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Collaborating Authors
Reservoir Description and Dynamics
Patricia de Lugão received a Bachelor of Science degree in environmental engineering and water resources from the University of South Carolina in 1988, a master's degree in geophysics from the Observatório Nacional in Rio de Janeiro in 1992 and a Ph.D. in geophysics from University of Utah in 1997. At Observatório Nacional, she worked with Sergio Fontes on the acquisition, processing, and modeling of magnetotelluric data from the Recôncavo Basin, Brazil. During her Ph.D. studies at the University of Utah, de Lugão had the good fortune to work with Phil Wannamaker and Michael Zhdanov on the development of modeling and inversion algorithms for magnetotellurics. After her Ph.D., de Lugão worked in the research department at Western Atlas in Houston with Kurt-Martin Strack, where she applied her knowledge in modeling and inversion to the development of algorithms for array borehole tools. In the Geosignal division of Western Atlas, Patricia worked with Lee Bell on two- and three-dimensional refraction tomography techniques for statics correction and initial velocity model for prestack depth migration of seismic data from the foothills of South America to the Gulf of Mexico.
- North America > United States > Utah (0.47)
- South America > Brazil > Rio de Janeiro > Rio de Janeiro (0.26)
- Geophysics > Electromagnetic Surveying (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (0.72)
- Geophysics > Seismic Surveying > Seismic Processing (0.57)
- South America > Brazil > Brazil > South Atlantic Ocean > Santos Basin (0.99)
- South America > Brazil > Bahia > Reconcavo Basin (0.99)
- Information Technology > Knowledge Management (0.76)
- Information Technology > Communications > Collaboration (0.76)
The SEG Research Committee CO2 Sub-Committee is happy to announce the Geophysical Research for Gigatonnes CO2 Storage workshop scheduled for 14–19 July 2024 at the Colorado School of Mines in Golden, Colorado, USA. This SEG and Geologic Carbon Storage (GCS) summer research workshop is dedicated to advancing geophysics in the context of GCS. It emphasizes interdisciplinary research and best practices with a focus on site characterization and monitoring, site integrity, and risks related to injectivity, and seals and faults. The workshop's distinctive format encourages networking and open discussions, aiming to cultivate a dynamic knowledge nexus for the secure geological storage of CO2 consistent with governmental regulations and guidelines, and international best practices. With two key segments, the workshop will assess the current status, gaps, and challenges in geophysics for GCS and provide recommendations for regulators and operators, positioning itself as a vital platform for geophysical advancements in GCS.
- Energy (0.38)
- Transportation > Passenger (0.35)
- Transportation > Ground > Road (0.35)
- Automobiles & Trucks > Manufacturer (0.35)
- Reservoir Description and Dynamics > Storage Reservoir Engineering > CO2 capture and sequestration (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Health, Safety, Environment & Sustainability > Sustainability/Social Responsibility > Sustainable development (1.00)
- (2 more...)
Multichannel deconvolution with a high-frequency structural regularization
Wang, Pengfei (China University of Petroleum) | Zhao, Dongfeng (China University of Petroleum) | Niu, Yue (National Engineering Research Center for Oil and Gas Exploration Computer Software) | Li, Guofa (China University of Petroleum) | Gu, Weiwei (China University of Petroleum)
The resolution of seismic data determines the ability to characterize stratigraphic features from observed seismic record. Sparse spike inversion (SSI) as an important processing method can effectively improve the band-limited property of the seismic data. However, the approch ignores the spatial information along seismic traces, which causes the unreliability of the reconstructed high-resolution data. In this article, we have developed a high-frequency structure constrained multichannel deconvolution (HFSC-MD) to alleviate this issue. This method allows the cost function to incorporate high-frequency spatial information in the form of prediction-error filter (PEF), to regularize the components of the result beyond the original frequency. The PEF also called high-frequency structural characterization operator (HFRSC operator), is estimated from the mapping relationship of low and high-frequency components. We adopt the alternating direction method of multipliers (ADMM) to solve the cost function in HFSC-MD. Synthetic and field data demonstrate that the proposed method recovers more reliable high-resolution data, and enriches the reflective structures.
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (0.34)
ABSTRACT Prismatic reflections in seismic data carry abundant information about subsurface steeply dipping structures, such as salt flanks or near-vertical faults, playing an important role in delineating these structures when effectively used. Conventional linear least-squares reverse time migration (L-LSRTM) fails to use prismatic waves due to the first-order Born approximation, resulting in a blurry image of steep interfaces. We develop a nonlinear LSRTM (NL-LSRTM) method to take advantage of prismatic waves for the detailed characterization of subsurface steeply dipping structures. Compared with current least-squares migration methods of prismatic waves, our NL-LSRTM is nonlinear and thus avoids the challenging extraction of prismatic waves or the prior knowledge of L-LSRTM results. The gradient of NL-LSRTM consists of the primary and prismatic imaging terms, which can accurately project observed primary and prismatic waves into the image domain for the simultaneous depiction of near-horizontal and near-vertical structures. However, we find that the full Hessian-based Newton normal equation has two similar terms, which prompts us to make further comparison between the Newton normal equation and our NL-LSRTM. We determine that the Newton normal equation is problematic when applied to the migration problem because the primary reflections in the seismic records will be incorrectly projected into the image along the prismatic wavepath, resulting in an artifact-contaminated image. In contrast, the nonlinear data-fitting process included in the NL-LSRTM contributes to balancing the amplitudes of primary and prismatic imaging results, thus making NL-LSRTM produce superior images compared with the Newton normal equation. Several numerical tests validate the applicability and robustness of NL-LSRTM for the delineation of steeply dipping structures and illustrate that the imaging results are much better than the conventional L-LSRTM.
