scholarly journals Anisotropy extraction using a deviated well in the Mahanadi Basin, East Coast of India

2019 ◽  
Vol 10 (3) ◽  
pp. 911-918
Author(s):  
Biplab Kumar Mukherjee ◽  
G. Karthikeyan ◽  
Karanpal Rawat ◽  
Hari Srivastava
Keyword(s):  
Seismic Data ◽  
East Coast ◽  
Velocity Model ◽  
P Wave ◽  
S Wave ◽  
Gardner Equation ◽  
East Coast Of India ◽  
Nonlinear Fitting ◽  
Sonic Log ◽  
Mahanadi Basin

Abstract Shale is the primary rock type in the shallow marine section of the Mahanadi Basin, East Coast of India. Shale, being intrinsically anisotropic, always affects the seismic data. Anisotropy derived from seismic and VSP has lower resolution and mostly based on P wave. The workflow discussed here uses Gardner equation to derive vertical velocity and uses a nonlinear fitting to extract the Thomsen’s parameters using both the P wave and S wave data. These parameters are used to correct the sonic log of a deviated well as well as anisotropic AVO response of the reservoir. The presence of negative delta was observed, which is believed to be affected by the presence of chloride and illite in the rock matrix. This correction can be used to update the velocity model for time–depth conversion and pore pressure modelling.

Prestack 2D parsimonious Kirchhoff depth migration of elastic seismic data

Geophysics ◽  
2011 ◽  
Vol 76 (4) ◽  
pp. S157-S164 ◽  
Author(s):  
Robert Sun ◽  
George A. McMechan
Keyword(s):  
Seismic Data ◽  
Velocity Model ◽  
P Wave ◽  
P Value ◽  
Common Source ◽  
S Wave ◽  
P Values ◽  
Depth Migration ◽  
Reflector Surface ◽  
Kirchhoff Depth Migration

We have extended prestack parsimonious Kirchhoff depth migration for 2D, two-component, reflected elastic seismic data for a P-wave source recorded at the earth’s surface. First, we separated the P-to-P reflected (PP-) waves and P-to-S converted (PS-) waves in an elastic common-source gather into P-wave and S-wave seismograms. Next, we estimated source-ray parameters (source p values) and receiver-ray parameters (receiver p values) for the peaks and troughs above a threshold amplitude in separated P- and S-wavefields. For each PP and PS reflection, we traced (1) a source ray in the P-velocity model in the direction of the emitted ray angle (determined by the source p value) and (2) a receiver ray in the P- or S-velocity model back in the direction of the emergent PP- or PS-wave ray angle (determined by the PP- or PS-wave receiver p value), respectively. The image-point position was adjusted from the intersection of the source and receiver rays to the point where the sum of the source time and receiver-ray time equaled the two-way traveltime. The orientation of the reflector surface was determined to satisfy Snell’s law at the intersection point. The amplitude of a P-wave (or an S-wave) was distributed over the first Fresnel zone along the reflector surface in the P- (or S-) image. Stacking over all P-images of the PP-wave common-source gathers gave the stacked P-image, and stacking over all S-images of the PS-wave common-source gathers gave the stacked S-image. Synthetic examples showed acceptable migration quality; however, the images were less complete than those produced by scalar reverse-time migration (RTM). The computing time for the 2D examples used was about 1/30 of that for scalar RTM of the same data.

Lithospheric architecture of the Ligurian Basin from seismic travel time tomography

2021 ◽  
Author(s):  
Anke Dannowski ◽  
Heidrun Kopp ◽  
Ingo Grevemeyer ◽  
Grazia Caielli ◽  
Roberto de Franco ◽  
...  
Keyword(s):  
Seismic Data ◽  
Velocity Model ◽  
P Wave ◽  
Central Basin ◽  
Uppermost Mantle ◽  
Mantle Boundary ◽  
Oceanic Spreading ◽  
Refraction Seismic ◽  
Back Arc ◽  
Ligurian Basin

