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Collaborating Authors
Matar, Saad
Abstract The Minagish Oolite is a thick undersaturated carbonate oil reservoir in the Minagish field in West Kuwait (Fig. 1) containing several billion STB. It is a mature but relatively undeveloped reservoir. Since discovery in 1959, it has produced 10% of its OOIP under a combination of natural depletion, gas re-injection and aquifer drive. Initial reservoir pressure had declined by about 450 psi prior to the Gulf war in 1990. The well blowouts following the war caused a significant pressure drop of another 700 psi. Following the blowout, plans were made to redevelop the West Kuwait fields and increase the production rate starting in 2001 and to sustain the plateau for at least 5 years. This strategy called for three-fold increase in the production rate of Minagish Oolite reservoir. Since the existing well inventory and the loss of the gas re-injection facility could not sustain the desired plateau rate, additional field development was required. To achieve the production target, a multidisciplinary team was formed to evaluate options. The recommended plan required the drilling of additional producers and installing a field-wide peripheral waterflood. The reservoir, however, presented a number of significant challenges to waterflooding, such as the presence of a substantial and not well defined tarmat near the oil/water contact, and uncertainties of lateral and vertical heterogeneities. In 1997 a full-field simulation model was developed, but this model didn't capture the water movement properly because of insufficient reservoir data at that time. As new core was obtained, a refined reservoir description was developed. Building on lessons learned from the previous full-field model and sector models, a new full-field model was developed which significantly improved well-by-well history matches. Although containing twice as many grid cells, the new model ran up to four times faster than the previous model by making use of the Analytical Aquifer option within the model, improved relative permeability curves and other model refinements. This paper traces the history of the field and the systematic evolution of the development plan. The reservoir simulation efforts including modeling strategy, history matching events, prediction runs, future direction and challenges are also addressed. Introduction Numerical simulators are an important tool for reservoir management, providing management the ability to observe how alternate development plans and operating strategies will affect future oil production and recovery. As additional information is acquired and new technologies are developed, it is necessary to periodically update the reservoir simulation tools. This paper identifies the reasons for building a new model, the differences between it and the previous model, and documents the data-sources, files and the methodology used to construct the new model. The previous model (FFM 97) was constructed and initialized in 1997. The model was based on a course 12-layer reservoir description and history matched reservoir performance up through the start of dumpflood water injection. In predictive mode, however, the model did not adequately predict the rapid water movement in the northeast quarter of the field or the arrival of initial water in the peripheral producers. Sector models constructed at the same time indicated that a refined reservoir description that incorporated the observed barriers and high permeability streaks should provide an improved match of the observed water movement. Since completion of the FFM 97, significant drilling activity and data acquisition has improved the understanding of the reservoir. Between January 1998 and August 2000, 25 wells have been drilled (including 7 wells being cored) and 10 wells were RFT'd across the entire reservoir. This additional data, particularly the core, has significantly improved the geological understanding of the reservoir. One significant improvement has been in defining the areal extent and vertical distribution of the tarmat, which is the major controlling factor affecting water influx and pressure support from the surrounding aquifer and dumpflood injection rates.
- Asia > Middle East > Kuwait > Jahra Governorate (0.25)
- Asia > Middle East > Israel > Mediterranean Sea (0.24)
- North America > United States > California > San Joaquin Basin > Elk Hills Field (0.99)
- Asia > Middle East > Saudi Arabia > Saudi Arabia - Kuwait Neutral Zone ("Partitioned Zone") > Arabian Basin > Widyan Basin > Wafra Joint Operations Block > Wafra Field (0.99)
- Asia > Middle East > Kuwait > Saudi Arabia - Kuwait Neutral Zone ("Partitioned Zone") > Arabian Basin > Widyan Basin > Wafra Joint Operations Block > Wafra Field (0.99)
- (22 more...)
