GB2476519A - Technique and system for deriving a time lapse low frequency model using both seismic data and a flow simulation model - Google Patents

Technique and system for deriving a time lapse low frequency model using both seismic data and a flow simulation model Download PDF

Info

Publication number
GB2476519A
GB2476519A GB1006941A GB201006941A GB2476519A GB 2476519 A GB2476519 A GB 2476519A GB 1006941 A GB1006941 A GB 1006941A GB 201006941 A GB201006941 A GB 201006941A GB 2476519 A GB2476519 A GB 2476519A
Authority
GB
United Kingdom
Prior art keywords
change
velocity
elastic property
time
interest
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
GB1006941A
Other versions
GB2476519B (en
GB201006941D0 (en
Inventor
Kjetil Westeng
Thomas Andreas Hope
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Gemalto Terminals Ltd
Schlumberger Holdings Ltd
Original Assignee
Gemalto Terminals Ltd
Schlumberger Holdings Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US12/765,307 external-priority patent/US8325560B2/en
Application filed by Gemalto Terminals Ltd, Schlumberger Holdings Ltd filed Critical Gemalto Terminals Ltd
Publication of GB201006941D0 publication Critical patent/GB201006941D0/en
Publication of GB2476519A publication Critical patent/GB2476519A/en
Application granted granted Critical
Publication of GB2476519B publication Critical patent/GB2476519B/en
Expired - Fee Related legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01VGEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28Processing seismic data, e.g. for interpretation or for event detection
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01VGEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/282Application of seismic models, synthetic seismograms
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01VGEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V11/00Prospecting or detecting by methods combining techniques covered by two or more of main groups G01V1/00 - G01V9/00
    • G06F17/5018
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00Computer-aided design [CAD]
    • G06F30/20Design optimisation, verification or simulation
    • G06F30/23Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01VGEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00Details of seismic processing or analysis
    • G01V2210/60Analysis
    • G01V2210/61Analysis by combining or comparing a seismic data set with other data
    • G01V2210/612Previously recorded data, e.g. time-lapse or 4D

Landscapes

  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Remote Sensing (AREA)
  • General Physics & Mathematics (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • Geophysics (AREA)
  • Acoustics & Sound (AREA)
  • Geology (AREA)
  • Environmental & Geological Engineering (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Hardware Design (AREA)
  • Evolutionary Computation (AREA)
  • Geometry (AREA)
  • General Engineering & Computer Science (AREA)
  • Geophysics And Detection Of Objects (AREA)

Abstract

The invention relates to determining a change 40 in an elastic property of a region of interest of an earth formation at a second time relative to a prior first time based on a flow simulation model 31. The change 40 is then scaled 46 in response to data 22, 24 acquired in a seismic survey. The act of scaling may comprise determining a change 46 in a velocity of the region of interest by performing a time-shift analysis 20. The change 40 in the elastic property may therefore be scaled based on the determined change in the velocity. The elastic property may comprise an acoustic impedance, a Poisson's ratio or a density.