<p>The Ligurian Basin is located north-west of Corsica at the transition from the western Alpine orogen to the Apennine system. The Back-arc basin was generated by the southeast retreat of the Apennines-Calabrian subduction zone. The opening took place from late Oligocene to Miocene. While the extension led to extreme continental thinning little is known about the style of back-arc rifting. Today, seismicity indicates the closure of this back-arc basin. In the basin, earthquake clusters occur in the lower crust and uppermost mantle and are related to re-activated, inverted, normal faults created during rifting.</p><p>To shed light on the present day crustal and lithospheric architecture of the Ligurian Basin, active seismic data have been recorded on short period ocean bottom seismometers in the framework of SPP2017 4D-MB, the German component of AlpArray. An amphibious refraction seismic profile was shot across the Ligurian Basin in an E-W direction from the Gulf of Lion to Corsica. The profile comprises 35 OBS and three land stations at Corsica to give a complete image of the continental thinning including the necking zone.</p><p>The majority of the refraction seismic data show mantle phases with offsets up to 70 km. The arrivals of seismic phases were picked and used to generate a 2-D P-wave velocity model. The results show a crust-mantle boundary in the central basin at ~12 km depth below sea surface. The P-wave velocities in the crust reach 6.6 km/s at the base. The uppermost mantle shows velocities >7.8 km/s. The crust-mantle boundary becomes shallower from ~18 km to ~12 km depth within 30 km from Corsica towards the basin centre. The velocity model does not reveal an axial valley as expected for oceanic spreading. Further, it is difficult to interpret the seismic data whether the continental lithosphere was thinned until the mantle was exposed to the seafloor. However, an extremely thinned continental crust indicates a long lasting rifting process that possibly did not initiate oceanic spreading before the opening of the Ligurian Basin stopped. The distribution of earthquakes and their fault plane solutions, projected along our seismic velocity model, is in-line with the counter-clockwise opening of the Ligurian Basin.</p>

Using Seismic Data to Predict Shale Pore Pressure and Overpressure Sweet Spots: A Case Study from the Lower Silurian Longmaxi Formation in Sichuan Basin, China

2021 ◽  
Author(s):  
Sheng Chen ◽  
Qingcai Zeng ◽  
Xiujiao Wang ◽  
Qing Yang ◽  
Chunmeng Dai ◽  
...  
Keyword(s):  
Prediction Model ◽  
Wave Velocity ◽  
Shale Gas ◽  
Seismic Data ◽  
P Wave ◽  
Formation Pressure ◽  
S Wave ◽  
P Wave Velocity ◽  
New Formation ◽  
Sweet Spots

Abstract Practices of marine shale gas exploration and development in south China have proved that formation overpressure is the main controlling factor of shale gas enrichment and an indicator of good preservation condition. Accurate prediction of formation pressure before drilling is necessary for drilling safety and important for sweet spots predicting and horizontal wells deploying. However, the existing prediction methods of formation pore pressures all have defects, the prediction accuracy unsatisfactory for shale gas development. By means of rock mechanics analysis and related formulas, we derived a formula for calculating formation pore pressures. Through regional rock physical analysis, we determined and optimized the relevant parameters in the formula, and established a new formation pressure prediction model considering P-wave velocity, S-wave velocity and density. Based on regional exploration wells and 3D seismic data, we carried out pre-stack seismic inversion to obtain high-precision P-wave velocity, S-wave velocity and density data volumes. We utilized the new formation pressure prediction model to predict the pressure and the spatial distribution of overpressure sweet spots. Then, we applied the measured pressure data of three new wells to verify the predicted formation pressure by seismic data. The result shows that the new method has a higher accuracy. This method is qualified for safe drilling and prediction of overpressure sweet spots for shale gas development, so it is worthy of promotion.

Scalar reverse‐time depth migration of prestack elastic seismic data

Geophysics ◽  
2001 ◽  
Vol 66 (5) ◽  
pp. 1519-1527 ◽  
Author(s):  
Robert Sun ◽  
George A. McMechan
Keyword(s):  
Seismic Waves ◽  
Velocity Model ◽  
P Wave ◽  
Common Source ◽  
Reverse Time ◽  
S Wave ◽  
Wave Source ◽  
Reflected Waves ◽  
S Waves ◽  
Elastic Data

Reflected P‐to‐P and P‐to‐S converted seismic waves in a two‐component elastic common‐source gather generated with a P‐wave source in a two‐dimensional model can be imaged by two independent scalar reverse‐time depth migrations. The inputs to migration are pure P‐ and S‐waves that are extracted by divergence and curl calculations during (shallow) extrapolation of the elastic data recorded at the earth’s surface. For both P‐to‐P and P‐to‐S converted reflected waves, the imaging time at each point is the P‐wave traveltime from the source to that point. The extracted P‐wave is reverse‐time extrapolated and imaged with a P‐velocity model, using a finite difference solution of the scalar wave equation. The extracted S‐wave is reverse‐time extrapolated and imaged similarly, but with an S‐velocity model. Converted S‐wave data requires a polarity correction prior to migration to ensure constructive interference between data from adjacent sources. Synthetic examples show that the algorithm gives satisfactory results for laterally inhomogeneous models.

