Combining reflection and transmission information in time-lapse velocity inversion: A new hybrid approach

Geophysics ◽  
2019 ◽  
Vol 84 (4) ◽  
pp. R601-R611 ◽  
Author(s):  
Maria Kotsi ◽  
Jonathan Edgar ◽  
Alison Malcolm ◽  
Sjoerd de Ridder
Keyword(s):  
Waveform Inversion ◽  
Hybrid Approach ◽  
Time Lapse ◽  
Velocity Model ◽  
Nonlinear Inversion ◽  
Image Domain ◽  
Velocity Inversion ◽  
Reflected Waves ◽  
Deep Diving ◽  
Velocity Changes

Full-waveform inversion (FWI) uses the information of the full wavefield to deliver high-resolution images of the subsurface. Conventional time-lapse FWI primarily uses the transmitted component (diving waves) of the wavefield to reconstruct the low-wavenumber component of the velocity model. This requires large-offset surveys and low-frequency data. When the target of interest is deep, diving waves cannot reach the target and FWI will be dominated by the reflected component of the wavefield. Consequently, the retrieved model resembles a least-squares migration instead of a velocity model. Image-domain methods, especially image-domain wavefield tomography (IDWT), have been developed to obtain a model of time-lapse velocity changes in deeper targets using reflected waves. The method is able to recover models of deep targets. However, it also tends to obtain smeared time-lapse velocity changes. We have developed a form of time-lapse waveform inversion that we call dual-domain time-lapse waveform inversion (DDWI), whose objective function joins FWI and IDWT, combining information from the diving waves in the data-domain FWI term with information from the reflected waves in the image-domain IDWT term. During the nonlinear inversion, the velocity model is updated using constraints from both terms simultaneously. Similar to sequential time-lapse waveform inversion, we start the time-lapse inversion from a baseline model recovered with FWI. We test DDWI on a variety of synthetic models of increasing complexity and find that it can recover time-lapse velocity changes more accurately than when both methods are used independently or sequentially.

Time-lapse velocity imaging via deep learning

2019 ◽  
Author(s):  
Congcong Yuan ◽  
Xiong Zhang ◽  
Xiaofeng Jia ◽  
Jie Zhang
Keyword(s):  
Iterative Methods ◽  
Network Architecture ◽  
Waveform Inversion ◽  
Time Lapse ◽  
Velocity Model ◽  
Double Difference ◽  
Target Velocity ◽  
Waveform Data ◽  
Processing Cost ◽  
Velocity Changes

Summary It is of great significance and a great challenge to quickly and effectively monitor subsurface time-lapse velocities in the earth. Over the past few decades, regularized iterative methods, such as traveltime and waveform inversions, have been presented to monitor velocity changes. Due to high processing cost, these iterative methods have been hardly employed in practice to investigate the subsurface velocity changes in real time. In this study, we propose a new time-lapse imaging technique that effectively eliminates these limitations and directly produces accurate velocity changes from the time-lapse data. The approach uses a fully convolutional neural network (FCN) to perform the inverse problem. The network architecture consists of a contracting path to quickly extract the features of waveform data and a symmetric expanding path to yield an accurate velocity model. With the known baseline velocity and data, we cast a mapping between time-lapse data and target velocity changes via the proposed FCN algorithm. Along with the observed time-lapse data, this mapping will generate a predictive estimation of the target velocity changes. We demonstrate the efficiency and accuracy of our approach in three 2D synthetic tests. The proposed technique is able to invert the velocity changes successfully with much higher efficiency than the regular double-difference full waveform inversion.

scholarly journals Using image warping for time-lapse image domain wavefield tomography

Geophysics ◽  
2014 ◽  
Vol 79 (3) ◽  
pp. WA141-WA151 ◽  
Author(s):  
Di Yang ◽  
Alison Malcolm ◽  
Michael Fehler
Keyword(s):  
Cost Function ◽  
Oil And Gas ◽  
Waveform Inversion ◽  
Time Lapse ◽  
Gas Production ◽  
Reservoir Properties ◽  
Image Domain ◽  
Seismic Waveform ◽  
Realistic Situation ◽  
Velocity Changes

Time-lapse seismic data are widely used for monitoring subsurface changes. A quantitative assessment of how reservoir properties have changed allows for better interpretation of fluid substitution and fluid migration during processes such as oil and gas production and carbon sequestration. Full-waveform inversion (FWI) has been proposed as a way to retrieve quantitative estimates of subsurface properties through seismic waveform fitting. However, for some monitoring systems, the offset range versus depth of interest is not large enough to provide information about the low-wavenumber component of the velocity model. We evaluated an image domain wavefield tomography (IDWT) method using the local warping between baseline and monitor images as the cost function. This cost function is sensitive to volumetric velocity anomalies, and it is capable of handling large velocity changes with very limited acquisition apertures, where traditional FWI fails. We described the theory and workflow of our method. Layered model examples were used to investigate the performance of the algorithm and its robustness to velocity errors and acquisition geometry perturbations. The Marmousi model was used to simulate a realistic situation in which IDWT successfully recovers time-lapse velocity changes.

