scholarly journals Rock property modelling and sensitivity analysis for hydrocarbon exploration in OSSY field, Niger Delta Basin

2021 ◽  
Vol 11 (4) ◽  
pp. 1809-1822
Author(s):  
Alexander Ogbamikhumi ◽  
Osakpolor Marvellous Omorogieva
Keyword(s):  
Wave Velocity ◽  
Structural Complexity ◽  
Rock Physics ◽  
Hydrocarbon Exploration ◽  
P Wave ◽  
Rock Properties ◽  
Fluid Substitution ◽  
Quantitative Interpretation ◽  
S Wave ◽  
Reflection Pattern

AbstractThe application of quantitative interpretation techniques for hydrocarbon prospect evaluation from seismic has become so vital. The effective employment of these techniques is dependent on several factors: the quality of the seismic and well data, sparseness of data, the physics of rock, lithological and structural complexity of the field. This study adopts reflection pattern, amplitude versus offset (AVO), Biot–Gassmann fluid substitution and cross-plot models to understand the physics of the reservoir rocks in the field by examining the sensitivity of the basic rock properties; P-wave velocity, S-wave velocity and density, to variation in lithology and fluid types in the pore spaces of reservoirs. This is to ascertain the applicability of quantitative seismic interpretation techniques to explore hydrocarbon prospect in the studied field. The results of reflection pattern and AVO models revealed that the depth of interest is dominated by Class IV AVO sands with a high negative zero offset reflectivity that reduces with offset. The AVO intercept versus gradient plot indicated that both brine and hydrocarbon bearing sands can be discriminated on seismic. Fluid substitution modelling results revealed that the rock properties will favourably respond to variation in oil saturation, but as little as 5% gas presence will result in huge change in the rock properties, which will remain constant upon further increments of gas saturation, thereby making it difficult to differentiate between economical and sub-economical saturations of gas on seismic data. Rock physics cross-plot models revealed separate cluster points typical of shale presence, brine sands and hydrocarbon bearing sands. Thus, the response of the rock properties to the modelling processes adopted favours the application of quantitative interpretation techniques to evaluate hydrocarbon in the field.

Elastic reflectivity vectors and the geometry of intercept-gradient crossplots

2019 ◽  
Vol 38 (10) ◽  
pp. 762-769
Author(s):  
Patrick Connolly
Keyword(s):  
Elastic Property ◽  
Elastic Properties ◽  
Wave Velocity ◽  
Seismic Data ◽  
Measurement Errors ◽  
Rock Physics ◽  
P Wave ◽  
S Wave ◽  
P Wave Velocity ◽  
Well Log Analysis

Reflectivities of elastic properties can be expressed as a sum of the reflectivities of P-wave velocity, S-wave velocity, and density, as can the amplitude-variation-with-offset (AVO) parameters, intercept, gradient, and curvature. This common format allows elastic property reflectivities to be expressed as a sum of AVO parameters. Most AVO studies are conducted using a two-term approximation, so it is helpful to reduce the three-term expressions for elastic reflectivities to two by assuming a relationship between P-wave velocity and density. Reduced to two AVO components, elastic property reflectivities can be represented as vectors on intercept-gradient crossplots. Normalizing the lengths of the vectors allows them to serve as basis vectors such that the position of any point in intercept-gradient space can be inferred directly from changes in elastic properties. This provides a direct link between properties commonly used in rock physics and attributes that can be measured from seismic data. The theory is best exploited by constructing new seismic data sets from combinations of intercept and gradient data at various projection angles. Elastic property reflectivity theory can be transferred to the impedance domain to aid in the analysis of well data to help inform the choice of projection angles. Because of the effects of gradient measurement errors, seismic projection angles are unlikely to be the same as theoretical angles or angles derived from well-log analysis, so seismic data will need to be scanned through a range of angles to find the optimum.

