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Abstract We have validated with superior results that the direct measurement of porosity using Nuclear Magnetic Resonance (NMR), in Naturally Fractured Clastics Reservoirs of very low porosity (ห 3.5%) in the Devonian of the Bolivian Sub-Andean, reveals information till now incoherent compared with core data. As it is well known, when the rock does not have paramagnetic elements, the porosity measured with the NMR is not affected by the minerals within the matrix and the tool answers mainly to the contained fluids in the pores of the rock. This peculiar characteristic of the NMR response in these low porosity reservoirs, with complex and variable lithology, become fundamentally beneficial at the time of determining an immediate porosity value with less uncertainty in comparison to the one from conventional logging tools, such as the Neutron, the Density and the Sonic, where there is a need to assume variable values of density and transit time for the matrix. To corroborate that the obtained effective NMR porosity, is the best to be easily and truthfully correlated to true formation porosity, core data information are available. The key to obtain a reliable and precise measurement of porosity through NMR in these complex environments is based on the optimum selection of the acquisition parameters for the tool, like the polarization time, the echo-spacing and the use of a fit-for-purpose T2 cutoff time, tailored for this type of reservoirs. Furthermore, it will be demonstrated that the effects of nuclear diffusion on the transversal relaxation time distribution (T2 mode), primarily caused by gas, are not significant in these reservoirs, since an underestimate of the porosity was not noticed with regard to the one from cores. Additionally with the aim of obtaining a better correlation among cores and NMR porosities, it has been used a specific high resolution acquisition and processing method, achieving continuous porosity measurements with a dynamic vertical resolution of 22 inches, more suitable to the sampling core interval and to the real petrophysical characteristics of our fields. Introduction The gas-condensate reservoirs from Devonian age of the Bolivian Sub-Andean that lie between the 3000 and 5500 m of depth, have been produced by the existence of a thick column of clastics sediments of scarce to null primary intergranular porosity, but with an important development of secondary porosity for fissures and fractures taking place during the characteristic tectonics of this region. The lithology of these Devonian formations is characterized mainly by shoreface sediments; quartzitic sandstones, litharenites, micaceous, and laminated sandstones together with shaly intervals that can be found in the productive formations of the region (i.e. Huamampampa, Icla and Santa Rosa). For e-log interpretation purposes, we have classified lithology of the Devonian formations under three main petrofacies:quartzitic sandstones, micaceous and laminated sandstones and shaly intervals. Sandstones composition is rather complex except for the massive quartzitic sandstone bodies because quartz, volcanic lithics, mica, and minor accessory heavy minerals are present in the rock composition concurrently with thin shale-silt laminations over certain intervals. In these formations the petrophysical analysis is affected by strong limitations in core analysis and log interpretation, due to the very low porosity and naturally fractured reservoir environment. Despite of this, the measurement of a reliable porosity with the NMR technique in these naturally fractured clastics reservoirs, has demonstrated to be a viable and reliable alternative, this means, without the necessity of assuming a fixed lithology parameters that have turned out to be variable in these kind of formation and not easy to estimate with conventional logs. The selection of the acquisition parameters for the tool takes here an important role. For the wells showed as example in this paper, the acquisition was done with a logging speed of 700 ft/hr corresponding to a wait time of 15.8 seconds, enough for the complete polarization of the fluids near the wall of the borehole, with a number of echoes of 3000 and an echo spacing of 0,2 ms (200ms). The high resolution EPM mode (Enhanced Precision Mode) allowed the acquisition of short CPMG pulses of 30 echoes each one with a repetition of 10 times for better signal/noise ratio at fast relaxation decay. After an exhaustive LQC, the data are reprocessed to improve the overall NMR response and signal by using the CMRTM application, part of the customary Geoframe TM software platform.