Target-oriented inversion using the Patched Green′s function method

Geophysics ◽  
2021 ◽  
pp. 1-77
Author(s):  
Danyelle da Silva ◽  
Edwin Fagua Duarte ◽  
Wagner Almeida ◽  
Mauro Ferreira ◽  
Francisco Alirio Moura ◽  
...  
Keyword(s):  
Condensed Matter ◽  
Waveform Inversion ◽  
Time Lapse ◽  
Velocity Model ◽  
Dyson Equation ◽  
Computational Time ◽  
Green Functions ◽  
Target Area ◽  
Computational Domain ◽  
Full Waveform

We have designed a target-oriented methodology to perform Full Waveform Inversion using a frequency-domain wave propagator based on the so-called Patched Green’s Function (PGF) technique. Originally developed in condensed matter physics to describe electronic waves in materials, the PGF technique is easily adaptable to the case of wave propagation in a spatially variable media in general. By dividing the entire computational domain into two sections, namely the target area and the outside target area, we calculate the Green Functions related to each section separately. The calculations related to the section outside the target are performed only once at the beginning of inversion, whereas the calculations in the target area are performed repeatedly for each iteration of the inversion process. With the Green Functions of the separate areas, we calculate the Green Functions of the two systems patched together through the application of a Recursive Dyson equation. By performing 2D and time-lapse experiments on the Marmousi model and a Brazilian Pre-salt velocity model, we demonstrate that the target-oriented PGF reduces the computational time of the inversion without compromising accuracy. In fact, when compared with conventional FWI results, the PGF-based calculations are identical but done in a fraction of the time.

Ten years of time-lapse seismic on Atlantis Field

2021 ◽  
Vol 40 (7) ◽  
pp. 494-501
Author(s):  
Jean-Paul van Gestel
Keyword(s):  
Waveform Inversion ◽  
Water Injection ◽  
Main Tool ◽  
Time Lapse ◽  
Velocity Model ◽  
Lessons Learned ◽  
Ocean Bottom ◽  
Injection Wells ◽  
History Match ◽  
Business Decisions

In 2019, the fourth ocean-bottom-node survey was acquired over Atlantis Field. This survey was quickly processed to provide useful time-lapse (4D) observations two months after the end of the acquisition. The time-lapse observations were immediately valuable in placing wells, refining final drilling target locations, updating well prioritization, and sequencing production and water-injection wells. These data are indispensable pieces of information that bring geophysicists and reservoir engineers together and focus the conversation on key remaining uncertainties such as fault transmissibilities and drainage areas. Time-lapse observations can confirm the key conceptional models already in place but are even more valuable when they highlight alternative models that have not yet been considered. The lessons learned from the acquisition, processing, analysis, interpretation, and integration of the data are shared. Some of these lessons are reiterations of previous work, but several new lessons originated from the latest 2019 acquisition. This was the first survey in which independent simultaneous sources were successfully deployed to collect a time-lapse survey. This resulted in a much faster and less expensive acquisition. In addition, full-waveform inversion was used as the main tool to update the velocity model, enabling a much faster turnaround in processing. The fast turnaround enabled incorporation of the latest acquisition to better constrain the velocity model update. The updated velocity model was used for the final time-lapse migration. In the integration part, the 4D-assisted history-match workflow was engaged to update the reservoir model history match. All of the upgrades led to an overall faster, less expensive, and better way to incorporate the acquired data in the final business decisions.

scholarly journals 4D Travel-Time Tomography as a Tool for Tracking Fluid-Driven Medium Changes in Offshore Oil–Gas Exploitation Areas

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 5878
Author(s):  
Grazia De Landro ◽  
Ortensia Amoroso ◽  
Guido Russo ◽  
Aldo Zollo
Keyword(s):  
Seismic Velocity ◽  
Fluid Pressure ◽  
Time Lapse ◽  
Velocity Model ◽  
Target Volume ◽  
Fluid Injection ◽  
Pressure Perturbation ◽  
Operational Conditions ◽  
Starting Point ◽  
Velocity Changes

The monitoring of rock volume where offshore exploitation activities take place is crucial to assess the corresponding seismic hazard. Fluid injection/extraction operations generate a pore fluid pressure perturbation into the volume hosting the reservoir which, in turn, may trigger new failures and induce changes in the elastic properties of rocks. Our purpose is to evaluate the feasibility of reconstructing pore pressure perturbation diffusion in the host medium by imaging the 4D velocity changes using active seismic. We simulated repeated active offshore surveys and imaged the target volume. We constructed the velocity model perturbed by the fluid injection using physical modeling and evaluated under which conditions the repeated surveys could image the velocity changes. We found that the induced pressure perturbation causes seismic velocity variations ranging between 2–5% and 15–20%, depending on the different injection conditions and medium properties. So, in most cases, time-lapse tomography is very efficient in tracking the perturbation. The noise level characterizing the recording station sites is a crucial parameter. Since we evaluated the feasibility of the proposed 4D imaging strategy under different realistic environmental and operational conditions, our results can be directly applied to set up and configure the acquisition layout of surveys aimed at retrieving fluid-induced medium changes in the hosting medium. Moreover, our results can be considered as a useful starting point to design the guidelines to monitor exploitation areas.

