scholarly journals Use of maximum likelihood sparse spike inversion and probabilistic neural network for reservoir characterization: a study from F-3 block, the Netherlands

2019 ◽  
Vol 10 (2) ◽  
pp. 829-845 ◽  
Author(s):  
Prabodh Kumar Kushwaha ◽  
S. P. Maurya ◽  
N. P. Singh ◽  
Piyush Rai
Keyword(s):  
Neural Network ◽  
Maximum Likelihood ◽  
The Netherlands ◽  
Wave Velocity ◽  
Seismic Data ◽  
Probabilistic Neural Network ◽  
P Wave ◽  
Time Interval ◽  
Petrophysical Parameters ◽  
P Wave Velocity

AbstractMaximum likelihood sparse spike inversion (MLSSI) method is commonly used in the seismic industry to estimate petrophysical parameters in inter-well region. In present study, maximum likelihood sparse spike inversion technique is applied to the processed 3D post-stack seismic data from the F-3 block, the Netherlands, for estimation of acoustic impedance in the region between the wells. The analysis shows that the impedance varies from 2500 to 6200 m/s/*g/cc in the region which is relatively low and indicates the presence of loose formation in the area. The correlation between synthetic seismic trace and original seismic trace is found to be 0.93 and the synthetic relative error as 0.369, which indicate good performance of the algorithm. The analysis also shows low-impedance anomaly in between 600 and 700 ms time interval which may be due to the presence of sand formation. Thereafter, the probabilistic neural network analysis is performed to predict porosity along with multi-attribute transform analysis to estimate P-wave velocity and porosity in inter-well region. These parameters strengthen the seismic data interpretation which is very crucial step of any exploration and production project. The method is first applied to the composite traces near to well locations, and results are compared with well log data. After getting reasonable results, the whole seismic section is inverted for the P-wave velocity and porosity volume. The analysis shows anomaly in between 600 and 700 ms time interval which corroborates well with the low-impedance zone which may correspond to the reservoir. This is preliminarily interpretation; however to confirm a reservoir, there is need for more petrophysical parameters to be studied.

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.

scholarly journals Semiglobal viscoacoustic full-waveform inversion

Geophysics ◽  
2019 ◽  
Vol 84 (2) ◽  
pp. R271-R293 ◽  
Author(s):  
Nuno V. da Silva ◽  
Gang Yao ◽  
Michael Warner
Keyword(s):  
Physical Properties ◽  
Wave Velocity ◽  
Seismic Data ◽  
Waveform Inversion ◽  
P Wave ◽  
Full Waveform Inversion ◽  
Data Set ◽  
Intrinsic Attenuation ◽  
Full Waveform ◽  
P Wave Velocity

Full-waveform inversion deals with estimating physical properties of the earth’s subsurface by matching simulated to recorded seismic data. Intrinsic attenuation in the medium leads to the dispersion of propagating waves and the absorption of energy — media with this type of rheology are not perfectly elastic. Accounting for that effect is necessary to simulate wave propagation in realistic geologic media, leading to the need to estimate intrinsic attenuation from the seismic data. That increases the complexity of the constitutive laws leading to additional issues related to the ill-posed nature of the inverse problem. In particular, the joint estimation of several physical properties increases the null space of the parameter space, leading to a larger domain of ambiguity and increasing the number of different models that can equally well explain the data. We have evaluated a method for the joint inversion of velocity and intrinsic attenuation using semiglobal inversion; this combines quantum particle-swarm optimization for the estimation of the intrinsic attenuation with nested gradient-descent iterations for the estimation of the P-wave velocity. This approach takes advantage of the fact that some physical properties, and in particular the intrinsic attenuation, can be represented using a reduced basis, substantially decreasing the dimension of the search space. We determine the feasibility of the method and its robustness to ambiguity with 2D synthetic examples. The 3D inversion of a field data set for a geologic medium with transversely isotropic anisotropy in velocity indicates the feasibility of the method for inverting large-scale real seismic data and improving the data fitting. The principal benefits of the semiglobal multiparameter inversion are the recovery of the intrinsic attenuation from the data and the recovery of the true undispersed infinite-frequency P-wave velocity, while mitigating ambiguity between the estimated parameters.

