Comparison of P- and S-Wave Seismic Data: A New Method for Detecting Gas Reservoirs

1984 ◽  
Vol 15 (3) ◽  
pp. 189-191
Author(s):  
R. A. Ensley
Keyword(s):  
Seismic Data ◽  
New Method ◽  
S Wave ◽  
Gas Reservoirs

On: “Comparison of P‐ and S‐wave seismic data: A new method for detecting gas reservoirs” by R. A. Ensley (GEOPHYSICS, 49, 1420‐1431, September, 1984).

Geophysics ◽  
1985 ◽  
Vol 50 (11) ◽  
pp. 1793-1793
Author(s):  
D. F. Winterstein
Keyword(s):  
Shear Wave ◽  
Seismic Data ◽  
New Method ◽  
Wave Group ◽  
S Wave ◽  
Gas Reservoirs ◽  
Wave Data

Ensley’s paper was based on data of the Conoco Shear Wave Group Shoot of 1977–1978; hence, many companies besides Exxon, including my own, have the Putah Sink data he showed. The stacked sections he showed look identical to the Conoco brute stacks furnished to group shoot participants. The brute stacks had no residual statics or stacking velocity corrections. Those of us who have processed S-wave data often have been pleasantly surprised at how much improvement can come from careful application of residual statics and stacking velocity corrections. Our experience shows that it is unwise to interpret stacked S-wave data that have not had benefit of such refinements.

Comparison of P‐ and S‐wave seismic data: A new method for detecting gas reservoirs

Geophysics ◽  
1984 ◽  
Vol 49 (9) ◽  
pp. 1420-1431 ◽  
Author(s):  
Ross Alan Ensley
Keyword(s):  
Shear Wave ◽  
Seismic Data ◽  
Quantitative Methods ◽  
Qualitative Evaluation ◽  
Compressional Wave ◽  
Bright Spot ◽  
S Wave ◽  
Gas Reservoirs ◽  
Compressional Waves

Compressional waves are sensitive to the type of pore fluid within rocks, but shear waves are only slightly affected by changes in fluid type. This suggests that a comparison of compressional‐ and shear‐wave seismic data recorded over a prospect may allow an interpreter to discriminate between gas‐related anomalies and those related to lithology. This case study documents that where a compressional‐wave “bright spot” or other direct hydrocarbon indicator is present, such a comparison can be used to verify the presence of gas. In practice, the technique can only be used for a qualitative evaluation. However, future improvement of shear‐wave data quality may enable the use of more quantitative methods as well.

scholarly journals Prediction of Shale Gas Reservoirs Using Fluid Mobility Attribute Driven by Post-Stack Seismic Data: A Case Study from Southern China

2020 ◽  
Vol 11 (1) ◽  
pp. 219
Author(s):  
Jing Zeng ◽  
Alexey Stovas ◽  
Handong Huang ◽  
Lixia Ren ◽  
Tianlei Tang
Keyword(s):  
Shale Gas ◽  
Seismic Data ◽  
Spectral Decomposition ◽  
Southern China ◽  
Gas Reservoirs ◽  
Seismic Attribute ◽  
Longmaxi Formation ◽  
Shale Gas Reservoirs ◽  
The Sichuan Basin ◽  
Shale Reservoirs

Paleozoic marine shale gas resources in Southern China present broad prospects for exploration and development. However, previous research has mostly focused on the shale in the Sichuan Basin. The research target of this study is expanded to the Lower Silurian Longmaxi shale outside the Sichuan Basin. A prediction scheme of shale gas reservoirs through the frequency-dependent seismic attribute technology is developed to reduce drilling risks of shale gas related to complex geological structure and low exploration level. Extracting frequency-dependent seismic attribute is inseparable from spectral decomposition technology, whereby the matching pursuit algorithm is commonly used. However, frequency interference in MP results in an erroneous time-frequency (TF) spectrum and affects the accuracy of seismic attribute. Firstly, a novel spectral decomposition technology is proposed to minimize the effect of frequency interference by integrating the MP and the ensemble empirical mode decomposition (EEMD). Synthetic and real data tests indicate that the proposed spectral decomposition technology provides a TF spectrum with higher accuracy and resolution than traditional MP. Then, a seismic fluid mobility attribute, extracted from the post-stack seismic data through the proposed spectral decomposition technology, is applied to characterize the shale reservoirs. The application result indicates that the seismic fluid mobility attribute can describe the spatial distribution of shale gas reservoirs well without well control. Based on the seismic fluid mobility attribute section, we have learned that the shale gas enrich areas are located near the bottom of the Longmaxi Formation. The inverted velocity data are also introduced to further verify the reliability of seismic fluid mobility. Finally, the thickness map of gas-bearing shale reservoirs in the Longmaxi Formation is obtained by combining the seismic fluid mobility attribute with the inverted velocity data, and two favorable exploration areas are suggested by analyzing the thickness, structure, and burial depth. The present work can not only be used to evaluate shale gas resources in the early stage of exploration, but also help to design the landing point and trajectory of directional drilling in the development stage.