Joint microseismic event location and anisotropic velocity inversion with the cross double-difference method using downhole microseismic data

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. KS63-KS73
Author(s):  
Yangyang Ma ◽  
Congcong Yuan ◽  
Jie Zhang
Keyword(s):  
Arrival Time ◽  
Velocity Model ◽  
P Wave ◽  
Double Difference ◽  
S Wave ◽  
Arrival Times ◽  
Monitoring Well ◽  
The Cross ◽  
Microseismic Event ◽  
Wave Arrival

We have applied the cross double-difference (CDD) method to simultaneously determine the microseismic event locations and five Thomsen parameters in vertically layered transversely isotropic media using data from a single vertical monitoring well. Different from the double-difference (DD) method, the CDD method uses the cross-traveltime difference between the S-wave arrival time of one event and the P-wave arrival time of another event. The CDD method can improve the accuracy of the absolute locations and maintain the accuracy of the relative locations because it contains more absolute information than the DD method. We calculate the arrival times of the qP, qSV, and SH waves with a horizontal slowness shooting algorithm. The sensitivities of the arrival times with respect to the five Thomsen parameters are derived using the slowness components. The derivations are analytical, without any weak anisotropic approximation. The input data include the cross-differential traveltimes and absolute arrival times, providing better constraints on the anisotropic parameters and event locations. The synthetic example indicates that the method can produce better event locations and anisotropic velocity model. We apply this method to the field data set acquired from a single vertical monitoring well during a hydraulic fracturing process. We further validate the anisotropic velocity model and microseismic event locations by comparing the modeled and observed waveforms. The observed S-wave splitting also supports the inverted anisotropic results.

scholarly journals Full waveform inversion of short-offset, band-limited seismic data inthe Alboran basin (SE Iberia)

2019 ◽  
Author(s):  
Clàudia Gras ◽  
Valentí Sallarès ◽  
Daniel Dagnino ◽  
C. Estela Jiménez ◽  
Adrià Meléndez ◽  
...  
Keyword(s):  
Travel Time ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Velocity Model ◽  
P Wave ◽  
Full Waveform Inversion ◽  
Time Inversion ◽  
Full Waveform ◽  
Travel Time Tomography ◽  
Band Limited

Abstract. We present a high-resolution P-wave velocity model of the sedimentary cover and the uppermost basement until ~ 3 km depth obtained by full-waveform inversion of multichannel seismic data acquired with a 6 km-long streamer in the Alboran Sea (SE Iberia). The inherent non-linearity of the method, especially for short-offset, band-limited seismic data as this one, is circumvented by applying a data processing/modeling sequence consisting of three steps: (1) data re-datuming by back-propagation of the recorded seismograms to the seafloor; (2) joint refraction and reflection travel-time tomography combining the original and the re-datumed shot gathers; and (3) FWI of the original shot gathers using the model obtained by travel-time tomography as initial reference. The final velocity model shows a number of geological structures that cannot be identified in the travel-time tomography models or easily interpreted from seismic reflection images alone. A sharp strong velocity contrast accurately defines the geometry of the top of the basement. Several low-velocity zones that may correspond to the abrupt velocity change across steeply dipping normal faults are observed at the flanks of the basin. A 200–300 m thick, high-velocity layer embedded within lower velocity sediment may correspond to evaporites deposited during the Messinian crisis. The results confirm that the combination of data re-datuming and joint refraction and reflection travel-time inversion provides reference models that are accurate enough to apply full-waveform inversion to relatively short offset streamer data in deep water settings starting at field-data standard low frequency content of 6 Hz.

On plane‐wave decomposition: Alias removal

Geophysics ◽  
1989 ◽  
Vol 54 (10) ◽  
pp. 1339-1343 ◽  
Author(s):  
S. C. Singh ◽  
G. F. West ◽  
C. H. Chapman
Keyword(s):  
Plane Wave ◽  
Seismic Data ◽  
P Wave ◽  
S Wave ◽  
Plane Wave Decomposition ◽  
Time Migration ◽  
Time Distance ◽  
P Domain ◽  
Wave Decomposition ◽  
Rapid Modeling