- North America (0.93)
- Europe > Norway > Norwegian Sea (0.24)
Abstract This paper describes the implementation of a petroelastic model (PEM) based on Gassmann's equation to calculate seismic attributes into a commercial reservoir flow simulator. This implementation is the first step of a project to integrate time-lapse (4D) seismic attributes into an assisted history matching tool developed in a previous project. The paper includes the description of the PEM and some implementation issues, such as the coupling of the model with the flow simulator with the purpose of using its basic calculated properties, discuss some user options (such as properties input through correlation or geoestatisticaly obtained maps) and the model variants and extensions (such as lithology influence and pressure effects). Three applications of this petroelastic model are shown: the first is a synthetic model based on outcrop data; the second is a 4D feasibility study for water injection monitoring in an offshore field; and the last one is a comparison between observed and calculated pressure impedances for an offshore field. The resulting tool is applicable, for example, in 4D seismic feasibility studies, in seismic modeling for comparison with observed surveys and makes possible further implementations for incorporating the seismic data in assisted history matching. Introduction The use of petroelastic attributes has several useful purposes 1, such as feasibility of applying 4D seismic monitoring, optimize 4D seismic monitoring program and prepare more accurate production forecasts. A possible workflow for applying 4D seismic in the monitoring of fluid flow in porous media follows the iterative steps 2:Acquire, process and interpret 3D seismic data at two or more different points in time; Combine the 3D seismic interpretation with other geoscience and engineering data to characterize the reservoir; Use a flow simulator to model fluid flow performance; Use flow simulator properties such as pressure and saturation at the acquisition times in a PEM to calculate reservoir seismic attributes for comparison with the observed ones. Steps 3 and 4 are unnecessarily cumbersome because most flow simulators do not calculate reservoir seismic attributes. As a result, information from the flow simulation Step 3 must be converted to a format suitable for analysis in the PEM Step 4. In addition, errors may be introduced into the calculation of seismic attributes if fluid properties in the PEM do not match the corresponding fluid properties in the flow simulator, like using standard correlations of fluid properties. These problems can be avoided if the PEM is incorporated into the flow simulator, eliminating the need of a third-party software to calculate the seismic attributes, so that it uses exactly the same fluid property model. Fanchi 1 shows the results for some reservoir management scenarios, applying successfully the petroelastic properties information calculated through an integrated flow simulator using the Gassmann's equation 3, improving the reservoir management and monitoring processes. Gosselin et al.4 also implemented an integrated flow simulator tool 5 using the Gassmann's equation in a project to integrate 4D data into an assisted history matching process. The ultimate aim of this project is to incorporate time-lapse seismic attributes into an assisted history match (AHM) tool, which combines efficient derivative calculation and robust optimization techniques, already developed in a previous project 6 through an integrated reservoir flow simulator, facilitating a lot the viable use of this kind of data.
- South America > Brazil (0.69)
- North America > United States (0.68)
- Europe (0.68)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type (0.91)
- Geophysics > Time-Lapse Surveying > Time-Lapse Seismic Surveying (1.00)
- Geophysics > Seismic Surveying > Seismic Interpretation (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (0.89)
- Energy > Oil & Gas > Upstream (1.00)
- Water & Waste Management > Water Management > Lifecycle > Disposal/Injection (0.34)
Abstract The use of seismic attributes has increased, especially when extracted from interpreted horizons. The various available attributes are not independent from each other but represent, in fact, different ways of presenting and studying fundamental information from seismic data (time, amplitude, frequency and attenuation). However, statistical analysis using attributes must be based on geological knowledge and not only on mathematical correlation. Petrophysical studies and seismic modeling are sources of understanding. Such knowledge is necessary to improve confidence in observed correlations with reservoir parameters and must be part of all attribute analysis. However, the use of seismic attributes leads to several questions, for example, what do they all mean? When to use one or another? How to use them on geologic modeling? How reliable those data are? The answers to these questions are not easy, but considering about petrophysical modeling (Porosity, NTG and permeability) what is the best approach: to consider only well data, that are punctual and need to be interpolated, or try to find correlation with physical measurements (seismic data)? Not to consider seismic attributes makes one feel coming back in time, when this important tool was not available. On a giant oilfield offshore Brazil seismic attributes ('conventional', complex trace, polynomial decomposition, geometric and coherence) have been used to create geological models and to reduce uncertainties. The attribute choice must be performed by the geophysicist and the geologist working together, in order to check geological meaning of attribute maps, possible physical meaning of the attribute, etc. Plots of the highest correlation values should be visually inspected in order to choose the attribute with best correlation to the desired parameters. The results show attributes have been favourable to porosity and NTG prediction, but regular (at maximum) to permeability. For permeability even if the results are not so good, the correlation are improving for the latest models (as long as new wells are used). Polynomial decomposion and complex trace attributes have shown better results. Introduction: seismic attribute definitions and discussions The use of seismic attribute data for prediction of detailed reservoir properties began more than 30 years ago. In fact, a seismic attribute is any property derived from seismic reflection signal. Attributes may be compared to lithology in an attempt to devise a method of property prediction away from well control. The method of prediction can vary from a simple linear correlation to multi-attribute analysis, geostatistical methods, etc. As an evidence of current proliferation the use of attributes, Chen and Sidney (1997) have catalogued more than 60 commom seismic attributes along with a description of their apparent significance and utility. Although there is a rich history of seismic attributes use in reservoir prediction, the practice remains a difficult and uncertain task. The bulk of this uncertainty arises from the nature of the physics connecting a number of attributes to a corresponding reservoir property. Due to the complex and varied physical processes responsible for various attributes the unambiguous use of attributes for direct prediction will probably remain a challenge for the years to come. In addition to the fact above described, there is the possibility of coming across statistical pitfalls while using multiple attributes for empirical reservoir property prediction. In addition, many attributes are derived using similar signal processing methods and can, in some cases, be considered largely redundant with respect to their description of the seismic signal.