Description

TECHNIQUE AND SYSTEM FOR DERIVING A TIME LAPSE LOW
FREQUENCY MODEL USING BOTH SEISMIC DATA AND A FLOW
SIMULATION MODEL
BACKGROUND
The invention generally relates to determining technique and system for deriving a time lapse low frequency model using both seismic data and a flow simulation model.
Seismic exploration involves surveying subterranean geological formations for hydrocarbon deposits. A survey typically involves deploying seismic source(s) and seismic sensors at predetermined locations. The sources generate seismic waves, which propagate into the geological formations creating pressure changes and vibrations along their way. Changes in elastic properties of the geological formation scatter the seismic waves, changing their direction of propagation and other properties. Part of the energy emitted by the sources reaches the seismic sensors. Some seismic sensors are sensitive to pressure changes (hydrophones), others to particle motion (e.g., geophones), and industrial surveys may deploy only one type of sensors or both. In response to the detected seismic events, the sensors generate electrical signals to produce seismic data.
Analysis of the seismic data can then indicate the presence or absence of probable locations of hydrocarbon deposits.
Some surveys are known as marine" surveys because they are conducted in marine environments. However, "marine" surveys may be conducted not only in saltwater environments, but also in fresh and brackish waters. In one type of marine survey, called a "towed-array" survey, an array of seismic sensor-containing streamers and sources is towed behind a survey vessel.
For purposes of observing changes in a producing field over time, a series of towed seismic surveys of the producing field, separated by months or years, may be conducted. Thus, an initial survey (called a base survey') may be conducted before or after the well completion equipment is installed, and thereafter, subsequent surveys (called "monitor," or repeat surveys") are conducted for purposes of observing changes in the producing field. Ideally, the only change between any two of the surveys should be in the fluids (i.e., oil, gas and/or water) that are produced or displaced from the producing field. In time lapse analysis, also called "4-D analysis," differences are taken between the surveys to ideally reveal only the changes in the produced/displaced fluids, with the geology (ideally being the same for each survey) canceling out.
SUMMARY
In an embodiment of the invention, a technique includes determining a change in an elastic property of a region of interest at a second time relative to a first time based on a flow simulation model. The technique includes scaling the determined change in the elastic property based on data acquired in a seismic survey.
In another embodiment of the invention, a system includes an interface and a processor. The interface receives seismic data acquired in a seismic survey conducted in a region of interest. The processor applies a flow simulation model to determine a change in an elastic property of the region of interest at a second time relative to a first time and scales the determined change in the elastic property based on the seismic data.
In yet another embodiment of the invention, an article includes a computer readable storage medium to store instructions that when executed by a computer cause the computer to receive seismic data acquired in a seismic survey conducted in a region of interest. The instructions when executed cause the computer to apply a flow simulation model to determine a change in an elastic property of the region of interest at a second time relative to a first time and scale the determined change in the elastic property based on the seismic data.
Advantages and other features of the invention will become apparent from the
following drawing, description and claims.
BRIEF DESCRIPTION OF THE DRAWINGS
Fig. 1 is a schematic diagram illustrating a system to derive a time lapse, low frequency model based on seismic data and a flow simulation model according to an embodiment of the invention.
Figs 2 and 3 are flow diagrams depicting techniques to derive a time lapse, low frequency model based on seismic data and a flow simulation model according to embodiments of the invention.
Fig. 4 is a schematic diagram of a data processing system according to an embodiment of the invention.
DETAILED DESCRIPTION