The delay‐time (τ‐p) parameterization, which is also known as the plane‐wave decomposition (PWD) of seismic data, has several advantages over the more traditional time‐distance (t‐x) representation (Schultz and Claerbout, 1978). Plane‐wave seismograms in the (τ, p) domain can be used for obtaining subsurface elastic properties (P‐wave and S‐wave velocities and density as functions of depth) from inversion of the observed oblique‐incidence seismic data (e.g., Yagle and Levy, 1985; Carazzone, 1986; Carrion, 1986; Singh et al., 1989). Treitel et al. (1982) performed time migration of plane‐wave seismograms. Diebold and Stoffa (1981) used plane‐wave seismograms to derive a velocity‐depth function. Decomposing seismic data also allows more rapid modeling, since it is faster to compute synthetic seismograms in the (τ, p) than in the (t, x) domain. Unfortunately, the transformation of seismic data from the (t, x) to the (τ, p) domain may produce artifacts, such as those caused by discrete sampling, of the data in space.

scholarly journals The potential of seismic cross-hole tomography for geotechnical site investigation

2019 ◽  
Vol 92 ◽  
pp. 18006
Author(s):  
Yannick Choy Hing Ng ◽  
William Danovan ◽  
Taeseo Ku
Keyword(s):  
Wave Velocity ◽  
Site Investigation ◽  
Velocity Model ◽  
P Wave ◽  
Reconstruction Technique ◽  
Sufficient Information ◽  
Eastern Region ◽  
S Wave ◽  
Near Surface ◽  
Soil Layers

Seismic cross-hole tomography has been commonly used in oil and gas exploration and the mining industry for the detection of precious resources. For near-surface geotechnical site investigation, this geophysical method is relatively new and can be used to supplement traditional methods such as the standard penetration test, coring and sampling, thus improving the effectiveness of site characterization. This paper presents a case study which was carried out on a reclaimed land in the Eastern region of Singapore. A seismic cross-hole test was performed by generating both compressional waves and shear waves into the ground. The signals were interpreted by using first-arrival travel time wave tomography and the arrival times were subsequently inverted using Simultaneous Iterative Reconstruction Technique (SIRT). A comparison with the borehole logging data indicated that P-wave velocity model cannot provide sufficient information about the soil layers, especially when the ground water table is near the surface. The S-wave velocity model seemed to agree quite well with the variation in the SPT-N value and could identify to a certain extent the interface between the different soil layers. Finally, P-wave and S-wave velocities are used to compute the Poisson's ratio distribution which gave a good indication of the degree of saturation of the soil.

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.

Comparison of P‐ and S‐wave velocities and Q’S from VSP and sonic log data

Geophysics ◽  
1994 ◽  
Vol 59 (10) ◽  
pp. 1512-1529 ◽  
Author(s):  
Gopa S. De ◽  
Donald F. Winterstein ◽  
Mark A. Meadows
Keyword(s):  
Velocity Dispersion ◽  
P Wave ◽  
S Wave ◽  
S Waves ◽  
Wave Velocities ◽  
Sonic Log ◽  
Transit Times ◽  
Wave Slope ◽  
Northern Alberta ◽  
Q Values

We compared P‐ and S‐wave velocities and quality factors (Q’S) from vertical seismic profiling (VSP) and sonic log measurements in five wells, three from the southwest San Joaquin Basin of California, one from near Laredo, Texas, and one from northern Alberta. Our purpose was to investigate the bias between sonic log and VSP velocities and to examine to what degree this bias might be a consequence of dispersion. VSPs and sonic logs were recorded in the same well in every case. Subsurface formations were predominantly clastic. The bias found was that VSP transit times were greater than sonic log times, consistent with normal dispersion. For the San Joaquin wells, differences in S‐wave transit times averaged 1–2 percent, while differences in P‐wave transit times averaged 6–7 percent. For the Alberta well, the situation was reversed, with differences in S‐wave transit times being about 6 percent, while those for P‐waves were 2.5 percent. For the Texas well, the differences averaged about 4 percent for both P‐ and S‐waves. Drift‐curve slopes for S‐waves tended to be low where the P‐wave slopes were high and vice versa. S‐wave drift‐curve slopes in the shallow California wells were 5–10 μs/ft (16–33 μs/m) and the P‐wave slopes were 15–30 μs/ft (49–98 μs/m). The S‐wave slope in sandstones in the northern Alberta well was up to 50 μs/ft (164 μs/m), while the P‐wave slope was about 5 μs/ft (16 μs/m). In the northern Alberta well the slopes for both P‐ and S‐waves flattened in the carbonate. In the Texas well, both P‐ and S‐wave drifts were comparable. We calculated (Q’s) from a velocity dispersion formula and from spectral ratios. When the two Q’s agreed, we concluded that velocity dispersion resulted solely from absorption. These Q estimation methods were reliable only for Q values smaller than 20. We found that, even with data of generally outstanding quality, Q values determined by standard methods can have large uncertainties, and negative Q’s may be common.

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