improvement of the reservoir characterization of fluvial sandstones … · 2015-08-07 ·...

R e s e r v o i r d e s c r i p t i o n

508 The Leading Edge May 2014

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Improvement of the reservoir characterization of fluvial sandstones with geostatistical inversion in Golfo San Jorge Basin, Argentina

AbstractIn the Golfo San Jorge Basin, Argentina, the main tra-

ditional reservoirs are fluvial sandstones, which are thin and usually fall below seismic tuning frequency. The need for an aggressive development of the reserves and the need to open new areas led to a pilot job in a zone of approximately 100 km2, assuming the possibility to go vertically across structural and stratigraphic traps and their combination, using high tech-nology added to the proved workflow. After the carefully tra-ditional seismic structural interpretation of faults and horizons in the area and using seismic attributes, the seismic reservoir characterization was aimed toward the processing and analy-sis of a seismic geostatistical inversion. Using this technique improved the knowledge of the emplacement, behavior, and occurrence of the reservoir sand bodies, opening new zones and determining development drilling. The method includes geo-statistical inversion and its interpretation, results, and improve-ment of the sequence. Time thickness of the reservoirs, sand probability, and effective-porosity maps for each separate layer were generated and interpreted at the end of the sequence. The results in proposed wells are based on this technique.

IntroductionThe Golfo San Jorge Basin, in central Patagonia, covers an

area of approximately 170,000 km2. Regarding hydrocarbon production, the basin presents the highest cumulative value in Argentina. This intracratonic basin is predominantly ex-tensional, trending roughly east-west, from the Andean Belt to the Atlantic Ocean.

After a regional tilt of the main axis of the basin, the Chubutian sedimentary cycle starts. The Pozo D-129 Forma-tion (Barremian-Aptian), of mainly lacustrine origin, is the most important source rock of the basin. Overlying it, a group of fluvial–shallow lacustrine units was deposited under late sag conditions. These units contain the reservoirs that host the bulk hydrocarbon accumulations of the basin. The main phase of Tertiary compression uplifts the north-south-trending San Bernardo fold belt by reactivating previous normal faults.

Volcanic activity throughout the history of the basin is expressed in the high tuffaceous content of the entire col-umn, affecting the quality of the reservoirs. After migrating through a network of faults and pathways, the oil is trapped in extensional and compressional structures.

The area of interest for this job is characterized by its fluvial sand tuffaceous reservoirs, which have variable thickness, gener-ally no more than 15 m. Almost all are below seismic tuning fre-quency, and if they are stacked, they are represented seismically.

That is one of the difficulties to solve in each reservoir; in addition, the reservoirs are developed vertically in more than 1000 m of pay zone. The quality of the 3D seismic in

Luis Vernengo, raúL CzepLowodzki, and eduardo TrinChero, Pan American Energy LLCaLberTo sabaTé, inna TsybuLkina, and FranCisCo MoriLLo, CGG GeoSoftware

the area of interest is medium. The source used is vibroseis, and it was recorded five to 15 years ago. In this study, the average frequency is about 35 Hz. It is usual to find more than 40 reservoirs in one well, although no more of 10% of them are productive. Hydraulic fractures are often produced. The impedance of the reservoirs is higher than the seal, partly because of the presence of tuff.

Traditional seismic interpretation on 3D seismic includes structural maps adjusted to wells and attribute extractions such as amplitude and phase (Figure 1a). Spectral decompo-sition, curvature, and coherency filters are among the best indicators of channels and faults (Hunt et al., 2011).

Nonetheless, it is necessary to test other technologies to obtain more accuracy in the vertical component and spatial distribution so as to understand the architectural features of the reservoirs (Slatt and Abousleiman, 2011). In that way, this study is based on geostatistical inversion, which uses seismic and wells together to give cubes with more resolu-tion than conventional seismic and other realizations such as probability cubes.

Figure 1. (a) Structural map at the top of the interest zone. Step-out well (red circle). (b) Seismic line with interpretation, producing wells, picks, and location of the step-out well.

May 2014 The Leading Edge 509

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Geostatistical inversion helps in the integral solution be-cause it includes in one product the advantages of full-stack seismic, angle gathers, P- and S-sonic logs, density well logs, and simultaneous inversion. Finally, it is possible to interpret high-frequency impedance, sand, and lithotype-probability maps.

This method integrated in the interpretative workflow has been demonstrated to be effective not only for development-well locations but also for mid- to high-risk wells (mainly in stratigraphic traps) and to add a new dimension in the decision tree of determining sand occurrence. Geostatistical inversion has been proved to provide subtle details and quan-tification of uncertainty.

