A multi-physics feasibility study using wells and CSEM …€¦ · · 2015-04-14A multi-physics...
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A multi-physics feasibility study using wells and CSEM modeling for a Miocene reservoir, Gulf of Mexico Paola Vera de Newton* and Amanda Geck Alvarez, Rock Solid Images; Marco Vázquez and Humberto Salazar, PEMEX Summary Changes in rock properties, fluid content and their combination can be related to multiple seismic and EM anomalies. Rock physics models are commonly used to better understand the underlying physics of observed log responses and how they are governed by local petrophysical properties (Smith, 2011). In this study from the Gulf of Mexico, we present a case that shows a strong EM anomaly that can be tied to the rock properties at the wellbore location by generating 1D CSEM models. This multi-physics approach addresses the importance of understanding of rock physics fundamentals and their integration with other technologies, such as seismic and EM. Introduction This feasibility study aims to understand the impact of elastic and electric reservoir properties using the discovery well as the calibration well for such anomaly response in a Miocene reservoir. Theoretical rock physics models are used to predict elastic curves (Bulk Density, Vp, and VS) at in situ conditions, and then main reservoir rocks are perturbed in terms of water saturation by applying Gassmann theory in the elastic domain, and modified Simandoux’s relationship in the electrical domain. Seismic feasibility is accomplished using synthetic seismograms and CSEM feasibility is achieved by calculating the synthetic amplitudes. In this study, three main steps are followed: Step 1: Geophysical Well-Log Analysis (GWLA) and Rock Physics Diagnostics (RPD) – This is required to build a good and consistent well-log data set for the rock physics modeling. In this step, we estimate volumetric fractions of solid and fluid constituents. Step 2: Rock physics modeling and fluid substitution is conducted for different fluid saturation scenarios. Gassmann’s fluid substitution is used for the elastic data and Simaundoux’s equation is used for resistivity data. Step 3: Geophysical modeling and sensitivity analysis: The objective is to assess the effect of changes in the fluid content on the reservoir on the measured seismic and CSEM responses. Synthetic seismic and AVO modeling is used to calculate the effect of variations in reservoir properties and physical conditions on synthetic seismograms. Similarly, 1D modeling based on upscaled resistivities from the GWLA and rock physics analysis is used to investigate the sensitivity of measured CSEM data to changes in the reservoir charge. GWLA and Rock Physics Diagnostics Once the main petrophysical properties are estimated, and elastic logs are fully conditioned, the elastic trends of each reservoir are analyzed using theoretical models. In these Miocene reservoirs two main trends were observed in the elastic domain. Figure 1 shows the total porosity (φ t ) versus p wave velocities (V p ) cross plots color coded by water saturation within the Miocene sediments. Highly gas saturated upper Miocene sediments (left) show distinctive lower V p than deeper Miocene sections (middle and right). Theoretical gas saturated lines (bright red) for the Unconsolidated Sand Figure 1: Total porosity versus V p color coded by water saturation. Gas sands in Upper and Lower Miocene fall in trend with the unconsolidated sand model, while Middle Miocene reservoirs shows better agreement with the stiff sand model. DOI http://dx.doi.org/10.1190/segam2013-1392.1 © 2013 SEG SEG Houston 2013 Annual Meeting Page 2878 Downloaded 08/14/14 to 216.198.85.26. Redistribution subject to SEG license or copyright; see Terms of Use at http://library.seg.org/
Transcript of A multi-physics feasibility study using wells and CSEM …€¦ · · 2015-04-14A multi-physics...
A multi-physics feasibility study using wells and CSEM modeling for a Miocene reservoir, Gulf of Mexico Paola Vera de Newton* and Amanda Geck Alvarez, Rock Solid Images; Marco Vázquez and Humberto Salazar, PEMEX Summary Changes in rock properties, fluid content and their combination can be related to multiple seismic and EM anomalies. Rock physics models are commonly used to better understand the underlying physics of observed log responses and how they are governed by local petrophysical properties (Smith, 2011). In this study from the Gulf of Mexico, we present a case that shows a strong EM anomaly that can be tied to the rock properties at the wellbore location by generating 1D CSEM models. This multi-physics approach addresses the importance of understanding of rock physics fundamentals and their integration with other technologies, such as seismic and EM.
Introduction This feasibility study aims to understand the impact of elastic and electric reservoir properties using the discovery well as the calibration well for such anomaly response in a Miocene reservoir. Theoretical rock physics models are used to predict elastic curves (Bulk Density, Vp, and VS) at in situ conditions, and then main reservoir rocks are perturbed in terms of water saturation by applying Gassmann theory in the elastic domain, and modified Simandoux’s relationship in the electrical domain. Seismic feasibility is accomplished using synthetic seismograms and CSEM feasibility is achieved by calculating the synthetic amplitudes.
