Weijermans, Peter-Jan (Neptune Energy Netherlands B.V.) | Huibregtse, Paul (Tellures Consult) | Arts, Rob (Neptune Energy Netherlands B.V.) | Benedictus, Tjirk (Neptune Energy Netherlands B.V.) | De Jong, Mat (Neptune Energy Netherlands B.V.) | Hazebelt, Wouter (Neptune Energy Netherlands B.V.) | Vernain-Perriot, Veronique (Neptune Energy Netherlands B.V.) | Van der Most, Michiel (Neptune Energy Netherlands B.V.)
The E17a-A gas field, located offshore The Netherlands in the Southern North Sea, started production in 2009 from Upper Carboniferous sandstones, initially from three wells. Since early production history of the field, the p/z plot extrapolation has consistently shown an apparent Gas Initially In Place (GIIP) which was more than 50% higher than the volumetric GIIP mapped. The origin of the pressure support (e.g. aquifer support, much higher GIIP than mapped) and overall behavior of the field were poorly understood.
An integrated modeling study was carried out to better understand the dynamics of this complex field, evaluate infill potential and optimize recovery. An initial history matching attempt with a simulation model based on a legacy static model highlighted the limitations of existing interpretations in terms of in-place volumes and connectivity. The structural interpretation of the field was revisited and a novel facies modeling methodology was developed. 3D training images, constructed from reservoir analogue and outcrop data integrated with deterministic reservoir body mapping, allowed successful application of Multi Point Statistics techniques to generate plausible reservoir body geometry, dimensions and connectivity.
Following a series of static-dynamic iterations, a satisfying history match was achieved which matches observed reservoir pressure data, flowing wellhead pressure data, water influx trends in the wells and RFT pressure profiles of two more recent production wells. The new facies modeling methodology, using outcrop analogue data as deterministic input, and a revised seismic interpretation were key improvements to the static model. Apart from resolving the magnitude of GIIP and aquifer pressure support, the reservoir characterization and simulation study provided valuable insights into the overall dynamics of the field – e.g. crossflows between compartments, water encroachment patterns and vertical communication. Based on the model a promising infill target was identified at an up-dip location in the west of the field which looked favorable in terms of increasing production and optimizing recovery. At the time of writing, the new well has just been drilled. Preliminary logging results of the well will be briefly discussed and compared to pre-drill predictions based on the results of the integrated reservoir characterization and simulation study.
The new facies modeling methodology presented is in principle applicable to a number of Carboniferous gas fields in the Southern North Sea. Application of this method can lead to improved understanding and optimized recovery. In addition, this case study demonstrates how truly integrated reservoir characterization and simulation can lead to a revision of an existing view of a field, improve understanding and unlock hidden potential.
One of the targets of time-lapse seismic monitoring of CO2 sequestration is to determine the parts of the reservoir reached by the injected CO2 and to estimate their saturation. Such information could be extracted from the time-lapse measurement using AVO or impedance information. But non-repeatability of the source positions and time-lapse changes in the overburden could lower the accuracy of the estimation of saturation. We propose the utilization of non-physical (ghost) reflections retrieved by seismic interferometry to estimate the CO2 saturation inside the reservoir. We use the ghost reflections to directly estimate the layer specific velocities inside the reservoir and the cap rock and thus eliminate the two mentioned causes of inaccuracies. We apply this idea and demonstrate its added value and potential using numerically modelled data for a simplified model based on the Sleipner underground storage site in the North Sea.
Time-lapse seismic data provide information on the dynamics of multiphase reservoir fluid flow in places where no production data from wells are available. This information, in principle, could be used to estimate unknown reservoir properties. However, the amount, resolution, and character of the data have long posed significant challenges for quantitative use in assisted-history matching workflows. Previous studies, therefore, have generally investigated methods for updating single models with reduced parameter-uncertainty space. Recent developments in ensemble-based history-matching methods have shown the feasibility of multimodel history and matching of production data while maintaining a full uncertainty description. Here, we introduce a robust and flexible reparameterization for interpreted fluid fronts or seismic attribute isolines that extends these developments to seismic history matching. The seismic data set is reparameterized, in terms of arrival times, at observed front positions, thereby significantly reducing the number of data while retaining essential information. A simple 1D example is used to introduce the concepts of the approach. A synthetic 3D example, with spatial complexity that is typical for many waterfloods, is examined in detail. History-matching cases based on both separate and combined use of production and seismic data are examined. It is shown that consistent multimodel history matches can be obtained without the need for reduction of the parameter space or for localization of the impact of observations. The quality of forecasts based on the history-matched models is evaluated by simulating both expected production and saturation changes throughout the field for a fixed operating strategy. It is shown that bias and uncertainty in the forecasts of production both at existing wells and in the flooded area are reduced considerably when both production and seismic data are incorporated. The proposed workflow, therefore, enables better decisions on field developments that require optimal placement of infill wells.