Petroelastic forward modeling of a reservoir flow model may be employed for purposes of simulating elastic rock properties (seismic velocity, acoustic impedance, a ratio of the compression wave velocity (Vp) to the shear wave velocity (Vs), Poisson's ratio and density, as non-limiting examples) from measured reservoir properties (porosity, pressure, net to gross, fluid/gas saturation, etc.) It is useful to obtain a time lapse analysis of the elastic rock properties, as enhanced property predictions support better wellbore planning, formation integrity forecasting and the characterization of subsidence, compaction, damage to the cap rock integrity and fault sealing. Furthermore, rock property predictions also help determine the maximum completion and optimum production for a field when coupled to reservoir pressures and field stress developments.
In accordance with embodiments of the invention described herein, both seismic time lapse analysis and a flow simulation model are used for purposes of deriving a low frequency, time lapse model of the elastic parameters. More specifically, referring to Fig. 1, a system 10 in accordance with embodiments of the invention employs the use of time lapse seismic data and a flow simulation model for purposes of producing scaled elastic property change cubes 48 for the various elastic properties. Each change cube 48, as it name implies, indicates the change over time in a particular elastic property for a three-dimensional (3 -D) region of interest, and the change indicated by the cube 48 is scaled by seismic data acquired in a seismic survey.
More specifically, the system 10 includes a petroelastic model 30 that, in response to measured reservoir properties 32 (measured porosities, pressures, net to gross, fluid/gas saturation, etc.) and a reservoir flow model 31 forecasts elastic properties at two different times (called "Time 1' and "Time 2" in Fig. 1) to produce two types of cubes: cubes 36, which represent the forecasted elastic properties at Time 1; and cubes 38, which represent the elastic properties at Time 2. Thus, a particular elastic property cube 36, 38 indicates a given elastic property, at a given time. The system 10 includes a combiner 40, which combines the cubes 36 and 38 to generate elastic property relative ratio change cubes 44. Each change cube 44 indicates two changes: a first percentage change of the elastic property from Time 1 to Time 2; and a percentage change of the elastic property relative to the change in the compression wave velocity (Vp) from Time 1 to Time 2. More specifically, the elastic property relative ratio change cubes 44 for acoustic impedance, Poisson's ratio and density may be described according to Eqs. 1, 2 and 3, respectively, below: 1 Acoustic Imped.ance1Jk (time m) Acoustic Impedance.k (time n) rAJVP.k(timen-'m)= VeIocity(time m) Velocity Vk(time n) Eq. 1 Poisson's Ratiok(time m) / Poisson's Ratiofk(timen) rPRVP.kttimen-m)-- 1 Velocity,k(time m) VeIocity(time n) Eq. 2 Densiy(tiIne m) Density..k(time n) rRHOBVPkVlmen-m)= . ,and 1 Veloclty(tlme m) VeIocity(time n) Eq. 3 where the notation "ijk" represents the orientation in the seismic volume or reservoir grid. The relative ratios are calculated for each sample/cell in the seismic volume/reservoir grid. Thus, as can be seen, each relative ratio change cube 44 indicates the change in a particular elastic property relative to the change of the compression wave velocity.
As also depicted in Fig. 1, the system 10 receives seismic data 22 acquired at Time 1 and seismic data 24 acquired at Time 2. Time shift analysis 20 is employed for purposes of translating the observed time shifts into corresponding velocity changes that appear in a velocity change cube 26. As shown below in Eq. 4, the compression velocity change from Time 1 to Time 2 is approximately equal to the observed time shifts: DVP.k(timen -in) DT.k(timen -in) D Vmjh/k(time n -) -VP0 gk(time n) TOqk(time n) Eq. 4 The elastic property relative ratio change cubes 34 are scaled 46 with the velocity changes to produce the scaled elastic property change cubes 48. The result of this scaling is described below for acoustic impedance (Eq. 5), Poisson's ratio (Eq. 6) and density (Eq. 7), as described below: dAI_LFM/k rAIV1k(timen -b m)*dVPejftlk(timen -), Eq. 5 dPR_LFMVk = rPR VFk(tIme n -m)* dVPmjft/k(timen -in), Eq. 6 dRHOB_LFM)k = rRHOB V1k(time n -m)* n - Eq. 7 Thus, the low frequency model realized in the form of the scaled elastic property change cubes 48, utilizes inputs from both time lapse seismic data and utilizes the output of the petroelastic model 30. The model 30 has an accurate rock physics in each sample throughout the volume. The relationship between the between the changes of each elastic parameter in the low frequency model defined by the model 30. However, the amplitudes of the changes are determined/scaled by the changes in seismic velocity.
It is noted that the architecture that is depicted in Fig. 1 merely illustrates one out of many possible architectures for generating the low frequency, time lapse model. Thus, the skilled artisan would appreciate numerous modifications and deviations therefrom.