Iterative P-wave velocity inversion using Markov chain Monte Carlo method

2020 ◽  
Author(s):  
Hyunggu Jun ◽  
Hyeong-Tae Jou ◽  
Han-Joon Kim ◽  
Sang Hoon Lee
Keyword(s):  
Wave Velocity ◽  
Seismic Data ◽  
Velocity Structure ◽  
Low Frequency ◽  
P Wave ◽  
Velocity Analysis ◽  
Seismic Section ◽  
Traveltime Tomography ◽  
P Wave Velocity ◽  
Frequency Components

<p>Imaging the subsurface structure through seismic data needs various information and one of the most important information is the subsurface P-wave velocity. The P-wave velocity structure mainly influences on the location of the reflectors during the subsurface imaging, thus many algorithms has been developed to invert the accurate P-wave velocity such as conventional velocity analysis, traveltime tomography, migration velocity analysis (MVA) and full waveform inversion (FWI). Among those methods, conventional velocity analysis and MVA can be widely applied to the seismic data but generate the velocity with low resolution. On the other hands, the traveltime tomography and FWI can invert relatively accurate velocity structure, but they essentially need long offset seismic data containing sufficiently low frequency components. Recently, the stochastic method such as Markov chain Monte Carlo (McMC) inversion was applied to invert the accurate P-wave velocity with the seismic data without long offset or low frequency components. This method uses global optimization instead of local optimization and poststack seismic data instead of prestack seismic data. Therefore, it can avoid the problem of the local minima and limitation of the offset. However, the accuracy of the poststack seismic section directly affects the McMC inversion result. In this study, we tried to overcome the dependency of the McMC inversion on the poststack seismic section and iterative workflow was applied to the McMC inversion to invert the accurate P-wave velocity from the simple background velocity and inaccurate poststack seismic section. The numerical test showed that the suggested method could successfully invert the subsurface P-wave velocity.</p>

Guidelines for building a detailed elastic depth model

Geophysics ◽  
2000 ◽  
Vol 65 (1) ◽  
pp. 35-45
Author(s):  
Jarrod C. Dunne ◽  
Greg Beresford ◽  
Brian L. N Kennett
Keyword(s):  
Wave Velocity ◽  
Mode Conversion ◽  
Poisson Ratio ◽  
Velocity Model ◽  
P Wave ◽  
Time Interval ◽  
S Wave ◽  
Sea Floor ◽  
P Wave Velocity ◽  
Inelastic Attenuation

We developed guidelines for building a detailed elastic depth model by using an elastic synthetic seismogram that matched both prestack and stacked marine seismic data from the Gippsland Basin (Australia). Recomputing this synthetic for systematic variations upon the depth model provided insight into how each part of the model affected the synthetic. This led to the identification of parameters in the depth model that have only a minor influence upon the synthetic and suggested methods for estimating the parameters that are important. The depth coverage of the logging run is of prime importance because highly reflective layering in the overburden can generate noise events that interfere with deeper events. A depth sampling interval of 1 m for the P-wave velocity model is a useful lower limit for modeling the transmission response and thus maintaining accuracy in the tie over a large time interval. The sea‐floor model has a strong influence on mode conversion and surface multiples and can be built using a checkshot survey or by testing different trend curves. When an S-wave velocity log is unavailable, it can be replaced using the P-wave velocity model and estimates of the Poisson ratio for each significant geological formation. Missing densities can be replaced using Gardner’s equation, although separate substitutions are required for layers known to have exceptionally high or low densities. Linear events in the elastic synthetic are sensitive to the choice of inelastic attenuation values in the water layer and sea‐floor sediments, while a simple inelastic attenuation model for the consolidated sediments is often adequate. The usefulness of a 1-D depth model is limited by misties resulting from complex 3-D structures and the validity of the measurements obtained in the logging run. The importance of such mis‐ties can be judged, and allowed for in an interpretation, by recomputing the elastic synthetic after perturbing the depth model to simulate the key uncertainties. Taking the next step beyond using simplistic modeling techniques requires extra effort to achieve a satisfactory tie to each part of a prestack seismic record. This is rewarded by the greater confidence that can then be held in the stacked synthetic tie and applications such as noise identification, data processing benchmarking, AVO analysis, and inversion.