Glegola, Marcin A. (Delft University of Technology) | Ditmar, Pavel (Delft University of Technology) | Hanea, Remus (TNO) | Eiken, Ola (Statoil) | Vossepoel, Femke C. (Shell International Exploration and Production B.V.) | Arts, Rob (TNO) | Klees, Roland (Delft University of Technology)
Water influx is an important factor influencing production of gas reservoirs with an active aquifer. However, aquifer properties such as size, porosity, and permeability are typically uncertain and make predictions of field performance challenging. The observed pressure decline is inherently nonunique with respect to water influx, and large uncertainties in the actual reservoir state are common. Time-lapse (4D) gravimetry, which is a direct measure of a subsurface mass redistribution, has the potential to provide valuable information in this context.
Recent improvements in instrumentation and data-acquisition and -processing procedures have made time-lapse gravimetry a mature monitoring technique, both for land and offshore applications. However, despite an increasing number of gas fields in which gravimetric monitoring has been applied, little has been published on the added value of gravity data in a broader context of modern reservoir management on the basis of the closed-loop concept. The way in which gravity data can contribute to improved reservoir characterization, production-forecast accuracy, and hydrocarbon-reserves estimation is still to be addressed in many respects.
In this paper, we investigate the added value of gravimetric observations for gasfield-production monitoring and aquifer-support estimation. We perform a numerical study with a realistic 3D gasfield model that contains a large and complex aquifer system. The aquifer support and other reservoir parameters (i.e., porosity, permeability, reservoir top and bottom horizons) are estimated simultaneously using the ensemble smoother (ES). We consider three cases in which gravity only is assimilated, pressure only is assimilated, and gravity and pressure data are assimilated jointly. We show that a combined estimation of the aquifer support with the permeability field, porosity field, and reservoir structure is a very challenging and nonunique history-matching problem, in which gravity certainly has an added value. Pressure data alone may not discriminate between different reservoir scenarios. Combining pressure and gravity data may help to reduce the nonuniqueness problem and provide not only an improved gas- and water-production forecast and gas-in-place evaluation, but also a more-accurate reservoir-state description.
Glegola, Marcin A. (Delft University of Technology) | Ditmar, Pavel (Delft University of Technology) | Hanea, Remus G. (TNO) | Vossepoel, Femke C. (Shell International E&P) | Arts, Rob (TNO) | Klees, Roland (Delft University of Technology)
Peters, Lies (TNO) | Arts, Rob (TNO) | Brouwer, Geert (TNO) | Geel, Cees (TNO) | Cullick, Stan (Halliburton) | Lorentzen, Rolf J. (International Research Institute of Stavanger) | Chen, Yan (University of Oklahoma) | Dunlop, Neil (Roxar) | Vossepoel, Femke C. (Shell International) | Xu, Rong (Schlumberger) | Sarma, Pallav (Stanford University) | Alhuthali, Ahmed H.H. (Texas A&M University) | Reynolds, Albert (University of Tulsa)
In preparation for the SPE Applied Technology Workshop (ATW) held in Brugge in June 2008, a unique benchmark project was organized to test the combined use of waterflooding-optimization and history-matching methods in a closed-loop workflow. The benchmark was organized in the form of an interactive competition during the months preceding the ATW. The goal set for the exercise was to create a set of history-matched reservoir models and then to find an optimal waterflooding strategy for an oil field containing 20 producers and 10 injectors that can each be controlled by three inflow-control valves (ICVs). A synthetic data set was made available to the participants by TNO, consisting of well-log data, the structure of the reservoir, 10 years of production data, inverted time-lapse seismic data, and other information necessary for the exercise. The parameters to be estimated during the history match were permeability, porosity, and net-to gross- (NTG) thickness ratio. The optimized production strategy was tested on a synthetic truth model developed by TNO, which was also used to generate the production data and inverted time-lapse seismic. Because of time and practical constraints, a full closed-loop exercise was not possible; however, the participants could obtain the response to their production strategy after 10 years, update their models, and resubmit a revised production strategy for the final 10 years of production. In total, nine groups participated in the exercise. The spread of the net present value (NPV) obtained by the different participants is on the order of 10%. The highest result that was obtained is only 3% below the optimized case determined for the known truth field. Although not an objective of this exercise, it was shown that the increase in NPV as a result of having three control intervals per well instead of one was considerable (approximately 20%). The results also showed that the NPV achieved with the flooding strategy that was updated after additional production data became available was consistently higher than before the data became available.
The Ensemble Kaiman Filter (EnKF) is considered a fast and efficient algorithm in the data assimilation process to estimate reservoir properties from measured data. 4D seismics is an important source of information for the reservoir monitoring and the improvement of the geological model. The use of low frequencies for deep surface seismic makes it very complicated to discriminate and estimate properties for fine-grid reservoir models. In this paper it is demonstrated that using vertically averaged seismic data, inverted as time-lapse differences in pore pressure and saturation, greatly improves the quality of the history match and the estimation of the reservoir state. The EnKF may present some problems when assimilating large amounts of data (frequent 4D seismic), as the flexibility of the model solution is strongly reduced. The conditioning of the covariance matrix in the Kaiman gain is a key to avoid the filter divergence. In this study the localization criterion is based on the mere distance or on the streamlines trajectories. Results from 2D and 3D synthetic examples show the importance of localization to ensure the correct functioning of the filter.