Regardless of the particular architecture that is used, a technique 50 that is depicted in Fig. 2 may be used to generate a low frequency in accordance with embodiments of the invention.
Referring to Fig. 2, the technique 50 includes determining (block 54) a change in an elastic property of a region of interest at a second time relative to a first time based on a flow simulation model and scaling (block 58) the change in the elastic property in response to data acquired in a seismic survey. More specifically, as a depicted in technique 80 that is illustrated in Fig. 3, the technique to derive a low frequency model includes determining (block 84) a change in velocity in a region of interest at a second time relative to a first time based on data acquired in a seismic survey and determining (block 88) a change in velocity in the region of interest in response to time lapse seismic data. The change in the elastic property is multiplied (block 90) by the determined change in the velocity.
As a non-limiting example, the seismic survey that is described herein may be one of numerous different types of seismic surveys. As non-limiting examples, the seismic surveys may be towed seismic surveys in which streamers are towed in a marine environment over a region of interest; a sea bed-based survey in which a sea bed cable is used to acquire seismic data; a terrestrial or land-based seismic survey that may, for example, use a vibroseis survey to acquire seismic data, a borehole-based seismic survey, etc. Regardless of the type of seismic survey employed, the time lapse seismic survey data is formed from a first set of seismic data acquired by the survey at a first time and another set of seismic data acquired in a seismic survey conducted in the same region of interest at a second time.
Referring to Fig. 4, in accordance with some embodiments of the invention, a processing system 400 may be used for purposes of efficiently computing the fracture area inside a cube, pursuant to the techniques that are disclosed herein. It is noted that the architecture of the processing system 400 is illustrated merely as an example, as the skilled artisan would recognize many variations and deviations therefrom.
In the example that is depicted in Fig. 4, the processing system 400 includes a processor 404, which executes program instructions 412 that are stored in a system memory 410 for purposes of causing the processor 404 to perform some or all of the techniques that are disclosed herein. As non-limiting examples, the processor 404 may include one or more microprocessors and/or microcontrollers, depending on the particular implementation. In general, the processor 404 may execute program instructions 412 for purposes of causing the processor 404 to determine a change in an elastic property at a region of interest at a second time relative to a first time based on a flow simulation model, scale the determined change in the elastic property in response to data acquired in a seismic survey, determine cubes depicting an elastic property at Time 1, determine cubes representing the elastic property at Time 2, combine the cubes representing the elastic property at Time 1 and Time 2 to derive elastic property relative ratio change cubes, etc. The memory 410 may also store datasets 414 which may be initial, intermediate and/or final datasets produced by the processing by the processor 404. For example, the datasets 414 may include data indicative of elastic property at Time 1 cubes, elastic property at Time 2 cubes, elastic property relative ratio change cubes, velocity change cubes, scaled elastic property change cubes, etc. As depicted in Fig. 4, the processor 404 and memory 410 may be coupled together by at least one bus 408, which may couple other components of the processing system 400 together, such as a network interface card (NIC) 424. As a non-limiting example, the NIC 424 may be coupled to a network 426, for purposes of receiving such data as seismic data acquired at different times, data indicative of reservoir properties, etc. As also depicted in Fig. 4, a display 420 of the processing system 408 may display initial, intermediate or final results produced by the processing system 400. In general, the display 420 may be coupled to the system 400 by a display driver 416. As a non-limiting example, the display 420 may display an image, which graphically depicts the cubes and the tensors, which are determined pursuant to the techniques that are disclosed herein.
While the present invention has been described with respect to a limited number of embodiments, those skilled in the art, having the benefit of this disclosure, will appreciate numerous modifications and variations therefrom. It is intended that the appended claims cover all such modifications and variations as fall within the true spirit and scope of this present invention.

