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Summary Shallow water environment typical of the Arabian Gulf presents unique challenges and opportunities to 4C ocean bottom cable (OBC) seismics, including strong surface waves and multiples. A case study of a set of 2D 4C OBC data acquired in the Arabian Gulf suggests that acquisition of 4C OBC seismic data should consider long time delay between shots, small spacing interval and other parameter modifications so that severe signal contamination and spatial aliasing can be avoided. Importantly, a combination of shallow water depth and hard bottom can result in efficient conversion of acoustic energy to downward point-source- like shear-wave energy at the water/seabed interface that is more than thousand times stronger than in deepwater environments. Special 4C data processing incorporating forward elastic wave modeling may help utilize the PS-S reflections to estimate the shear wave properties of the seabed and the subsurface carbonate reservoirs. Introduction Time-lapse 4C ocean bottom cable seismic data could potentially provide us not only a better quality P-wave but also vital shear wave information needed to detect bypassed oil, reservoir connectivity, pressure and saturation changes due to injection and production. 4C OBC data have been usually acquired in deep-water environments. However, some of the worldโs important oil and gas fields are in the Arabian Gulf where the average water depth could be as shallow as about 10-15m (Sun et al., 2009). Permanent installation of 4C ocean bottom cables has now been considered as one of the economically viable tools for long-term monitoring of hydrocarbon productions from these super-giant carbonate reservoirs. In this paper, we report the major challenges and issues that are unique to shallow water environment typical of the Arabian Gulf, using a 2D 4C ocean bottom seismic data set in the offshore of the United Arab Emirates. We also illustrate the possible technical advantages of 4C ocean bottom seismics in shallow water environment for estimation of shear-wave properties of the seabed and the subsurface carbonate reservoirs. This case study is aimed to reveal the peculiarities and new aspects of 4C ocean bottom seismic acquisition, processing and interpretation in shallow water environment that are different from in other environments where 4C ocean bottom seismics conventionally applied, including a better understanding of near-field acoustic radiation and sediment physics. In contrast to deep-water environments, shallow-water carbonate environment with varying seabed conditions including hard coral reefs presents special challenges including strong surface wave contamination and spatial aliasing, severe internal multiples due to high impedance contrasts of overburden rocks, and low temporal resolution due to elevated wave velocities in carbonate rocks. Nevertheless, we demonstrate that the shallow-water environment may allow efficient energy penetration and PS conversion into the poroelastic rocks underlying the water bottom, which is important to obtaining shear wave formation from converted waves for reservoir monitoring. Method Because of the extremely shallow water depth, the hard bottom, and relatively low seismic frequency, the problem of energy partition and P-S wave conversion at the water/rock interface may not be addressed adequately using classical plane wave theory and boundary value solutions.
Summary A complex paleocave system in the San Andres Formation, Permian basin, Texas is characterized through detailed seismic analysis, integrating core, log and seismic inversion. The karst controlled paleocave system intensively occluded by anhydrite is a dominant factor controlling carbonate reservoir compartmentalization of the studied field, verified by operator-interpreted tracer and pressure data. The paleocave system represents karst-controlled collapsed features of different sizes and patterns. In the area of high volume production, the collapsed paleocave system is characterized by irregularly developed crackle and fracture breccias, mosaic breccias and cave fillings in the upper 100 feet of San Andres Formation. Along the transition from the platform to basin, it is marked by a linear collapse with the occurrences of sags and small vertical faults that are observable in seismic data. The complexity of the paleocave system can be explained using an outcrop analogy to modern hydrological environments. Our method may be useful to interpret occurrence of similar subsurface paleocave systems in other areas. Introduction Presence of karst-controlled palecaves systems is one of the major factors causing carbonate reservoir heterogeneity and compartmentalization. If porous and permeable, the paleokarst systems can be important hydrocarbon reservoirs in world class fields. However, the filling sediment and collapse during burial also destroy most of the cavernous porosity. The field studied in this research is located on the eastern Central Basin platform of the Permian Basin, west Texas. The carbonate reservoir experienced substantial subaerial exposure during the Guadalupian period. Reservoir production from the field has been problematic due to the fluid barriers caused by tight gypsum and anhydrite-cemented dolostones, as revealed by available cores. The average porosity and permeability of the tight dolostone intervals are usually lower than 2 % and 1 MD, respectively. Our analysis indicates that the occurrence of these tight barriers may be the result of a fully developed paleokarst system with vertical and lateral spatial complexity. Detailed knowledge of 3-D distribution of this paleocave system is very important for optimizing development strategy and improving reservoir recovery. Spatial variation and complexity of a paleocave system present immense challenges for seismic reservoir characterization (Sullivan, 2005, Marfurt, 2006, and others). A correct geologic model for the occurrence of a paleocave system is also important for a successful prediction of the 3-D distribution of the system. In this study, we focus on part of the east central flank of the Central Basin platform and develop a method of paleocave system identification by integrating core and log, seismic inversion and 3-D geometric attribute analysis. Using results of this seismic characterization, we further explore an island hydrologic model to explain the development of the paleocave system and its control on the reservoir compartmentalization of the field. Method Using available core data in two cored wells, we first identify the karst features and their related collapsed paleocave packages in the San Andres Formation. For the high volume area of the field where both core and log data are available, we interpret the collapsed karst features using well log data calibrated by core description.