- Geophysics > Seismic Surveying > Seismic Processing > Seismic Migration (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (0.46)
ABSTRACT The explicit finite-difference (EFD) method is widely used in numerical simulation of seismic wave propagation to approximate spatial derivatives. However, the traditional and optimized high-order EFD methods suffer from the saturation effect, which seriously restricts the improvement of numerical accuracy. In contrast, the implicit FD (IFD) method approximates the spatial derivatives in the form of rational functions and thus can obtain much higher numerical accuracy with relatively low orders; however, its computational cost is expensive due to the need to invert a multidiagonal matrix. We derive an explicit strategy for the IFD method to reduce the computational cost by constructing the IFD method with the discrete Fourier matrix; then, we transform the inversion of the multidiagonal matrix into an explicit matrix multiplication; next, we construct an objective function based on the norm to reduce approximation error of the IFD method. This explicit strategy of the IFD method can avoid inverting the multidiagonal matrix, thus improving the computational efficiency. This constant coefficient optimization method reduces the approximation error in the medium-wavenumber range at the cost of tolerable deviation (smaller than 0.0001) in the low-wavenumber range. For the 2D Marmousi model, the root-mean-square error of the numerical results obtained by this method is one-fifth that of the traditional IFD method with the same order (i.e., 5/3) and one-third that of the traditional EFD method with much higher orders (i.e., 72). The significant reduction of numerical error makes the developed method promising for numerical simulation in large-scale models, especially for long-time simulations.
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
The Nova field is in the northeastern North Sea. Reservoir sands are at depths of 2500–2800 m, with a pressure of approximately 290 bar and temperatures up to 110 C. The field will be operated with six wells--three oil producers and three water injectors--with an injector/producer pair in each of the main fault compartments.
- Europe > Norway > North Sea > Northern North Sea > North Viking Graben > PL 418 > Block 35/9 > Nova Field > Viking Formation > Heather Formation (0.99)
- Europe > Norway > North Sea > Northern North Sea > North Viking Graben > PL 418 > Block 35/9 > Nova Field > Rannoch Formation > Heather Formation (0.99)
- Europe > Norway > North Sea > Northern North Sea > North Viking Graben > PL 418 > Block 35/8 > Nova Field > Viking Formation > Heather Formation (0.99)
- (3 more...)
All are playful nicknames for the oil and gas icon known as a pumpjack. To the uninformed, the pumpjack is a thing-a-ma-jig that has something to do with oil, probably "fracking" because that's what drilling rigs do, right? But as an industry-educated and well-informed reader of JPT, you know this is inaccurate. By whatever name you call it, you know that the pumpjack is the visible manifestation of an invisible physics equation, a mechanism buried deep underground that lifts reservoir fluids to the surface. You also know it is one type of artificial lift available in a stable of systems with equally curious and technical names like progressive cavity, plunger, jet, gas lift, and electrical submersible pump (ESP).
- Oceania > Australia > Western Australia > North West Shelf > Carnarvon Basin > Dampier Basin > WA-209-P > Stag Field (0.99)
- Oceania > Australia > Western Australia > North West Shelf > Carnarvon Basin > Dampier Basin > WA-15-L > Stag Field (0.99)
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- (26 more...)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Shale oil (0.70)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Shale gas (0.70)
- Health, Safety, Environment & Sustainability > Sustainability/Social Responsibility > Sustainable development (0.69)
- (5 more...)
ABSTRACT Prismatic reflections in seismic data carry abundant information about subsurface steeply dipping structures, such as salt flanks or near-vertical faults, playing an important role in delineating these structures when effectively used. Conventional linear least-squares reverse time migration (L-LSRTM) fails to use prismatic waves due to the first-order Born approximation, resulting in a blurry image of steep interfaces. We develop a nonlinear LSRTM (NL-LSRTM) method to take advantage of prismatic waves for the detailed characterization of subsurface steeply dipping structures. Compared with current least-squares migration methods of prismatic waves, our NL-LSRTM is nonlinear and thus avoids the challenging extraction of prismatic waves or the prior knowledge of L-LSRTM results. The gradient of NL-LSRTM consists of the primary and prismatic imaging terms, which can accurately project observed primary and prismatic waves into the image domain for the simultaneous depiction of near-horizontal and near-vertical structures. However, we find that the full Hessian-based Newton normal equation has two similar terms, which prompts us to make further comparison between the Newton normal equation and our NL-LSRTM. We determine that the Newton normal equation is problematic when applied to the migration problem because the primary reflections in the seismic records will be incorrectly projected into the image along the prismatic wavepath, resulting in an artifact-contaminated image. In contrast, the nonlinear data-fitting process included in the NL-LSRTM contributes to balancing the amplitudes of primary and prismatic imaging results, thus making NL-LSRTM produce superior images compared with the Newton normal equation. Several numerical tests validate the applicability and robustness of NL-LSRTM for the delineation of steeply dipping structures and illustrate that the imaging results are much better than the conventional L-LSRTM.