Claims (21)

  1. CLAIMS1. A method comprising: determining a change in an elastic property of a region of interest at a second time relative to a prior first time based on a flow simulation model; and scaling the change in the elastic property in response to data acquired in a seismic survey.
  2. 2. The method of claim 1, wherein the act of scaling the change comprises: determining a change in a velocity of the region of interest at the second time relative to the first time based on the data acquired in the seismic survey; and scaling the change based on the determined change in the velocity.
  3. 3. The method of claim 2, wherein the act of determining the change in the velocity comprises performing a time shift analysis.
  4. 4. The method of claim 2, wherein the velocity comprises a velocity of a compression wave.
  5. 5. The method of claim 1, wherein the act of determining the change in the elastic property comprises determining a change in the elastic property relative to a velocity.
  6. 6. The method of claim 5, wherein the act of scaling the change comprises: determining a change in a velocity of the region of interest at the second time relative to the first time based on the data acquired in the seismic survey; and multiplying the change in the elastic property with the determined change in the velocity.
  7. 7. The method of claim 1, wherein the elastic property comprises an acoustic impedance, a Poisson's ratio or a density.
  8. 8. A system comprising: an interface to receive seismic data acquired in a seismic survey conducted in a region of interest; and a processor to: apply a flow simulation model to determine a change in an elastic property of the region of interest at a second time relative to a prior first time; and scale the change in the elastic property in response to the seismic data.
  9. 9. The system of claim 8, wherein the processor is adapted to: determine a change in a velocity of the region of interest at the second time relative to the first time based on the data acquired in the seismic survey; and scale the change based on the determined change in the velocity.
  10. 10. The system of claim 9, wherein the velocity comprises a velocity of a compression wave.
  11. 11. The system of claim 9, wherein the processor is adapted to perform a time shift analysis to determine the change in velocity.
  12. 12. The system of claim 9, wherein the change in the elastic property comprises a change in the elastic property relative to a velocity.
  13. 13. The system of claim 12, wherein the processor is adapted to: determine a change in a velocity of the region of interest at the second time relative to the first time based on the data acquired in the seismic survey; and multiply the change in the elastic property with the determined change in the velocity.
  14. 14. The system of claim 8, wherein the elastic property comprises an acoustic impedance, a Poisson's ratio or a density.
  15. 15. An article comprising a computer readable storage medium to store instructions that when executed by a computer cause the computer to: receive seismic data acquired in a seismic survey conducted in a region of interest; apply a flow simulation model to determine a change in an elastic property of the region of interest at a second time relative to a prior first time; and scale the change in the elastic property in response to the seismic data.
  16. 16. The article of claim 15, the storage medium storing instructions that when executed cause the computer to: determine a change in a velocity of the region of interest at the second time relative to the first time based on the data acquired in the seismic survey; and scale the change based on the determined change in the velocity.
  17. 17. The article of claim 16, wherein the velocity comprises a velocity of a compression wave.
  18. 18. The article of claim 16, wherein the storage medium storing instructions that when executed cause the computer to determine the change in velocity.
  19. 19. The article of claim 15, wherein the change in the elastic property comprises a change in the elastic property relative to a velocity.
  20. 20. The article of claim 19, the storage medium storing instructions that when executed cause the computer to: determine a change in a velocity of the region of interest at the second time relative to the first time based on the data acquired in the seismic survey; and multiply the change in the elastic property with the determined change in the velocity.
  21. 21. The article of claim 15, wherein the elastic property comprises an acoustic impedance, a Poisson's ratio or a density.
GB1006941A 2009-05-08 2010-04-26 Technique and system for deriving a time lapse low frequency model using both seismic data and a flow simulation model Expired - Fee Related GB2476519B (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US17657109P 2009-05-08 2009-05-08
US12/765,307 US8325560B2 (en) 2009-05-08 2010-04-22 Technique and system for deriving a time lapse low frequency model using both seismic data and a flow simulation model

Publications (3)

Publication Number Publication Date
GB201006941D0 GB201006941D0 (en) 2010-06-09
GB2476519A true GB2476519A (en) 2011-06-29
GB2476519B GB2476519B (en) 2011-11-09

Family

ID=43308297

Family Applications (1)

Application Number Title Priority Date Filing Date
GB1006941A Expired - Fee Related GB2476519B (en) 2009-05-08 2010-04-26 Technique and system for deriving a time lapse low frequency model using both seismic data and a flow simulation model

Country Status (2)

Country Link
GB (1) GB2476519B (en)
NO (1) NO20100659L (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR3076626A1 (en) * 2018-01-10 2019-07-12 Landmark Graphics Corporation PREDICTION OF EARLY TIME SEISMIC ROCH PROPERTIES BASED ON 4D SEISMIC ANALYSIS

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2409900A (en) * 2004-01-09 2005-07-13 Statoil Asa Processing seismic data from two different states
GB2438306A (en) * 2006-05-15 2007-11-21 Pgs Geophysical As Method for sub-salt migration velocity analysis

