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Keywords: seg seg denver 2010

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Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2010 SEG Annual Meeting, October 17–22, 2010

Paper Number: SEG-2010-2284

... prediction artificial intelligence flow in porous media co 2 reservoir modeling

**seg****seg****denver****2010**Permeability prediction and its impact in reservoir modeling. Postle Field, Oklahoma Crucelis Cucha López*, Colorado School of Mines now ExxonMobil and Thomas L. Davis, Colorado School of Mines...
Abstract

Summary A permeability model was developed for the reservoir within the Reservoir Characterization Project (RCP) study area at Postle Field, so that flow simulation could be performed. The biggest challenge was the heterogeneity within the reservoir, especially the presence of high permeability zones. The effects of these high permeability zones within a CO2 injection framework need to be analyzed in detail because the CO2 and miscible oil bank will flow through the path of least resistance and may cause early breakthrough and poor sweep efficiency. Permeability modeling based on multiple permeability distributions to characterize the Morrow A sandstone produced a more reliable reservoir model to simulate CO2 flooding within the fluvial system. The integrated permeability model was tested against a binary (sandstone – shale) model. A base case history match of liquid production (oil + water) was performed. This match showed that the performance of the integrated permeability model was better than the binary model with respect to matching early liquid arrival in specific wells High permeability zones within some wells were the cause of early fluid arrival and the integrated permeability model more accurately predicted these zones. An accurate characterization of these high permeability zones leads to a more reliable reservoir model for CO2 flow prediction. Introduction Detailed understanding of the heterogeneities and complexity of reservoir architecture and flow properties are of utmost importance in the development and exploitation of commercial hydrocarbon reservoir. Characterization and simulation studies are performed on a continuous basis during the life of a field from initial exploration through appraisal, development and eventual abandonment. A key component of these studies is the knowledge of the reservoir permeability across the field. However, permeability data is sparse therefore; estimation methods have included empirical and statistical approaches, as well as the emerging pattern recognition techniques. The accuracy of most methods is greatly enhanced when the reservoir is subdivided into units with common flow properties (Facci, 2005) A Multiclass classification of reservoir facies based on Support Vector Machines (SVM) is used to identify sandstone petrofacies from core and log data (López and Davis, 2009). Each facies represents a specific flow unit. An SVM multivariate regression was used to predict permeability with the objective of making a reliable reservoir model to simulate CO2 flooding. The inclusion of high permeability zones within the reservoir model is of great importance and the integration of geophysical, geological and petroleum engineering data helps to constrain, and validate the results. Field Description Postle Field, located in Texas County, Oklahoma, produces from the A sandstone member of the Pennsylvanian age Morrow Formation. The reservoir underwent water flooding since 1974, near shut-in in 1998, and CO2 flooding which began in late 2007. Average net pay thickness is 30 ft and it is interpreted as an incised valley. The production unit is the Hovey Morrow Unit (HMU) (Figure 1). These data are used as a constraint and validation tool for the permeability modeling of the A sandstone in order to predict CO2 flow.

Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2010 SEG Annual Meeting, October 17–22, 2010

Paper Number: SEG-2010-2664

... eventually be dissolved into the brine, the sample could now be considered fully saturated.

**seg****seg****denver****2010**porosity elastic property s-wave velocity geophysics log analysis brine-saturated sample triaxial cell relation porosity measurement rock sample rock physics model correlation...
Abstract

Summary A series of tests were carried out employing a modified triaxial cell able to measure both elastic and electric properties simultaneously at reservoir conditions. Measured quantities like porosity, resistivity and P- and S-wave velocities were cross plotted, and possible correlations were investigated. Identified experimental trends were then tested against a variety of rock physics models. A good correlation was obtained in most cases. INTRODUCTION This paper analyses data from a modified triaxial cell (Wang et al. 2009), where the electrical conductivity and Pand S-wave velocities can be measured simultaneously. This provides the feasibility of investigating both the electrical and elastic properties of the rock samples at the same conditions. The paper is organized as follows. First, a brief discussion of the experimental setup and procedure is given. It is followed by a description of all samples used for the various experiments. In the third section the results obtained are described and discussed. The possible correlations between porosity, electrical resistivity and acoustic velocities of reservoir rocks were investigated. This study is supported by various rock physics models. Experimental setup and procedure In order to make the simultaneous acquisition of velocity and resistivity feasible, a modified triaxial cell has been used (Wang et al. 2009). A schematic drawing of the triaxial cell is shown in Figure 1. Two pieces of insulators have been inserted at the top of the top cap and the bottom of the pedestal respectively, so that the top cap and the pedestal can be used as electrodes. Note that the P- and Swave transducers are also mounted inside the top cap and pedestal. Such a design ensured that only minor changes were introduced to the original cell. Velocity measurements in such a triaxial cell are typically represented by an overall error of about 4%. Moreover, the overall error of the resistivity measurements is in the order of 1% (Wang et al. 2009). A total of six samples from three different wells were used in these experiments. Initially, the samples were cleaned and dried, and the porosities were measured employing a gravimetric analysis (i.e. measuring volume, mass and grain density of each sample). The major uncertainty of the porosity measurement is due to the determination of the volume of the sample, especially when the sample is small and has an irregular shape (samples T1714 and T1716 in this study, see Table 1 for all porosities). The relative error of the porosity measurements is calculated to be about 3%. The process of saturating a sample with brine was implemented as follows. Then carbon dioxide (CO2) was injected into the sample and kept for a few minutes whereupon the vacuum was applied again. This CO2 injection-vacuum process was repeated several times and in the final round the vacuum was applied for 30 minutes to ensure a small residual amount of CO2. Finally, brine was introduced and as the residual CO2 in the sample would eventually be dissolved into the brine, the sample could now be considered fully saturated.

Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2010 SEG Annual Meeting, October 17–22, 2010

Paper Number: SEG-2010-2411

... vernik modeling elastic property equation porosity range sand diagenesis model reservoir characterization upstream oil & gas marathon oil corporation well logging porosity arenite relation

**seg****seg****denver****2010**prediction sandstone consolidated regime shape factor Modeling elastic...
Abstract

Summary: Modeling dry frame moduli to such factors as rock composition, porosity, microstructure, and stress state remains a challenge in rock physics. We have modeled these relations for the most ubiquitous petrophysical group of sands(tones) – arenites – in two distinctly different levels of consolidation: at f <f con (consolidated sandstones) the rock can be modeled as a continuous solid containing pores and microcracks whereas at f >f con it is transitioning to the granular material. In the consolidated regime, we have developed a micromechanics-based model that expresses the effective elastic constants in terms of porosity, pore shape factors, and microcrack density. We then extended this model beyond f con by empirical relations to create the sand diagenesis model covering the entire porosity range from zero to critical porosity f c . This model was supplemented with the poorly consolidated sand model , which was derived from the Mori-Tanaka''s scheme by allowing pore shape factors to assume large values, consistently with very soft, cuspoidal pore shapes typical of sands undergoing mechanical compaction with elements of initial pressure solution. This model complements the global sand diagenesis model by accounting for local, grain sorting-induced porosity variation in a sand interval occurring in a narrow effective stress window. Introduction: No theoretical model covering the entire porosity range from zero to f c can be viewed as well established. It is particularly difficult to model evolution of the microstructure from the unconsolidated sediment to tight, low porosity sandstone due to geological processes that can be collectively referred to as diagenesis. Indeed, as was pointed out by Nur et al (1991), Vernik (1998), and Avseth et al (2005), no single effective medium theory describes the entire range of sand diagenesis. The present work develops microstructure-elasticity relations in sandstones with porosity below f con ˜0.22-0.30. The micromechanics-based modeling at f f con is supplemented by a combination of empirical and effective field (Mori-Tanaka''s) theory models at f > f con . We apply the theoretical framework described above to the database of ultrasonic core measurements of velocities and porosity as well as worldwide wireline log data, focusing on the most ubiquitous group of sands(tones) - arenites - that have negligible variation of their matrix properties, characterized by the following parameters: vcl = 0.02-0.12, ? m = 2.65 g/cm3 , Km = 35.6 GPa, Gm = 33.0 GPa, and ? m = 0.146 (Vernik, 1998). The core database is primarily based on publications by Han et al (1986), Jizba (1991), Blangy et al (1993), and Han and Batzle (2006) supplemented by inhouse data. Consolidated sandstones We start with the non-interaction approximation (NIA) and assume that the orientation distribution of cracks and pores is random so that the overall elastic properties are isotropic. The importance of NIA lies, firstly, in that it constitutes the basic building block for various approximate models accounting for interactions (such as the Mori-Tanaka’s scheme, the differential scheme, etc) and, secondly, in the fact that shape factors for pores and cracks are identified in the NIA framework.

Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2010 SEG Annual Meeting, October 17–22, 2010

Paper Number: SEG-2010-2314

... dolomite reservoir delineation upstream oil & gas coherence curvature porous dolomite reservoir spectral decomposition paleotopography dolomite reservoir reservoir characterization class ii well target formation

**seg****seg****denver****2010**thick porous dolomite reservoir fracture reservoir...
Abstract

Summary Dolomite reservoir characterization poses great challenges to exploration geophysicists, mainly because of its complex geologic origin and peculiar petrophysical properties. The formation of good dolomite reservoir is closely related to dissolution effect which is controlled by paleotopography. Therefore, we implement dolomite reservoir characterization at a study area in the Precaspian Basin using an integrated seismic attribute analysis approach constrained by paleotopography. The paleotopography provides background for determining the favorable facies belt with strong dissolution effect. Coherence and volume curvature attributes are used to characterize faults and fractures which determine the formation of porous dolomite reservoir. Finally, spectral decomposition is utilized to highlight amplitude anomalies caused by dissolved dolomite reservoirs. Using this integrated approach, we successfully delineated the distribution of the dolomite reservoir in the study area, and the results were calibrated and validated by the 36 drilled wells. Introduction Dolomite reservoirs are attractive exploration targets throughout the world. However, its characterization is difficult due to its complex geological origin and peculiar petrophysical properties (Strecker et al. , 2004). There are numerous kinds of mechanism for dolomitization and to determine which one is dominant in a given area is quite difficult (Logel, 2004). Furthermore, the petrophysical property of the dolomite reservoir is peculiar, with high velocity, high modulus and small contrast between reservoir and non-reservoir rocks. The formation of good dolomite reservoir is closely related to dissolution effect and the resulted karsting, which is controlled by the paleotopography. Therefore, we first recover the paleotopography of the dolomite formation to recognize favorable facies belt with strong dissolution effect. Since dissolution is closely related to the faults and fractures which determine the formation of good dolomite reservoir, we use coherence and 3D curvature to delineate faults and fractures (Hart et al ., 2009). Finally, spectral decomposition is combined with coherence and curvature to better delineate the distribution of the dolomite reservoir. The reservoir characterization results were calibrated with the 36 drilled wells and the results verified the effectiveness of our approach. Paleotopography Recovery Paleotopography has a significant effect on the distribution of reservoir porosity and permeability (Dubois, 1980). To recover the paleotopography of the target formation, a reference horizon above the target formation is selected. The time thickness between the reference horizon and the top of the target formation (isochron map) roughly reflects the paleotopography of target formation where dissolution occurs. The selection of reference horizon above the target formation is crucial for the recovery of the paleotopography. Generally, a maximum flooding surface under which shales deposit can be a good candidate. It is because that after the deposition of the target formation and post-depositional exposure, shales will deposit and quickly fill the paleotopography lows. Thus the isochron map could roughly reflect the paleotopography of the target formation if we neglect the compaction effect which is minor in our study area. Real Data Example The real data example is from the Precaspian Basin where Carboniferous carbonate reservoirs are developed under the Permian salt overhangs, which are obvious in the seismic profile in Figure 1.

Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2010 SEG Annual Meeting, October 17–22, 2010

Paper Number: SEG-2010-2639

...

**seg****seg****denver****2010**experimental data pore pressure change injection permeability variation microseismicity experiment shapiro weibull distribution variation flow in porous media fluid dynamics permeability equation poro-elastic equation permeability evaluation upstream oil & gas...
Abstract

Summary Results of laboratory experiments on study of permeability change in time during non-stationary stage of fluid flow due to injection with constant rate and fluid runoff are considered. Permeability variations were calculated based on data of pore pressure change in time at several points along laboratory porous sample. A method of permeability estimation with the help of microseismic activity variation data is suggested. Results of permeability evaluations based on two above methods are compared with each other. The presented research showed that it is possible to estimate local permeability by registering microseismic activity change in time in particular volume of porous medium. Introduction An increase of interest to passive seismic is reflected in a number of research publications (Maxwell, 2010; Eisner et al, 2009). Most of them are related with the problem of hypocenter definition accuracy improvement and use of microseismic event registration for detection of hydro fracture position, sizes and orientation. Meanwhile there are a series of publications dedicated to a possibility of microseismic data application to estimation of strata permeability. It was shown (Shapiro et al, 2002, 2005; Grechka et al, 2010), that it is possible to use data on propagation of cloud of microseismic events triggered by fluid injection to estimate the permeability. Meanwhile, it is well known, that permeability depends strongly on porosity, so there is a contradiction in neglecting of permeability variation when one takes into account the porosity change, as it is adopted in poro-elastic model. It was suggested by Nikolaevsky (1984) that both porosity and permeability can be considered as exponentially dependent on pressure. In the presented paper we estimate variation of permeability in laboratory experiments. In the experiments, pore pressure as well as acoustic emission AE (which corresponds to microseismic emission in real scale) was measured at several points of a long cell. The permeability variation was estimated with the help of equation (1) and experimental data on pore pressure variations in space and time; the results were compared with permeability evaluation from data on AE activity change in time. The proposed approach is aimed to expand ideas suggested by Shapiro et al. (2002, 2005) for estimation of permeability of inhomogeneous medium by using not only an information on microseismic event front propagations but also data on microseismicity variation in time. A model is suggested which describes a relation between acoustic events and pore pressure change in time. The model is based on an assumption that the microseismic (acoustic) events occurred when pore pressure reaches a critical value, which is distributed under Weibull distribution. The possibility to resolve an inverse problem of the permeability estimation from microseismic data for inhomogeneous medium is shown. Experimental procedure Experiments were made with the help of laboratory setup described in (Turuntaev et al., 2007, 2009). The setup (the cell - Fig.1) consists of long rod (1060 mm in length) with rectangular cross-section (117×82.5 mm). A channel with rectangular cross-section (65×62.5 mm) was milled in the rod.

Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2010 SEG Annual Meeting, October 17–22, 2010

Paper Number: SEG-2010-2334

... and by the very costly forward solutions. inverse problem upstream oil & gas machine learning evolutionary algorithm reservoir model reservoir characterization particle swarm optimization principal component analysis history

**seg****seg****denver****2010**artificial intelligence reservoir simulation...
Abstract

Summary We apply different particle swarm optimizers (PSO) to a history matching problem for the synthetic Stanford VI sand-and-shale reservoir. The ill-posed character of this inverse problem is attenuated by reducing the model complexity using a Spatial Principal Component base and by combining as observables flow production measurements and cross-well seismic data. Additionally the inverse problem is solved in a stochastic framework searching for the family of reservoir models that equally fit the data. We show that PSO algorithms have a very good convergence rate and in addition provide approximate measures of uncertainty around the optimum facies model. The uncertainty estimation, although it is a proxy for the true posterior distribution of model parameters, allow us to perform risk analysis.. Introduction Characterizing the spatial distribution of heterogeneous reservoir properties is one of the major challenges in reservoir modeling for optimizing production. Well data together with seismic data are typically used to infer the spatial distribution of properties such as facies, porosity and permeability. The seismic history matching problem consists then in obtaining reservoir models that match production data as well as seismic time lapse data. The main challenge of this inverse problem is that the production data alone does not uniquely constrain the porosity and permeability of the reservoir. Combining flow production measurements with time lapse seismic data has been useful for better constraining the history matching (Huang et al., 1997; EcheverrÍa and Mukerji, 2009; Xia and Huang, 2009; and Dadashpour et al., 2009, among others). Furthermore, to run the flow simulator and produce accurate results a detailed description of the reservoir is needed. This causes the inversion problem to be highly illposed due to its underdetermined character. One solution commonly found in the literature is to use non-linear leastsquares methods with Tikhonov regularization around a reservoir reference model that is constructed using prior geological and geophysical knowledge. The result is a unique reservoir model that shows the best trade-off between the data prediction and the model complexity. No uncertainty estimation is usually performed around this model due to the high computational cost.. Stochastic approaches to inverse problems consist in shifting attention to the probability of existence of certain interesting subsurface structures instead of looking for a unique model. Global optimization methods are well suited to perform this task. Although global algorithms can be used in exploitative form, their main advantage is that they can potentially address the inverse problem as a sampling problem. Their use as samplers requires a reasonably fast forward modeling and a small number of independent parameters. The use of Particle Swarm Optimization in geosciences still remains restrained (Shaw and Srivastava, 2007; Fernández-MartÍnez et al., 2010 a,b). In reservoir engineering PSO has been used to determine the optimum well location and type in very heterogeneous reservoirs (Onwunalu and Durlofsky, 2009). The use of global optimization techniques is hampered in real history matching problems by the large number of parameters needed to accurately describe the reservoir and by the very costly forward solutions.

Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2010 SEG Annual Meeting, October 17–22, 2010

Paper Number: SEG-2010-2773

...

**seg****seg****denver****2010**upstream oil & gas inverse problem geophysics information inversion van den berg artificial intelligence reference model characteristic full waveform inversion abubakar waveform inversion iteration full-waveform inversion Regularization and full-waveform...
Abstract

SUMMARY Geophysical inverse problems are usually ill-posed and illconditioned, meaning that their solution is not unique and/or unstable. This kind of inverse problems can be solved using regularization techniques from optimization theory, where an additive or multiplicative regularizer will help obtain an automatic selection of a unique and stable solution for a given cost function. Regularization helps condition an inverse problem by providing the search for possible solutions with a priori information that comes from inferences about the physics of the problem. There is a toolbox of regularization techniques for inverse problems. Thus, it is necessary to assess the effect of different regularizers and different a priori information to select the best combination for the problem at hand. In this expanded abstract, we focus on regularization techniques aimed at stabilizing and improving the model estimated through full waveform inversion by adopting a two-step approach in light of the computational challenges posed by inverting for large 3D models. INTRODUCTION The information that seismic processing aims to extract from the data is: (1) an estimate of the structural map of the earth, and (2) estimates about the mechanical properties of the target (possible hydrocarbon reservoirs). The process of estimating the earth’s material properties (porosity, velocity, density, etc.) is called inversion. Ultimately, this information is interpreted to deduce the geological structure, size, and type of possible hydrocarbon accumulations. In this abstract, we focus on methods to estimate the earth’s subsurface model given the best fit to the recorded data in the sense of minimizing the data misfit using a specific metric (e.g., the L2 norm). Most of these methods use iterative schemes, in which the model is updated based on a search direction computed from a gradient of a cost function. These types of optimization problems have been given a lot of attention in seismic exploration in recent years; one example is full waveform inversion (FWI). The FWI theory was originally developed by Tarantola (1984, 1988); its most general formulation involves a quadratic objective function measuring the differences (in terms of dynamics and kinematics) between model data and measured data. The inverted model, which generates a realization of model data that minimizes the objective function, is the output of FWI. The goal of FWI is to invert for a model that closely describes the actual model (earth) that produced the measured data (Crase et al., 1990; Ikelle et al., 1986; Sirgue et al., 2008; Vigh and Starr, 2008). The final solution to a geophysical inverse problem like FWI might differ significantly from any ideal solution due to data incompleteness, errors in the model parameterization, violation of assumptions (e.g., assuming an acoustic model to invert elastic measured data), noise, intrinsic issues with the specific mathematical description of the problem (e.g., under-determined problems), etc. Seismic inverse problems are, in general, ill-posed and ill-conditioned; thus, their null space can be significantly large and many different solutions (models) can exist that fit a given dataset equally well.

Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2010 SEG Annual Meeting, October 17–22, 2010

Paper Number: SEG-2010-2517

... parameters analytically. anisotropy tensor axis upstream oil & gas traction

**seg****seg****denver****2010**anisotropic stress analytical model geophysics p-wave anisotropy sayer application compliance fracture discontinuity reservoir characterization stress-induced anisotropy orientation...
Abstract

Summary One of the main causes of azimuthal anisotropy in sedimentary rocks is anisotropy of tectonic stresses in the earth’s crust. In this paper we analytically derive the pattern of seismic anisotropy caused by application of a small anisotropic stress. We first consider an isotropic elastic medium (porous or non-porous) permeated by a distribution of discontinuities with random (isotropic) orientation (such as randomly oriented compliant grain contacts or cracks). Geometry of individual discontinuities is not specified. Instead, their behaviour is defined by a ratio B of the normal to tangential excess compliances. When this isotropic rock is subjected to a small compressive stress (isotropic or anisotropic), the density of cracks along a particular plane is reduced in proportion to the normal stress traction acting on that plane. In particular, if the stress is a uniaxial compression along the x axis, then the density of cracks normal to x axis will reduce most, while the density of cracks parallel to x axis will not reduce at all. This effect is modelled using Sayers-Kachanov (1995) non-interactive approximation. The results of this derivation show that such anisotropic crack closure yields elliptical anisotropy, regardless of the value of the compliance ratio B . It also predicts the ratio of anisotropy parameters e / ? as function of the compliance ratio B and Poisson’s ratio of the unstressed rock. These results are useful for differentiating stress-induced anisotropy from fracture-induced anisotropy. Conversely, if the cause of anisotropy is known, then the anisotropy pattern allows one to estimate P-wave anisotropy from S-wave anisotropy. Introduction One of the main causes of azimuthal anisotropy in sedimentary rocks is anisotropy of tectonic stresses in the earth’s crust. Stresses affect elastic properties of rocks due to presence of discontinuities such as cracks and compliant grain contacts. Non-hydrostatic stress can cause elastic anisotropy since the effect of a stress field on a discontinuity depends on the orientation of the discontinuity with respect to the stress field. Knowledge of the pattern of stress-induced anisotropy (as expressed, for example, by the ratio of anisotropy parameters) can be useful for distinguishing it from other causes of anisotropy, such as presence of aligned fractures. Such patterns can also be used to distinguish, say, P-wave anisotropy from Swave anisotropy estimated from S-wave splitting. A number of authors have modeled stress-induced anisotropy by assuming the rock to contain a distribution of penny-shaped cracks, and considering variation of this distribution due to applied stress (see e.g., Nur, 1971, Sayers, 1988). However, penny-shaped crack geometry may not give an adequate quantitative description of discontinuities in rocks (Sayers and Han, 2002; Gurevich et al., 2009; Angus et al., 2009). Alternatively, Mavko et al. (1995) and Sayers (2002) developed modeling approaches that do not restrict the shape of discontinuities but instead infer their parameters from measurements. These approaches require numerical calculations to obtain an insight into anisotropy patterns. To obtain a more simple and general insight to these patterns, we make some simplifying assumptions that allow us to compute the anisotropy parameters analytically.

Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2010 SEG Annual Meeting, October 17–22, 2010

Paper Number: SEG-2010-2697

... used in carbonate reservoir characterization (e.g., Bracco Gartner et al., 2005; Dou and Sun, 2008). rock-physics-based estimation porosity artificial intelligence log analysis sandstone

**seg****seg****denver****2010**well logging geophysics aspect ratio compressional velocity wave velocity...
Abstract

Summary Knowledge of sand/shale ratio is important for predicting reservoir quality. Estimation of shale content in inter-well regions from seismic data, however, remains to be a challenging problem. In this paper, a pore aspect ratio parameter is derived from well log based on the theory of poroelasticity to study the inter-relationships among shale content, pore aspect ratio and acoustic velocity. It is found that this parameter could be used to quantitatively describe the evolution of pore aspect ratio with changes in the volume of shale within clastic reservoir rocks, using a publicly available dataset from the North Sea. The study reveals a good correlation among the pore aspect ratio parameter, volume of shale, and acoustic velocity. The porosity and volume of shale for the studied reservoir range from 2 to 36 percent and from 5 to 43%, respectively. Pore aspect ratio is relatively constant at 0.23 for volume of shale less than 32% with a significant decrease to 0.04 for volume of shale above 32% in the studied reservoir. The point of inflexion at 32% (volume of shale) is defined as the critical volume of shale. Much of the scatters in the velocity-porosity cross-plots are observed in the region where volume of shale is above this critical value with a relatively less scattered linear trend for values below it. Quantitative understanding of the dependence of seismic properties on clay content and pore aspect ratio could thus provide physical insights and additional means to delineate sand/shale ratio within shaly clastic reservoirs using seismic data. Introduction A well grounded rock physics model is essential to link observed seismic properties to reservoir rock and fluid properties. The time-average equation (Wyllie et al., 1956) and other statistical equations (e.g., Raymer et al., 1980) have been used for years to obtain lithology and porosity data from acoustic velocity based on empirical relationships. Despite widespread use, such empirical fits do not account for the scatter in the velocity-porosity crossplots derived from wireline logs. Han et al. (1986) show with experimental data that much of the scatter in the velocity-porosity cross-plots can be attributed to clay content. Earlier work by Tosaya and Nur (1982), and Kowallis et al. (1984) demonstrate that a systematic decrease in acoustic velocity results from an increase in clay content in both well-consolidated and poorly consolidated clastic reservoir rocks. While presence of clay in clastic reservoir rocks has significant influence on both their elastic properties and flow behavior, few studies have quantitatively determine and relate the amount of clay to the variation of pore aspect ratio and/or pore size. Therefore, it is important to be able to determine the critical volume of shale (clay) above which the properties of clastic reservoir rocks and reservoir quality are most affected. The rock physics model introduced by Sun (2000) defines a key pore structure parameter (the gamma parameter), which has been used in carbonate reservoir characterization (e.g., Bracco Gartner et al., 2005; Dou and Sun, 2008).

Proceedings Papers

#### Light Oil Measurement: Density, Velocity And Modulus From 23 to 200?C And At Pressures Up to 150 MPa

Publisher: Society of Exploration Geophysicists

Paper presented at the 2010 SEG Annual Meeting, October 17–22, 2010

Paper Number: SEG-2010-2470

... velocity dead oil transducer constant temperature light oil measurement sample-storage vessel dv vessel

**seg****seg****denver****2010**hthp condition reservoir surveillance production control density measurement Light oil measurement: density, velocity and modulus from 23 to 200 C and at pressures up...
Abstract

Introduction Summary A new density vessel has been made and calibrated. And measurement procedures have been improved for measuring density and velocity of hydrocarbon fluids at temperature up to 200 Figure1. Schematic experimental setup. 1. fluid sample; 2. transducers; 3. density and velocity vessel (DV vessel); 4. temperature controller; 5.CSD transducer; 6.scope; 7. Sample-storage vessel; 8. digital pump; 9. tiny O-ring. C? and pressure to 150 MPa. Measured data of light oil samples reveal systematic correlations of density, velocity and modulus with extended range of temperature, pressure and Gas-Oil Ratio (GOR). To investigate property of fluid (oil/gas) in high temperature and high pressure condition (HTHP) is increasingly important with increasing efforts of exploring ultra-deep, ultra-hot reservoirs. But there are limited laboratory measurements of density and velocity of hydrocarbon fluids, especially at HTHP condition (Rao, K. and Rao, B., 1959, Batzle and Wang, 1992, Han & Batzle, 2000, McCain, 1990). We have performed laboratory measurements to investigate properties of fluid in situ condition successfully in recent years. But when approaching to HTHP condition, the biggest challenge is seal of the test vessel and transducers. We have tried different O-ring and back-up for vessel and piston sealing. They have worked well if temperature and pressure are not too high. But over to 150 C? and 100 MPa, they only worked by chance. We have tried various epoxies for sealing transducers at HTHP condition for repeating measurement, but they cannot hold transducers and keep the same condition (sample volume and distance between two transducers) either. Any leakage of test sample affected quality of measured data and disrupted experiment, especially for density measurement. The vessel we used has two chambers: one is used for measurement; and the other for pressure control. They are separately sealed by three groups of O-rings. Unexpected O-ring deformation in HTHP condition will cause uncontrollable volume increase of the measured chamber. In addition, internal leak, even just minimal, can cause under-estimated oil volume. Both of cases will cause systematically low estimate of oil density data. Accuracy of calculation mainly depends on quality of density measurement. We can measure density with relative error around 0.5%, which can produce much large errors in density variance, because variance of density is very small (in order of 0.005 gm/cc) for pressure variance, such as 10 MPa. If variance of density drops to 0.004 with error of 0.001 gm/cc, static modulus will shot up 25%. In thermal dynamics, the dynamic modulus of liquid should be higher than the static modulus. However, a tiny error in ?? can bring significant difference of static modulus, even a reversed result that static modulus is higher than dynamic one at HTHP condition. In order to keep step with the developing trend of HTHP technology and requirement, we have designed and made a new density vessel that can also be used to measure velocity of liquid. By using the vessel and improved measuring methods, we measured several oil samples provided by our industrial sponsors.

Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2010 SEG Annual Meeting, October 17–22, 2010

Paper Number: SEG-2010-2746

... snieder dipole source wapenaar point charge reservoir characterization dipole field fluctuation

**seg****seg****denver****2010**upstream oil & gas equation realization function extraction application correlation static field continuous monitoring static problem Extracting the Green s function...
Abstract

Summary The theory of Green’s function extraction from field fluctuations has originally been derived in geoscience applications for wave propagation problems. Although current application of this technique is not restricted to wave propagation, there was no theory for Green’s function extraction of static fields. We present the theory of Green’s function extraction of static fields and illustrate the theory with a numerical example. The theory presented here is applicable to potential fields and to DC resistivity problems. The ability to extract static fields from field fluctuations makes it possible, in principle, to extract static fields from passive measurements of field fluctuations. This can be particularly relevant for continuous monitoring of the subsurface with electrical fields. Introduction Extraction the Green’s function from field fluctuations is a rapidly growing field in science and engineering (Curtis et al., 2006; Larose et al., 2006; Wapenaar et al., 2008). In seismological applications this technique is usually referred to as seismic interferometry. The principle of Green’s function extraction has up to this point been applied to time-dependent problems. This may have been caused by the fact the underlying theory was originally related to time-reversal, e.g. (Derode et al., 2003). The current theory has mostly been applied to problems involving wave propagation, such problems are inherently time-dependent. It may appear to be a paradox that one can extract the Green’s function of static fields from dynamic field fluctuations. The underlying principle is, however, not complicated. As an example, we show numerical simulations where random dipoles are present in an electrostatic system. At every moment in time, different dipoles generate the field, which at any moment in time depends only on the instantaneous dipole distribution. (This is actually the definition of the quasi-static response.) We show theoretically and numerically that by averaging over all dipole distributions one can extract the electrostatic response. Since the response is quasi-static, it does not matter whether the fields are generated by time-dependent electrical dipoles, or by an ensemble of dipoles; in the end this gives the same response. In the following section we derive the theory, which we illustrate with a numerical example in the subsequent section. Green’s function extraction in electrostatics In reality one may not have an ensemble of identical electrostatic systems, but one may have a system where random sources fluctuate with time. When the characteristic time of the temporal variations in these dipole sources is large compared to the time it takes for light to propagate through the system, the response of the system is quasistatic. In that case the ensemble average can be replaced by a temporal average over the field fluctuations. In fact, the approach to replace an ensemble average by an average over time is common in seismology where averaging over multiple non-overlapping time windows is used to extract the dynamic Green’s function (Larose et al., 2006; Sabra et al., 2005; Shapiro et al., 2005). By applying the same principle to quasi-static field fluctuations one can extract the electrostatic Green’s function from temporal field fluctuations.

Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2010 SEG Annual Meeting, October 17–22, 2010

Paper Number: SEG-2010-2441

... oil & gas interpretation lithology

**seg****seg****denver****2010**Rock physics workflows for exploration in frontier basins Mario A. Gutierrez*, BHP Billiton Petroleum, and Jack Dvorkin, Stanford University. Summary Figure 1: The application of quantitative seismic interpretation techniques in poorly...
Abstract

Summary Figure 1: The application of quantitative seismic interpretation techniques in poorly-calibrated basins where the nearest well control is some distance away is problematic. Quantitative seismic interpretation has been successfully applied to predicting lithology and fluids in areas with high-quality local-well control. By contrast, the application of the same techniques is problematic in frontier basins where the nearest well control is some distance away. The reason is when rock property models are extended out the range of calibration, seismic responses can not be always reliably predicted. Here we introduce an integrated methodology for frontier exploration that combines rock physics modeling and seismic-based evaluation, allowing the interpreter to consistently quantify seismic responses for a number of geologic scenarios. The key is to understand the underlying geological processes and, by so doing, focus on the main effects of geology on the seismic properties and variations thereof from a distant well to the prospect location. Stateof- art rock physics models have to be integrated with existing thermal, burial, and reservoir quality prediction models based on regional basin modeling and petrographic analysis. By combining geology and rock physics, this methodology helps generate a catalog of seismic responses of potential exploration successes and failures. From such catalogues, real seismic amplitudes are interpreted in terms of pore pressure, lithology, rock texture, fluid content, and porosity, thus providing a rock property-based seismic interpretation framework to de-risk exploration and support business decisions. Introduction Direct application of quantitative seismic interpretation techniques in frontier basins where the nearest well is far away can be problematic. Seismic responses cannot be reliably extrapolated and predicted without a clear understanding of the key geological and geophysical effects on the elastic properties and their variations from wells to lead locations (Figure 1). Here we describe how to use modern rock physics transforms that are consistently integrated with existing thermal, burial, and reservoir quality prediction models from regional basin modeling and petrographic analog data. These rock physics transforms link the rock elastic properties to their bulk properties (porosity, lithology), physical conditions (pressure, temperature, and pore fluid properties) and geological characteristics (texture and composition). Only the application of integrated rock physics workflow, that encompasses geological constrains, can provide robust predictions. Figures 2 and 3 show the generalized workflows that are applied to interpret the seismic amplitude anomalies in frontier basins. The interpretation framework includes the following tasks: (1) Log and seismic quality control and conditioning. (2) Time-depth calibrations. (3) Calibration of well and seismic processing velocities. (4) Rock typing and upscaling. (5) Rock property trend analysis, rock-physics diagnostic, and model formulation. (6) Integration with existing thermal, burial, and reservoir quality prediction models. Fluid acoustic properties modeling and extrapolation to the expected pressures and temperatures in prospect area. (7) Generation of synthetic seismograms for the key wells. Improvement of poor-quality logs using rock physics transforms. The basis of quantitative seismic interpretation is built upon quality-controlled seismic and borehole data. In order to illustrate these workflows, we have selected an example from a poorly-calibrated offshore deepwater Tertiary basin.

Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2010 SEG Annual Meeting, October 17–22, 2010

Paper Number: SEG-2010-2612

... Weatherford’s Source Rock Analyzer TPH/TOC Version 1.0. maturity variation contribution clay content stiffness variation image show microcrack upstream oil & gas structural geology organic-rich shale maturity index mineralogy

**seg****seg****denver****2010**displacement stiffness shale anisotropy...
Abstract

Summary Mineralogy of eleven Bakken shale samples with varying thermal maturities were studied as a function of their contribution to anisotropy and kerogen stiffness variations between the shales. It was found that anisotropy increased with increasing clay content and that kerogen stiffness increased with maturity. The understanding of the controls on anisotropy and stiffness of the soft components (kerogens and clays) of organic-rich shales is important in developing methods for indirect and in-situ detection of maturity. Introduction Present day detection of maturity in organic-rich shales is still hinged on geochemical processes where core and drillcuttings are analyzed by a pyrolysis technique to determine an index of maturity. This index is typically in the form of Total Organic Carbon (TOC), Hydrogen Index (HI), a measure of hydrocarbon already generated in shale (S1), a measure of kerogen being converted to hydrocarbon at a certain temperature (S2), the amount of CO2 produced during pyrolysis of kerogen (S3), TMax (maximum temperature at S2 pyroysis) and other derivative indices thereof. Recent efforts are targeted towards in-situ detection of maturity through indirect geophysical means. Some of these efforts are channeled towards understanding the acoustic properties of organic-rich shales through static down-hole measurements, dynamic bench-top measurements along with other assorted techniques in determining what controls acoustic and stiffness changes in the shales (Prasad et al, 2009). We know from literature that anisotropy increases in the direction perpendicular to bedding for organic-rich shales due to their high kerogen content and the presence of horizontal micro-cracks formed from hydrocarbon escape from over-pressured pores (Vernik and Nur, 1992). We also know that as thermal maturity increases, organic content reduces, micro-cracks increase and the shales become more anisotropic. However the anisotropic nature of post-mature shales becomes more variable as the shales are buried deeper. This is because greater overburden pressures would cause micro-cracks to close and the rocks appear more uniform, so causing microcrack contribution to anisotropy to be less significant (Vanorio et al, 2008). A major rock component which may be overlooked in understanding anisotropy is mineralogy. The dominant mineralogy in shale and its compliance during pressure loading would determine how well anisotropy-controlling micro-cracks would form in the shale. Theoretically at high internal pore pressures, shales with higher silica content would be brittle and more likely to allow micro-crack generation than shales with higher clay content which are more ductile. The Bakken shales which are organic-rich source-rock shales in the Williston basin were chosen for this study due to their relatively low clay content, and widely variable silica content. One source of uncertainty in these studies is the variation in physical properties of organic matter with maturity. The variation of kerogen stiffness with maturity was also studied with changing mineralogy. From this study, relationships of kerogen stiffness with maturity were determined. Study Methods Eleven Bakken shale samples with varying maturities were used for the study. Maturity indices were determined by pyrolysis using Weatherford’s Source Rock Analyzer TPH/TOC Version 1.0.

Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2010 SEG Annual Meeting, October 17–22, 2010

Paper Number: SEG-2010-2537

... measurements are made using torsional and flexural forced oscillation. university effective pressure jackson specimen reservoir characterization frequency measurement frequency

**seg****seg****denver****2010**upstream oil & gas mpa australian national university ultrasonic measurement velocity...
Abstract

Introduction Summary Seismic properties of saturated, cracked rock are expected to be strongly frequency dependent as a result of reversible fluid flow within the crack porosity at all scales caused by the oscillating stress induced by seismic waves. Laboratory measurements, typically made with frequencies on the order of MHz, must systematically overestimate in-situ seismic wave velocities that are typically measured with frequencies on the order of mHz-kHz, a range of frequencies applicable to earthquake teleseisms (<10 Hz) through active exploration seismic and microseismic investigations (~10 to 300 Hz), mine seismology (~1 kHz) and finally geophysical logging (~ 10 kHz). Forced flexural and torsional oscillation of core samples in the laboratory allows measurement of seismic properties at lower frequencies (0.01-1 Hz) that are more directly comparable to typical in-situ frequencies. Here we describe progress in the development of flexural oscillation methods at the Australian National University (ANU) laboratory for use alongside the established torsional mode capability, as well as preliminary results from a thermally cracked synthetic sample of polycrystalline alumina and a thermally cracked core of Cape Sorell quartzite for comparison with ultrasonic measurements on the samples. Pore fluids affect seismic wave speeds by increasing the effective stiffness of the pore, and thus the overall stiffness of the rock, when compressed by passing seismic waves. The stiffness of the rock can be related to seismic wave speeds by the Christoffel equations. The amount of additional stiffness induced by the pore fluid when compressed by a seismic wave depends strongly on the frequency of the seismic wave and the viscosity of the fluid. Maximum stiffness is achieved when the frequency is high enough that the pore fluid does not have sufficient time to flow. For this reason, measurements conducted in the laboratory with ultrasonic transducers (MHz) can be expected to measure higher stiffnesses and seismic velocities than measurements made using sonic logging (kHz), active source in-situ seismic (Hz) and passive source seismic (= Hz). Measurements can also be expected to be sensitive to the pore pressure; as the effective pressure changes (confining pressure minus pore pressure) the crack apertures and distribution of fluid within the fracture network will change, causing changes in the rock stiffness and seismic velocities. Numerous theoretical models exist which predict the frequency dependence of the seismic velocity based on factors such as pore fluid viscosity, fluid density, rock porosity, permeability, crack aspect ratio and tortuosity of fractures [e.g. Biot , 1956a; b; Geertsma and Smit , 1961; Mavko and Jizba , 1991; O''Connell and Budiansky , 1977]. Despite this extensive theoretical investigation of frequency dependence, the effect of fluid saturation on wave speeds through liquid saturated rock remains largely untested experimentally. As interest in time-lapse seismic monitoring of petroleum production, geothermal energy, geological CO2 sequestration, repositories for nuclear waste, etc. grows, the ability to accurately characterize fracture networks, pore pressure and fluid flow within low porosity crystalline rock becomes increasingly important. Method The low frequency measurements are made using torsional and flexural forced oscillation.

Proceedings Papers

Femke Vossepoel, Mathieu Darnet, Stéphane Gesbert, Ezequiel Gonzalez, Folkert Hindriks, Roseleen Kelly, Alessandro Sandrin, Line Jensen, Anette Uldall

Publisher: Society of Exploration Geophysicists

Paper presented at the 2010 SEG Annual Meeting, October 17–22, 2010

Paper Number: SEG-2010-2391

... rocks, however, the AI sensitivity to fluids is limited because of the relative competence of the rock matrix. carbonate joint interpretation fluid fill

**seg****seg****denver****2010**resistivity csem data joint interpretation column height porosity acoustic impedance interpretation hydrocarbon...
Abstract

Summary Quantitative interpretation (QI) of seismic data has been successfully used to predict reservoir properties such as porosity and fluid fill. In specific cases, however, adding resistivity estimates of the subsurface to the QI can reduce ambiguities in the properties prediction. Controlled-source electromagnetic (CSEM) sounding methods provide these estimates. By making use of their complementary nature, integration of seismic and electromagnetic datasets has been used to estimate the height of hydrocarbon-filled porous carbonate. In this paper, we present a methodology for joint interpretation, which can help us to discriminate tight carbonates from porous carbonates, and to differentiate between brine and hydrocarbon fill. When applied to a field in a carbonate setting, the estimated hydrocarbon column height corresponds qualitatively with the saturation height estimate based on production data. Introduction Controlled-source electromagnetic (CSEM) is a method for imaging subsurface resistivity. This resistivity is a function of variations in lithology and brine salinity, but more importantly of porosity and associated pore fluid. The physics of the CSEM is such that the spatial resolution of the resistivity images (especially vertical) is still low compared to seismic imaging, even though 3D multiazimuth acquisitions provide higher resolution and more robust resistivity estimates of the subsurface than 2D profiling. Therefore, some uncertainties still remain on the origin of the resistive anomaly(ies) at the target level. Incorporating additional constraints (e.g., seismic, petrophysical) into the inversion process provide a possible solution to overcome this limitation (e.g. Hansen and Mittet, 2009, Brevik et al., 2009). This is not straightforward, as changes in elastic properties do not necessarily correspond to resistivity changes and viceversa. Only when this imaging hurdle has been resolved, a quantitative interpretation of the resistivity image in terms of hydrocarbon presence can give reliable results. Assuming a reliable constrained inversion, interpretation of the resistivity image can be further enhanced through combination with seismic. Recent examples of joint seismic/CSEM interpretation (e.g. Hoversten et al, 2006, Harris et al., 2009) have demonstrated the potential of this approach in clastic settings. Carbonate settings pose a particular challenge, because the resistivity of the tight layers can be similar to that of hydrocarbon bearing porous carbonate. CSEM alone can, therefore, not unequivocally detect the presence of hydrocarbons. The seismic method faces a similar challenge in this environment, having limited sensitivity to fluid fill with respect to hydrocarbon presence. In the present paper, we present an application of a quantitative joint interpretation of CSEM and seismic data to a field in a carbonate setting. Methodology The resistivity and acoustic impedance of a rock depend on its lithological composition, porosity and fluid fill. Figure 1, a cross plot of well log data in our region of interest, illustrates this dependency. A decrease in porosity or water saturation can both increase resistivity considerably (Archie’s law). Acoustic impedance ( AI ) is also related to lithology, porosity and fluid fill; in many carbonate rocks, however, the AI sensitivity to fluids is limited because of the relative competence of the rock matrix.

Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2010 SEG Annual Meeting, October 17–22, 2010

Paper Number: SEG-2010-2366

... data. fault network reservoir simulation fault geometry

**seg****seg****denver****2010**maximum fault size fault surface realization stochastic simulation input data trace initial reservoir characterization fault trace trace initial upstream oil & gas fault family displacement...
Abstract

SUMMARY The structural interpretation of 2D seismic lines and their correlation to deduce a 3D structure, e.g. a fault network, is a hard task for geoscientists. Interpretations may introduce a prior geological knowledge bias and/or human bias (Bond et al., 2007). Consequently, decisions based on one single deterministic model of the subsurface may be highly risky. The goal of our study is to show that the 3D organization of a fault network, especially fault connections, is uncertain and to investigate how these uncertainties evolve depending on the quantity of input data. For this, we realized stochastic simulations of fault networks using a new method that allows both geometry and fault connection changes from statistical parameters about fault geometry and fault traces interpreted on 2D seismic lines. Simulations have been performed for two scenarios in which the quantity of input data was different, and generate a set of possible models whose diversity decreases as more data are added. INTRODUCTION Inferring 3D geological structures from a few seismic lines and exploration wells is a big challenge. Bond et al. (2007) show that among 412 geoscientist interpretations of a synthetic seismic image, only 21% of the participants found the right tectonic setting. The goal of this paper is not to explicitly address such conceptual geological uncertainty, but to focus on topological and geometrical uncertainties about fault networks, under prior knowledge about fault statistical properties. Indeed, fault uncertainty is often first order in structural interpretation and related applications, because faults significantly affect not only gross rock volumes but also fluid flow in the subsurface. Uncertainty about fault networks goes beyond the geometric uncertainty (Caumon et al., 2007; Charles et al., 2001; Lecour et al., 2001; Thore et al., 2002), because it also concerns fault existence and connectivity. In their pioneering work, Hollund et al. (2002) have proposed sampling this uncertainty through multiple realizations on cornerpoint reservoir grids. Suzuki et al. (2008) consider topological uncertainty by selecting an ensemble of models generated by combining manual interpretations and stochastic model perturbations. The stochastic model used in this paper also generates several alternative models, but is tailored to accurately represent more complex fault networks by using implicit surfaces on a tetrahedral mesh (Cherpeau et al., rev). Such multirealization approach can help understanding risks related to structural interpretation, both in underground resource exploration or management and assessment of natural hazards. This abstract briefly explains the features of this stochastic model, then presents its application to a Middle East field: from the 3D seismic of the area, a few 2D seismic lines have been extracted and interpreted; the stochastic fault simulation is then used and the various ouptut fault networks are compared to the reference model obtained by interpreting the 3D seismic. STOCHASTIC MODEL OVERVIEW We hereby present the stochastic model used to generate fault surfaces, based on previous work (Cherpeau et al., rev). The method is applied and adapted to model laterally dying and synsedimentary faults and to account for fault locations interpreted from available data.

Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2010 SEG Annual Meeting, October 17–22, 2010

Paper Number: SEG-2010-2564

... randomly layered media, compared to periodically layered media or finite randomly layered media. upstream oil & gas biot simulation equation consolidation fluid flow heterogeneity dispersion seismic attenuation petrophysical parameter media reservoir characterization

**seg****seg****denver**...
Abstract

Introduction Summary The finite element method is used to solve Biot’s quasistatic equations of consolidation. We perform 1D and 2D numerical creep tests of partially saturated porous rocks to calculate the frequency-dependent seismic attenuation and phase velocity from the modeled stress-strain relations. The resulting attenuation and velocity dispersion are due to fluid flow induced by pressure differences between mesoscopic-scale regions of the rock fully saturated with different fluids (White’s model). Comparisons of our numerical results with analytical solutions show accuracy for a wide range of frequencies. The algorithm is applied to a 1D partially saturated rock with a random distribution of saturation. We show that the numerical results for the random distribution can be approximated with a volume average of analytical solutions for periodic media. Attenuation of seismic waves in partially saturated rocks is of great interest because it has been observed that gas and oil reservoirs often exhibit high attenuation (e.g., Dasgupta and Clark, 1998; Rapoport et al., 2004), especially at low frequencies (Chapman et al., 2006). Data, from both laboratory and field, and theoretical work show that attenuation can be related to an increase in reflectivity in the low-frequency range (Korneev et al., 2004; Quintal et al., 2009). Goloshubin et al. (2006) showed three examples of field data in which oil-rich reservoirs exhibit increased reflectivity at low seismic frequencies (around 10 Hz). At low seismic frequencies, wave-induced fluid flow on the mesoscopic scale is presumably the major cause of wave attenuation and velocity dispersion in partially saturated porous rocks (e.g., Norris, 1993; Johnson, 2001; Pride and Berryman, 2003a, b). White (1975) and White et al. (1975) were the first to introduce the wave-induced fluid flow mechanism for a 3D model of a water-saturated medium with spherical gas-saturated inclusions and a 1D layered model. In White’s model, a partially saturated rock is represented by a poroelastic solid with regions fully saturated by one fluid and regions fully saturated by another fluid. Wave-induced fluid flow is caused by pore pressure differences between the two regions. Dutta and Odé (1979a, b) showed that wave-induced fluid flow can be modeled using Biot’s equations (Biot, 1962) for wave propagation in poroelastic media with spatially varying petrophysical parameters. Several theoretical studies, based on White’s model and Biot’s theory (Biot, 1962), provide various closed-form analytical solutions for seismic attenuation in porous saturated media with periodic mesoscopic-scale heterogeneities of particular geometries, such as layered media or media with spherical inclusions (e.g., Johnson, 2001; Pride and Berryman, 2003a, b). There are also closed-form analytical solutions for randomly layered media (e.g., Gurevich and Lopatnikov, 1995), however, they are restricted to infinite media and to particular autocorrelation functions. Müller and Gurevich (2005) showed that significant differences in the magnitude and frequency dependence of attenuation are caused by only the use of different autocorrelation functions. Additionally, in the low-frequency limit, 1 Q (Q is the quality factor, 1 Q is a measure of attenuation) scales differently in infinite randomly layered media, compared to periodically layered media or finite randomly layered media.

Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2010 SEG Annual Meeting, October 17–22, 2010

Paper Number: SEG-2010-2416

... rock physics model

**seg****seg****denver****2010**laboratory glauconite fraction elastic property stiff-sand model reservoir characterization coordination number glauconite grain quartz s-wave velocity hertz-mindlin contact model porosity greensand Rock physics model of glauconitic greensand from...
Abstract