Pore-Scale Characterization and Productivity Analysis by Integration of NMR and Openhole Logs: A Verification Study
Galarza, Tania (Occidental Oil & Gas Corporation) | Giordano, Sergio (Occidental Oil & Gas Corporation, Inc. Suc. Argentina) | Fontanarosa, Michael B. (Occidental of Argentina) | Saubidet, Marcelo E. (OXY Argentina Cities Services) | Altumbay, Mehmet (Baker Atlas) | Saavedra, Benito Eduardo (Baker Atlas) | Romero, Pedro Antonio (Baker Atlas)
Abstract Presently, NMR logs are routinely used to estimate the irreducible portion of the bulk volume of wetting phase (BVI), permeability and the type of hydrocarbons present when filtrate invasion is limited or non-existent. In most cases, the rest of the pore-scale information contained in NMR log data is overlooked or ignored as the model equations used to infer it are empirical in nature. Our aim in this study is to demonstrate the use of and the degree of accuracy of porescale data derived from NMR logs by presenting an actual case study with production test results and comparison to corebased information. This study also demonstrates the importance of NMR based pore-scale characterization if or when the filtrate invasion limits the hydrocarbon typing capabilities. Pore scale characterization is obtained by transforming the NMR data to synthetic capillary pressure and evaluating the dynamic properties of the formation in conjunction with the fluids present. The capillary transformation of NMR data is performed based on experimental models. The BVI from this transformation (RBVI) is tested against the conventional T2cutoff based BVI for accuracy and reliability prior to the determination of Swanson-based Swirr, relative permeability transformations and the critical Swirr that can be used for identifying transition zone. T2cutoff independent BVI (from which Swirr is derived), threshold pressure, mean pore-throat radius, effective permeability to wetting and non-wetting phases and the md-ft evaluation of subject intervals are the end products of this study. The results are compared to production test data to demonstrate the utility of NMR logs both to production/completion engineering and to well/formation economics. Introduction Prior to elaborating on the productivity analysis, we would like to identify the pore-scale features that control the fluid flow. In a petrophysical analysis of "flow through porous media"; porosity, permeability, pore-size distribution and capillary pressure characteristics are the main features of the pore space. Capillary pressure is the dynamic link between the pore space characteristics and the wetting/non-wetting phases of the liquids that fill the pore space. In reservoir rocks filled with more than one fluid, the ability of each fluid to flow under a pressure differential is a function of the relative permeability of that phase. Relative, absolute and effective permeability information will have to be available before one can attempt to study the productivity potential of a formation. However, the lack of core-based tests for capillary pressure and relative permeability or the data availability in the timeline of the sequential field activities led us to use commonly accepted relative permeability approximations for this study. We will compare study results from the approximations to the actual field results. Capillary Pressure Data And Its Importance In general, a "Productivity Analysis" study is comprised of three sections:Matching past/current productivity, Predicting future behaviour, Recommending the best completion/fracturing plan for the future. In this study, we are concentrating on a sample study of predicting productivity in the lack of experimental data. However, we are able to compare our predictions with the actual field production test data. Water saturation in productive formations is controlled by size, shape and configuration of the pore spaces and the height above the water level. The capillary pressure behaviour of the rocks is the controlling mechanism.
- North America > United States (1.00)
- Europe > Norway > Norwegian Sea (0.25)