Methodology for seismic geostatistical inversionThe geostatistical inversion proposed in this workflow

has the main objective of generating multiple scenarios (from now on called realizations) of the elastic properties and litho-facies (simultaneously and not derived afterward) of the res-ervoir in high detail (Chopra et al., 2004; Close et al., 2011). The realizations are the result of integrating rigorously

1) all prior knowledge about the field in the form of a geo-statistical model per lithofacies in the interest interval; as a typical model, it will have vertical and lateral variograms, histograms, proportions, and any other prior knowledge in the form of a trend (1D, 2D, or 3D)

2) probability density functions (PDF) for each of the litho-facies defined in the domain of elastic properties (IP, VP /VS, and ρ)

3) a rock-physics model that allows

a) synthetizing logs in sections that have been affected by washout or invasion

b) characterizing rocks based on elastic propertiesc) establishing the link between petrophysical and elastic

properties4) a stratigraphic grid built on interpreted surfaces and faults5) wells6) seismic data as partial-angle stacks7) wavelets, one for each partial-angle stack

The proposed way to overcome the problem of integration of different types, units, and scales is to represent every source of information as a probability density function (PDF) (Figures 2a and 2b) and then combine all the PDFs into one multivariate posterior PDF through Bayesian inference (Figure 3a).

The next step is to obtain statistically correct and equi-probable samples of this multivariate PDF by using a custom-ized Markov-chain Monte Carlo algorithm. In this process, one sample (realization) consists of highly detailed volumes of P-impedance, VP/VS, density, and lithofacies that honor all input parameters as well as the seismic data.

Forward-modeled seismic response of each sample will match the input seismic data with a minimal residual (Figure 3b). It is important to mention that the high detail found in each realization volume does not constitute an increment in resolution. The details beyond the seismic bandwidth come from the geostatistical model, and it is required to analyze

Figure 2. (a) and (b) Integration of different types of information, units, and scales, representing each source of data included in the geostatistical-inversion workflow. Figure 3. (a) Combination of all the information in a multivariate

realization and then through Bayesian inference. (b) Forward-modeleld seismic response of each sample will match the input seismic data with minimal residual. (c) Summary of described process.



all realizations to better understand the uncertainty (Torres-Verdín et al., 1999; Shanor et al., 2001).

After we obtain a fair number of samples and through a cosimulation process for each realization, porosity volumes are estimated. Finally, summary statistics of the results can be generated; this is generating mean and standard deviation for each of the continuous properties and probability of occur-rence for the lithofacies.

For the purposes of this project, lithofacies were defined as sands (reservoir) and shale (nonreservoir). Other useful re-sults for analysis include maps (sand thickness, effective po-rosity) that can be generated from the summaries. Summary of the process is showed in Figure 3c.

Application for a step-out well proposalOne of the products of this workflow was the proposal of

a 2400-m-deep step-out well. It was planned to drill across 20 tuffaceous sandstones with about 80-m permeable thick-ness. The map in Figure 1a (red circle) shows how structur-ally compromised the well is because it was planned to go approximately 30 m deeper than nearby wells for the same marker and outside the fault closure. The position is three times the normal distance between wells.

Figure 4. (a) North-south seismic section showing structural complexity. (b) The same section with wells and logs and seismic horizons.

Figure 6. (a) Deterministic inversion (acoustic P) near the oil field and a stratigraphic slice of a productive reservoir. (b) Deterministic inversion (acoustic P) near the oil field and a stratigraphic slice of another productive reservoir.

Figure 5. (a) Structural map referred to the central marker of the pay zone and overposted deterministic-inversion slice showing stratigraphy downstructure. (b) Structural model of low-frequency model, constructed from 70 ms above K marker up to 70 ms below the base (P marker). This padding layer should have a thickness on the order of half the wavelet length.



In the northeast-southwest line, is easy to see that most of the seis-mic horizons are not continuous from the producing wells to the pro-posed one (in Figure 1b, the propsed well is shown in red). This could be a good sign because oil and gas ac-cumulations in this field have strong stratigraphic control. The producing wells, to the west, are quite far from the proposed well.

The north-south line with and without interpretation (Figures 4a and 4b) clearly shows the presence and development of faults and struc-tures. We first needed to identify res-ervoirs in the seismic with the help of attributes, where it shows that they are below seismic resolution (3 to 5 m), although sometimes we can see the “stack” of reservoir sand-stones. On the other hand, we used several seismic attributes, spectral decomposition, and coher-ency calculations to make a better model.