Useful approximations for converted‐wave AVO

Geophysics ◽  
2001 ◽  
Vol 66 (6) ◽  
pp. 1721-1734 ◽  
Author(s):  
Antonio C. B. Ramos ◽  
John P. Castagna
Keyword(s):  
Wave Velocity ◽  
Random Noise ◽  
P Wave ◽  
Rock Properties ◽  
Negative Slope ◽  
High Contrast ◽  
Fractional Change ◽  
S Wave ◽  
Converted Wave ◽  
Low Contrast

Converted‐wave amplitude versus offset (AVO) behavior may be fit with a cubic relationship between reflection coefficient and ray parameter. Attributes extracted using this form can be directly related to elastic parameters with low‐contrast or high‐contrast approximations to the Zoeppritz equations. The high‐contrast approximation has the advantage of greater accuracy; the low‐contrast approximation is analytically simpler. The two coefficients of the low‐contrast approximation are a function of the average ratio of compressional‐to‐shear‐wave velocity (α/β) and the fractional changes in S‐wave velocity and density (Δβ/β and Δρ/ρ). Because of its simplicity, the low‐contrast approximation is subject to errors, particularly for large positive contrasts in P‐wave velocity associated with negative contrasts in S‐wave velocity. However, for incidence angles up to 40° and models confined to |Δβ/β| < 0.25, the errors in both coefficients are relatively small. Converted‐wave AVO crossplotting of the coefficients of the low‐contrast approximation is a useful interpretation technique. The background trend in this case has a negative slope and an intercept proportional to the α/β ratio and the fractional change in S‐wave velocity. For constant α/β ratio, an attribute trace formed by the weighted sum of the coefficients of the low‐contrast approximation provides useful estimates of the fractional change in S‐wave velocity and density. Using synthetic examples, we investigate the sensitivity of these parameters to random noise. Integrated P‐wave and converted‐wave analysis may improve estimation of rock properties by combining extracted attributes to yield fractional contrasts in P‐wave and S‐wave velocities and density. Together, these parameters may provide improved direct hydrocarbon indication and can potentially be used to identify anomalies caused by low gas saturations.

Converted-wave model building and imaging based on common-focus-point methodology

Geophysics ◽  
2020 ◽  
Vol 85 (6) ◽  
pp. U139-U149
Author(s):  
Hongwei Liu ◽  
Mustafa Naser Al-Ali ◽  
Yi Luo
Keyword(s):  
Elastic Wave ◽  
Wave Velocity ◽  
Seismic Data ◽  
Model Building ◽  
P Wave ◽  
Rock Properties ◽  
S Wave ◽  
Converted Wave ◽  
Seismic Images ◽  
Focus Point

Seismic images can be viewed as photographs for underground rocks. These images can be generated from different reflections of elastic waves with different rock properties. Although the dominant seismic data processing is still based on the acoustic wave assumption, elastic wave processing and imaging have become increasingly popular in recent years. A major challenge in elastic wave processing is shear-wave (S-wave) velocity model building. For this reason, we have developed a sequence of procedures for estimating seismic S-wave velocities and the subsequent generation of seismic images using converted waves. We have two main essential new supporting techniques. The first technique is the decoupling of the S-wave information by generating common-focus-point gathers via application of the compressional-wave (P-wave) velocity on the converted seismic data. The second technique is to assume one common VP/ VS ratio to approximate two types of ratios, namely, the ratio of the average earth layer velocity and the ratio of the stacking velocity. The benefit is that we reduce two unknown ratios into one, so it can be easily scanned and picked in practice. The PS-wave images produced by this technology could be aligned with the PP-wave images such that both can be produced in the same coordinate system. The registration between the PP and PS images provides cross-validation of the migrated structures and a better estimation of underground rock and fluid properties. The S-wave velocity, computed from the picked optimal ratio, can be used not only for generating the PS-wave images, but also to ensure well registration between the converted-wave and P-wave images.

Petroacoustics and composition of presalt rocks from Santos Basin

2019 ◽  
Vol 38 (5) ◽  
pp. 342-348 ◽  
Author(s):  
Guilherme Fernandes Vasquez ◽  
Marcio José Morschbacher ◽  
Camila Wense Dias dos Anjos ◽  
Yaro Moisés Parisek Silva ◽  
Vanessa Madrucci ◽  
...  
Keyword(s):  
Clay Minerals ◽  
Chemical Elements ◽  
Pore Space ◽  
Rock Physics ◽  
P Wave ◽  
Rock Properties ◽  
Tectonic Activity ◽  
S Wave ◽  
Wave Velocities ◽  
Lacustrine Carbonate

