the first map in Figure 11a correspond to a seismic velocity increase. The change was caused by oil production from the wells Prod 3 and Prod 5. During the next time period (Figure 11b) the well Inj 4 started water injection. The time-shift anomalies reflect water front propagation to the wells Prod 3 and Prod 4 between years 1999-2003. The negative anomalies around the toe of well Prod 3 were due to pressure depletion from production. A number of anomalies around the well Prod 4 suggest that the area was waterflooded. In reality, the well Prod 4 exhibited rapid water cut development between years 1999-2003. However, the reservoir simulation model predicted no injection water breakthrough to this well (Figure 10b). The time-shift signal for the next time period 2003-2006 (Figure 11c) indicates that water front has reached the vicinity of the wells Prod 3 and Prod 4. This conclusion is supported by the well observation data that indicates oil production rate decline and increase in water cut after year 2003 (Figure 10a). The final time-lapse difference (Figure 11d) highlights that the injection water front continued its movement to the south.

Figure 10 Production profiles for selected wells Prod 3 (a) and Prod 4 (b). Observed daily oil production rate is shown in dashed red lines; observed water cut in purple dashed lines; simulated daily oil production rate is shown in solid green lines; and simulated water cut is shown in solid blue lines. In Figure 10a, vertical red line denotes year 2003. Please note that displayed oil production profiles are normalized

Figure 11 Map view of observed time-shift data in Tor Formation for different time periods: (a) negative time-shift signal is driven by pressure depletion; (b) positive time-shift signal is due to depletion around well Prod 3 at toe level, water front is moving towards Prod 3; (c) water front has reached wells Prod 3 and Prod 4; (d) water front moves south

The analysis of the 4D seismic data in the region reveals the following simulation model shortcomings. The shape and distribution of 4D anomalies suggests that the water front propagates through the matrix and/or an interconnected set of short fractures rather than through large and extended high permeability streaks (fractures). Time-shift anomalies reflect water front propagation towards both wells Prod 3 and Prod 4. On the contrary, the original simulation model predicted injection water front propagation mainly towards the well Prod 3 and Prod 5. The latter was caused by a permeability distribution within the region where a high permeability fracture corridor was introduced that connected the wells Inj 4 and Prod 3 (Figure 14a). Here 4D seismic data provides valuable insights in the waterflood pattern; interpretation using only well production data is uncertain and challenging due to complex interference with neighboring regions and wells.

The goals of this detailed history match study within the region were to improve predictability of the simulation model, map the remained oil pockets in the formation, and to improve water cut history match in Prod 4 while honoring observed production data from the neighboring wells.

The starting point for the SHM workflow is the current reservoir simulation model (see Figure 4). Following the described Ekofisk SHM workflow, the simulation model perturbation routine was set up. The routine perturbs both static and dynamic

a b c d

a b

SPE 154347 11

parameters. For this example, the object modeling technique is used to generate alternative realizations of fracture network indicators. Moreover, 4D seismic anomalies were used to provide information about a presence of the reservoir facies or fractures. This information was included as fracture trend map for the object modeling.

The second step of the workflow serves to reduce the size of the problem. The uncertain parameters are provided with corresponding distributions. Uniform distributions are assigned in cases where there is insufficient data to construct the statistical distributions with certain characteristics. At this initial stage, ranges of uncertainties are selected to cover all the possible outcomes and possible geological scenarios. The main physical parameters and phenomena that control the flow in the Ekofisk field are fracture network geometry, fracture properties and characteristics fracture and matrix permeability, porosity, geomechanical effects, chalk compaction trends, pressure, faults conductivity, capillary hold-up effects, relative permeability functions, fluid exchange mechanisms between fracture and matrix, etc. Overall, twenty-one uncertain parameters are identified that might affect prediction of depletion and water front propagation in the region. The sensitivity analysis was initially constructed based on 43 simulation runs, with outputs displayed in a tornado plot (Figure 12a). From this analysis, the size of the problem is reduced to 8 main parameters: distribution of fractures, fracture orientation angle, average fracture width and width-to-length ratio, average fracture permeability, average fracture density, fault transmissibility multipliers along two major faults present within the region. This number of uncertain parameters is incorporated into 80 Latin Hypercube exploratory runs that are simulated to sample the space of possible outcomes (Figure 13) and assign the weight-factors for misfit functions. Standard weighted mean-square error functions as the misfit functions are specified to evaluate a mismatch between the observed and simulated production and 4D seismic data. The weight factors for production and 4D seismic misfit functions are adjusted for each well and 4D seismic data set individually to honor observed data quality.