- Geology > Geological Subdiscipline > Economic Geology > Petroleum Geology (0.49)
- Geology > Mineral > Sulfate > Anhydrite (0.48)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock > Dolomite (0.45)
- North America > United States > Texas > Permian Basin > Midland Basin (0.99)
- North America > United States > Texas > Fort Worth Basin (0.99)
- Oceania > Australia > Victoria > Bass Strait > Gippsland Basin (0.98)
- (25 more...)
Introduction Summary Pore structure and grain size play an important role in controlling the complexity of velocity-porosity relationships and permeability heterogeneity in carbonate reservoirs, in addition to mineral composition and pore fluid. A frame flexibility factor (g) has been found to be able to quantify effect of pore structure changes on sonic wave velocity and permeability in carbonate reservoir rocks. Our study of a San Andres carbonate reservoir, Permian Basin, shows that for core samples of given porosity, the lower the frame flexibility factor g, the higher the sonic wave velocity. A frame flexibility factor g value of 3.85 could be used to separate permeability zones of different geological origin for the studied San Andres carbonate reservoir. Samples with frame flexibility factor g <3.85 are either dolograinstone and dolopackstone with visible vuggy pores or tight dolowackstone. On the other hand, samples with frame flexibility factor g >3.85 are dolopackstone with dominant interparticle pores, dolowackstone or dolomudstone with microcrack pores. Using the frame flexibility factor g, different porosity-velocity and porosity-permeability trends can be classified with clear geologic interpretation such as carbonate rock and pore types. New porosity-permeability relations with g classification help delineate high-permeability zones in the San Andres reservoir, and may be useful for other similar carbonate reservoirs as well. A robust and practical rock physics model is important in understanding the complicated relationships between the acoustic properties and the reservoir parameters of carbonate rocks. The Differential Effective Medium model, Kuster-Toksรถz model, extended Xu-White model and revised Saleh-Castagna model have been used in predicting carbonate reservoir velocity (Anselmetti et al., 1993, Wang, 1997, Sun, 2004, Saleh and Castagna, 2004, Baechle et al., 2007, Xu et al., 2007). Baechle et al. (2008) argued that spherical pores may not have a dominant effect on velocity in carbonate reservoir as some models suggest. Studies also reveal that mineral composition and grain size can dramatically influence the acoustic properties in carbonate reservoir (Anselmetti et al., 2003, Scotellaro et al., 2007, Sun, 2007). A carbonate rock-physics velocity model that considers dominant factors including mixed mineral composition, grain size and pore structure remains to be developed. Sun (2000, 2004) introduced a rock physics model by defining elastic parameters called frame flexibility factors. These frame flexibility factors depend less on porosity than wave velocity does. They are not only related to pore structure but also to solid/pore connectivity and grain size. This poroelasticity model has been successfully proven at the core-plug scale by measured core data for its effectiveness in quantifying pore structure (Sun, 2007). These frame flexibility factors have been successfully used for carbonate reservoir permeability inversion from seismic data (Bracco Gartner et al., 2005). In this paper, we use the rock physics model introduced by Sun (2000) to analyze the velocity-porosity complexity and to understand the permeability heterogeneity of a San Andres carbonate reservoir. This progress report is based on analysis of core measurements and well logging data. Results will be used for simultaneous porosity and permeability inversion from seismic data. Method and Data Sets Based on an extended Biot theory of poroelasticity, Sun (2000, 2004) derived a simplified rock physics model for carbonate rocks.