New tools for 2D full-waveform inversion: applications on Brazilian Pre-Salt velocity model from Santos Basin

2020 ◽  
Author(s):  
Henrique Santos ◽  
Claus Eikmeier ◽  
Ernani Volpe
Keyword(s):  
Oil And Gas ◽  
Model Building ◽  
Waveform Inversion ◽  
Numerical Algorithms ◽  
Velocity Model ◽  
Southeastern Brazil ◽  
Full Waveform Inversion ◽  
Case Scenario ◽  
Velocity Inversion ◽  
Full Waveform

<p>In this work, we present full-waveform inversion (FWI) results of a typical Brazilian Pre-Salt model (Santos Basin) using new open-source tools. The large accumulations of oil with excellent quality and high commercial value discovered in the pre-salt carbonates of southeastern Brazil, especially in the Santos Basin, have made this province one of the most prospective in the world. Velocity model building in areas of highly complex geology (like the Santos Basin) remains a challenging step in seismic processing. FWI proved to be an efficient tool for the determination of high-resolution details in multiparameter models of complex subsurface structures, and it has been applied in different geophysical problem scales. However, since FWI is a computationally and mathematically challenging problem, many issues remain open, such as more efficient ways to deal with multiparameter inversion problems such crosstalk and different orders of magnitude in the seismic signal for different classes of parameters. Inversions for more than one class of parameters are of particular importance in the estimation of the physical properties of rocks (poroacoustic or poroelastic applications), for example, to monitoring oil and gas reservoirs and for monitoring the injection of carbon dioxide into geological structures. Also, programming complex numerical algorithms for each application is time-consuming and often evades the expertise of researchers from the geoscientific community. In this sense, a high-level computational tool for simulations and inversions would greatly improve the working time for researchers. Existing finite difference based FWI tools such as Devito, and finite elements based partial differential equations (PDE) solvers tools such as FEniCS and Firedrake are being explored and used for these purposes. In this work, we initially present an FWI acoustic isotropic inversion test (velocity inversion only), performed with the Devito software while a particular code is being developed in FEniCS and Firedrake computer programs. Devito is also a new and under development software and therefore must be tested under different conditions. Our first numerical results indicate the potential of using freely available computational programs in a real case scenario.</p>

Source-domain full waveform inversions

Geophysics ◽  
2020 ◽  
pp. 1-50
Author(s):  
Yulang Wu ◽  
George A. McMechan
Keyword(s):  
Differential Operators ◽  
Waveform Inversion ◽  
Synthetic Data ◽  
Velocity Model ◽  
Low Noise ◽  
Full Waveform Inversion ◽  
Virtual Source ◽  
Reflected Waves ◽  
Source Domain ◽  
Full Waveform

Conventional full waveform inversion (FWI) updates a velocity model by minimizing the data residuals between predicted and observed data, at the receiver positions. We propose a new full waveform inversion to update the velocity model by minimizing virtual source artifacts, at the receiver positions, in the source domain (SFWI). Virtual source artifacts are created by replacing the propagating source wavefield by the forward-time observed data at the receiver positions, as a data-residual constraint. Therefore, no matter whether the velocity model is correct or not, the data residuals, at the receiver positions, are always forced to be zero. If the velocity model is correct, this data-residual constraint has no effect on the wavefield, since the predicted data is the same as the observed data. However, if the estimated velocity model is incorrect, the mismatch between the replaced forward-time observed data and the incorrect predicted upgoing waves (e.g., reflected waves) at the receiver positions, will produce downgoing artifact waves. Thus, the data-residual constraint behaves as a virtual source to create artifact wavefields. By minimizing the virtual source artifacts (equivalent to producing the artifact wavefield), the velocity model can be iteratively updated toward the true velocity model. Similar to conventional FWI, SFWI can be implemented in either the frequency or the time domain, which is unlike previous source-domain solutions, which have to be implemented only in the frequency domain, to solve the normal equations. SFWI does more over-fitting of noisy observed data than conventional FWI does, because noise is amplified by the differential operators when calculating the virtual source artifacts. Tests on synthetic data show that the SFWI inverts for the velocity model more accurately than conventional FWI for noise-free or low-noise data.

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