Figure 12 Application of the Ekofisk SHM workflow to a field region: uncertainty analysis and computer-assisted history matching steps. (a) normalized Tornado chart; (b) contribution to variance in cumulative well production, red line denotes threshold for parameters; (c) Latin Hypercube rank correlation; (d) Latin Hypercube contribution to variance in cumulative well production

At the next stage, the PSO method is set-up with a population size of 24 samples and 30 generations of parameters

realizations. Each sample corresponds to a realization of static and dynamic parameters (Figure 14 a, b). The ranges for the uncertain parameters are narrowed based on the results of the performed Latin Hypercube exploratory runs and the corresponding variances. An evolution of the normalized objective function is shown in Figure 14d. History match quality criteria are set to be +/-5% for well production and +/-2% for the overall region production. The previously mentioned K-means Cluster Analysis helped identify the five most different models out of a number of simulation runs that give the desired history match quality. These selected models were evaluated by a multi-disciplinary team, and one realization (Figure 14c) was selected. Additionally, the five history matched models are used for reservoir management applications to include uncertainty analysis and quantification in the production forecast within the described region.

Figure 15 presents a comparison between simulation results for the original and the best history matched mode. Application of the SHM workflow process allows for significant improvement of both oil production and water cut prediction

a b

c d

12 SPE 154347

Figure 13 Summary of Latin Hypercube exploratory runs: (a) average daily oil production rate for well Prod 3 (normalized); (b) water cut for well Prod 3 (normalized); (a) average daily oil production rate for well Prod 4 (normalized); (b) water cut for well Prod 4 (normalized); Observed production data is denoted by black cross symbols, profiles for each sensitivity run are displayed in color

Figure 14 Summary of computer-assisted history matching step of Ekofisk SHM workflow using PSO method: (a) original simulation model, permeability map; (b) permeability maps realizations, performed by model perturbation routine within 4D Close-The-Loop using object modeling technique; (c) best history matched model; (d) SHM objective function evolution with generations

Figure 15 Results of Ekofisk SHM workflow for wells Prod 3 (a) and Prod 4 (b). Observed daily oil production rate is shown in dashed red lines with cross symbols; observed water cut in purple solid lines with cross symbols; simulated daily oil production rate using original model is shown in solid green lines; and simulated water cut using original model is shown in blue solid lines; simulated daily oil production rate using the best history matched in solid pink color; and simulated water cut using the best history matched in light blue solid color oil Displayed profiles are normalized. Please, note improvement for both oil and water history match for well Prod 4

a b

c d

a b

a

b

c

d

SPE 154347 13

for the well Prod 4 (Figure 15b). The best history matched model correctly predicts the injection water front advancement and honors the observed data. The improved overall understanding of the production mechanisms in the considered region is crucial for reservoir management and well planning studies. Conclusions

The paper demonstrates the Ekofisk 4D Seismic History Matching Workflow. The Ekofisk field is one of the largest fields on the Norwegian Continental Shelf and still a vital giant. Current and future development plans at Ekofisk include an active drilling programme. The task of planning infill drilling in a mature field sets high requirements with respect to the precision of well targets and well trajectories. The overall goal of the workflow is to improve reservoir simulation model predictability in general, and to improve reservoir management decisions that are to be taken in particular.

The workflow steps include geological modeling, model parameterization and perturbation techniques. The model perturbation routine uses 4D seismic information to constrain the geomodeling process. Additionally, the workflow allows identifying reservoir model shortcomings using 4D data. Forward rock physics modeling is employed to quantitatively estimate a misfit between simulated and observed 4D seismic data. Uncertain geological parameters are then updated in an optimization loop through minimization of the objective function that contains both production and 4D seismic misfits. The workflow is an automated closed loop system, and optimization is performed using an in-house computer-assisted history matching tool that employs various techniques for uncertainty analysis and PSO method.The successful application of the workflow is demonstrated on a field segment, and the associated benefits of incorporating 4D seismic data into history matching process are highlighted. The interpretation of 4D seismic attributes from consequent time-lapse surveys between 1999 and 2008 helped identify the main production mechanisms and track injection water front advancement in the field region. This information also helped to set-up the computer-assisted history matching process that results in the updated fracture network distribution and improved model predictability on the well level.

To conclude, the SHM workflow is a multi-disciplinary process that requires collaboration across the disciplines, and it is becoming a core tool in reservoir management and well planning practices on Ekofisk. Acknowledgements

The authors would like to thank the ConocoPhillips management and PL018 license partners ConocoPhillips Skandinavia AS, Total E&P Norge AS, ENI Norge AS, Statoil Petroleum AS and Petoro AS for the permission to publish this paper. References Benguigui A. and MacBeth C., 2009. Updating the Simulation Model using 4D-derived fault transmissibility multipliers: An Application to

the Heidrun Field. EAGE Conference, Amsterdam, 8-11 June 2009 Castro, S., Caers, J., Otterlei, C., Høye, T., Andersen, T., and Gomel, P. 2006. A probabilistic integration of well log, geological information,

3D/4D seismic, and production data: application to the Oseberg Field, SPE Annual Technical Conference and Exhibition, San Antonio, Texas, September 24–27, 2006, SPE 103152