The deposition of the presalt section from Santos Basin began when Gondwana started to break up and South America and Africa were separating. Initial synrift carbonate deposits affected by relatively severe tectonic activity evolved to a lacustrine carbonate environment during the later stages of basin formation. Although the reservoirs are composed of carbonate rocks, the occurrence of faults and the intense colocation of igneous rocks served as a source of chemical elements uncommon in typical carbonate environments. Consequently, beyond the presence of different facies with complex textures and pore geometries, the presalt reservoir rocks present marked compositional and microstructural variability. Therefore, rock-physics modeling is used to understand and interpret the extensive laboratory measurements of P-wave velocities, S-wave velocities, and density that we have undertaken on the presalt carbonate cores from Santos Basin. We show that quartz and exotic clay minerals (such as stevensite and other magnesium-rich clay minerals), which have different values of elastic moduli and Poisson's ratio as compared to calcite and dolomite, may introduce noticeable “Poisson's reflectivity anomalies” on prestack seismic data. Moreover, although the authors concentrate their attention on composition, it will become clear that pore-space geometry also may influence seismic rock properties of presalt carbonate reservoirs.

Acoustic signature of fluid substitution in reservoir rocks

2020 ◽  
Author(s):  
Christian David ◽  
Joël Sarout ◽  
Christophe Barnes ◽  
Jérémie Dautriat ◽  
Lucas Pimienta
Keyword(s):  
Wave Velocity ◽  
Water Injection ◽  
P Wave ◽  
Rock Properties ◽  
Fluid Distribution ◽  
Fluid Substitution ◽  
Ultrasonic Monitoring ◽  
Reservoir Rocks ◽  
Water Imbibition ◽  
P Wave Velocity

&lt;p&gt;During the production of hydrocarbon reservoirs, EOR operations, storage of CO2 underground or geothermal fluid exchanges at depth, fluid substitution processes can lead to significant changes in rock properties which can be captured from the variations in seismic waves attributes. In the laboratory, fluid substitution processes can be investigated using ultrasonic monitoring.&amp;#160;&lt;/p&gt;&lt;p&gt;The motivation of our study was to identify the seismic attributes of fluid substitution in reservoir rocks through a direct comparison between the variation in amplitude, velocity, spectral content, energy, and the actual fluid distribution in the rocks. Different arrays of ultrasonic P-wave sensors were used to record at constant time steps the waveforms during fluid substitution experiments. Two different kinds of experiments are presented: (i) water injection experiments in oil-saturated samples under stress in a triaxial setup mimicking EOR operations, (ii) spontaneous water imbibition experiments at room conditions.&lt;/p&gt;&lt;p&gt;In the water injection tests on a poorly consolidated sandstone saturated with oil and loaded at high deviatoric stresses, water weakening triggers mechanical instabilities leading to the rock failure. The onset of such instabilities can be followed with ultrasonic monitoring either in the passive mode (acoustic emissions recording) or in the active mode (P wave velocity survey).&lt;/p&gt;&lt;p&gt;In the water imbibition experiments, a methodology based on the analytical signal and instantaneous phase was designed to decompose each waveform into discrete wavelets associated with direct or reflected waves. The energy carried by the wavelets is very sensitive to the fluid substitution process: the coda wavelets are impacted as soon as imbibition starts and can be used as a precursor for remote fluid substitution. It is also shown that the amplitude of the first P-wave arrival is impacted by the upward moving fluid front before the P-wave velocity is. Several scenarios are discussed to explain the decoupling between P wave amplitude and velocity variations during fluid substitution processes.&lt;/p&gt;

Sediments with gas hydrates: Internal structure from seismic AVO

Geophysics ◽  
1998 ◽  
Vol 63 (5) ◽  
pp. 1659-1669 ◽  
Author(s):  
Christine Ecker ◽  
Jack Dvorkin ◽  
Amos Nur
Keyword(s):  
Internal Structure ◽  
Wave Velocity ◽  
Seismic Data ◽  
Pore Space ◽  
Rock Physics ◽  
P Wave ◽  
S Wave ◽  
High Concentration ◽  
Amplitude Variation With Offset ◽  
Bottom Simulating Reflector