The next step is imaging those sand bodies with other tools, and simultaneous inversion proved helpful. It reinforc-es the frequency content and adds the lower frequency from wells. It is possible to have not only P-impedance, S-imped-ance, and sometimes density cubes but also lambda-rho and mu-rho parameters (Figures 5a and 6). The slice of a reservoir represented as P-impedance and overposted on the structural map is an excellent way to understand the deposition pattern (Latimer et al., 2000).

To guide the low-frequency model (Figure 5b), the hori-zons and faults are integrated. The frequency increment ob-tained in the geostatistical inversion is remarkable, especially in the area of interest (Figure 7). During the crossplot phase of the simultaneous-inversion job using two and three vari-ables, the reservoir and nonreservoir were clearly separated, encouraging continuation of the study (Figures 8a and 8b).

Finally, the decision was to run a geostatistical inversion, which gave results in terms of impedance and sand-cube probability, sandstone occurrence, and effective-porosity cube. Therefore, the interpretation procedure changed dra-matically. We looked into these new cubes and developed skills to describe the occurrence of sandstone bodies.

The process started with the discrimination in a well-log crossplot (Figures 9 and 10) where a relationship between P-impedance and resistivity was established. Using a cutoff is possible to highlight reservoirs in wells of the area. The next crossplot show that P- and S-impedance are related not only to shale volume but also to effective porosity. Moreover, the crossplot of the VP/VS with P-impedance shows a very strong separation between sand and seal (Castagna et al., 1985). In this case, more than 30% of shale is not a reservoir. This phase concluded, showing in three intervals guided by geology picks the proportion of reservoir/nonreservoir.

Figure 8. (a) Crossplot of porosity versus P-impedance of deter min istic inversion. (b) The same wells as shown in part (a) with highlighted high impedances inside the blue polygon. Sand bodies are highlighted in the well logs.

Figure 7. Frequencies content: (a) conventional seismic data, (b) deterministic inversion, and (c) geostatistical inversion.

Correlation of a well with the whole set of logs (such as P- and S-sonic, density, electric logs, and so forth) (Figure 11) shows the good seismic match and a test of deterministic and geostatistical inversion. In this case, it shows the differ-ence in vertical resolution for the seismic and how it can be



Figure 11. (a) Proportional sand-shale graphic for each interval (K-L2/L2-M7/M7-P). (b) Composite comparing conventional seismic with syn thetic seismic and deterministic inversion (well impedance in center and seis mic impedance in background) and geostatistical inversion where the resolution increment can be appreciated. Porosity and resistivity well logs are at right.

Figure 9. (a) Crossplot of P-impedance versus S-impedance with color code = Vshale. Warm colors indicate reservoirs (low shale content), and cold colors indicate the seal. (b) Crossplot of P-impedance versus S-impedance with color code = effective porosity. The best reservoirs are associated with medium values of impedance (warm colors).

Figure 12. (a) Line with deterministic inversion across one input data well and six blind wells. It is possible to appreciate the good correlation between the wells and the section. (b) The deterministic inversion is still of low resolution, but good matching can be seen among all realizations.

Figure 10. (a) VP /VS crossplot versus P-impedances with a cutoff. When the rock has < 30% of shale, it is considered sandstone. (b) When the sands are highlighted in crossplot (a), they are represented in brown in part (b). This is referred to as Marker K.



improved. Figure 12 shows an inverted test line where, with the input of only one well, the rest are blind tests. Correlation is fairly good, encouraging continuation of the process.

It includes generation of an unconstrained-to-wells mod-el, using the seismic in various realizations, to how and where it converges. The parameters calculated are the most probable lithotype volume and sand-probabilities volume. In the next step, the same realizations were made for constrained vol-umes to wells. The results are excellent, taking into account the small size of the reservoirs and the quality of the seismic.

Different useful plots are generated during the process to add consistency to the interpretation, for example, the most probable lithotype (12 realizations), with the effective-poros-ity volume and the sand-probability volume. The combina-tion of displays marks the big improvement in the resolution of the method, the deterministic inversion being the one of-ten used because it produced a better image than the seismic (Figures 13 and 14).

One of the tests was conducted in a blind well where there was one sand, and in the next, there were no such reservoir. Af-ter checking in the logs, the result was as predicted (Figure 15a). Examples such as this one are common because we add this procedure to the interpretation workflow. The representation of probabilities for one reservoir and the combination in other plots combined with P-impedance were helpful (Figure 15b).