Identification of gas hydrate dissociation from wireline-log data in the Shenhu area, South China Sea

Geophysics ◽  
2012 ◽  
Vol 77 (3) ◽  
pp. B125-B134 ◽  
Author(s):  
Xiujuan Wang ◽  
Myung Lee ◽  
Shiguo Wu ◽  
Shengxiong Yang
Keyword(s):  
Wave Velocity ◽  
Gas Hydrate ◽  
Seismic Data ◽  
Pore Space ◽  
P Wave ◽  
Well Log ◽  
Hydrate Dissociation ◽  
Free Gas ◽  
P Wave Velocity

Wireline logs were acquired in eight wells during China’s first gas hydrate drilling expedition (GMGS-1) in April–June of 2007. Well logs obtained from site SH3 indicated gas hydrate was present in the depth range of 195–206 m below seafloor with a maximum pore-space gas hydrate saturation, calculated from pore water freshening, of about 26%. Assuming gas hydrate is uniformly distributed in the sediments, resistivity calculations using Archie’s equation yielded hydrate-saturation trends similar to those from chloride concentrations. However, the measured compressional (P-wave) velocities decreased sharply at the depth between 194 and 199 mbsf, dropping as low as [Formula: see text], indicating the presence of free gas in the pore space, possibly caused by the dissociation of gas hydrate during drilling. Because surface seismic data acquired prior to drilling were not influenced by the in situ gas hydrate dissociation, surface seismic data could be used to identify the cause of the low P-wave velocity observed in the well log. To determine whether the low well-log P-wave velocity was caused by in situ free gas or by gas hydrate dissociation, synthetic seismograms were generated using the measured well-log P-wave velocity along with velocities calculated assuming both gas hydrate and free gas in the pore space. Comparing the surface seismic data with various synthetic seismograms suggested that low P-wave velocities were likely caused by the dissociation of in situ gas hydrate during drilling.

scholarly journals Seismic P  Wave Velocity Model From 3‐D Surface and Borehole Seismic Data at the Alpine Fault DFDP‐2 Drill Site (Whataroa, New Zealand)

2021 ◽  
Author(s):  
V Lay ◽  
S Buske ◽  
SB Bodenburg ◽  
John Townend ◽  
R Kellett ◽  
...  
Keyword(s):  
New Zealand ◽  
Wave Velocity ◽  
Seismic Data ◽  
Velocity Model ◽  
P Wave ◽  
Alpine Fault ◽  
P Wave Velocity ◽  
Drill Site ◽  
Borehole Seismic

No description supplied

A fall in P-wave velocity before the Gisborne, New Zealand, earthquake of 1966

1974 ◽  
Vol 64 (5) ◽  
pp. 1501-1507 ◽  
Author(s):  
D. J. Sutton
Keyword(s):  
New Zealand ◽  
Wave Velocity ◽  
Significant Variation ◽  
Arrival Time ◽  
P Wave ◽  
Time Interval ◽  
P Waves ◽  
Arrival Times ◽  
P Wave Velocity ◽  
Seismograph Station

Abstract A fall in P-wave velocity before the Gisborne earthquake of March 4, 1966 is indicated by arrival-time residuals of P waves from distant earthquakes recorded at the Gisborne seismograph station. Residuals were averaged over 6-month intervals from 1964 to 1968 and showed an increase of about 0.5 sec, implying later arrival times. The change began about 480 days before the earthquake. This precursory time interval is about that expected for an earthquake of this magnitude (ML = 6.2), but unlike most other reported instances, there was no obvious delay between the return of the velocity to normal and the occurrence of the earthquake. Similar analyses were carried out over the same period for two other New Zealand seismograph stations; at Karapiro there was no significant variation in mean residuals, and at Wellington the scatter was too large for the results to be meaningful. The Gisborne earthquake had a focus in the lower crust, about 25 km deep and was deeper than other events for which such precursory drops in P-wave velocity have been reported.

P-wave velocity structure of Piton de la Fournaise volcano deduced from seismic data recorded between 1996 and 1999

2009 ◽  
Vol 184 (1-2) ◽  
pp. 49-62 ◽  
Author(s):  
Elodie Prôno ◽  
Jean Battaglia ◽  
Vadim Monteiller ◽  
Jean-Luc Got ◽  
Valérie Ferrazzini
Keyword(s):  
Wave Velocity ◽  
Seismic Data ◽  
Velocity Structure ◽  
P Wave ◽  
Piton De La Fournaise ◽  
P Wave Velocity ◽  
P Wave Velocity Structure
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