Reservoir simulation was used to develop a set of correlation charts for predicting steamflood oil recovery and oil/steam ratio as functions of predicting steamflood oil recovery and oil/steam ratio as functions of reservoir characteristics and operating conditions. The correlations emphasize the effects of steam quality, mobile oil saturation, reservoir thickness, and net/gross ratio. Introduction Heavy-oil properties that classify as candidates for steamflooding often need to be screened for priority ranking due to budget, manpower development and permitting limitations. Also, sensitivity studies often permitting limitations. Also, sensitivity studies often are run on steamflood projects to determine the effects of various operating strategies on project performance and economic feasibility. Steamflood performance and economic feasibility. Steamflood performance predictions required in such screening performance predictions required in such screening and sensitivity studies certainly can be made using one or more of the analytical and empirical models available in the literature. Numerical reservoir models that simulate the process of steamflooding also can be used to make the required predictions. While these analytical and/or numerical models could suffice, they generally require somewhat lengthy computations and necessitate the use of a computer. There is a need for a simplified easy-to-use method for predicting steamflood performance. This paper describes the development of such a method and its basis, procedures, and limitations of applicability. Basic Concept and Assumptions The basic concept of the method is to define the minimum set of parameters that have the most influence on steamflood oil recovery and are easy to determine for any given project. Oil recovery then is determined as a function of these parameters using field data and/or numerical simulation. Generalized correlations or charts are prepared from these results and used for prediction purposes. In a steamflood, oil recovery should be dependent on (1) rock properties such as permeability, porosity, compressibility, relative permeability, capillary pressure, and net/gross ratio; (2) fluid properties pressure, and net/gross ratio; (2) fluid properties such as specific gravity, viscosity, compressibility, and PVT relationships; (3) flood geometry such as pattern shape, spacing, and sand thickness; (4) pattern shape, spacing, and sand thickness; (4) thermal properties such as thermal conductivity, heat capacity, and thermal expansion; (5) reservoir conditions such as initial oil saturation, temperature, pressure, and residual oil saturation after pressure, and residual oil saturation after steamflood; and (6) injection conditions such as rate, pressure, and steam quality. pressure, and steam quality.Because most steamflood applications are focused on shallow heavy-oil-bearing sands, typical unconsolidated sand characteristics were used in this work. This meant that parameters such as absolute permeability, capillary pressure, compressibility, permeability, capillary pressure, compressibility, thermal properties, and fluid properties were not considered as variables in the development of these correlations. Instead, these parameters were fixed at acceptable typical values. In most projects, reservoir temperature and pressure prior to steamflooding generally are low. pressure prior to steamflooding generally are low. JPT P. 325
This paper describes the design and development of a steamflood pilot consisting of six inverted five-spot patterns in Section 26C of the Midway-Sunsetfield. Steam injection will be in the 330-ft Monarch sand. A steamflood simulation study indicated a potential of 60- to 70-percent oil recovery and defined the relative importance of various steamflood parameters. Introduction The reservoir characteristics and production history of Section 26C of Midway-Sunset field make it a favorable candidate for thermal recovery. Current well productivity under cyclic steaming is only about 0.05 BOPD/ft. The estimated ultimate recovery by this method is only 15 percent because of its continuously decreasing percent because of its continuously decreasing effectiveness. Underground combustion is thought to be undesirable for this section because of the reservoir's large thickness and low dip. Based on its predicted and actual performance in other heavy oil reservoirs, performance in other heavy oil reservoirs, steamflooding is the most promising thermal recovery technique for this property. The high capital and operating costs of steamflooding made a pilot desirable to evaluate the process in this field. Other reasons were uncertainties about the geological structure, sand continuity, and the role of gravity override in steamflooding a very thick reservoir. This paper describes the reservoir and its geology, the production history under cyclic steaming, pilot project production history under cyclic steaming, pilot project details, and the results of a simulation study to optimize steam injection and predict performance. Reservoir Characteristics Geology The structural feature associated with the various productive zones in Section 26C of Midway-Sunset field is a productive zones in Section 26C of Midway-Sunset field is a large southeasterly plunging nose. The main productive sands lie on the southwest flank and plunge, where the dip is about 10 deg. . Little production comes from the northeast flank, where the dip is up to 50 deg. . The two areas are separated by a thrust fault that offsets the oil-water contact. A northeasterly trending sand channel in the northern half of the section is developed and productive mainly over the northwest quarter. No production comes from the southwest comer of the section because of sand truncation. The Monarch sand, considered the main productive zone, is of Miocene age and is at an average depth of 1,300 ft. Its thickness varies from 0 to 600 ft and averages 350 ft in the main productive area. Well logs show that the sand is vertically continuous, without any significant shale breaks. A typical induction-electric log is shown in Fig. 1. However, recent cores contain a number of diatomite beds from 0.25 to 6 in. thick. Their areal extent and ability to restrict vertical fluid movement are not known. Rock and Fluid Properties The reservoir rock is an unconsolidated, poorly sorted sand with very fine to very coarse grains. The productive interval consists of turbidite beds generally 2 to 5 ft thick that exhibit normal upward fining of grain size, and that usually are separated by thin, low-permeability diatomite beds. Average porosity is 27 percent and air permeability is 520 md under reservoir conditions. The over-all net-to-gross ratio for the zone is 0.74. The rock is saturated with a 14 deg. API oil whose viscosity is a strong function of temperature (Fig. 2). Current oil saturation in the main part of the reservoir is 59 percent but only 36 percent in the depleted portions near the top. JPT P. 1559