To characterize one layer (Figure 16), the specific strati-graphic-slice technique can be used, showing different param-eters with P-impedance, VP/VS, and probability slices. This final comparison (Figure 17a) between simultaneous and geo-statistical inversion points out different accuracies and varia-tions in favor of geostatistical inversion. The conjunction of those three, in this case, allows testing not only their presence but also their possibilities as reservoir.

Figure 13. (a) Most probable lithotype volumes, based on 12 realizations. (b) Most probable lithotype based in 12 realizations, effective-porosity volume, and sand-probability volume.

Figure 14. (a) Deterministic inversion, (b) probability cube, (c) effective-porosity cube.

Figure 15. (a) Blind-test wells. It is possible to see the good results obtained in geostatistical inversion. (b) Example over only one sand.



Another representation, in three dimensions in this case, put all the sand (time) thickness for one interval in one plot, summarizing the best areas in a quick view to make well pro-posals (Figure 17b).

ConclusionsAfter careful consideration, a methodology which in-

cludes geostatistical inversion at the end of normal processes has been developed to attend inherent problems of vertical seismic resolution, reservoir existence, and placement. The results are valuable because they can be used not only to drill in development areas but also to add new parameters in zones for step-out wells.

This procedure combines geophysical and geologic tech-niques more closely, also taking into account the importance of petrophysics involved in the geologic column.

The ability to go from one place to any other in an in-verted cube of seismic without losing accuracy, even though in a geostatistical way, is considered one of the most positive answers to the problem of thin layers.

ReferencesCastagna, J. P., M. L. Batzle, and R. L. Eastwood, 1985, Relation-

ships between compressional-wave and shear-wave velocities in clastic silicate rocks: Geophysics, 50, no. 4, 571–581, http://dx.doi.org/10.1190/1.1441933.

Chopra, S., S. Singleton, C. Hall, R. Nickerson, and D. Carlson, 2004, Integrated reservoir characterization — A successful interdisciplin-ary working model: CSEG National Convention, Abstracts, 1–6.

Close, D. I., M. Pérez, B. Goodway, F. Caycedo, and D. Monk, 2011, Workflows for integrated interpretation of rock properties and geomechanical data: Part 2: Application and interpretation: Re-covery, CSPG CSEG CWLS Convention, 1–3.

Hunt, L., S. Reynolds, S. Hadley, J. Downton, and S. Chopra, 2011, Causal fracture prediction: Curvature, stress, and geomechan-ics: The Leading Edge, 30, no. 11, 1274–1286, http://dx.doi.org/ 10.1190/1.3663400.

Latimer, R. B., R. Davison, and P. van Riel, 2000, An interpreter’s guide to understanding and working with seismic-derived acoustic impedance data: The Leading Edge, 19, no. 3, 242–256, http://dx.doi.org/10.1190/1.1438580.

Shanor, G., M. Rawanchaikul, M. Sams, R. Muggli, G. Tiley, and J. Ghulam, 2001, A geostatistical inversion to flow simulation work-flow example — Makarem field, Oman: 63rd Conference and Ex-hibition, EAGE, Extended Abstracts, 1–4.

Slatt, R. M., and Y. Abousleiman, 2011, Merging sequence stratigra-phy and geomechanics for unconventional gas shales: The Leading Edge, 30, no. 3, 274–282, http://dx.doi.org/10.1190/1.3567258.

Torres-Verdín, C., M. Victoria, G. Merletti, and J. Pendrel, 1999, Trace-based and geostatistical inversion of 3-D seismic data for thin-sand delineation: An application in San Jorge Basin, Argentina: The Lead-ing Edge, 18, no. 9, 1070–1077, http://dx.doi.org/10.1190/1.1438434.

Acknowledgments: We would like to thank Pan American Energy LLC and CGG GeoSoftware for permission to show the examples in this article.

Corresponding author: [email protected]

Figure 17. (a) Stratigraphic slice across five wells in the oil field where sand lateral variation and geometry are shown. (b) Stratigraphic slice with 3D structure visualization.

Figure 16. (a) Stratigraphic slice from the interest zone, extracted from the VP/VS cube of the deterministic inversion (simultaneous). (b) The same stratigraphic slice extracted from the sand-probability cube of geostatistical inversion. Inside the pointed ellipse, it is possible to appreciate the differ-ent behavior of each inversion, which is less homogeneous in part (b).

improvement of the reservoir characterization of fluvial sandstones … · 2015-08-07 ·...

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