- Geophysics > Seismic Surveying > Seismic Processing > Seismic Migration (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (0.46)
Time-domain extended-source full-waveform inversion: Algorithm and practical workflow
Guo, Gaoshan (University Côte d’Azur — CNRS — IRD — OCA) | Operto, Stéphane (University Côte d’Azur — CNRS — IRD — OCA) | Gholami, Ali (Polish Academy of Sciences) | Aghamiry, Hossein S. (University Cote d’Azur — CNRS — IRD — OCA, Charité-Universitätsmedizin Berlin)
ABSTRACT Extended-source full-waveform inversion (ES-FWI) first computes wavefields with data-driven source extensions such that the simulated data in inaccurate velocity models match the observed counterpart sufficiently well to prevent cycle skipping. Then, the source extensions are minimized to update the model parameters toward the true medium. This two-step workflow is iterated until data and sources are matched. It has been recently indicated that the source extensions are the least-squares solutions of the scattered data-fitting problem. As a result, the source extensions are computed by propagating backward in time the deconvolved data residuals by the damped data-domain Hessian of the scattered data-fitting problem. Estimating these weighted data residuals is the main computational bottleneck of time-domain ES-FWI. To mitigate this burden, we approximate the inverse data-domain Hessian by mono- and multidimensional matching filters with two simulations per source. We implement time-domain ES-FWI with the alternating-direction method of multipliers and total-variation regularization. Moreover, we apply ES-FWI with a multiscale approach involving frequency continuation and layer stripping, with the latter being implemented with an offset-time-dependent weighting operator. In this framework, we further regularize the inversions while mitigating their computational burden by matching the grid interval to the frequency bandwidth. Finally, the overall workflow combines ES-FWI and classical FWI during the early and late stages of the multiscale approach, respectively. We illustrate that the sensitivity of ES-FWI to the accuracy of the approximated inverse data-domain Hessian depends on the complexity of the targeted model, the data anatomy, and the accuracy of the starting model. In the case of the 2004 BP salt model, we determine that the layer stripping is necessary when the inverse data-domain Hessian is approximated by a 2D Gabor matching filter and the starting model is crude, whereas this feature is not necessary with the Marmousi II model.
Protecting the weak signals in distributed acoustic sensing data processing using local orthogonalization: The FORGE data example
Oboué, Yapo Abolé Serge Innocent (Zhejiang University) | Chen, Yunfeng (Zhejiang University) | Fomel, Sergey (The University of Texas at Austin) | Chen, Yangkang (The University of Texas at Austin)
ABSTRACT The development of the distributed acoustic sensing (DAS) technique enables us to record seismic data at a significantly improved spatial sampling rate at meter scales, which offers new opportunities for high-resolution subsurface imaging. However, DAS recordings are often characterized by a low signal-to-noise ratio (S/N) due to the presence of data noise, significantly degrading the reliability of imaging and interpretation. Current DAS data noise reduction methods remain insufficient in simultaneously preserving weak signals and eliminating various types of noise. Particularly when dealing with DAS data that are contaminated by four types of noise (i.e., high-frequency noise, high-amplitude erratic noise, horizontal noise, and random background noise), it becomes challenging to attenuate the strong noise while maintaining fine-scale features. To address these issues, we develop an integrated local orthogonalization (LO) method that can remove a mixture of different types of noise while protecting the useful signal. Our LO method effectively eliminates the aforementioned noise by concatenating multiple denoising operators including a band-pass filter, a structure-oriented, spatially varying median filter, a dip filter in the frequency-wavenumber domain, and a curvelet filter. Next, the local orthogonalization weighting operator is applied to extract signal energy from the removed noise section. We demonstrate the robustness of our LO method on various challenging DAS data sets from the Frontier Observatory for Research in Geothermal Energy geothermal field. The denoising results demonstrate that our LO method can successfully minimize the levels of different types of noise while preserving the energy of weak signals.
- Research Report > New Finding (0.66)
- Research Report > Experimental Study (0.48)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production logging (1.00)
- Information Technology > Artificial Intelligence (0.66)
- Information Technology > Sensing and Signal Processing (0.61)
- Information Technology > Communications > Networks > Sensor Networks (0.61)
- Information Technology > Data Science > Data Mining > Big Data (0.48)