Generally, industry “lookbacks” continue to show the difficulty of achieving a production forecast within an uncertainty band (P90 and P10) for both “greenfield” projects with limited data and “brownfield” projects with abundant data. One main reason for industry underperformance is that the evaluation methods do not account for the “full range of subsurface uncertainties.”
Three-dimensional reservoir models are used routinely for various purposes in the E&P business to support value-based decisions. One of the key challenges in 3D reservoir modeling is distributing the identified facies and their associated properties in the defined 3D structural/stratigraphic framework respecting geologic knowledge and available well data. Different geostatistical techniques are used for populating the reservoir facies and properties in the 3D reservoir models which have different working inputs and assumptions. Most reservoir-characterization studies use variogram-based geostatistical-modeling methods to accurately and efficiently represent reservoir heterogeneities. The variogram-based techniques constrain 3D reservoir models to local data which represent the geologic knowledge and help to create appropriate flow behaviors through dynamic simulation. However, simulation results obtained from those techniques are highly dependent on available data, selected variogram model, and the geomodeler's geostatistical knowledge and geologic experience. To illustrate the impact of variogram modeling on 3D reservoir-modeling outcomes, multiple 3D reservoir models were generated using different variograms for a siliciclastic reservoir. As expected, the results obtained from the models show significant variations, which indicate that selection of appropriate variogram models, which is critical for facies and property distribution in 3D static models, affects original hydrocarbon in place (OHIP) and recoverable resources/reserves estimation and production forecasts. A sensitivity analysis of the variogram parameters in the 3D static models and its impact in the dynamic simulation should be considered an integral part of the 3D modeling workflow.
TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThe Santa Barbara and Bosque (SB/BSQ) fields, located in northern Monagas State of Venezuela, are in an area of the largest and most prolific oil fields in Venezuela and currently produce nearly 1 million BOPD. The fields are operated by Petroleos de Venezuela, S.A. (PDVSA). The proven SB/BSQ hydrocarbon column is more than 6,000 feet thick. The vertical compositional variation creates a complex system where relatively low yield condensate gas changes to high yield condensate, near critical fluid, volatile oil, and finally, highly under saturated oils. The fluid system is complex and area differences exist because of structural compartmentalization. Original field management strategies identified the high potential of increasing reserves by highpressure injection of produced gas. Modeling of the complex interactions of injected gas is important, thus a full and detailed study of the PVT samples, evaluation of area fluid distributions, and the definition of an equation of state to allow reservoir modeling were performed. This paper documents the effort of 1998 and the ongoing process to define an Equation-of-State (EoS) to meet reservoir modeling and management objectives. The Problem -Santa Barbara and Bosque FieldsThe fields were discovered in 1988 with the drilling of the SBC-001 exploration well. Figure 1 (Santa Barbara Bosque Fields Location) shows the approximate location of the fields in eastern Venezuela. The PIC-001 well was credited for delineating the Bosque area, in the northern
The Orocual field is located in the northern Monagas state of Venezuela and is owned and operated by Petroleos de Venezuela S.A. (PDVSA), the national oil company of Venezuela.Reservoir compartmentalization adds complexity to the field, and structurally equivalent, noncommunicating fluid regions exist. An equation of state (EOS) is needed for reservoir modeling, requiring a review of available data and area fluid distributions.A seven-pseudocomponent EOS with a single characterization defining the compositional gradient of the hydrocarbon column from gas to black oil is defined. The method demonstrates that composition relative to depth can be predicted in those parts of the reservoir in which samples do not exist but in which production and test data must be matched, and thus where a gas-to-oil transition occurs.This paper demonstrates a technique to identify representative samples for use in developing an EOS and for initializing fluids in place. A method is presented to adjust the component composition vs. depth, providing consistent vertical composition distribution and compositional-model stability. This method meets the objectives of matching field production observations. A method is presented to quickly initialize a full-field model using a 1D compositional simulator to give full-field-model stability using the local high-temperature gradient.Results of compositional simulation show a single EOS, and vertical compositional and thermal variation reproduce the complex character of the field hydrocarbon column, matching fieldmeasured observations of saturation pressure (p s ), gas/oil ratio (GOR), and fluid densities.
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