We interpret amplitude variation with offset (AVO) data from a bottom simulating reflector (BSR) offshore Florida by using rock‐physics‐based synthetic seismic models. A previously conducted velocity and AVO analysis of the in‐situ seismic data showed that the BSR separates hydrate‐bearing sediments from sediments containing free methane. The amplitude at the BSR are increasingly negative with increasing offset. This behavior was explained by P-wave velocity above the BSR being larger than that below the BSR, and S-wave velocity above the BSR being smaller than that below the BSR. We use these AVO and velocity results to infer the internal structure of the hydrated sediment. To do so, we examine two micromechanical models that correspond to the two extreme cases of hydrate deposition in the pore space: (1) the hydrate cements grain contacts and strongly reinforces the sediment, and (2) the hydrate is located away from grain contacts and does not affect the stiffness of the sediment frame. Only the second model can qualitatively reproduce the observed AVO response. Thus inferred internal structure of the hydrate‐bearing sediment means that (1) the sediment above the BSR is uncemented and, thereby, mechanically weak, and (2) its permeability is very low because the hydrate clogs large pore‐space conduits. The latter explains why free gas is trapped underneath the BSR. The seismic data also indicate the absence of strong reflections at the top of the hydrate layer. This fact suggests that the high concentration of hydrates in the sediment just above the BSR gradually decreases with decreasing depth. This effect is consistent with the fact that the low‐permeability hydrated sediments above the BSR prevent free methane from migrating upwards.

Quantitative prediction of total organic carbon content in shale-gas reservoirs using seismic data: A case study from the Lower Silurian Longmaxi Formation in the Chang Ning gas field of the Sichuan Basin, China

2018 ◽  
Vol 6 (4) ◽  
pp. SN153-SN168 ◽  
Author(s):  
Sheng Chen ◽  
Wenzhi Zhao ◽  
Qingcai Zeng ◽  
Qing Yang ◽  
Pei He ◽  
...  
Keyword(s):  
Wave Velocity ◽  
Shale Gas ◽  
Rock Physics ◽  
Elastic Parameter ◽  
P Wave ◽  
Quantitative Prediction ◽  
S Wave ◽  
Gas Field ◽  
Lower Silurian ◽  
Data Volume

We present a quantitative prediction of total organic carbon (TOC) content for shale-gas development in the Chang Ning gas field of the Sichuan Basin (China). We have used the rock-physics analysis method to define the geophysical characteristics of the reservoir and the most sensitive elastic parameter to TOC content. We established a quantitative prediction template of the TOC content by rock-physics modeling. Well data and 3D seismic data were combined for prestack simultaneous inversion to obtain the most sensitive elastic parameter data volume. According to the prediction template, we transformed the sensitive elastic parameter data volume to the TOC content volume. The rock-physics analysis indicates that the reservoir with a high TOC content in the Lower Silurian Longmaxi Formation (Fm) of the Chang Ning (CN) gas field is characterized by low density, low P-wave velocity ([Formula: see text]), low S-wave velocity ([Formula: see text]), low Poisson’s ratio (PR), and low ratio of P-wave velocity to S-wave velocity ([Formula: see text]). Density is the most sensitive elastic parameter to TOC content. The rock-physics model suggests that density is negatively correlated with TOC content, and the relationship between them changes under different porosities. The reservoir with high TOC content is mainly distributed at the bottom of the Longmaxi Fm and in the central and east central area of the study field. The quantitative prediction results are in good agreement with the log interpretation and production test. Therefore, it has important implications for the efficient development of the shale-gas reservoir in the basin.

scholarly journals Identification of hydrocarbons in chalk reservoirs from surface seismic data: South Arne field, North Sea

2005 ◽  
Vol 7 ◽  
pp. 13-16
Author(s):  
Peter Japsen ◽  
Anders Bruun ◽  
Ida L. Fabricius ◽  
Gary Mavko
Keyword(s):  
Wave Velocity ◽  
North Sea ◽  
Seismic Data ◽  
Pore Space ◽  
Sedimentary Rocks ◽  
Rock Physics ◽  
Acoustic Properties ◽  
P Wave ◽  
S Wave ◽  
Reservoir Rocks

