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.

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.

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.

Time-lapse inverse theory with applications

Geophysics ◽  
2016 ◽  
Vol 81 (6) ◽  
pp. R485-R501 ◽  
Author(s):  
Musa Maharramov ◽  
Biondo L. Biondi ◽  
Mark A. Meadows
Keyword(s):  
Waveform Inversion ◽  
Significant Risk ◽  
Time Lapse ◽  
Long Wavelength ◽  
Full Waveform ◽  
Total Variation Model ◽  
Velocity Anomalies ◽  
Velocity Changes ◽  
Primary Focus ◽  
Production Effects

Compaction in the reservoir overburden can impact production facilities and lead to a significant risk of well-bore failures. Prevalent practices of time-lapse seismic processing of 4D data above compacting reservoirs rely on picking time displacements and converting them into estimated velocity changes and subsurface deformation. This approach relies on prior data equalization and requires a significant amount of manual interpretation and quality control. We have developed methods for automatic detection of production-induced subsurface velocity changes from seismic data. We have evaluated a time-lapse inversion technique based on a simultaneous regularized full-waveform inversion (FWI) of multiple surveys. In our approach, baseline and monitor surveys are inverted simultaneously with a model-difference regularization penalizing nonphysical differences in the inverted models that are due to survey or computational repeatability issues. The primary focus of our work was the inversion of long-wavelength “blocky” changes in the subsurface model, and this was achieved using a phase-only FWI with a total-variation model-difference regularization. However, we have developed a multiscale extension of our method for recovering long- and short-wavelength production effects. We have developed a theoretical foundation of our method and analyzed its sensitivity to a realistic 1%–2% velocity deformation. The method was applied in a study of overburden dilation above the Gulf of Mexico Genesis field and recovered blocky negative-velocity anomalies above compacting reservoirs.

Time-lapse velocity analysis — Application to onshore continuous reservoir monitoring

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. B105-B117 ◽  
Author(s):  
Julien Cotton ◽  
Hervé Chauris ◽  
Eric Forgues ◽  
Paul Hardouin
Keyword(s):  
Heavy Oil ◽  
Reservoir Characterization ◽  
Real Data ◽  
Time Lapse ◽  
Velocity Model ◽  
Steam Injection ◽  
Calendar Time ◽  
Data Set ◽  
Time Migration ◽  
Velocity Changes

In 4D seismic, the velocity model used for imaging and reservoir characterization can change as production from the reservoir progresses. This is particularly true for heavy oil reservoirs stimulated by steam injection. In the context of sparse and low-fold seismic acquisitions, conventional migration velocity analyses can be inadequate because of a poorly and irregularly sampled offset dimension. We update the velocity model in the context of daily acquisitions with buried sources and receivers. The main objective is to demonstrate that subtle time-lapse effects can be detected over the calendar time on onshore sparse acquisitions. We develop a modified version of the conventional prestack time migration to detect velocity changes obtained after crosscorrelation of the base and monitor surveys. This technique is applied on a heavy oil real data set from the Netherlands and reveals how the steam diffuses over time within the reservoir.

A new approach to separate seismic time-lapse time shifts in the reservoir and overburden

Geophysics ◽  
2017 ◽  
Vol 82 (6) ◽  
pp. Q67-Q78 ◽  
Author(s):  
Yi Liu ◽  
Martin Landrø ◽  
Børge Arntsen ◽  
Joost van der Neut ◽  
Kees Wapenaar
Keyword(s):  
Linear Equations ◽  
Time Lapse ◽  
Velocity Model ◽  
Borehole Data ◽  
Lapse Time ◽  
Surface Reflection ◽  
The North ◽  
Reflection Data ◽  
The North Sea ◽  
Velocity Changes

For a robust way of estimating time shifts near horizontal boreholes, we have developed a method for separating the reflection responses above and below a horizontal borehole. Together with the surface reflection data, the method uses the direct arrivals from borehole data in the Marchenko method. The first step is to retrieve the focusing functions and the up-down wavefields at the borehole level using an iterative Marchenko scheme. The second step is to solve two linear equations using a least-squares minimizing method for the two desired reflection responses. Then, the time shifts that are directly linked to the changes on either side of the borehole are calculated using a standard crosscorrelation technique. The method is applied with good results to synthetic 2D pressure data from the North Sea. One example uses purely artificial velocity changes (negative above the borehole and positive below), and the other example uses more realistic changes based on well logs. In the 2D case with an adequate survey coverage at the surface, the method is completely data driven. In the 3D case in which there is a limited number of horizontal wells, a kinematic correct velocity model is needed, but only for the volume between the surface and the borehole. Possible error factors related to the Marchenko scheme, such as an inaccurate source wavelet, imperfect surface multiples removal, and medium with loss are not included in this study.

Source-independent time-lapse full-waveform inversion for VTI media

Geophysics ◽  
2021 ◽  
pp. 1-51
Author(s):  
Yanhua Liu ◽  
Ilya Tsvankin
Keyword(s):  
Waveform Inversion ◽  
Transversely Isotropic ◽  
Time Lapse ◽  
Model Complexity ◽  
Double Difference ◽  
Full Waveform Inversion ◽  
Reservoir Properties ◽  
Hydrocarbon Production ◽  
Full Waveform ◽  
Adjoint State

Time-lapse full-waveform inversion can provide high-resolution information about changes in the reservoir properties during hydrocarbon production and CO2 injection. However, the accuracy of the estimated source wavelet, which is critically important for time-lapse FWI, is often insufficient for field-data applications. The so-called “source-independent” FWI is designed to reduce the influence of the source wavelet on the inversion results. We incorporate the convolution-based source-independent technique into a time-lapse FWI algorithm for VTI (transversely isotropic with a vertical symmetry axis) media. The gradient of the modified FWI objective function is obtained from the adjoint-state method. The algorithm is tested on a model with a graben structure and the modified VTI Marmousi model using three time-lapse strategies (the parallel-difference, sequential-difference, and double-difference methods). The results confirm the ability of the developed methodology to reconstruct the localized time-lapse parameter variations even for a strongly distorted source wavelet. The algorithm remains robust in the presence of moderate noise in the input data but the accuracy of the estimated time-lapse changes depends on the model complexity.

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