Saturation/height functions on the basis of unique flow units have been developed as part of an integrated petrophysical analysis of a gas field. Furthermore, coupling the saturation/height functions with appropriate relative permeability models has effectively quantified hydrocarbon saturation, classified producibility of intervals, and defined critical water saturation. The results show that linking depositional and diagenetic rock fabric with flow units and then linking the flow units with zones that have similar core capillary pressure and relative permeability relationships have enhanced the utility of the saturation models. The saturation/height functions provided more-accurate water saturation in the study field, and potentially they can overcome uncertainties associated with log interpretation by use of Archie or shaly-sand models.The saturation/height models were developed from core capillary pressure (P c ) data to calculate water saturation vs. depth, which is independent of logs. The relative permeability models were obtained from special-core analysis (SCA). Consequently, the core-based saturation/height functions can be useful in the calibration of log-based petrophysical models and with relative permeability can also be used to estimate water/gas ratios (WGRs) and critical water saturation.Capillary pressure and relative permeability curves from SCA studies were distributed into corresponding flow units, on the basis of the calculated flow-zone indicators (FZIs). Saturation/height functions were then developed for each unit and were used to calculate water saturation in the study field. The most accurate flow-unit-based saturation model that evolved is a function only of porosity and of height above the free-water level; it does not require permeability in its application; and it performed better than the Leverett J-function in this field.Coupled with hydraulic unit (HU)-based relative permeability curves, the saturation models may provide more comprehensive petrophysical interpretation in gas-bearing formations and may highlight potential differences in reservoir producibility.
Hydrocarbon in place calculations and recovery predictions require data from a variety of sources. Of these sources, core data are, potentially, the most accurate because they are direct measurements. Therefore, reliable and representative core analysis data are essential to calibrate and validate tool-acquired data, especially, wireline logs. Special core analysis (SCAL) vendor laboratories are the primary sources or supplemental resources for the measurements and acquisition of core data. These laboratories are set up for more rapid throughput of large quantities of samples at costs lower than those of an oil company's internal laboratory. But very poor quality data can be generated by some vendors. This paper discusses Quality Control/Quality Assurance analysis and assessments of SCAL data from four laboratories. The multi-vendor SCAL program was formulated to evaluate specific petrophysical properties capabilities of the selected laboratories. The tests included porosity and permeability at varying stress, formation water resistivity (Rw), electrical properties (m and n), and capillary pressure by the centrifuge and mercury intrusion methods. Core plugs from two different formations were used in the evaluation. One set consisted of limestone plugs and the other set included sandstone samples. Baseline porosity and air permeability measurements and CT scans were used to screen the samples and divide them into subsets for the individual vendors. The only information provided to the vendors was plug designation. Some laboratories provided very poor quality data on some of the tests. They reported wrong results for porosity at varying stress, electrical properties, and mercury capillary pressure. Probe maintenance, contamination, and calibration were given as possible reasons for erroneous brine resistivity. Wrong saturation exponent values were attributed to saturation instability. The errors identified in this study can have significant economic impact on a field development program. Therefore, it is important that we exercise very strict supervision of the program and engage with the vendor throughout the data acquisition program. The QC/QA analysis used in this study can improve the performance of these vendors and the quality of data.
The use of key wells to improve formation evaluation programs is well established as this method has been proven to improve petrophysical accuracy. In the key wells, extensive logging, coring and fluid sampling programs provide the data used to develop better predictive models for water saturation, lithology, porosity, and permeability. To be effective, key well programs must obtain representative special core (SCAL) data from all cored facies, but this often requires high-density sampling, which may not be cost effective. This paper presents a strategy to meet technical and business objectives by selecting optimum number of samples required to provide the data needed to develop improved petrophysical models. A case study is provided showing the development of an optimum SCAL sampling strategy for a carbonate reservoir. This example highlights how an optimum sampling program for capillary pressure, electrical properties and relative permeability tests leads to enhanced formation evaluation. The case study demonstrates how effective sampling strategies improve determination of water saturation and permeability. It also shows how relative permeability data from a carbonate reservoir may not be directly usable for reservoir simulation if all rock types are not sampled. To represent all rock types, porosity and permeability were initially used to develop hydraulic units. Next, geologic descriptions, which incorporate thin section petrography, X-ray diffraction, and scanning electron microscopy, were used to characterize the reservoir rock in terms of geological facies. X-ray computerized tomography (CT scanning) was used to determine core plug heterogeneity, especially vugginess and mineralogical changes not visible on inspection. Lastly, mercury porosimetry was used to group core plugs of the same rock type based on similarity in pore size distribution. These composited core plugs were then subjected to relative permeability measurements at reservoir conditions. Using the data from this study, core-calibrated saturation and permeability profiles were developed for all rock types. In addition, an analytical relative permeability model for direct input into flow simulators was developed and validated. This model is easy to use and can be modified quite easily during reservoir simulation by changing some predetermined parameters in the model. This should reduce the time and expense of history matching. The model incorporates the scatter in relative permeability data that is usually encountered in laboratory measurements. Introduction The defined goal of coring and core analysis is to reduce uncertainty in reservoir evaluation by providing data that is representative of the reservoir at in-situ conditions. This goal is met by coring to acquire core samples for laboratory analysis which are representative of the reservoir. The well is cored and logged with the purpose of collecting petrophysical data to achieve strategic objectives that result in more accurate hydrocarbon-in-place calculations and better (smarter) reservoir development. Every coring program has several stakeholders and each has a different objective. Geoscientist might be primarily interested in porosity, permeability, diagenesis, pore and rock types, etc.; reservoir engineers might require heterogeneity, anisotropy, relative permeability, capillary pressure, wettability, etc.; petrophysicists would want formation water resisitivity, lithology, water saturation and parameters for calibration of well logs. Drilling and completion engineers would want rock mechanical properties, stress orientation, formation damage, grain size distribution, etc. For the work described in this paper, we shall concentrate on the desires of the reservoir engineers and petrophysicists. But to achieve those set goals we first fulfilled the deliverables of the geoscientists.
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