Seismic data are mainly used to map out structures in the subsurface, but are also increasingly used to detect differences in porosity and in the fluids that occupy the pore space in sedimentary rocks. Hydrocarbons are generally lighter than brine, and the bulk density and sonic velocity (speed of pressure waves or P-wave velocity) of hydrocarbon-bearing sedimentary rocks are therefore reduced compared to non-reservoir rocks. However, sound is transmitted in different wave forms through the rock, and the shear velocity (speed of shear waves or S-wave velocity) is hardly affected by the density of the pore fluid. In order to detect the presence of hydrocarbons from seismic data, it is thus necessary to investigate how porosity and pore fluids affect the acoustic properties of a sedimentary rock. Much previous research has focused on describing such effects in sandstone (see Mavko et al. 1998), and only in recent years have corresponding studies on the rock physics of chalk appeared (e.g. Walls et al. 1998; Røgen 2002; Fabricius 2003; Gommesen 2003; Japsen et al. 2004). In the North Sea, chalk of the Danian Ekofisk Formation and the Maastrichtian Tor Formation are important reservoir rocks. More information could no doubt be extracted from seismic data if the fundamental physical properties of chalk were better understood. The presence of gas in chalk is known to cause a phase reversal in the seismic signal (Megson 1992), but the presence of oil in chalk has only recently been demonstrated to have an effect on surface seismic data (Japsen et al. 2004). The need for a better link between chalk reservoir parameters and geophysical observations has, however, strongly increased since the discovery of the Halfdan field proved major reserves outside four-way dip closures (Jacobsen et al. 1999; Vejbæk & Kristensen 2000).

The Petrophysical Study for Shale Sample Based on Autoscan-II Platform

2013 ◽  
Vol 788 ◽  
pp. 701-704
Author(s):  
Xin Gong Tang ◽  
Jian Bin Ma ◽  
Kui Xiang ◽  
Liang Jun Yan ◽  
Wen Bao Hu
Keyword(s):  
Wave Velocity ◽  
Shale Gas ◽  
Rock Physics ◽  
Core Sample ◽  
P Wave ◽  
S Wave ◽  
Low Velocity ◽  
Good Correspondence ◽  
Complex Resistivity ◽  
Shale Sample

Petrophysical study is playing an important role in oil and gas exploration. Shale gas and shale oil is blooming in recent years in many countries. Less rock physics knowledge is known about shale relatively to other rock type such as sandstone and limestone. In this paper, we carried out a rock physical study of shale core sample which is drilled from north China. The plan distribution of permeability, P wave velocity, S wave velocity and complex resistivity were acquired based on AutoScan-IIplatform. The results show that the permeability of the shale sample is basically low with values of 0.1 to several micro Darcy (mD) except some fracture areas in the surface, which has values of about several tens mD. The permeability can basically describe the distribution of the fracture. The complex resistivity has the similar characteristics with permeability, which is also roughly corresponding to the position of the facture. As for the Vp and Vs, although not very good correspondence with the surface, they are still approximately present the high and low velocity feature of the core sample as well. This result is significantly helpful for shale gas exploration and production.

Fluid substitution: Estimating changes in VP without knowing VS

Geophysics ◽  
1995 ◽  
Vol 60 (6) ◽  
pp. 1750-1755 ◽  
Author(s):  
Gary Mavko ◽  
Christina Chan ◽  
Tapan Mukerji
Keyword(s):  
Wave Velocity ◽  
P Wave ◽  
Large Set ◽  
Fluid Substitution ◽  
S Wave ◽  
Shear Moduli ◽  
P Wave Velocity ◽  
The Common ◽  
Common Situation ◽  
Rock Bulk

Two methods are presented for estimating the change of seismic P‐wave velocity that accompanies pore fluid changes in a rock in the common situation when S‐wave velocity is unknown. In contrast, Gassmann’s relation operates on the rock bulk modulus, which can only be calculated when both [Formula: see text] and [Formula: see text] are measured. The first method operates directly on the P‐wave modulus and is equivalent to replacing the bulk moduli of the rock and mineral in Gassmann’s relation with the corresponding P‐wave moduli. The second method uses a graphical construction to estimate the decomposition of the measured P‐wave modulus into bulk and shear moduli, which then allows the conventional Gassmann’s formula to be used. When applied to a large set of sandstone data, the predictions of both methods, computed with [Formula: see text] only, are within a few percent of the Gassmann’s relation, using both [Formula: see text] and [Formula: see text].

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