Permeability estimation for un-cored wells is a classic issue. A simple model that widely used is using core porosity-core permeability cross plot to determine the linier regression. Then we estimate permeability in un-cored well after making adjustment for porosity log to porosity core. The difficulties using that method is most of cross plot did not show clear relationship (scatter data) due to effect of rock heterogeneity. Therefore another effort is needed by separate it based on rock type to get better relationship. Permeability itself is not depend only on porosity but also other properties like clay content, grain size, tortuosity and etc. Part of this phenomenon had been modeled by Carmen-Kozeny which illustrates strong dependency of permeability on average grain size, tortuosity and flow zone index. The conventional way to reduce data scatter is by using additional correlation parameters. Commonly shale content (Vsh) and reservoir facies are used to give reliable transform or regression analysis for estimating the permeability. Start with this concept; we try to simplify the correlation by modified the Carmen Kozeny eq. (used flow zone index term) using clay content as another parameter that influences permeability value. Because we assume that porosity and clay content are the most important properties that have significant effect on permeability. In this paper, we will describe permeability estimation for un-cored well as function of porosity and clay content using modified flow zone index-permeability cross plot. This cross plot has been test in three clastic reservoirs in Lower Sihapas formation either for consolidated or unconsolidated sandstone. The result shows this cross plot give better relationship compare to conventional cross plot and more simple transform to estimate permeability in un-cored well for input to geologic and reservoir simulation models. Introduction EMP Malacca Strait has develop reservoir integrated study which involves data review, G & G remodeling, reservoir characterization, reservoir modeling and prediction to identify the reliable hydrocarbon potential and developing a reliable reservoir model for choosing the optimum development plan for Lower Sihapas formation in three clastic reservoir1,2. One of the main subjects during the integrated study workflow is the reservoir rock characterization and core data analysis which includes the permeability transform to develop correlation between core data and log data. Permeability is one of the fundamental rock properties which represent the quality of a reservoir. The appropriate permeability value in each well is needed in order to represent the permeability distribution in a reservoir model. Usually the data that we have from cored well is very limited. Beside that, in order to get the data from each well we need to perform coring process that quite time consuming and require expensive laboratory measurements. For that reason, in term of practical use, we need a mathematical function that can represent the permeability value from each key well (cored well) by using the existing data (log data).
A 1000 bopd was achieved from successful integrated study of Kurau Field in 2007 year. All best practices of the study describe in this paper. The objective of this study is to build a complete reservoir model from an integrated study involving all aspects of engineering & G&G, such as data review, G&G modeling, reservoir characterization, reservoir modeling and prediction, in order to optimize the development of Kurau Field. The milestone starts from interpreting the 3D seismic data, structural or zonation model, petrophysics analysis, reservoir characterization, static modeling, dynamic modeling, and proceeds until it reaches the development and performance prediction of Kurau Field. More than just a simple model, Kurau reservoir model resulted from all data and results from G&G such as interpretation of 3D seismic, G&G modeling (static model), environmental correction for well logs from Petrophysical Software analysis together with reservoir engineering works such as rock and fluid properties analysis, rock characterization and also production engineering aspects. Geomodeling software was used to build 3D seismic to reservoir modeling, with additional Simulation Software to complete the simulation. The history matching analysis shows a satisfactory level of production profile comparison results of model performances with actual production data. Several infill wells and even EOR potential can be identified from this reservoir simulation model. Using an integrated study workflow from G&G, Petrophysic and Reservoir Engineering work had greatly improved the reliability of this reservoir model. Recently updated, those infill wells proved able to yield an additional 1000 bopd to Kurau field, as previously predicted. An integrated study from Engineering & G&G Department was required when we attempted to build a reservoir model. Such a model can be used to determine infill well potential, work over result estimation and also the EOR project. Achieving an increment of the oil recovery factor is the most beneficial result from this reservoir simulation. Introduction Nowadays, reservoir simulations play an important role in the petroleum industry. Geological modeling, reservoir characterization, well performance analysis, infill well proposal and even EOR methods simulation can be applied. While it would seem to be more complicated than conventional techniques, reservoir simulation can on the other hand achieve or at least a yield preliminary pictures of the entire reservoir. With so many software releases available at present, the user just picks whichever is most suitable, depending on needs, compatibility with currently used software and also, of course, on the budget. The important rule to be borne in mind is that "garbage in, garbage out". Kurau Field in the EMP-Malacca Strait has been generated into a reservoir simulation. Within a 9 months' integrated study, a team can achieve a reliable reservoir simulation. Beginning with a 3D seismic interpretation and then combining this with a geological interpretation and reservoir characterization, the team can also generate a reasonable history matching. Geomodeling Software and Simulation Software were exploited to generate and run the model. Kurau Field itself has never been modeled before. The structure was quite complicated. Even it was only from the Lower Sihapas Formation, reservoir characterization plays an important role when a model is built. The new geophysics interpretation to define faults was carried out. Further, sand correlation was involved, along with reinterpretation due to new data from previous infill wells.
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