In this study, three main steps are followed:
Step 1: Geophysical Well-Log Analysis (GWLA) and Rock Physics Diagnostics (RPD) – This is required to build a
good and consistent well-log data set for the rock physics modeling. In this step, we estimate volumetric fractions of solid and fluid constituents.
Step 2: Rock physics modeling and fluid substitution is conducted for different fluid saturation scenarios. Gassmann’s fluid substitution is used for the elastic data and Simaundoux’s equation is used for resistivity data.
Step 3: Geophysical modeling and sensitivity analysis: The objective is to assess the effect of changes in the fluid content on the reservoir on the measured seismic and CSEM responses. Synthetic seismic and AVO modeling is used to calculate the effect of variations in reservoir properties and physical conditions on synthetic seismograms. Similarly, 1D modeling based on upscaled resistivities from the GWLA and rock physics analysis is used to investigate the sensitivity of measured CSEM data to changes in the reservoir charge.
GWLA and Rock Physics Diagnostics
Once the main petrophysical properties are estimated, and elastic logs are fully conditioned, the elastic trends of each reservoir are analyzed using theoretical models. In these Miocene reservoirs two main trends were observed in the elastic domain.
Figure 1 shows the total porosity (φt) versus p wave velocities (Vp) cross plots color coded by water saturation within the Miocene sediments. Highly gas saturated upper Miocene sediments (left) show distinctive lower Vp than deeper Miocene sections (middle and right). Theoretical gas saturated lines (bright red) for the Unconsolidated Sand
Figure 1: Total porosity versus Vp color coded by water saturation. Gas sands in Upper and Lower Miocene fall in trend with the unconsolidated sand model, while Middle Miocene reservoirs shows better agreement with the stiff sand model.
DOI http://dx.doi.org/10.1190/segam2013-1392.1© 2013 SEGSEG Houston 2013 Annual Meeting Page 2878
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A multi-physics feasibility study using wells and CSEM modeling
model (Dvorkin and Nur, 1996) show good agreement with measured data, suggesting that this model can be used as a good proxy for understanding in situ velocity-density conditions. A composite model was used for Middle and Lower Miocene, using both the unconsolidated and stiff sand models, due to more compaction in these rocks.
Rock properties are analyzed in other elastic spaces and compared to electrical responses. Figure 2 shows the comparison between the elastic and electrical spaces at the calibration well within the Miocene section. Arbitrary lines are plotted to show the separation between pay (red samples) and non-reservoir rocks (background). Note that as rocks get older and buried deeper in the section (Middle and Lower Miocene), they exhibit in general higher
resistivities (upper) and acoustic impedances (lower).
The Upper Miocene shows significant separation in resistivity domain, but when compared to the elastic attributes of Poisson’s ratio and acoustic impedance, wet sands, commercial and non-commercial gas plot within the same space. This similar elastic trend is also observed in Middle and Lower Miocene sediments. In contrast, as sediments become rich in clay and highly laminated (shales and siltstones) below the Middle Miocene (generally at the top of each sand interval), vertical resistivity increases significantly. Shales tend to exhibit high levels of elastic and electrical anisotropy (Ellis et al., 2010), and by combining both horizontal and vertical measurements at the well it is observed that this effect causes an overlap between the electrical responses of non-reservoir and commercial gas sands.
Rock Physics Modeling and Fluid Substitution
Elastic and electrical logs are perturbed in order to understand fluid content effect on seismic and electromagnetic data. Gassman’s theory and Modified Simandoux’s relationship are used in the elastic and electrical domain respectively. Figure 3 shows fluid sensitivity up-scaled responses per reservoir level for the main attributes analyzed in this study.
Elastic attributes in Figure 3 (upper set of cross plots) show that both commercial and non-commercial gas (red and orange samples) fall within the same AI-PR space. In terms of resistivity (lower set of cross plots), modeled hydrocarbon scenarios show that vertical resistivity is a good discriminator for pay responses in the Upper Miocene reservoir. In contrast, as the sands get more compacted in the Middle and Lower Miocene, the elastic matrix has a stronger effect than fluid content. The same set of attributes (upper set in Figure 3) shows minimal separation between modeled cases, and challenging responses in resistivity domain since the background in this section becomes more anisotropic.