Abstract Three methods are proposed for quickly evaluating the historymatch of a numerical simulation to actual reservoirperformance. All of the methods rely on computing a set ofdeviation values, each of which is defined to be a calculatedsimulator result minus the corresponding surveillancemeasurement value. For any particular type of surveillance data, such as rates, watercuts, or gas-oil ratios, the deviation values can begrouped by well, by area, or combining all measurements inthe database. The first two proposed methods rely on simplegraphical presentations of each group of deviation values toshow how well the simulation results match the surveillancedata. Plotting together the results from more than onesimulation run allows a quick comparison of the match foreach run, which is useful during the history match process. The third method converts each deviation value to aquantity called Match Factor, which is a relative measure ofthe confidence that the simulator actually reproduced theparticular reservoir performance at the time the surveillancemeasurement was made. Weighted-average Match Factors canreveal the degree of match by well, by area, and by data type. These techniques are especially valuable when matchingreservoirs with a large volume of surveillance data. They canhelp focus the history matching process by identifying areasless well matched. They can identify when the historymatching process is not significantly improving the match andcan stop. Introduction One of the more challenging aspects of history matching largenumerical simulators is assessing how well the simulatorresults match observed field behavior. Unfortunately, there arefew objective measures of the degree of match readilyavailable. The traditional approach is to plot the observed data valuesversus time, along with the corresponding simulator output, and visually assess how well the simulator reproduced themeasured values. The quality of this evaluation can vary, depending on the experience and judgment of the simulationengineer. It can also be very time consuming, especially forfields with many wells and years of surveillance data. This paper describes a two-stage approach, developed byBP Kuwait for Kuwait Oil Company, and validated on largemodels of giant reservoirs in Kuwait. In the first stage, observed surveillance data values are directly compared to thecorresponding predicted values extracted from simulatoroutput. Each pair of observed and predicted values defines adeviation value. Groups of deviation values are presented intwo graphical ways, showing the overall degree of match forthe type of data in that group for that run. Plotting togetherresults from two or more simulation runs can be used toquickly compare the matches for the runs. In the second stage of the analysis, each deviation isconverted to a Match Factor value, which represents theconfidence level that the simulated result matches the actualfield behavior represented by the observed surveillance value.This conversion takes into account the inherent uncertainty ofthe field measurement technique, and the limits of thesimulator calculation. Plotting on a map the Match Factorsaveraged by well quickly shows where a model is bettermatched compared with other areas. This helps guide wherechanges in the reservoir description should be made insubsequent history match runs. Averaging the Match Factors for all values in one datatype quickly provides a measure of the overall degree ofmatch for that data type for that simulation run. Comparingthis average to that from other runs, shows whether the historymatch is improving or deteriorating. If the average MatchFactor value has stabilized, the useful end of the history matchprocess may have been reached (unless a significant alterationin the reservoir description is made). This method can also prove valuable when doingsensitivity analysis on simulators which have already beenhistory matched. Different reservoir descriptions which givesubstantially the same degree of match are equally likely todescribe the actual reservoir. Forecasts based on thesedifferent reservoir descriptions are also equally probable, andcan help to define the uncertainty range around officialforecasts.
Abstract Three methods are proposed for quickly evaluating the history match of a numerical simulation to actual reservoir performance. All of the methods rely on computing a set of deviation values, each of which is defined to be a calculated simulator result minus the corresponding surveillance measurement value. For any particular type of surveillance data, such as rates, watercuts, or gas-oil ratios, the deviation values can be grouped by well, by area, or combining all measurements in the database. The first two proposed methods rely on simple graphical presentations of each group of deviation values to show how well the simulation results match the surveillance data. Plotting together the results from more than one simulation run allows a quick comparison of the match for each run, which is useful during the history match process. The third method converts each deviation value to a quantity called Match Factor, which is a relative measure of the confidence that the simulator actually reproduced the particular reservoir performance at the time the surveillance measurement was made. Weighted-average Match Factors can reveal the degree of match by well, by area, and by data type. These techniques are especially valuable when matching reservoirs with a large volume of surveillance data. They can help focus the history matching process by identifying areas less well matched. They can identify when the history matching process is not significantly improving the match and can stop. Introduction One of the more challenging aspects of history matching large numerical simulators is assessing how well the simulator results match observed field behavior. Unfortunately, there are few objective measures of the degree of match readily available. The traditional approach is to plot the observed data values versus time, along with the corresponding simulator output, and visually assess how well the simulator reproduced the measured values. The quality of this evaluation can vary, depending on the experience and judgment of the simulation engineer. It can also be very time consuming, especially for fields with many wells and years of surveillance data. This paper describes a two-stage approach, developed by BP Kuwait for Kuwait Oil Company, and validated on large models of giant reservoirs in Kuwait. In the first stage, observed surveillance data values are directly compared to the corresponding predicted values extracted from simulator output. Each pair of observed and predicted values defines a deviation value. Groups of deviation values are presented in two graphical ways, showing the overall degree of match for the type of data in that group for that run. Plotting together results from two or more simulation runs can be used to quickly compare the matches for the runs. In the second stage of the analysis, each deviation is converted to a Match Factor value, which represents the confidence level that the simulated result matches the actual field behavior represented by the observed surveillance value. This conversion takes into account the inherent uncertainty of the field measurement technique, and the limits of the simulator calculation. Plotting on a map the Match Factors averaged by well quickly shows where a model is better matched compared with other areas. This helps guide where changes in the reservoir description should be made in subsequent history match runs. Averaging the Match Factors for all values in one data type quickly provides a measure of the overall degree of match for that data type for that simulation run. Comparing this average to that from other runs, shows whether the history match is improving or deteriorating. If the average Match Factor value has stabilized, the useful end of the history match process may have been reached (unless a significant alteration in the reservoir description is made).
- North America > United States (1.00)
- Asia > Middle East > Kuwait (0.95)