- North America > United States > Texas (0.85)
- North America > United States > New Mexico (0.60)
- Geology > Mineral (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (0.99)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (0.48)
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- North America > United States > Texas > Permian Basin > Yates Formation (0.99)
- North America > United States > Texas > Permian Basin > Wolfcamp Formation (0.99)
- (21 more...)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Carbonate reservoirs (1.00)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management (1.00)
Summary Accurate high resolution reservoir models are critical to better understanding of the reservoir heterogeneity for enhanced oil recovery. We define three parameters, permeability dart coefficient (SK), variation coefficient (VK) and contrast coefficient (NK), to characterize quantitatively the heterogeneity levels. Incorporation of these parameters in reservoir facies models proves to help build the quantitative reservoir heterogeneity models, including detailed delineation of intrabedded and interbedded heterogeneity within the reservoir. This method has been successfully tested in two pilot areas in the Bohai Bay Basin using core and log data and greatly enhanced the thermal production. Further upscaling of these heterogeneity parameters from log to seismic scale and their seismic signature identification will enable us to predict the reservoir connectivity in interwell regions. Introduction Reservoir heterogeneity plays an important role in reservoir performance. The factors that affect production are reservoir continuity, connectivity and the spatial distribution of permeability (Hovadik and Larue, 2007). Geological model and geostatistical model have been widely applied on these studies (Larue and Legarre, 2004, and many others). Some quantitative methods have been attempted on describing reservoir heterogeneity. Dykstra- Parsons coefficient of permeability variation is a common descriptor of reservoir heterogeneity. It measures reservoir uniformity by the dispersion or scatter of permeability values. Jenson and Lake used D-P coefficient, Lorenz coefficient and coefficient of variations to measure the heterogeneity. Jenson and Currie (1990) developed a new method for estimating the D-P coefficient to characterize reservoir heterogeneity. In this report, we develop a new set of permeability coefficients to build reservoir heterogeneity model with well log and core data for highly heterogeneous reservoirs in Bohai Bay Basin. Clastic heavy oil reservoirs of lower Tertiary age, in the Bohai Bay basin, China, have been under thermal production for more than twenty years. However, production performance has remained unstable, largely due to the fact that the producing formations are highly heterogeneous. A better understanding of reservoir heterogeneity and establishment of accurate high-resolution reservoir heterogeneity models are highly needed to optimize the field development. The focus of the study is SHUA Es reservoir A which is located at the western slope of the west depression in the Bohai Bay basin, and characterized by thick sandstone and conglomerate deposition in the middle and lower fan delta environment. It has been divided into seven sand units, and the top five units are the major producing formations with average porosity of 26% and permeability in the range 100 to 1936 md (Figure 1). Methods In order to identify the reservoir heterogeneity accurately, quantitative heterogeneity levels need to be determined rather than qualitative descriptions. The important step is to develop the relationship between a heterogeneity level and a lithofacies model that is genetically related to deposition patterns. Quantitative heterogeneity levels are determined by calculation of permeability variation coefficient (VK), dart coefficient (SK) and contrast coefficient (NK): The distributions of the three permeability coefficients within each sand unit are generated to show the interbedded heterogeneity of the reservoir Four lithofacies models are built based on cyclic features on well logs and core description.
- Asia > China (1.00)
- Europe > Norway > Norwegian Sea (0.25)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.92)
- Geology > Sedimentary Geology > Depositional Environment > Transitional Environment > Deltaic Environment (0.35)
- Asia > China > Bohai Basin (0.99)
- Africa > Nigeria (0.91)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Geologic modeling (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery (1.00)