Geophysical Modeling
The next stage in the analysis is to assess the effect of the fluid changes investigated above on the measured seismic and CSEM responses. In the seismic case, the main sensitivity was observed to the Upper Miocene section, for which a strong AVO signature at the main pay sandstone was observed. However, the presence of residual gas in underlying sandstones also results in an AVO signature (Figure 4) leaving an ambiguity in the saturation when only seismic data are considered.
Figure 2: Log10 of vertical resistivity versus total porosity andPoisson’s ratio (PR) versus acoustic impedance (AI), color codedby litho-classes. Upper Miocene gas sand stands out from non-reservoir sediments in resistivity domain (upper plot) but not in PR-AI. Middle and Miocene reservoirs generally remain withinbackground in these spaces.
DOI http://dx.doi.org/10.1190/segam2013-1392.1© 2013 SEGSEG Houston 2013 Annual Meeting Page 2879
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A multi-physics feasibility study using wells and CSEM modeling
Figure 3: Poisson’s ratio (PR) versus acoustic impedance (AI) and AI versus Log10 of vertical resistivity, color coded by fluid cases.
Figure 4: Synthetic seismic modeling at the calibration well within the Miocene section. Note that residual gas below main pay in UpperMiocene results in strong AVO signatures as well. No significant AVO is observed in Middle and Lower Miocene target sands.
DOI http://dx.doi.org/10.1190/segam2013-1392.1© 2013 SEGSEG Houston 2013 Annual Meeting Page 2880
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A multi-physics feasibility study using wells and CSEM modeling
In the CSEM case, a 1D modeling approach was applied to assess sensitivity. 1D sensitivity analysis, highlighting the change in the inline electric field caused by the target reservoir layer, was undertaken for multiple target scenarios. Figure 5 shows that good sensitivity to the Upper Miocene target is seen in a wide range of frequencies. The modeling shows only marginal sensitivity to the Middle Miocene reservoir at the offsets and frequencies typically encountered in CSEM data. Sensitivity to the stacked Lower Miocene targets is limited to long source-receiver offsets of the two lowest acquisition frequencies. In the presence of the Upper Miocene target, sensitivity to the stacked Lower Miocene targets is further decreased.
This sensitivity analysis allows for an improved understanding of the data requirements for higher dimensional analysis. In order to preserve sensitivity to the Lower Miocene targets, as much long-offset data must be retained as possible.
Conclusions Hydrocarbon charged Upper Miocene rocks can be differentiated from wet clay rich intervals in Poisson’s and resistivity domains, whereas Middle and Lower Miocene responses may not clearly distinguished in the elastic or electrical domains. The use of seismic alone for interpreting the Upper Miocene may result in misleading results due to false positive anomalies caused by residual
gas in the sands. Resistivity may assist in the hydrocarbon response understanding if reservoir changes its laminated nature away from the calibration well location.
Modeling and sensitivity analysis shows that the CSEM dataset contains limited sensitivity to the Lower Miocene targets. With careful application of constrained inversion techniques and integration with seismic reservoir properties, a more thorough understanding of the reservoir may be achieved.
This feasibility analysis demonstrates the importance of understanding of rock physics fundamentals and their integration with other technologies, such as seismic and EM.
Acknowledgements The authors would like to thank PEMEX for permission to publish this work.
Figure 5: Normalized results for 1D CSEM sensitivity analysis shows good sensitivity to the Upper Miocene target (left). Sensitivity to the Middle Miocene target (second from left) is poor. Sensitivity to the stacked Lower Miocene targets (third from left) is limited to long offsets ofthe two lowest acquisition frequencies. In the presence of the Upper Miocene target, sensitivity to the stacked Lower Miocene targets is further decreased (right plots). Areas shaded blue correspond to survey data included in initial higher-dimensional analysis; longer offset data may be incorporated to increase the sensitivity of the survey dataset to the Lower Miocene targets.
DOI http://dx.doi.org/10.1190/segam2013-1392.1© 2013 SEGSEG Houston 2013 Annual Meeting Page 2881
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http://dx.doi.org/10.1190/segam2013-1392.1 EDITED REFERENCES Note: This reference list is a copy-edited version of the reference list submitted by the author. Reference lists for the 2013 SEG Technical Program Expanded Abstracts have been copy edited so that references provided with the online metadata for each paper will achieve a high degree of linking to cited sources that appear on the Web. REFERENCES
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Ellis , M. H., M. C. Sinha, and R. Parr, 2010a, Role of fine-scale layering and grain alignment in the electrical anisotropy of marine sediments: First Break, 28, no. 9, 49–57.
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