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.

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.

Estimating shear‐wave velocities from P-wave and converted‐wave data

Geophysics ◽  
1999 ◽  
Vol 64 (2) ◽  
pp. 504-507 ◽  
Author(s):  
Franklyn K. Levin
Keyword(s):  
Wave Velocity ◽  
Seismic Data ◽  
P Wave ◽  
S Wave ◽  
Converted Wave ◽  
Wave Data ◽  
Wave Velocities ◽  
Shear Wave Velocities ◽  
P Wave Velocity ◽  
Growing Body

Tessmer and Behle (1988) show that S-wave velocity can be estimated from surface seismic data if both normal P-wave data and converted‐wave data (P-SV) are available. The relation of Tessmer and Behle is [Formula: see text] (1) where [Formula: see text] is the S-wave velocity, [Formula: see text] is the P-wave velocity, and [Formula: see text] is the converted‐wave velocity. The growing body of converted‐wave data suggest a brief examination of the validity of equation (1) for velocities that vary with depth.

Poststack, prestack, and joint inversion of P- and S-wave data for Morrow A sandstone characterization

2015 ◽  
Vol 3 (3) ◽  
pp. SZ59-SZ92 ◽  
Author(s):  
Paritosh Singh ◽  
Thomas L. Davis ◽  
Bryan DeVault
Keyword(s):  
Wave Velocity ◽  
Seismic Data ◽  
Joint Inversion ◽  
Wave Impedance ◽  
P Wave ◽  
S Wave ◽  
Wave Data ◽  
Wave Inversion ◽  
Density Maps ◽  
P Wave Velocity

Exploration for oil-bearing Morrow sandstones using conventional seismic data/methods has a startlingly low success rate of only 3%. The S-wave velocity contrast between the Morrow shale and A sandstone is strong compared with the P-wave velocity contrast, and, therefore, multicomponent seismic data could help to characterize these reservoirs. The SV and SH data used in this study are generated using S-wave data from horizontal source and horizontal receiver recording. Prestack P- and S-wave inversions, and joint P- and S-wave inversions, provide estimates of P- and S-wave impedances, and density for characterization of the Morrow A sandstone. Due to the weak P-wave amplitude-versus-angle response at the Morrow A sandstone top, the density and S-wave impedance estimated from joint P- and S-wave inversions were inferior to the prestack S-wave inversion. The inversion results were compared with the Morrow A sandstone thickness and density maps obtained from well logs to select the final impedance and density volume for interpretation. The P-wave impedance estimated from prestack P-wave data, as well as density and S-wave impedance estimated from prestack SV‐wave data were used to identify the distribution, thickness, quality, and porosity of the Morrow A sandstone. The stratal slicing method was used to get the P- and S-wave impedances and density maps. The S-wave impedance characterizes the Morrow A sandstone distribution better than the P-wave impedance throughout the study area. Density estimation from prestack inversion of SV data was able to distinguish between low- and high-quality reservoirs. The porosity volume was estimated from the density obtained from prestack SV-wave inversion. We found some possible well locations based on the interpretation.

Interpretation of AVO anomalies

Geophysics ◽  
2010 ◽  
Vol 75 (5) ◽  
pp. 75A3-75A13 ◽  
Author(s):  
Douglas J. Foster ◽  
Robert G. Keys ◽  
F. David Lane
Keyword(s):  
Wave Velocity ◽  
Seismic Data ◽  
P Wave ◽  
Rock Properties ◽  
Light Hydrocarbons ◽  
S Wave ◽  
Amplitude Variation With Offset ◽  
P Wave Velocity ◽  
Fluid Properties ◽  
Porosity Changes

We investigate the effects of changes in rock and fluid properties on amplitude-variation-with-offset (AVO) responses. In the slope-intercept domain, reflections from wet sands and shales fall on or near a trend that we call the fluid line. Reflections from the top of sands containing gas or light hydrocarbons fall on a trend approximately parallel to the fluid line; reflections from the base of gas sands fall on a parallel trend on the opposing side of the fluid line. The polarity standard of the seismic data dictates whether these reflections from the top of hydrocarbon-bearing sands are below or above the fluid line. Typically, rock properties of sands and shales differ, and therefore reflections from sand/shale interfaces are also displaced from the fluid line. The distance of these trends from the fluid line depends upon the contrast of the ratio of P-wave velocity [Formula: see text] and S-wave velocity [Formula: see text]. This ratio is a function of pore-fluid compressibility and implies that distance from the fluid line increases with increasing compressibility. Reflections from wet sands are closer to the fluid line than hydrocarbon-related reflections. Porosity changes affect acoustic impedance but do not significantly impact the [Formula: see text] contrast. As a result, porosity changes move the AVO response along trends approximately parallel to the fluid line. These observations are useful for interpreting AVO anomalies in terms of fluids, lithology, and porosity.