Summary The objective of this study is to establish a rock physics model of North Sea Paleogene greensand. First, our approach is to develop a Hertz-Mindlin contact model for a mixture of quartz and glauconite. Next, we used this Hertz- Mindlin contact model of two types of grains as initial modulus for a soft-sand model and a stiff-sand model. By using these rock physics models, we examine the elastic modulus-porosity relationships of laboratory and logging measured data and link rock physics properties to greensand diagenesis. Results of rock physics modeling and thin section observations indicate that variations in elastic properties of greensand can be explained by two main diagenetic phases: microcrystalline quartz or silica cementation and berthierine cementation. These diagenetic phases dominate in separate parts of the greensand reservoir bodies. Initially greensand is a mixture of mainly quartz and glauconite; when weekly cemented, it has relatively low elastic modulus, and can modeled by a Hertz-Mindlin contact model of two types of grains. Microcrystalline quartz cemented greensand have relatively high elastic modulus and can be modeled by an intermediate-stiff-sand or a stiff-sand model. Berthierine cement has a different growth pattern in the greensand formation, resulting in a soft-sand model and an intermediate-stiff-sand model. Introduction Greensands are glauconite bearing sandstones composed of a mixture of stiff clastic quartz grains and soft glauconite grains. Glauconite grains are porous and composed of aggregates of iron-bearing clay. Porosity is thus found in two scales: macro-porosity between grains and microporosity within grains (Figure 1). Greensand petroleum reservoirs occur world-wide, however, evaluation of greensand reservoirs has challenged geologists, engineers and petrophysicsts. Glauconite has an effect on the elastic properties, porosity and permeability of reservoir rocks (Diaz et al., 2003). Glauconite is also ductile (Ranganathan and Ty, 1986) so it can cause non-elastic deformation of greensand (Hossain et al., 2009) which could affect reservoir quality. The Hertz-Mindlin contact model (Mindlin, 1949) is the most commonly used contact model for sandstone (Avseth et al., 2005). This model is used to calculate initial sandpack modulus of soft-sand (Dvorkin and Nur, 1996), of stiff-sand (Mavko et al., 2009), and of intermediate-stiffsand model (Mavko et al., 2009). For the initial sand-pack for sandstone it is assumed that only quartz grains are packed together and the normal and shear stiffness are calculated based on the contact of two quartz grains. However, for greensands the initial sand-pack is a mixture of quartz and glauconite and because both of them are load bearing, elastic properties between that of quartz and glauconite are anticipated. To address this, we present a Hertz-Mindlin contact model for mixtures of quartz and glauconite. Theory and Method We investigate the effective elastic properties of a granular pack of spheres, for which each pair of grains in contact under normal and tangential load determines the fundamental mechanics. Typically in granular media models for unconsolidated sand, the grains are taken to be of the same materials e.g. quartz.

Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2010 SEG Annual Meeting, October 17–22, 2010

Paper Number: SEG-2010-2725

... characterization crack density woodford shale middle woodford fracture upstream oil & gas modeling variation anisotropy sensitivity orthogonal ps wave sensitivity test seismic response azimuthal rpp alpha 0

**seg****seg****denver****2010**aspect ratio saturation Sensitivity of seismic response...
Abstract

Summary The Woodford Shale can act as source rock, seal and reservoir, and may have elastic anisotropy. We carried out seismic anisotropy modeling using a vertical impulsive source to characterize the seismic response (PP, PS and SS waves) of different anisotropy scenarios of VTI and HTI in the Woodford Shale. The seismic response is sensitive to Thomsen’s parameters in the VTI case and to crack density, crack aspect ratio, and fluid saturation in the HTI case. Modeling helps in understanding these seismic properties and their relation to rock and fluid demonstrating the importance of this sensitivity study to the seismic. Field Discription According to Comer (1991), The Woodford Shale in West Texas and Southeastern New Mexico contains on the order of 80 x 109 bbl of oil (240 x 10¹² ft³ of natural gas equivalent). Production may come from a range of lithofacies like chert, sandstone, dolostone and siltstone, any of which may be fractured. The well selected for seismic modeling was drilled in Pecos County, Delaware Basin, West Texas. The Woodford Shale interval is gas saturated and the porosity is less than 10%. Using the gamma ray, resistivity and density logs, the Woodford Shale can be divided into three units, upper, middle and lower. The middle Woodford has relatively higher resistivity, higher gamma ray response and lower density than those of the other two units. The rock properties of each of these Woodford units are shown in Table 1. Using the above properties, this study explores the use of seismic modeling to understand Woodford anisotropy signatures on different wave modes and to understand the sensitivity of the seismic response to anisotropy. Seismic Modeling using a Vertical Impulsive Source The seismic response is numerically simulated to aid anisotropy analysis in the field. The well, which has a dipole sonic, was used to generate synthetic seismic shot gathers with an impulsive vertical source, which generates PP, PS and SS waves. Since we used only a vertical source and a single line orientation, there are only Z and X components (vertical and radial respectively), but no Y (transverse) component. A careful observation of the log shows possible VTI in the middle Woodford, which is characterized as interbedded chert and shale by the gamma ray response, and possible HTI in the middle Woodford, whose dipole shear log shows a clear separation of fast and slow shear waves. Modeling Results (1) Isotropic and VTI model (models 1and 2) Figure 1 shows comparison of isotropic and VTI seismic responses. The maximum offset is associated with an incident angle of approximately 35 degrees. The background matrix is set according to Table 1. Figure 1a shows that the VTI and isotropic synthetic data look similar for each component of PP, PS and SS waves. Figure 1b is a difference plot between VTI and isotropic synthetic seismograms for the two components at different offsets. The SS wave difference appears to be weaker than the PP and PS waves.

Proceedings Papers

Publisher: Society of Exploration Geophysicists

Paper presented at the 2010 SEG Annual Meeting, October 17–22, 2010

Paper Number: SEG-2010-2289

.... eaton inversion velocity inversion pressure gradient upstream oil & gas gradient tomographic inversion pore pressure reservoir characterization bower approach overburden gradient pore pressure estimation west kuwait field information

**seg****seg****denver****2010**well location pore pressure...
Abstract

Summary: This paper presents the results of a pore pressure prediction study carried out for the Minagish field in West Kuwait. The objective of this study was to understand and map the abnormal pressure occurrence within the Cretaceous reservoirs and the deep Jurassic section, so that the well designs could be optimized by making recommendations on mud weight, casing type and cap seal integrity. To achieve this objective, a clear relationship between velocity and pore pressure had to be established. It was observed that high pressure had an impact on the velocity, which was detectable on seismic. As petroleum system of Minagish field is both complex and diverse, three different techniques for estimations of velocity were used namely high density picking of NMO velocities, pre stack elastic inversion and tomographic inversion. The derived velocity fields were compared and the most optimum one was chosen. Pore pressure prediction was attempted next by using three different estimation techniques namely; Eaton''s model approach, petro-physical model approach and Bowers approach. Rock physics measurements were used to provide a reliable quantification of the sensitivity of the velocity with pore pressure. The resulting pore pressure volumes as well as the related attributes were compared and analyzed. It was found that Bowers approach for pore pressure estimation, based on inversion velocities, yielded the most reliable information for the area under study. Introduction: High pore pressures are encountered in Kuwait, in Cretaceous and late Jurassic sections. In the Minagish field, three main reservoir levels, namely Minagish Oolite, Najmah/Sargelu and Marrat were studied. These reservoirs have different lithology and rock types. The Najmah/Sargelu reservoir is a source of reservoir system with overpressure due to the excellent sealing capability of the Gotnia salt. Besides the above mentioned three reservoirs, there is a risk of encountering even higher pressures in the deeper Khuff reservoir formations. The main objective of this pore pressure prediction project was to use the available methods to better understand the occurrences of abnormal pressure pockets within the Cretaceous and late Jurassic sections. Besides well planning and hazard prevention, this pore pressure prediction study was aimed at getting information about the trap seal integrity in different reservoirs as well as to assess the hydrocarbon accumulation column height for prospect evaluation. Various physical processes can cause anomalous pressures in a reservoir. Pore pressure refers to the in-situ pressure of the fluids in the pores and equals the hydrostatic pressure when the pore fluids only support the weight of the overlying pore fluids. The litho-static or confining pressure results from the weight of overlying sediments, including the pore fluid. The difference between confining and pore pressure is called effective stress which is the principal driving mechanism for the compaction of compressible sediments. It depends on the current and past values of Terzaghi’s (1943) effective stress (sv ), which equals the vertical total stress sv minus the pore pressure (p). The total vertical stress or the overburden is usually calculated from density logs, regional correlations or density/velocity transforms.