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Estimation of Reservoir Fluid Volumes Through 4 D Seismic Analysis On - 'JsTo'-r - W>v mTRmmN op w-oocuwar is 'u%mm FORBGN SALES PRGHHISO 7180 THE 7th CONFERENCE ON RESERVOIR MANAGEMENT The New Petroleum Age Clarion Admiral Hotel Bergen 18th- 19 th November 1998 RECEIVED JAN 1 1 2008 OSTI ESTIMATION OF RESERVOIR KLVI1) N OLI MES THROl GH 4 D SEISMIC ANALYSIS ON Gl LLFAKS Presented by: Helene H. Veire Schlumberger Geco-Prakla DISCLAIMER Portions of this document may be illegible in electronic image products. Images are produced from the best available original document. Estimation of reservoir fluid volumes through 4D seismic analysis on Gullfaks. H.H. Veire, S B. Reymond, C. Signer, P.O. Tennebp, L. Spnneland, Schlumberger Geco-Prakla. Introduction Reservoir management today is a science of approximation when it comes to the rate anddirection of fluid front movement. Optimal management requires up to date information throughout the entire reservoir volume. Ac­ cess to the latest data on fluid distribution in a reservoir, and knowledge of how the distribution is changing with time, allows engineers to develop cost-effective strategies to get the most out of every field at the lowest possible risk. In addition to static, or one-time measurements, time-dependent measurements from various oilfield disciplines help constrain, refine and improve the accuracy of reservoir models. Time-lapse logging of fluid saturation through casing can show which zones are contributing to production and which are watering out or being by­ passed. Permanent downhole sensors provide continual observations of pressure, temperature and other diag­ nostics of reservoir performance. These measurements supply crucial information about fluid behavior at the well location, but fail in the vast interwell region. The 3D seismic measurements has routinely been relied on to provide interwell data. In the past, seismic surveys were mainly interpreted for structural features and strati­ graphic variations within the reservoir, but they can also be sensitive to contrasts in fluid types. Applied in sur­ veys separated by periods of production, time-lapse, or four-dimensional (4D) - 3D plus time - seismic images can map fluid changes in a producing reservoir. 4D seismic has the potential to monitor hydrocarbon movement in reservoirs during production and could there­ by supplement the predictions of reservoir parameters offered by the reservoir simulator. The changes in the seismic response can be attributed to changes in the reservoir due to production. A procedure where data from the reservoir model are integrated with seismic data will be presented in this article. The potential of such a procedure is demonstrated through a case study from a recent 4D survey over the Gullfaks field in the North Sea. The Gullfaks field In 1995-1996 a time-lapse 3D seismic survey was shot over the northern part of the Gullfaks field in the North Sea. More than 50% of the estimated recoverable reserves in Gullfaks have been produced since the start-up in december 1986. One important objective of the 4D studyperformed onthe Gullfaks field was to identify poten­ tially undrained reservoir compartments after 9 years of production (1986-95). In addition, the changes in fluid saturation as a consequence of the production should be estimated by integrating a set of different data types (4D seismic, well-logs and reservoir models). The Gullfaks field is located in the central part of the East Shetland Basin of the North Sea, onthe western flank of the Viking Graben. The field covers about 50km2 of the north-eastern part of Block 34/10 on the Norwegian sector. The block was awarded in 1978 to Statoil, Norsk Hydro and Saga Petroleum, with Statoil as main oper­ ator. The field consists of the shallowest structural elements of the Tampen Spur, formed during Upper Jurassic to Lower Cretaceous as a sloping high with westerly structural dip gradually decreasing to the east. A set of major faults striking north to south with easterly dipping fault planes divides the field into severalrotated fault blocks. The top reservoir unit in Gullfaks is the Tarbert Formation which consists of 60 to 70 meters of Bathonian trans­ gressive sands deposited in a tide-fluvial dominated delta environment. These clean sands show an average po­ rosity of 34%. The Tarbert sands are subdivided into three sub-members bounded by shales and coal layers that can locally act as permeability barriers and therefore induce non-homogeneous drainage patterns in the vertical plane. The Tarbert formation is bounded on top by two different caprocks: shales of the upper Jurassic Heather Formation and the more carbonated sediments of the Early Cretaceous Shetland group. They will be the source of important lateral differences in acoustic response, that must be considered for classification of fluids in the reservoir below. Page 1 of 5 Seismic classification Attribute maps generated from seismic data enhance different features of the subsurface, but it is often difficult to extract and combine all the available information from a set of attribute maps by manual interpretation. A generalised inversion