Caers J., 2003. History matching under a training image-based geological model constraints. SPE 74716, 218-226 Folstad P. G., Hartline C., Haugvaldstad H. and Svela K. E. 2008. 4D Seismic Analysis at the Ekofisk Field. NPF Biennial Geophysics

Conference, 2008. Gosselin O., Aanonsen S.I., Aavatsmark I., Cominelli A., Gonard R., Kolasinski M., Ferdinandi F., Kovacic L. and Neylon K. 2003. History

matching Using Time-lapse Seismic (HUTS). SPE Annual Technical Conference and Exhibition, 5-8 October 2003, Denver, Colorado. SPE 84464

Guilbot, J., and Smith, B., 2002, 4-D constrained depth conversion for reservoir compaction estimation: Application to Ekofisk Field. The Leading Edge, 21, No. 03, 302.308.

Haugvaldstad, H., Lyngnes B., Smith P., Thompson A., Ekofisk time-lapse seismic – a continuous process of improvement. First Break journal. Issue 9. Volume 29. September 2011

Hermansen, H. 2008. The Ekofisk Field: Achieving Three Times the Original Value, 19th World Petroleum Congress, June 29 - July 3, 2008, Madrid, Spain. 19-3966

Jansen J.D., Douma S.D., Brouwer D.R., Van den Hof P.M.J., Bosgra O.H., and Heemink A.W. 2009. Closed Loop Reservoir Management. SPE Reservoir Simulation Symposium, 2-4 February 2009, The Woodlands, Texas. SPE 119098

Janssen A. L., Smith B. A., Byerley G. W., 2006. Measuring velocity sensitivity to production-induced strain at the Ekofisk Field using time-lapse time-shifts and compaction logs. SEG/New Orleans 2006 Annual Meeting

Jin, L., Sen, M., Stoffa, P., Seif, R., and Sena, A. 2008. Stochastic inversion for reservoir properties using time-lapse seismic and well production data. Paper presented at the 70th EAGE Conference and Exhibition, Rome, Italy, 9–12 June, 2008

Jin L., Alpak F. O., Van den Hoek, P., Pirmex C., Fehintola T., Tendo, F. and Olaniyan E. 2011. A Comparison of Stochastic Data-Integration Algorithms for the Joint History Matching of Production and Time-Lapse Seismic Data, SPE Annual Technical Conference and Exhibition held in Denver, Colorado, USA, 30 October–2 November 2011, SPE 146418

Kennedy, J., and Eberhart, R.C. 1995. Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks, IEEE Press, p. 1942–1948

Kretz V., Le Ravalec-Dupin, M., and Rogger, F. 2002. An Integrated Reservoir Characterization Study Matching Production Data and 4D Seismic, SPE Annual Technical Conference and Exhibition held in San Antonio, Texas, 29 September - 2 October 2002. SPE 77516

Landrø, M., 2002. Uncertainties in qualitative time-lapse seismic analysis. Geophysical Prospecting, 50 (5), 2002, p. 527-538 Landrø, M. and Amundsen L., 2009. Estimating velocity and thickness changes of compacting reservoirs combining 4D seismic and

gravimetric measurements. 79th Society of Exploration Geophysicists International Exposition and Annual Meeting, Houston 2009

14 SPE 154347

Roggero, F., Ding, D.Y., Berthet, P., Lerat, O., Cap, J., and Schreiber, P.E. 2007. Matching of production history and 4d seismic data-application to the Girassol Field, offshore Angola. SPE Annual Technical Conference and Exhibition, Anaheim, California, 11–14 November, 2007. SPE 109929

Skjervheim, J.-A., Evensen, G., Aanonsen, S.I., Ruud, B.O., and Johansen, T.A. 2007. Incorporating 4d seismic data in reservoir simulation models using Ensemble Kalman Filter. SPE Journal, v. 12, p. 282–292. 95789-PA

Smith B. A., Sylte J. E., Clausen, C. K., Guilbot J. 2002. Ekofisk 4D Seismic - Influence on Flow Simulation and Compaction Modeling. Offshore Technology Conference held in Houston, Texas U.S.A., 6–9 May 2002. OTC 14149

Stephen, K.D., Soldo, J., MacBeth, C., and Christie, M. 2006. Multiple-model seismic and production history matching: a case study. SPE Journal, v. 11, p. 418–430. 94173-PA

Strebelle, S., 2000, Sequential simulation drawing structures from training images, PhD thesis, Stanford University, Stanford, CA Thomas, K. O., Seiffert L., de Lanlay J., Nes K. N. 2010. Optimisation loop between reservoir characterization and flow simulation for the

Ekofisk Field. Production Geoscience 2010 conference, Stavanger, Norway, November 2 – 3, 2010 Walls J. D., Dvorkin J., Smith B. A. 1998. Modeling Seismic Velocity In Ekofisk Chalk. SEG Annual Meeting, September 13 - 18, 1998,

New Orleans, Louisiana