Reservoir parameters

Introduction and Commercial Application The objective of reservoir geology is the description and quantification of geologically controlled reservoir parameters and the prediction of their lateral variation. Three parameters broadly define the reservoir geology of a field ... 

The basic data gathering methods are direct methods which allow visual inspection or at least direct measurement of properties, and indirect methods whereby we infer reservoir parameters from a number of measurements taken in a borehole. The main techniques available within these categories are summarised in the following table ... 

A vast variety of logging tools are In existence and Section 5.4 will cover only those which enable the evaluation of essential reservoir parameters, specifically net reservoir thickness, lithology, porosity and hydrocarbon saturation. 

Reservoir quality maps are used to illustrate the lateral distribution of reservoir parameters such as net sand, porosity or reservoir thickness. It is important to know whether thickness values are isochore or isopach (see Figure 5.46). Isochore maps are useful if properties related to a fluid column are contoured, e.g. net oil sand. Isopach maps are used for sedimentological studies, e.g. to show the lateral thinning out of a sand body. In cases of low structural dip (<12°) isochore and isopach thickness are virtually the same. 

As will be shown in the next section, the methods discussed so far do not take account of the uncertainties and lateral variations in reservoir parameters. Hence the accuracy of the results is not adequate for decision making. The next section introduces a more comprehensive approach to volumetric estimation. 

Any well used to observe the changes in specific reservoir parameters. 

History matching in reservoir engineering refers to the process of estimating hydrocarbon reservoir parameters (like porosity and permeability distributions) so that the reservoir simulator matches the observed field data in some optimal fashion. The intention is to use the history matched-model to forecast future behavior of the reservoir under different depletion plans and thus optimize production. 

The mathematical model for a hydrocarbon reservoir consists of a number of partial differential equations (PDEs) as well as algebraic equations. The number of equations depends on the scope/capabilities of the model. The set of PDEs is often reduced to a set of ODES by grid discretization. The estimation of the reservoir parameters of each grid cell is the essence ofhistory matching. 

In practice when reservoir parameters such as porosities and permeabilities are estimated by matching reservoir model calculated values to field data, one has some prior information about the parameter values. For example, porosity and permeability values may be available from core data analysis and well test analysis. In addiction, the parameter values are known to be within certain bounds for a particular area. All this information can be incorporated in the estimation method of the simulator by introducing prior parameter distributions and by imposing constraints on the parameters (Tan and Kalogerakis, 1993). 

Zhou et al. [55], The most effective method to assess the capacity is the flow simulation which includes volumetric formulas and more reservoir parameters rather than other methods [56], Mass balance and constitutive relations are accounted in mathematical models to capacity assessment and dimensional analysis consists of fractional flow formulation with dimensionless assessment and analytical approaches [33], From the formulations demonstrated by Okwen and Stewart for analytical investigation, it can be deduced that the C02 buoyancy and injection rate have affected the storage capacity [57], Zheng et al. have indicated the equations employed in Japanese and Chinese methodology and have noted that some parameters in Japanese relation can be compared to the CSLF and DOE techniques [58]. 

Sternberg, R. S., Damon, P. E., Radiocarbon bating, Sensitivity of Radiocarbon Fluctuations and Inventory to Geomagnetic and Reservoir Parameters, p. 691-717, 1979. 

A further difficulty that faces oil field modelers is the lack of information they have about downhole conditions and reservoir characteristics. Forward predictions about production are distressingly uncertain. It is therefore common to fit observed production data retrospectively—a procedure known as history matching—and to infer reservoir parameters, particularly permeabilities and relative permeabilities. 

A depleted zone is particularly attractive because the main reservoir parameters, namely size and original pressure, are known. Thus, one can easily estimate how much gas can safely be injected. 

As the surfactant slug is injected into the reservoir, the mixing of injected slug with reservoir components takes place. The mixing of surfactant with reservoir oil and brine often produces emulsions. Moreover, the reservoir parameters such as porosity, pressure, temperature, composition of connate water and crude oil as well as gas-oil ratio affect the formation of oil field emulsions. 

A single base reservoir geometry is used for aU of the subsequent calculations see Figure 1. The reservoir consists of two adjacent segments, separated by a notional barrier. Each reservoir segment is 1000 mx 1000 m areally, and 10 m thick. The key reservoir parameters and symbols are listed in Table 1. All equations use SI units but the table also lists some parameter values in field units, as these may be more familiar to some readers. 

This paper presents the results of a study to investigate and establish the reliability of both the description and performance data estimates from numerical reservoir simulators. Using optimal control theory, an algorithm was developed to perform automated matching of field observed data and reservoir simulator calculated data, thereby estimating reservoir parameters such as permeability and porosity. Well known statistical and probability methods were then used to establish individual confidence limits as well as joint confidence regions for the parameter estimates and the simulator predicted performance data. The results indicated that some reservoir input data can be reliably estimated from numerical reservoir simulators. Reliability was found to be inversely related to the number of unknown parameters in the model and the level of measurement error in the matched field observed data. 

These reservoirs are described using well log, core description and analyses of fluids therein. The well log interpretation was done using LOGCALC. Various reservoir parameters were evaluated to (i) characterize each pay zone, (ii) correlate lithofacies distributions (iii) estimate hydrocarbon in place, and (iv) environment of deposition. 

Prior to the selection of effective EOR processes for West Sak and Ugnu reservoirs, it is essential to study the reservoir parameters and their spatial distributions In detail. For commercial production, a thorough understanding of reservoir framework, architect and characterization is of the utmost importance. The purpose of this paper is to describe these parameters for West Sak and Ugnu reservoirs and evaluate their oil recovery potentials. 

The reservoir parameters, i.e. average porosities, water saturation, and net pay zone in the Upper and Lower sands units were computed from well logs and are listed in Tables II and III, and their spatial variations are shown in Figure 7. Subsequently, variations the hydrocarbon potentials of West Sak sands were evaluated first by determining the weighted means of effective porosity, water saturation and net pay thickness of ail sand intervals in the West Sak Formation in each well (Table I). The sands of West Sak have high porosity and contain substantial amounts of oil. 

Detailed mineralogical and textural analysis of Ugnu Formation has been conducted by Mowatt et al. (1991) and Hallam et al. (1991). The sand bodies with good reservoir parameters in the Lower Ugnu Formation were deposited by the distributary channels of a prograding delta. The coarse channel sands are... 

The weighted average reservoir parameters of the Lower Ugnu Sands are listed in Table IX. The average porosity and the average water saturation in these sands varies from 27% to 28% and from 14% to 46% respectively. Net pay zone thickness ranges from 3 ft to 161 ft. In most wells, the net pay thickness exceeds 50 ft. 

The rock composition, pore geometry and reservoir contents play an important role in oil displacement. Therefore, a complete study of the reservoir is required before selecting a suitable surfactant for EOR. It is recommended that the following reservoir parameters should be examined ... 

Parameters Affecting Phase Behavior. In general, the phase behavior at reservoir conditions for oil-water-surfactant systems without alcohol present is very sensitive to the oil composition. Polar components in the crude oil may act as cosurfactants. On the other hand, model oils like n-alkanes do not contain this type of material. Different papers using the Exxon surfactant product termed RL-3011 (dodecyl-o-xylene sulfonate) illustrate the importance of oil composition on the phase behavior regarding changes in reservoir parameters like temperature, pressure, and salinity [47-50], Conclusions from these papers may be summarized in the following way ... 

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