Nonlinear two‐dimensional elastic inversion of multioffset seismic data

Geophysics ◽  
1987 ◽  
Vol 52 (9) ◽  
pp. 1211-1228 ◽  
Author(s):  
Peter Mora
Keyword(s):  
Wave Equation ◽  
Wave Velocity ◽  
A Priori ◽  
Gaussian Model ◽  
Wave Impedance ◽  
P Wave ◽  
S Wave ◽  
Elastic Wave Equation ◽  
Gradient Direction ◽  
P Wave Velocity

The treatment of multioffset seismic data as an acoustic wave field is becoming increasingly disturbing to many geophysicists who see a multitude of wave phenomena, such as amplitude‐offset variations and shearwave events, which can only be explained by using the more correct elastic wave equation. Not only are such phenomena ignored by acoustic theory, but they are also treated as undesirable noise when they should be used to provide extra information, such as S‐wave velocity, about the subsurface. The problems of using the conventional acoustic wave equation approach can be eliminated via an elastic approach. In this paper, equations have been derived to perform an inversion for P‐wave velocity, S‐wave velocity, and density as well as the P‐wave impedance, S‐wave impedance, and density. These are better resolved than the Lamé parameters. The inversion is based on nonlinear least squares and proceeds by iteratively updating the earth parameters until a good fit is achieved between the observed data and the modeled data corresponding to these earth parameters. The iterations are based on the preconditioned conjugate gradient algorithm. The fundamental requirement of such a least‐squares algorithm is the gradient direction which tells how to update the model parameters. The gradient direction can be derived directly from the wave equation and it may be computed by several wave propagations. Although in principle any scheme could be chosen to perform the wave propagations, the elastic finite‐ difference method is used because it directly simulates the elastic wave equation and can handle complex, and thus realistic, distributions of elastic parameters. This method of inversion is costly since it is similar to an iterative prestack shot‐profile migration. However, it has greater power than any migration since it solves for the P‐wave velocity, S‐wave velocity, and density and can handle very general situations including transmission problems. Three main weaknesses of this technique are that it requires fairly accurate a priori knowledge of the low‐ wavenumber velocity model, it assumes Gaussian model statistics, and it is very computer‐intensive. All these problems seem surmountable. The low‐wavenumber information can be obtained either by a prior tomographic step, by the conventional normal‐moveout method, by a priori knowledge and empirical relationships, or by adding an additional inversion step for low wavenumbers to each iteration. The Gaussian statistics can be altered by preconditioning the gradient direction, perhaps to make the solution blocky in appearance like well logs, or by using large model variances in the inversion to reduce the effect of the Gaussian model constraints. Moreover, with some improvements to the algorithm and more parallel computers, it is hoped the technique will soon become routinely feasible.

scholarly journals Metode Seismik dalam Pemodelan Fisis Letusan Gunung Lumpur Bledug Kuwu

2019 ◽  
Vol 2 (2) ◽  
pp. 61-66
Author(s):  
Ahmad Fauzi Pohan ◽  
Rusnoviandi Rusnoviandi
Keyword(s):  
Wave Velocity ◽  
Physical Modeling ◽  
Vertical Component ◽  
Dominant Frequency ◽  
P Wave ◽  
Physical Parameters ◽  
S Wave ◽  
Interesting Phenomenon ◽  
Central Java ◽  
P Wave Velocity