Geometrical spreading in a layered transversely isotropic medium with vertical symmetry axis

Geophysics ◽  
2003 ◽  
Vol 68 (6) ◽  
pp. 2082-2091 ◽  
Author(s):  
Bjørn Ursin ◽  
Ketil Hokstad
Keyword(s):  
Ray Tracing ◽  
Seismic Data ◽  
Symmetry Axis ◽  
Transversely Isotropic ◽  
Analytical Approximation ◽  
Geometrical Spreading ◽  
S Wave ◽  
Weak Anisotropy ◽  
P Waves ◽  
Amplitude Versus

Compensation for geometrical spreading is important in prestack Kirchhoff migration and in amplitude versus offset/amplitude versus angle (AVO/AVA) analysis of seismic data. We present equations for the relative geometrical spreading of reflected and transmitted P‐ and S‐wave in horizontally layered transversely isotropic media with vertical symmetry axis (VTI). We show that relatively simple expressions are obtained when the geometrical spreading is expressed in terms of group velocities. In weakly anisotropic media, we obtain simple expressions also in terms of phase velocities. Also, we derive analytical equations for geometrical spreading based on the nonhyperbolic traveltime formula of Tsvankin and Thomsen, such that the geometrical spreading can be expressed in terms of the parameters used in time processing of seismic data. Comparison with numerical ray tracing demonstrates that the weak anisotropy approximation to geometrical spreading is accurate for P‐waves. It is less accurate for SV‐waves, but has qualitatively the correct form. For P waves, the nonhyperbolic equation for geometrical spreading compares favorably with ray‐tracing results for offset‐depth ratios less than five. For SV‐waves, the analytical approximation is accurate only at small offsets, and breaks down at offset‐depth ratios less than unity. The numerical results are in agreement with the range of validity for the nonhyperbolic traveltime equations.

Automatic phase identification of earthquake based on the UBDN deep network

2021 ◽  
pp. 1-10
Author(s):  
Jianxian Cai ◽  
Xun Dai ◽  
Zhitao Gao ◽  
Yan Shi
Keyword(s):  
Seismic Data ◽  
Short Term Memory ◽  
Long Term Memory ◽  
Phase Identification ◽  
S Wave ◽  
Traditional Methods ◽  
Term Memory ◽  
Seismic Stations ◽  
2008 Wenchuan Earthquake ◽  
Deep Network

Seismic data obtained from seismic stations are the major source of the information used to forecast earthquakes. With the growth in the number of seismic stations, the size of the dataset has also increased. Traditionally, STA/LTA and AIC method have been applied to process seismic data. However, the enormous size of the dataset reduces accuracy and increases the rate of missed detection of the P and S wave phase when using these traditional methods. To tackle these issues, we introduce the novel U-net-Bidirectional Long-Term Memory Deep Network (UBDN) which can automatically and accurately identify the P and S wave phases from seismic data. The U-net based UBDN strongly maintains the U-net’s high accuracy in edge detection for extracting seismic phase features. Meanwhile, it also reduces the missed detection rate by applying the Bidirectional Long Short-Term Memory (Bi-LSTM) mode that processes timing signals to establish the relationship between seismic phase features. Experimental results using the Stanford University seismic dataset and data from the 2008 Wenchuan earthquake aftershock confirm that the proposed UBDN method is very accurate and has a lower rate of missed phase detection, outperforming solutions that adapt traditional methods by an order of magnitude in terms of error percentage.

Preliminary determination of crustal structure in the Katmai National Monument, Alaska

1967 ◽  
Vol 57 (6) ◽  
pp. 1367-1392
Author(s):  
Eduard Berg ◽  
Susumu Kubota ◽  
Jurgen Kienle
Keyword(s):  
Crustal Structure ◽  
Seismic Data ◽  
Bouguer Anomaly ◽  
Volcanic Area ◽  
Average Depth ◽  
S Wave ◽  
Contour Map ◽  
Alaska Peninsula ◽  
National Monument

Abstract Seismic and gravity observations were carried out in the active volcanic area of Katmai in the summer of 1965. A determination of hypocenters has been aftempted using S and P arrivals at a station located at Kodiak and two stations located in the Monument. However, in most cases, deviations of travel times from the Jeffreys-Bullen tables were rather large. Therefore hypocenters are not well located. A method based on P- and S-wave arrivals yields a Poisson's ratio of 0.3 for the upper part of the mantle under Katmai. This higher value is probably due to the magma formation. The average depth to the Moho from seismic data in the same area is 38 km and 32 km under Kodiak. Using Woollard's relation between Bouguer anomaly and depth to the Moho, a small mountain root under the volcanoes with a depth of 34 km was found dipping gently up to 31 km on the NW side. The active volcanic cones are located along an uplift block. This block is associated with a 35 mgal Bouguer anomaly. The Bouguer anomaly contour map for the Alaska Peninsula is given and an interpretation attempted.

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.

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