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2409900A (en) * 2004-01-09 2005-07-13 Statoil Asa Processing seismic data from two different states
GB2438306A (en) * 2006-05-15 2007-11-21 Pgs Geophysical As Method for sub-salt migration velocity analysis

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR3076626A1 (en) * 2018-01-10 2019-07-12 Landmark Graphics Corporation PREDICTION OF EARLY TIME SEISMIC ROCH PROPERTIES BASED ON 4D SEISMIC ANALYSIS
GB2582104B (en) * 2018-01-10 2022-06-01 Landmark Graphics Corp Seismic rock property prediction in forward time based on 4D seismic analysis
US11598893B2 (en) 2018-01-10 2023-03-07 Landmark Graphics Corporation Seismic rock property prediction in forward time based on 4D seismic analysis

Also Published As

Publication number Publication date
NO20100659L (en) 2010-11-09
GB2476519B (en) 2011-11-09
GB201006941D0 (en) 2010-06-09

Similar Documents

Publication Publication Date Title
US8325560B2 (en) Technique and system for deriving a time lapse low frequency model using both seismic data and a flow simulation model
CN110462445B (en) Deep learning of geophysical
US11428834B2 (en) Processes and systems for generating a high-resolution velocity model of a subterranean formation using iterative full-waveform inversion
Zeng et al. An improved vacuum formulation for 2D finite-difference modeling of Rayleigh waves including surface topography and internal discontinuities
KR101797451B1 (en) Simultaneous source inversion for marine streamer data with cross-correlation objective function
Melgar et al. Near‐field tsunami models with rapid earthquake source inversions from land‐and ocean‐based observations: The potential for forecast and warning
CN102124374B (en) Method for separating independent simultaneous sources
KR101948509B1 (en) Artifact reduction in iterative inversion of geophysical data
Aagaard et al. Ground-motion modeling of the 1906 San Francisco earthquake, part II: Ground-motion estimates for the 1906 earthquake and scenario events
US9229123B2 (en) Method for handling rough sea and irregular recording conditions in multi-sensor towed streamer data
EP3710867B1 (en) Noise attenuation of multiple source seismic data
Kumagai et al. Source process of a long-period event at Kilauea volcano, Hawaii
GB2491346A (en) Three dimensional full-wavefield seismic tomography for use in mining or in extraction of metalliferous mineral and/or diamond deposits
Wang et al. Review on recent progress in near-field tsunami forecasting using offshore tsunami measurements: Source inversion and data assimilation
Saito et al. Synthesizing sea surface height change including seismic waves and tsunami using a dynamic rupture scenario of anticipated Nankai trough earthquakes
Kamei et al. Passive seismic imaging and velocity inversion using full wavefield methods
RU2570827C2 (en) Hybrid method for full-waveform inversion using simultaneous and sequential source method
Miola et al. A computational approach for 3D modeling and integration of heterogeneous geo-data
Loreto et al. Geophysical investigation of Pleistocene volcanism and tectonics offshore Capo Vaticano (Calabria, southeastern Tyrrhenian Sea)
Vardy et al. Can high-resolution marine geophysical data be inverted for soil properties?
Köhn et al. A combination of waveform inversion and reverse-time modelling for microseismic event characterization in complex salt structures
L’Heureux et al. Building better models for seismic simulations with stochastic stratigraphy
CN111936888B (en) Wave field propagator for tilted orthorhombic media
US20220026593A1 (en) Implicit property modeling
Larsen et al. Next-generation numerical modeling: incorporating elasticity, anisotropy and attenuation

Legal Events

Date Code Title Description
AT Applications terminated before publication under section 16(1)
S20A Reinstatement of application (sect. 20a/patents act 1977)

Free format text: REQUEST FOR REINSTATEMENT ALLOWED

Effective date: 20110519

Free format text: REQUEST FOR REINSTATEMENT FILED

Effective date: 20110128

PCNP Patent ceased through non-payment of renewal fee

Effective date: 20170426