Aktivitas gunung lumpur Bledug Kuwu di Jawa  Tengah merupakan fenomena yang menarik dikaji menggunakan pemodelan fisis. Tujuan penelitian ini adalah mengetahui parameter dari medium gunung lumpur Bledug Kuwu. Adapun pemodelan fisis yang dilakukan dengan menggunakan media fisis akuarium berukuran 59 × 59 × 37,3 cm yang diisi material dari lumpur Bledug Kuwu. Sumber letusan dihasilkan dari tekanan kompresor yang dapat diatur kedalaman (10.5, 13, dan 15.5 cm) dan sudut (30o, 45o dan 60o) sumbernya. Sensor yang digunakan geophone komponen vertikal sebanyak 3 buah dengan durasi perekaman selama 5 dan 2,5 detik. Data diambil dengan frekuensi sampel 2 dan 4 kHz untuk masing-masing durasi perekaman. Konfigurasi sumber dan geophone dibuat sesuai dengan pemodelan fisisnya. Pengukuran desnsitas lumpur menunjukkan angka sebesar 1200 kg/m3. Berdasarkan hasil analisis seismogram model fisis diperoleh kecepatan perambatan gelombang-P pada medium lumpur Bledug Kuwu adalah sebesar 48,74 m/s,dan gelombang-S sebesar 28,14 m/s dengan frekuensi dominan antara 20 sampai 25 Hz.   Bledug Kuwu mud volcano activity in Central Java is an interesting phenomenon to be studied using both physical  modeling. The objective of this study was to determine the physical parameters of the medium of Bledug Kuwu. The Physical model was an aquarium with a dimension of 59 × 59 × 37.3 cm filled with Bledug Kuwu’s mud. The eruption source is generated by a compressor pressure that can be controled both the depth(10.5, 13, and 15.5 cm) and the angel of the source (30o, 45o and 60o). The resulting seismic signals were recorded by using 3 vertical component geophones for 10 and 5 seconds durations at a frequency of 2 and 4 kHz respectivel, mud density 1200 kg/m3 . The physical modeling shows that the P-wave velocity of the Bledug Kuwu’s medium is 48.7 m/s, S-wave velocity of Bledug Kuwu’s is 28,14 m/s  with a dominant frequency of 20 to 25 Hz.

Physical Properties and Structure of the Near Subsurface in the Săcel Area, Romania, from Shallow Seismic Measurements

2019 ◽  
Vol 24 (1) ◽  
pp. 151-158
Author(s):  
Ionelia Panea
Keyword(s):  
Wave Velocity ◽  
Signal To Noise Ratio ◽  
Soil Surface ◽  
Geological Structure ◽  
P Wave ◽  
S Wave ◽  
Sedimentary Deposits ◽  
Shallow Seismic ◽  
P Wave Velocity ◽  
Good Signal

Results are presented for shallow seismic reflection measurements performed southwest of Săcel village in Romania for the purpose of obtaining information about the geological structure in the near subsurface. The P-wave and S-wave velocity distributions were also obtained below the soil surface. The measurements were performed along a nearly linear profile on the top of an elongated hill. Most of the shot gathers were characterized by a good signal-to-noise ratio. A depth-converted migrated section was obtained after the processing of shot gathers, on which an image of sedimentary deposits with various thicknesses, separated by shallow faults until a depth of about 80 m, were observed. The P-wave and S-wave velocity-depth models for two segments were of considerable interest for a geotechnical study proposed for the construction of a windmill park. The two- and three-layered P-wave velocity-depth models were comparable until depths of about 10 m after first-arrival traveltime inversions. The lateral variations in the subsurface geological structure and lithology reflected the variations in the P-wave velocity values from both models. The S-wave velocity-depth models for comparable depth intervals were similar to those from the P-wave velocity-depth models. Reliable S-wave velocity distributions were obtained after inversion of fundamental-mode and higher-mode surface waves.

scholarly journals Determination of vp/vs on Bali Province: Case of Bali Earthquake March 22, 2017

2018 ◽  
Vol 19 (2) ◽  
pp. 73
Author(s):  
Febi Niswatul Auliyah ◽  
Komang Ngurah Suarbawa ◽  
Indira Indira
Keyword(s):  
Case Studies ◽  
Wave Velocity ◽  
P Wave ◽  
S Wave ◽  
Diagram Method ◽  
P Wave Velocity ◽  
Before And After ◽  
Earthquake Data

P-wave velocity and S-wave velocity have been investigated in the Bali Province by using earthquake case studies on March 22, 2017. The study was focused on finding out whether there were anomalies in the values of vp/vs before and after the earthquake. Earthquake data was obtained from the Meteorology, Climatology and Geophysics Agency (BMKG) Region III Denpasar, which consisted of the main earthquake on March 22, 2017 and earthquake data in August 2016 to May 2017. Data was processed using the wadati diagram method, obtained that the vp/vs on SRBI, IGBI, DNP and RTBI stations are shifted from 1.5062 to 1.8261. Before the earthquake occurred the anomaly of the value of vp/vs was found on the four stations, at the SRBI station at 10.35%, at the IGBI station at 16.16%, at DNP station at 12.27% and at RTBI station at 4.62%.

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