tool based on geostatistical classification has been used to combine the information con­ tained in such a set of seismic attributes. The classification could be done without prior knowledge of the field using unsupervised classification methods based on natural clustering techniques (nearest neighbour algo­ rithms), or by combining the seismic attributes with prior knowledge (for example well log information) in su­ pervised classification. The basic principle behind the supervised classification is the assignment of anattribute- response to a specific fluid or facies type. The prior information (called training data) is used to “calibrate” the classification, that is, to estimate the classification function that maps the points into the different fluid- or facies-classes. The result of the classification will be maps showing the distribution of facies and/or fluids and the confidence of the classified result. These fluid distribution maps could be used to condition the reservoir model building. The 4D seismic fluid mapping of the Tarbert Formation on Gullfaks was done using this seismic classification system, and the resulting fluid distribution maps are shown in Figure 1. The classification was done using an equivalent set of instantaneous and volume attributes for the 1985 and the 1995 data. The training data used to set up the classification function were guided by a set of wells available in the area. The resulting fluid distri­ bution maps constitute the hydrocarbon hypothesis used in the reservoir volume analysis outlined below. The 4D seismic mapping of the pore fill content has been confirmed by new horizontal wells that verified undrained compartments. Statistical reservoir volume analysis To optimize the reservoir characterization we need to be able to estimate saturation changes in the reservoir and calculate the net-to-gross ratio of each compartment. In the reservoir model domain the volume of oil is calcu­ lated between the top of the reservoir and a base grid generated as an intersection between the base reservoir grid and a set threshold of the saturation volumes generated from flow simulations. In the seismic domain, this could be done by combining the results from the supervised classification with reservoir-volume analysis and seismic inversion. The classification results will be combined with the structural information of the reservoir for reservoir-volume analysis ina statistical manner described below. The results of the analysis will be maps show­ ing how good the fluid distribution maps from the classification fits with the reservoir structure. In addition the volume of the reservoir down to the Oil-Water-Contact (OWC) or the base of the reservoir is also calculated. A hydrocarbon reservoir needs to be sealed bya caprock layer. The draping of this caprock layer influences how the hydrocarbons are trapped. The probability of hydrocarbon presence in a rock unit is calculated by combining the structural interpretation identifying the caprock interface with a hypothesis of hydrocarbon presence based on the seismic signal. Typically such a hypothesis is generated by inversion or classification of seismic hydro­ carbon indicators. The probability of the match between each hydrocarbon hypothesis and the structural closure is computed with a cost function taking into account a set of 4 constrains: depth, area, volume and outline of the hydrocarbon hypothesis. We assume a horizontal OWC in a gravity controlled reservoir for the pre-production stage. Well calibration and saturation To calculate the volume of oil in place we have to relate the measured seismic attribute responses to a saturation value through petrophysical modeling orstatistical analysis in attribute space. Crossplotting seismic saturation indicators and saturation values indicates that classification of fluid fronts based on seismic attribute grids is able to capture anddistinguish low and high hydrocarbon saturation in a homogeneous lithology in the reservoir conditions of the Gullfaks field. We observe also that the relationship between saturations measured in wells and attribute responses is non - linear. It remains to see whether this relationship can be used to build a function that will transform a seismic saturation indicator attribute into a saturation map. A Neural Network estimation method has been tested to find a non-linear relationship between a set of seismic attributes and the saturation values. The method shows promising results, but requires a larger number of wells than what is available in this study. Page 2 of 5 * Seismic inversion A new seismic inversion method has been developed based on a method called Best Feasible Approximation (BFA). The method ensures that the computed acoustic impedance cube is consistent with the amplitude infor­ mation contained in the seismic data, the well log data and interval velocities for the layers in the overburden. An initial model for the inversion of the 1985 seismic was generated through ordinary kriging of acoustic im­ pedance well logs. The kriging error was used together with interval velocities and seismic amplitudes to con­ strain the inversion.
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